---
title: "Lenny's Podcast — 2026 Q1 合集"
date: "2026-01-01"
source: "Lenny's Podcast"
url: "https://www.lennysnewsletter.com/"
---

<div v-pre>


# Lenny's Podcast - 2026 Q1 (15 episodes)

This file contains 15 articles/episodes.

---

## [1/15] We replaced our sales team with 20 AI agents—here’s what happened | Jason Lemkin (SaaStr)

**Jason Lemkin** (00:00:00):
Here's the mistake that 99% of founders and sales reps make. We're not really selling in B2B, we're solving problems. Our job as sales reps in SaaS is to not sell a used car, okay? We are selling a Tesla Model 3 performance. It has competition, I might not need it this week, but it's pretty darn good. Let me help you get you into that Model 3 performance today. I've even got a special discount for the end of this month, and let me just help you. I've spent four calls answering all your questions and I've explained to you all the things and why the supercharging network is better than the regular one that doesn't really work at the charger near your house. I've gone on Google and I've seen there's no charging network near your house. There's only Superchargers. I got you, don't I? That's the job of SaaS and sales because we're not selling commodities.

**Lenny** (00:00:48):
Today, my guest is Jason Lemkin. Jason created and runs Saastr, the world's largest community for SaaS and B2B founders. He also runs two of the biggest town conferences every year, one in the Bay Area, which attracts over 15,000 people, and one in Europe with over 3,000 SaaS executives, founders, and entrepreneurs. Before Saastr, Jason was the CEO and co-founder of EchoSign, which he grew to over 100 million ARR and then sold to Adobe where he ended up as a vice president of their web services business. If you follow Jason on Twitter or LinkedIn, you know how much wisdom he has to share about all aspects of building a successful SaaS business. 

**Lenny** (00:01:27):
In our conversation, we focus on what I find most product leaders have the least experience in, building a sales team. We get very practical and tactical on how long you should wait to hire your first salesperson, what your one to two first hire should look like, why you should actually hire two salespeople, not just one initially, how to comp them, how to interview them, when it's time to hire a VP of sales, how to avoid your salespeople flaming out and burning through all your cash. We also get into how to make the product and sales relationship healthier, including how to push back on sales and feature requests, why your head of product should be super involved in your sales process, how long you should make your trials, why you should avoid annual contracts, and so much more.

**Jason Lemkin** (00:05:03):
Hey, I'm so excited. Long time. What's the expression? Long time fan, first time dialer or something?

**Lenny** (00:05:03):
Caller? 

**Jason Lemkin** (00:05:09):
First time caller, yeah, first time caller. 

**Lenny** (00:05:10):
First time guest. 

**Jason Lemkin** (00:05:11):
I don't know if that exists in 2024 or not, but yeah, that's me. So thank you so much for letting me come.

**Lenny** (00:05:16):
It's absolutely my pleasure. I feel like you're the kind of guy that I knew would be on this podcast eventually, and I'm glad that we're finally doing this. I also feel like we can go in so many directions. I feel like you have so many insights on so many parts of building a business, especially a B2B business, but I thought it would be most interesting to dive deep into building your sales team, essentially trying to help people figure out how to start their sales team, scale their sales team, and all the elements they go into it. How does that sound?

**Jason Lemkin** (00:05:46):
I think it's great. I think it's an evergreen topic, but I think there's remains a consistent set of confusion, fears around how to build a sales team, and it's evergreen. The tools change and the pace has changed, but the issues, I love to dig into them because we keep making the same mistakes as founders and executives again and again and again.

**Lenny** (00:06:09):
100% agree. It's such a black box to me. Sales, I've never done sales, and I'm always just like, "I have no idea what's going on here." So this is how I learned from folks like you. You touched on this now. Now, I wasn't going to get on this, but I think it'd be interesting just this question of, does everyone need a sales team? Will you need a sales team if you're listening as a founder? Is it simply, if you're a B2B business, you'll need a sales team? How should people think about, "Will I need a sales team?"

**Jason Lemkin** (00:06:34):
If you truly build a self-serve product, you can either never have a sales team or Slack defer it or Canva really defer it. Canva didn't really build a sales team until they were well north of 500 million in revenue because it's epic self-serve. Slack started all self-serve, and by the time they went public, the majority of their revenue was enterprise sales. So you can sequence things. You can have hybrid models like a third of Asana's revenue is still from self-serve, and two-thirds are from a self-serve motion. So there's all different hybrid things. 

**Jason Lemkin** (00:07:06):
The most important thing though I've found is however you get those first 10, 15, 20 customers, be honest, be honest, and if you've talked to them as a founder and you know that they need a sales type motion, they need effort to deploy it, they have questions about security, they have questions about competition, they have onboarding requirements, and you say, "Hey, I don't like sales, so I'm going to do a PLG motion," you'll fail.

**Jason Lemkin** (00:07:34):
One of the things that I've done with so many founders over the years, and it shocks them at first, is your first 10, 15, 50 unaffiliated customers, the ones that find you from the ether, they're the next 50, 100, 200, 1,000, and you can either embrace them or run for them. Too many producty founders find that, "Hey, gosh, I'm going to have to do sales," and they exit it. They exit it. 

**Jason Lemkin** (00:08:00):
I'll tell you a company I invested in 18 months ago when things were easier, maybe 20 months ago, they were doing 5 million in revenue, growing over 100% with the sales like motion. Things got harder, things got harder as they did for many of us, and they decided to fire the whole sales team, and now they're doing less than one from 5 million with beloved customers. If I told you the logos, your jaw would drop. Over 5,000 20 months ago to 1 million today because they fired the sales team because the founders didn't like sales, they thought it was icky, they didn't want to do it, they were tired of doing it. I can tell you several stories like this. 

**Jason Lemkin** (00:08:34):
So you've got to be honest about the ... Sometimes we get it just right on day zero. Sometimes we know exactly how our company is going to work or Lenny's podcast is going to work. We can predict the future perfectly, but I find in B2B, a lot of times the industry, we end up winning. The segment of the industry, the type of customer is not what we initially thought and either lean into it and be a success or run from it because sales is icky and risk failure.

**Lenny** (00:09:00):
When you talk about being honest, what you need to be honest about is that you need sales help to close customers?

**Jason Lemkin** (00:09:07):
Be honest about that. Just be honest about, yes, the motion that it will take to get those first 10, 15, 20 customers, and if they were able to find you by putting their credit card and no one had to talk to them, then that's your DNA today. It doesn't mean you won't go up market later like a Slack or a Canva, but if you can get them all to do that and you never have to talk to them, then get really good at viral loops, get really good at almost B2C type motions, get really good at this kind of stuff and hire growth hackers and do the whole almost B2C-ish motion, which we have at self-serve, but if your first 10 customers come in and say, "You know what, Lenny, I'll pay you $5,000, but you've got to solve this problem I have with online video," that is not a self-serve motion. It is not a self-serve motion.

**Jason Lemkin** (00:09:51):
Even worse, sometimes worse is your first 10 customers, five of them sign up online, but it takes a huge amount of effort for 19 bucks a month, and then five say they'll pay you $5,000 a month. Some founders are like, "I'm going to work on the five that pay us 20 bucks a month, not the five that'll pay us $5,000 a year." I find too many folks that don't like sales, too many folks that have not had any life experience in a sales-led environment flee from those customers, and I've never seen that lead to success. We're surprised who our niche is sometimes. We talk about nailing our niche, but sometimes we're surprised that niche that finds us.

**Lenny** (00:10:28):
There's a lot of startups I've invested in that start off product-led growth and quickly realized, "This just isn't working," and many of them move to, "We need to actually hire salespeople and start to go top down."

**Jason Lemkin** (00:10:40):
Most companies are a hybrid. Even there's weird hybrids like there's folks like Mongo and Snowflake. Mongo has, well, maybe Mongo's much better example, Mongo has a free low end version, and there's open source too. It's a little confusing, and then there's not a lot in the middle, and then there's a lot of big. There's all different ways to combine a product-led or self-serve motion with sales-led. Too many founders in the beginning think it's either or. It's not either or. 

**Lenny** (00:11:06):
So I think the big takeaway here is sales is not a question of if you're going to need a sales team, it's a matter of when. The Canva example is crazy. Maybe that's the [inaudible 00:11:16]

**Jason Lemkin** (00:11:16):
But they still have one. But they still have one now. 

**Lenny** (00:11:18):
They still have one. I think Notion, I think at 10 million ARRs when they hired their first salesperson.

**Jason Lemkin** (00:11:23):
That makes sense.

**Lenny** (00:11:23):
So I think the question that leads to is, what is a sign that you should start to hire your first salesperson?

**Jason Lemkin** (00:11:31):
Even if you hate sales, even if you think it's icky, even if you don't like it, as a founder you've, 95 times out of 100, you've got to find a way at least to close the first 10 customers yourself. You've got to find a way. We could dig into how to do that if you don't like sales. The hack though is that even if you don't like sales, customers love to talk to the CEO. The customers love it. Here's the other thing, as a founder, you're really good at the product in the market, hopefully even in the early days. So what's important is even if you don't know how to do outbound, even if you don't know how to send a cold email, even if you don't how to do any of this stuff, and even if you don't know how to ask for a check, even if you don't know how to open or close, almost all founders are A+ middlers, A+ middlers, A+ middlers. Before we started, you and I talked about podcasts and content, and immediately it was an A+ conversation. Maybe you don't know how to close a big sponsor, I'm just making that up, and maybe you don't know how to find one, but once you have someone in your spider web, letting the conversation, you and I just had was A+, and any founder should be there by day one, by day one, and prospects love it. 

**Jason Lemkin** (00:12:40):
If I want to buy CRM, I don't get to talk to Yamini at HubSpot or Marc Benioff, but I get to talk to you and your CRM company. It's magical for early adopters. So bear in mind, no matter how bad you think you are as a founder, you're pretty good at the middle part of sales. So find a way to get any prospects, get really good at the middle, and then work up your courage. Work up your courage, and find how to ask for next steps and money. What would it take, Lenny? How can we get going on Riverside? What would it take? We would love to have you. What would it take to get you going next week? You've got to learn that little motion, but you don't have to learn all the stuff you think is smarmy other than how to go from middle to just ask for how we can get going.

**Lenny** (00:13:19):
I like this term, middlers. So just to make sure I understand what that means, in the middle of the conversation, you're not great at initiating, you're not great at closing, but you could talk about it.

**Jason Lemkin** (00:13:27):
Yeah, and this is where sales reps are terrible. Most sales reps, once you go beyond the five basic questions, they can't tell you how a database works or how to eliminate hallucinations from your product or how to do payroll in Eastern Southern Guam. The sales rep, and we can talk about this, they need help. They need help from the founders in the early days. They need help from sales engineers or product people, which is my favorite place to get the help as you scale, but they need help. 

**Jason Lemkin** (00:13:56):
The beauty to founders is they don't need help in the middle. You can be 10 times better in the middle than any sales rep at your bigger competitor. If you're running, I am just making up CRM, but if you're running a new CRM startup, you can run circles around 98% of the sales reps at Salesforce or HubSpot. They have the brand, they have the partners, they have the integrations, but they cannot answer the questions you can answer.

**Lenny** (00:14:21):
Okay. So you close the first 10 customers. What are other signs that, "Okay. Now is really the time we need to start hiring sales"?

**Jason Lemkin** (00:14:26):
I think as soon as you've got those 10 customers and more than 20% of your time is booked up with customers, you need leverage. You need leverage. You've got to get ... If you do it too early, if you're not spending 20% of your time in sales and 20% of your time in recruiting, you're failing as a founder. You need 20% of each, 20% in sales and 20% recruiting. Nothing else really matters at some level as a CEO, maybe not as founders, but as CEOs. Once you cross the 20%, you need leverage or your calendar will die. So you need to hire one rep and you've got to hire two because otherwise, there's no A-B test. You have to A-B test humans. You have to A-B test humans. You've got to hire two as hard as it is, and there's just one cheat code to those first two. There's one cheat code. 

**Jason Lemkin** (00:15:13):
I talked to so many founders that screwed up their first sales hires, and they always nod when they hear it. Those first couple reps have to be people you would buy your own product from. That's it. Now we can talk about other criteria. We can talk about normalizing for deal size. We can talk about industry expertise. We can talk about all this kind of stuff, but when you go out as a first time founder and interview 30 reps, and you're going to have to interview 30, and 20 are just going to break your brain because they don't do any work and they don't do any prep and they didn't even go to your website. Then eight of the next 10 are okay, but you know it's not really going to work, but you might hire them if you get tired.

**Jason Lemkin** (00:15:50):
If you're lucky, one or two of them, they're like magicians. They ask the right questions, which we could talk about. Then take a pause and say, "Look, no matter how strange their background was, whatever they did, it's not the right background. It's not our same deal size. It's not our industry. They actually weren't very good at their last job. They got let go at their last job, but I would buy from Lenny. I know. I've been doing this for a year. Every day, I would buy from Lenny because Lenny just explained to me my product and my customer's problems in a way that I actually believe. Forget about what's on her LinkedIn or his LinkedIn. I would buy from Lenny." 

**Jason Lemkin** (00:16:23):
That rep always works because you can trust them with those leads. Almost every first time founder hires someone because they worked at Twilio or CloudFlare or wherever, and they talk the talk and they can say ACV and NRR, and they can talk all these acronyms. None of which matter. None of which matter. We all get attracted to logos on our resume, but in our gut, I always ask them, "Did you know that Jason was going to ... Would you buy from Jason?" They're like, "You're right. I wouldn't have bought from Jason, but he worked at Twilio." So just wait, wait and interview 30 sales reps, however you find them through LinkedIn, through use recruiters, use contingent recruiters. It's exhausting. Do everything. Work your network and then be flexible in the beginning. We're looking for pirates and romantics in the early days. We are not looking for folks with massive sales operations teams and enablement teams. You're looking for that quirky one that's got a few extra IQ points, that for reasons that make no sense has fallen in love with your little product that is so feature poor and does nothing, but they love it, they love it, they love it. 

**Jason Lemkin** (00:17:28):
My first rep I had this back in the day, he had gotten let go by a prior startup and he was struggling and he was living in his brother's garage at the time. This was not your number one person at Snowflake, but of all the 30, he came in and what's now Adobe sign? EchoSign. He described the whole problem. He described how we would solve the problem for our customers in the early days of this category. It was clear, whatever it took we needed him and whatever we needed to backfill the garage or whatever, this was the only one that could sell the product, and he did it for a decade. Closed our first five-figure deal, first six-figure deal, first seven-figure TCV deal. Didn't scale completely. Inside of Adobe was harder to scale inside of a 20,000 person company than it was in a six-person company, but he was still there. He still made that far to the journey, but he was the only one that I knew that I would buy my product from, not that I would buy something from, but I would buy my product from. 

**Jason Lemkin** (00:18:24):
Leads are so precious in the early days. It's so hard to find leads. That's the problem. You hire this perfect person from Snowflake and you have three leads a month and you give him two and he doesn't close them, your company's going to die. Leads are so precious. That's what folks don't get. So wait, and I know it's painful, and I know most founders have hired this person with the right LinkedIn that they wouldn't buy their product from. Just keep interviewing. You need that quirky pyromaniac that loves your product and can sell it. They're out there.

**Lenny** (00:18:57):
Amazing advice. Wow. There's so much there. I'm going to follow a couple threads there. In terms of the level, I know a lot of people make the mistake of going with a VP of sales pretty quickly. What's your advice? I know you talked about general attributes, but what's your advice for the level and seniority of these first two hires?

**Jason Lemkin** (00:19:14):
Two things, one that's evergreen and one that's maybe especially apropos for today's environment. The evergreen one, which I've talked about for over a decade, and I think folks that have been through it will see it, which is you need two sales reps hitting quota closing deals before you're ready to hire a manager for them. Almost all VPs of sales, their job is to take you from rep three to 300, to take you from three to 300, to take something that's just starting to repeat, a script that's just starting to work, leads that are just starting to come in. Then take Lenny and Jason, the two reps I have, as quirky as they are, they're crushing it. 

**Jason Lemkin** (00:19:55):
Then I'm going to hire a more heterogeneous type of person going forward. I'm going to hire the person from Cloudfare. They can't all be like Lenny and Jason, they're pretty quirky, but at least I can learn from them. What's working? What are the objections? How do we get around that feature gap? We're one year old, we're two years old. We've got a lot of feature gaps. This integration doesn't quite work. How do we box around those issues? How do we sell our 10x feature? Because if you're a startup, your product isn't very good, your software is not very good, and you have a competitor, but you have some 10x feature that is driving people to buy you something you're doing that does not exist in the marketplace.

**Jason Lemkin** (00:20:29):
The really good reps get insanely good at selling that, and the folks off the street, the new VPs often doesn't even understand the 10x feature. It's too subtle. It's too subtle. Why? The fact that your product is localized in Portuguese magically means you can win these deals. I'm making up something that doesn't quite make sense. So you need two reps that are hitting quota and then you hire VP of sales. If you hire it before then, you're doing a Hail Mary. It ain't going to work. It never, ever, never, ever, ever, never, ever works. You're asking them to find product market fit to be the first rep, to be the second rep and scale it all at the same time. It's mission impossible to do all four at the same time. 

**Jason Lemkin** (00:21:09):
So that's the biggest mistake is, Lenny, I can't get sales going at my company. I can't do it, but I raised $4 million in my C+ round. I'm going to go out and hire the VP of sales. That VP of sales will not be there in eight months. You know what else? 2 million of the four will be gone because they'll never understand the product. They will never understand. There's so many things we could talk about, but if there's one thing I could reinforce for this audience, it's that early sales team, they've got to be product gurus for your product audience. They've got to be product experts. Later as you scale, they can't be or you can't scale. 

**Jason Lemkin** (00:21:42):
When I started, when I came out of my own startup and started interviewing other folks, I remember I would interview a lot of folks at places like GitHub back in the day. I'd be like, "Do you have a technical background? Are you an engineer? Have you ever built any software?" They're like, "No, I don't. I don't know. I don't know," and I was like, "I was curious how you could do it," but GitHub was so well-established that it's 10 questions. It's 10 questions and grab a sales engineer. It's 10 questions and grab ... So you got to be really good at 10 and then you grab a sales engineer. That doesn't work in the early estate startups. So you've got to find these sales folks that are a bit of product savants and dropping the VP of sales in that isn't a product savant, which is a big issue. That's all mister or miss process. This is the issue. They're all mister or miss process.

**Jason Lemkin** (00:22:24):
So the second point I wanted to make, and I was on the fence for years of when you hire a VP of sales, do they need to carry a bag? Do they need to sell themselves or not? Does it matter? Does it matter? I wasn't sure for a long time. The reason I wasn't sure for a long time, it's not that you don't want a VP of sales carrying a bag. I do think a VP of engineering also should commit code. We can talk about that. 

**Lenny** (00:22:45):
Carrying a bag, meaning close sales themselves? 

**Jason Lemkin** (00:22:46):
Yeah, not just be a boss. 

**Lenny** (00:22:47):
Have a quota. 

**Jason Lemkin** (00:22:49):
Yeah, have a quote. The reason I was on the fence was because I was like, "Well, listen, in theory it's great, but let's say you're a hot startup and you want to go from two to six this year." So I want to add 4 million in new bookings, and let's say I can do 400K in quota per rep. That means I got to have 10 reps. She walks in with two. I'm at a race, aren't I? I got to hire eight in the next six months to hit my plan, and I got to put numbers on the board. If I'm going to out there as an individual contributor sales rep, I'm never going to hire eight people, am I? It's never going to happen. 

**Jason Lemkin** (00:23:20):
So I used to say I don't know if it matters, but what I've learned the last 18 to 20 months when everyone has gotten lazy, Lenny, everyone in tech has gotten lazy, is I see way too many VPs of sales that come in and never have any idea how to sell the product, never, never. They only want to be managers.

**Jason Lemkin** (00:23:38):
Now, whether they carry a bag or whether what they do is they join calls with the reps, whether they backfill the reps, you want your VP of sales in deals 20, 30 hours a week when they start. I see VPs of sales today start, they're in no deals. I'll go to a board meeting and a VP of sales will be there two, three months, and they'll have a look on the board. I'm like, "How's it going with Airbnb?" and they'll be like, "I don't know. I got to ask John." Doesn't know because it isn't in the deal, isn't even in the deal. So I see this too often.

**Jason Lemkin** (00:24:08):
Another company I'm on the board on, I really like this VP of sales a lot. He joined and six months in, sales were down from where, the new bookings were down from where they were six months before. He was honest. He's like, "The biggest problem is I feel like I didn't have time to learn this product. It's a complicated product. It's more complicated than the product I last sold, and I just didn't have time."

**Lenny** (00:24:29):
Jason, this conversation is going exactly how I'd hoped. There's so much actionable advice here. Let me try to summarize a little bit of the things you've said and then keep going. So let me know what I'm missing here. I'm going to try to just highlight some of the important things you mentioned. So in terms of when it makes sense to start hiring your first salesperson, a couple of things you shared is you've closed the first 10 customers on your own as founders. If you're spending more than 20% of your time on sales and you need to start creating more leverage. I'll throw in one other thing I've heard and just to tell me if this is also true, that there's a repeatable process you've created. You can sell consistently enough or you can tell someone, "Here's how I've been selling." Is that right?

**Jason Lemkin** (00:25:08):
Yeah. If you hire a VP of sales before then, it's approaching 100% chance of failure.

**Lenny** (00:25:13):
I see. So you can hire the first two reps before you're like, "Here's a pretty good process that has worked for me a number of times."

**Jason Lemkin** (00:25:19):
Yeah. Listen, as hard as it is for a lot of founders to even get their arms around, the reason is you've got to be that crummy first head of sales with them. You've got to manage those reps. You've got to be in the field with them. You've got to be figuring it out before you can hire the person. You just have to even if you're terrible at it because you're still going to be a good middler. You're still going to be a good middler.

**Lenny** (00:25:41):
Okay. A few more things that stood out to me. One is hire two sales reps immediately, not just one. These reps you need to interview about 30 people. Eight will be pretty good, 20 will be terrible, and two, these are people that you can see selling your product and you would buy from them. They're often very quirky and not maybe a traditional sales background. They've never been VPs of sales, essentially. They're more like AEs and things like that historically or maybe not even salespeople historically. Is that right?

**Jason Lemkin** (00:26:10):
I think you will regret it if you don't hire these first couple of sales reps that have a couple years of experience if you're in B2B with B2B sales. They need a couple of years of experience and they need a certain amount of maturity, and maturity is a real issue, and these are in any early hire because we just don't have time to babysit people. There's no onboarding, there's no training. So they need a couple years of experience, ideally something close to your deal size, we could chat about why, that would be nice, and enough maturity that you can trust them with that lead. You can trust them with your customer.

**Lenny** (00:26:43):
Okay. Then you hire a VP of sales once you're ready to go from three to 300 reps. Your advice is also give the VP of sales a bag slash a quota slash they need to be doing sales themselves in today's world.

**Jason Lemkin** (00:26:56):
At least for a little while. More importantly, if they don't want to, I think in today's world you got to run from them. Exactly whether they fully carry a quota, a half quota, whether they backfill the sales team, you want a salesperson. Here's the thing, Lenny, you want a ... This is going to sound silly, but it's not. Trust me on this. For anyone watching, listening, you want a head of sales that actually wants to do sales. I got to tell you, and I'm still struggling with this myself after the last three years or so, so many folks in all functions, we could talk about other functions too, but especially in sales, don't want to do sales anymore. They don't want to do it. They don't want do it. 

**Jason Lemkin** (00:27:39):
2021 was crazy because everything was too crazy. There's too much money, too much going on. 2022 was the knife falling. That was crazy too. 2023 was just hard. Whether it's burnout or whatever out or just too much change, I would say I've probably interviewed for different reasons, 50 VPs of sales in the last 12 months, I would say the majority don't want to do sales. It's my first screening question. That's why they got to carry a bag because it proves they actually still care about the craft. 

**Jason Lemkin** (00:28:12):
The best sales folks love sales. It's a craft. They love money. Yes, they love money, and that does matter. Do not hire a sales rep that doesn't like money. Trust me on this one. There's 0% chance they'll work out either, but they also like the craft. They like honing the script. They like beating the competition. They like figuring out the counterfeit. They like figuring out the weapon and the 10x feature. They like working on a team. They like hunting. They love it, but then sometimes I just think burnout is always a reality, but I just think that the last three years have been so yo-yoed, that folks are just, they're out. They're out. Don't hire them no matter how smart they are, and that's the test. 

**Jason Lemkin** (00:28:52):
I'll tell you, I talked with a really great VP of sales candidate that had worked with a top 10 tech company and I asked him what he wanted to do in this next role. He's like, "I've got a great team. I've got eight people that I can bring with me. We're ready to lock and load." I'm like, "Okay. Well, let me tell you one of my views in today's world is that you've got to actually visit more customers now, not less. In a distributed world, it's more important to visit customers." He lived in the East Bay in Pleasanton in the East Bay, which is maybe 20 minutes east of Oakland. He said, "I'm willing to visit customers, but I won't go as far as the peninsula in San Francisco." He's just happy at home. He's happy at home, Lenny, and he don't want to sell.

**Jason Lemkin** (00:29:34):
This guy has the best LinkedIn and the best references, but he don't want to sell anymore. He needs a job. He wants to be a VP of sales. I see this across the industry and I'm struggling to find answers. I think the biggest hiring ... We talked about a lot of tactical hiring mistakes that you can make today in sales. The biggest strategic mistake you can make today in sales because there's so many veterans, there's so many folks that worked at Twilio and Mongo and wherever, pick any great company you want. There's so many veterans out there, you just can't hire the ones that don't actually want to sell anymore, and there's too many, there's too many.

**Lenny** (00:30:06):
You mentioned that you actually just asked them in the interview. The first question is, "Do you actually want to sell?" What other questions do you ask for either ... Let's go through both. You said you have these questions that you ask for the early hires and then the VP of sales. What are some tips for people when they're interviewing these folks?

**Jason Lemkin** (00:30:21):
It's interesting. VP of sales or VP of product, I look for the same answer. One of the Colombo style questions I ask them, and this is so odd. It barely even counts as a question is, what do you want to do your first 30 days? What do you want to do? Actually, I usually ask, "What do you want to do your first two weeks?" In B2B, if I don't hear from the VP of sales or the VP of product that I'm going to go meet customers, out, I'm out. There's too many VPs of product too, Lenny, that don't want to meet customers anymore either. The majority of VPs of products that I interview, they don't meet customers. Every single great VP of product, chief product officer I've ever worked with in my entire career, you know what the first thing they do in the first two weeks? They're like, "Leave me alone. I'm going to go talk to 20 customers. Leave me alone."

**Jason Lemkin** (00:31:00):
The first two weeks, they're like, "Leave me alone. I'm going to go talk to 20 customers. Leave me alone. Leave me alone, I'm going to go talk..." They don't want to sit out on meetings and look at PRDs and talk out of their navel on endless internal meetings. All the best ones, they say they just leave. They literally say, "Leave me alone. I will see you in two weeks. I'm going to go meet with 20 customers." And that's what products should do. Sales, whether it's prospects or existing customers, it can vary, but they should be like, "On my first day, I want to join five calls." 

**Jason Lemkin** (00:31:29):
And when you hear that story of, "I don't want to travel out of Pleasanton," or they say, here's what you're going to hear from the wrong person for anything, even up to 50 million in revenue, "I'm going to spend that first month working on process," "I'm going to spend that first month getting Salesforce up and running," "I'm going to send the first month on territory planning, Lenny, because we really, with our three reps, I really want to make sure we've nailed territories, right? Lenny will do the south, Elaine will do the east, Jason will do the west." 

**Jason Lemkin** (00:31:59):
When you hear process, process, process, from any, from marketing, product, sales, customer success, too, boy, it's the death of customer success. It's just... And it's not that you don't need process, it's just, it's even worse than it was than a few years ago. And I know this is going to trigger some people, but the truth is they don't want to work. They don't want to work. They don't want to work. 

**Jason Lemkin** (00:32:21):
I literally just got this LinkedIn email to work with me at SaaStr, from this person who'd been reading SaaStr for eight years. You're like, "I want to run account management, customer success for you. Here's what's, you're doing wrong, here's how to do it." I'm like, "Oh, this is pretty good." And I DMed her back, I'm like, "Great. To be honest, you realize that you're actually going to have to talk to customers and sponsors yourself in the beginning." No response. Just out. Just out. And that was the best one I got the last 30 days. That was the best inbound that I personally got. 

**Jason Lemkin** (00:32:51):
I know it sounds triggering or critical, but as founders, we have to be honest that there's this vast pool of veterans with great LinkedIns and great resumes that are so burnt out, Lenny. And let's not blame them. I know it sounds like I'm blaming them or being negative, and I'm trying to not be negative. I'm empathetic. I am empathetic to the burnout, but don't, you can't hire these. There's too many. 

**Jason Lemkin** (00:33:14):
The industry is littered with the burnt out. They're littered with it. And if you hear a touch of cynicism, if you hear anger, if you hear, "I don't want to meet customers in any..." and I'm going to say any VP role, product, sales, marketing, customers... If they don't want to meet customers their first, let's make it a 14-day test, you know they're never going to want to meet customers.

**Lenny** (00:33:33):
These folks that don't want to do sales that are VPs of sales, I imagine when you're further along, that's more, okay, because you don't need them to be doing sales, right? They want to manage, optimize. No. I see you shaking your head?

**Jason Lemkin** (00:33:46):
I mean, okay, you think, okay, there's an element, let's call it north of 50 or 100 million, where for, what's sometimes called commercial sales, SMB sales, it really is all process. Okay? So for sure, I agree. But at Salesforce, they're trying to close $100-million-plus deals. You think that the sales leadership doesn't need to be in those deals? You think they can just hit refresh on the dashboards and track them from home? No. Marc Benioff today is still flying to meet customers. He said the other day, why does Marc Benioff go to Davos? He sits, and I've never been to Davos. He sits at the same place, at the top of some staircase. Okay? 

**Jason Lemkin** (00:34:26):
And Salesforce drops millions of dollars, and he waits to meet customers, prospects, and partners in person all day, because it's efficient. Because it's efficient. Because he can do 50 or 100 customer or prospect meetings a day at Davos. He's still doing it at 30-something billion in revenue. Right? So this idea that, is there some truth at... For example, when I worked at Adobe back in the day, it is true that Adobe, which was, parts of the business, sales literally was dashboards. I get it. Okay, at four billion, 10 billion in revenue, but most of your audience is not ready to hire at four billion or 10 billion in revenue. They're not ready to hire that person. Don't hire them, right?

**Lenny** (00:35:08):
I don't know, maybe Satya's listening. You never know.

**Jason Lemkin** (00:35:12):
I think on the sales side of the business, they're flying out to the big deals?

**Lenny** (00:35:17):
To close the loop on this thread, when you're interviewing the early sales reps, those first two quirky folks, what are some questions and ways to know if there may be a good fit? You mentioned one of, you feel like you would buy from them?

**Jason Lemkin** (00:35:30):
Okay. The one, really, the simplest one, and early in my career, I thought this was silly or hated it, this Glengarry Glen Ross. I don't know if it was Glengarry Glen Ross or that toxic Leo movie about sales, which is still entertaining. Yeah, one way or another, Lenny, it's got to be, "Sell me this pen." But it's not "Sell me this pen," it's "Sell me this app." So I don't like surprises, I don't like games. I like to do this in a second interview or whatever. Give them time. And I don't like to judge too harshly, but whether it's the first interview or the second, they got to sell you this pen. They've got to put in the 30 minutes of work and maybe it's two hours.

**Jason Lemkin** (00:36:07):
But here's the thing, today's world, you can go on YouTube or someone's website... There's an explainer video for every product under the sun, isn't there? I am shocked how many salespeople I have met, Lenny, from SDR to SEB of sales that, by the time... And I'm doing an interview for a portfolio company, this isn't a screener interview, they haven't even watched an explainer video yet. I could be the fifth or eighth interview, and they don't know how the product works. They have not even gone to the effort of searching YouTube or the homepage and watching it. 

**Jason Lemkin** (00:36:38):
And forget about explainer videos enough. But so many companies do webinars now and they publish them on the... Really good stuff, customer testimonials. Right? If you're a sales rep, you don't actually have to know how the webhooks work or how to provision an API key, but you need to know the product as well as that webinar that's on YouTube. And 98 out of 100 folks won't bother in an interview. They won't bother. They're just going to click on "Apply" on the ATS and tell you, "Hey, I recently stumbled upon whatever. It looks like... Do you have time to discuss." Right?

**Jason Lemkin** (00:37:10):
But that one that actually does enough work and watches the video, and then can sell it to you, and they're going to make mistakes. They're not going to get it right, but they have enough confidence with the core problem to be solved. Because here's the mistake that 99% of founders and sales reps make. We're not really selling in B2B. We're solving problems. And this is why so many people are struggling in 2024, because they can't... their products, as sales reps, as companies, they can't solve big problems anymore. 

**Jason Lemkin** (00:37:41):
Our job as sales reps in SaaS is to not sell a used car, okay? We are selling a Tesla Model 3 performance. It has competition. I might not need it this week, but it's pretty darn good. And I'm going to help you understand, Lenny, because you're in the market for the car, why this is the best one. I'm going to be honest about where it's not. I'm going to answer all your questions. I'm such an expert, and then I'm going to ask you, Lenny, when is that... You have 280,000 miles on the Civic? Let me help you get you into that Model 3 performance today. And I've even got a special discount for the end of this month. And let me just help you."

**Jason Lemkin** (00:38:14):
And I've spent four calls answering all your questions and I'm explained to you all the things and why the supercharging network is better than the regular one that doesn't really work at the charger near your house. And I've gone on Google and I've seen there's no charging network near your house, there's only supercharges. I got you, don't I? And that's the job of SaaS and sales, because we're not selling commodities. And that's why the best reps will also tell reps when not to buy their product. 

**Jason Lemkin** (00:38:41):
As painful as that seems in tougher times, in 2024, the best reps say, "No, we're not there yet. We're not the right... If you need this feature, if you need this integration, I want you back in six months when you use this big product that isn't that great. But today, we're not the right solution for you." That's, the best ones do that. They have the confidence to know to close it. And the rest of the world thinks this is some sort of adversarial transactional thing and it's not. Right? 

Software... I think hopefully all your audience would agree, when it's done right, software's magical. It solves incredible problems, incredible problems. What Airbnb does, what Uber does, what SaaS... Things solve... I can track my customers, I can manage, I can automate my communications. It's magical. You shouldn't have to use boiler room tactics and use [inaudible 00:39:30] tactics when you have something. But, it doesn't sell itself, except for a little while in late 2020, early 2021, that screwed the whole world up when products actually sold themselves.

**Lenny** (00:39:42):
I feel like this is a separate podcast we should do of just how to get better at sales and how to sell. But, I feel like that's its own hour of conversation. I did a great episode with April Dunford where, I don't know if you follow her stuff, but she-

**Jason Lemkin** (00:39:54):
Yes. 

**Lenny** (00:39:54):
... essentially describes exactly your approach of help people understand the market, help them understand the entire landscape, and then talk about your problem. So anyway, we'll link to that episode. Has a lot of good advice there. In terms of this interview, selling-me-a-pen approach, to give people something very concrete to do. How do you actually set this up? Is it like, "Sell me your product," and then they put together a pitch for your team and they pitch you on a product?

**Jason Lemkin** (00:40:19):
I'll tell you, until the boom, until everyone in late 2020 got so desperate to hire anyone with a pulse, almost everyone in sales had to come and they had, during an interview process, they actually had to do the pitch. They would come and you used to do it in person with this screen. You can do it on Zoom now, it really doesn't matter, but do it for real. Do it for real, and everyone would see. And everyone in recruiting change, people stopped doing reference checks, they stopped doing these tests, and they would just hire warm bodies.

**Jason Lemkin** (00:40:51):
Late 2020, I can tell you a funny story in a second, and it was okay until it wasn't. And our hiring processes have not reverted back to pre-March, 2020, and I think it's failing founders left and right. I can't tell you, I would say 95% of the hiring I see out there, people don't do reference checks anymore, Lenny. And we can talk about when they work and when they don't, but no one does them. No one does them. You're going to invest so much... Forget about the salary, it doesn't really matter. So much time, your time, your leads, everything's so precious, to bring Jason into your company and you're going to go through all this recruiting process and you're not even going to do any reference checks? People don't even do reference checks.

**Jason Lemkin** (00:41:33):
And they don't, they stopped doing, "Do me this demo," because they were so worried the rep would go to Gusto or would go to Deal, or would go to remote. There was no time. I got to get... This SDR has 50 offers. Lenny, we got to hire this SDR with three weeks of experience. We've got to decide today. And that's the way, and hiring does not come back. If those hires bounce, not only is it bad for you, but it's a disservice to the... It's worse for the hire. I mean, that poor CloudFlare woman that blew up with the thing, there's so many issues there we could dig into, but it's all Cloudflare fault for hiring her. Okay, let's forget about that she didn't close a single deal. Okay, so objectively, in sales, should you retain your job in sales if you can't close a single deal? I don't want to get into some of the triggering things, but remember, if you hire someone and they fail, it's all your fault. She didn't know what she was getting herself into. No candidates can do enough diligence ever. There's not enough time. There isn't enough time. 

**Jason Lemkin** (00:42:32):
And this is true all the way to the top. And so, as an investor, a board member, it's funny, when I do interviews, I do this thing, I try to be the last interview sometimes, and the founders don't want me to do it. They want me to sell. I don't want to be the last one. And what I want to do is when, Lenny, you've decided to join Ellen at her startup, right? At the end, I said, "Look, Lenny, I've talked to Ellen, she loves you. You have the job. You've got the job. What I don't want you to do, Lenny is bouncing three months after. So let's slow down, let's talk about the questions you have on your mind, and let's have a safe space where we can make sure you're going to be happy and successful there." Right? 

**Jason Lemkin** (00:43:09):
And sometimes even the founders get mad at me for doing this, because sometimes, the candidate bails. But I'm like, "I know you're mad at me today, but it is your fault. A hundred percent your fault if any of your hires fail. Don't blame them. And I'm guilty of this. I get upset when people start and they don't give it their all or they don't do it. I mean, what if you hired someone for your podcast and they decided, "You know what? I don't want to do the podcast this week. I'm tired. I'll do it..." You're so mad at them, but you should've seen it during the hiring process. Right?

**Jason Lemkin** (00:43:44):
So, tying it back, that's why you've got to do this, "Sell me this pen." You've got to, before you hire the salesperson, have them sell your product real. Do the demo. And if they need more time, give them more time. Let them watch another demo. Don't have them feel like it's a trap or spook them. Treat them the way you would like to be treated. But if you skip this step, I mean, what's the point? You're not helping them.

**Lenny** (00:44:09):
What I love about this process is it connects exactly to what you recommended you look for in these hires is, "Would I buy my product from these people?" So this makes a lot of sense. And you give them this assignment at home and they do work on this and they come in, right? It's not like come to the office?

**Jason Lemkin** (00:44:22):
If you talk to a great transactional VP of sales that's hiring tons of reps that do it, they'll make them do... If they're still doing this, they'll make you sell the pen fast in the process. They don't have time. They're doing 20 interviews a week, 30 interviews a week, you've got to sell me this pen. But if you're a founder and I'm talking about you've already talked to a bunch of candidates, you're down to one or two, make them, tell them, "Look, this isn't a trick. I'm not that great at telling my product myself, but I got to know that you're going to be happy and successful here. So do a demo for me."

**Jason Lemkin** (00:44:49):
And if they won't do it, they're not a salesperson. And too many people went into sales, not... The other weird thing about B2B sales in good times is actually, a lot of these folks are not really what you might think as salespeople. They can't do outbound, they can't pick up the phone. All they know how to do is to manage leads that come in and say, "I want to buy today." And you need those people, too. Okay? And I'm not saying you don't need those people, too, but that's not always what we think of as sales.

**Lenny** (00:45:16):
Another black box for me, and I think a lot of people with sales, is comp and quota, had actually set up their comp for early hires, and then how to actually decide on a quota. What advice do you have for figuring that out early on? What percentage is commission, what percentage is salary? All that-

**Jason Lemkin** (00:45:31):
Yeah, everyone gets this wrong. We worry... Look, at scale, when you're extremely mature, extremely mature, and very profitable, you really got to tweak this stuff very carefully, and we could talk about that if it's relevant. But, in the beginning, we get this all wrong. What matters is, can a sales rep close more than they take home? In the very beginning, that's all that matters. It's just you selling, right?

**Jason Lemkin** (00:45:58):
The first three months of a new rep, you're lucky if they can close as much as their take-home pay is, their OT. And so, in the really early days, my early, early day comp plan is, look, your first three months, you keep a hundred percent of what you close. Of course, that's not profitable for the business, but you've got to invest in them, right? You keep a hundred percent of what you close. And if you have enough going on in your business, usually, that's enough to put supper on the stove. 

**Lenny** (00:46:24):
And that's without a salary? 

**Jason Lemkin** (00:46:26):
No, no. You pick an OT, a salary, but you got to... Okay, let's step back for a minute. You got to pay market. You got to pay market. And in the early days, if you're bootstrapped or really lean, this stresses folks out. I just talked to this great rep, Lenny, but he wants 150k OTE. And in fact, it's a step down. He had 170k at Slack, but he knows this isn't a startup. He wants 100. He might take 140.

**Lenny** (00:46:53):
And OTE is total comp, overall total comp.

**Jason Lemkin** (00:46:54):
Total comp. And the founder panics. "I don't have, I'm only making 60. How can I pay 140?" Well, let's break it down for a minute. First of all, let's be tactical. It's usually 50/50, right? 50% base, 50% bonus for a sales rep. So they're really only taking home a 70k base, trying to make another 70. Okay? Then 70 divided by 12, help me do my math. It's not quite 6k month. Well, maybe with taxes it is. You're really only paying 6k a month for a couple months to see if this experiment works out. You're investing 20,000, you don't have $20,000 to do sales? Then don't do sales, if you can't find $20,000. So people freak out too much about this comp-line number, this base and bonus, the on-target earnings, OTE, and they're not more practical about what am I committing to cash upfront? 

**Jason Lemkin** (00:47:44):
Two, you need a plan where it's a win-win. So if this person makes 140, but ultimately, a sales rep's got to bring in four to five times what they take home. That 140's got to be 500, 600 or more. The more enterprise you are, the bigger the multiple has to be, and the higher the comp. But it's got to be 3x to 5x at a minimum, 3x for small businesses, 4x for mid-market, 5... You got to get there, but you don't have to be there on day one and two. When you get there, the reps should be accretive, shouldn't they be? If they make 150 and they bring in 450, unless your marketing costs are out of control, which can happen, but that's not sales fault is it? If your marketing costs are reasonable and you pay a sales rep 150 for closing 450, if you engineer your plan right, you should be smiling, not frowning.

**Jason Lemkin** (00:48:34):
Where it gets tough is when you have a ton of folks not closing. When you have a ton of folks not closing and you have extremely high marketing costs, then it all breaks. Your cap breaks, your payback period breaks. But, if you have inbound leads, if you have demand, and you have sales reps that can sell you this pen, they're going to be accretive, Lenny, because of that math. And take the pressure off, don't make them close 5x their take on their first quarter, make them close 1x. Let them put points on board, let them eat. And so negotiating, here's the point if you think about it. Negotiating someone down from a 150k OTE to 130, it's not going to make any difference. You want them to close 600 or 700. It's not going to matter. 

**Jason Lemkin** (00:49:15):
At scale, does it matter? For Mongo maybe. Maybe. But, what you care about is how much more can they close and they bring in? And then you, that's why you want them to make a lot of money. You want them to be rich. You want them to take home a lot for a whole bunch of reasons. And so, don't freak out about the acronym OTE, what they take home. Figure out what it's really going to cost you and would you buy from them? And if you would, they're probably going to bring in more than they take home. Right? And on the marketing side, in early days, you're lucky if you can find a marketing channel that is that accretive.

**Lenny** (00:49:49):
Amazing. Okay. So just to summarize, you start a sales person with, say, 75k in salary and then say 75k in expected sales. And they take-

**Jason Lemkin** (00:50:01):
Bonus, bonus.

**Lenny** (00:50:02):
Bonus.

**Jason Lemkin** (00:50:04):
Bonus from sales, yep. 

**Lenny** (00:50:05):
Bonus from sales and the the idea there is they take 100% initially of their sales, for the first month or two, just to make it feel amazing. They're making money. See how they're doing, don't put a lot of pressure on them. And then the plan is, over time, get it to a point where they're bringing into the business four to five times what they're taking home in terms of bonus.

**Jason Lemkin** (00:50:24):
Yeah, yeah. 

**Lenny** (00:50:25):
I set expectations every month or two, or is it every quarter? We're going to revisit your comp and quota and adjust.

**Jason Lemkin** (00:50:32):
If you get it right, you never have to revisit it. Not for years. I mean, I'm using the same sales comp plan I have for a lot of years. It still works. Listen, you can make tweaks and you've got to listen, and we're all human beings. And people, and again, remember, your sales team has to eat. They've got to eat, okay? And you've got to be somewhat competitive. But, there's only so much you can tweak, Lenny. I mean, they have to bring, close more than they bring home. Right?

**Jason Lemkin** (00:50:55):
And so, you can make tweaks at the margin. The problem is if you make too many tweaks to this model, you burn up all the cash. You're trying to plug a leaky boat, there's no point. There's no point. There's no point. And remember, related to this, for a long time, for most folks, unless you're, even if you're hyper funded, you're better off with fewer, better reps than more reps.

**Jason Lemkin** (00:51:18):
There is a stage when you get big enough where this concept of capacity planning really does matter. Lenny, if I got to go from 50 to 100 this year, okay? I'm going to add 50 million. If each of my reps can do 500k, I need a hundred, don't I? Okay. And literally, if you have 20, you mathematically cannot hit the plan. So there's a certain truth at scale. In the early days, this is a rookie error. I'd much rather have two reps, each closing a million than 20 reps struggling to close 100k each. Culturally, it's terrible. There's no domain knowledge in the company. Everyone's miserable, everyone's fighting. 

**Jason Lemkin** (00:51:50):
What you want to do in the early days is concentrate your leads and your best closers, at the same time, bringing new people up. But you've got to concentrate your leads to your best closers. And that means year two, year three, year four, they should be making a lot of money. Lot of money, sometimes too much. I remember the first investment I ever made was a company called Pipedrive, which sold for a billion and a half. SMB CRM, and I put in the first sales rep there. He used to work for me. And the founders got mad at him. They got mad at him, because in three months, he was making more than any of the founders. They were pissed. 

**Jason Lemkin** (00:52:21):
I'm like, "Guys, I know we're have different backgrounds and cultures, but this is what you want." But it took them a long time culturally as self-serve... Because Pipedrive was 100% self-serve at the time. They'd never had a sales rep. And I put the guy in, and what he did was he went and he looked, all these self-serve customers, he's like, this was the company was about two million revenue at the time. And he's like, "Okay, who's bought more than one seat?" He just did a reverse sort and he called them. 

**Jason Lemkin** (00:52:48):
I remember he called AOL back in the day and he's like, "You guys have 20 seats of Pipedrive. Would you like to buy more for your sales team?" They're like, "Thank you very much. We'll take 100 seats." And he just went down the list, from the top to the bottom, and he took home 20% of those deals. Now that difference, that extra 80 seats at AOL, he brought it in. So Kim, keeping 20% of that is a great deal for the company, right? But he was pretty good at it and he was the only person for a while, so he made hundreds and hundreds of thousands of dollars when the poor company, the founders were paying themselves 50.

**Lenny** (00:53:20):
So your advice there is don't be sad if your salesperson is making tons of money.

**Jason Lemkin** (00:53:25):
If you architect the simple sales fund we talked about, and they're making a lot of money, you're making a lot of money, and your equity is worth a lot of money, your equity is worth a lot of money. Right?

**Lenny** (00:53:34):
When do you make that switch from they're taking home 100% of what they're selling to smaller percentage?

**Jason Lemkin** (00:53:40):
Look, some people are going to think it's goofy what I said about 100. I just view it as a simple ramp. I think you do it for one quarter max. One quarter. The problem is if you let this go for too long, more than a quarter, if you make it too easy for long a quarter, the problem is the mediocre lean into it too much, and that doesn't help. And not only does it not help you, it doesn't help them. You don't want to make mishires. They're so damn... I mean, you could have a whole podcast only just on mishires. It's probably the single most important thing in scaling startups. It's not hires, it's mishires.

**Jason Lemkin** (00:54:12):
It's a tragedy if you mis-hire a rep, but you don't want them there six months. It's not good for them either. You want to support them, you want to help them find a place where they're successful. And here's the quirky thing on the mishires, and this is why that first guy I hired was mid-pack. He was less than mid-pack. Here's the thing about sales is, someone can be very good in certain sales environments and completely fail in other ones, completely fail. It's probably more binary than any other function. And so you got to find it before you hire them, but you also got to root it out fast, because those leads are too precious.

**Jason Lemkin** (00:54:44):
And it doesn't mean they won't go on to another company and be wildly successful, but you could utterly fail. And the one related point I'll give, if I had to throw one hint on this first, any of these reps, first 10 and VP of sales, one hint more than anything else, trust... got to hire someone whose last product was harder to sell. This is so important. This is a recipe for utter disaster. If you've, take someone in sales and their last product was easier to sell, they will have none of the skills to sell your product. But, this is the... and I'll give you an example in a second. 

**Jason Lemkin** (00:55:27):
If you hire someone from something that was just a smidge harder, a smidge harder to sell, and they come into your company, it's like they're on Mars. It's like weight has been released from their feet. They learned how to sell a harder product and get it done, and then they come to you. And the first example I had, the first SDR I hired was a guy named Sam Blond, who went on to be CRO at Brex, and then now is a partner at Founders Fund. We worked together from the very beginning, first SDR. And he came, before that, he was an SDR at a company called Intacct, which was bought for a billion, which was online financials. Okay?

**Jason Lemkin** (00:56:03):
And back in the day, we know what the one thing people didn't want to put in their cloud was? Their financial statements, okay? It just wasn't trusted that. There are some things you would trust to the cloud, like A CRM, but they'd be like, "These are my crown jewels. I can't put these..." So he went from desperately trying to convince folks to move a business process to the cloud. They had no interest. And he came to us, and it was hard, but it wasn't that hard. And he instantly was number one. He went from SDR to SMB-AE to mid-market AE to director of sales, to head of this whole sale... 

**Jason Lemkin** (00:56:31):
Because, I mean, there are many reasons he was great, but he came to me and I said, "Sam, why are you crushing it when everyone else here is struggling?" He's like, "This is much easier than Intacct." So hire someone like that and don't make... No matter how much you like them, the sell me this pen thing will control for it a bit, okay? But, when we give, we give, we give in the hiring process. We never give what we want. Be very, very careful if the last product was easier to sell.

**Lenny** (00:56:57):
I love this advice. What is a heuristic that the last product was harder to sell than yours? What tells you that that's probably true?

**Jason Lemkin** (00:57:04):
Be very careful if it was more technical, very technical. Some folks can go from a business process sale. They can go from selling a Gong or a sales software and outreach, and they can go sell a complicated API to VPs of engineering or VPs of product, but most can't. Okay? So more technical is a tough heuristic. More competitive is a positive. What really works, Lenny, is if you hire someone from the number four in the space and they did well. You take someone that was number four in the space and then you're number two in the space? You think number two's really hard because number one's crushing you? But they're at number four in a space where no one can tell the difference between these products? That's a great one, right? So a space that's more competitive, a space that is a positive, more technical.

**Jason Lemkin** (00:57:55):
The other one related to technical, on the pure B2B side, is much more complicated business process. I see folks fail in B2B when they've sold us... Yeah, it's hard, but they've sold a simple business process. They go to something that's got a lot of integrations, a lot of complexity, and a lot of business process change, and they just melt because it's too complicated. They just melt, right? There's a company I invested in, it's doing over 20 million today. And for some reason, to simplify what they do, which is pretty complicated, they called it the Gong for X. Okay? The Gong for X.

**Jason Lemkin** (00:58:30):
And it's great to get a VC meeting and a few other, but it's not even on their homepage, okay? It doesn't really make sense. And the CEO had me listen to this senior sales exec he hired, and the only thing he could tell the prospect was that they were Gong for X. And the prospect kept asking, "Well, how does this deep integration with this business process flow and Zendesk works, and how does this go over to Salesforce?" He's like, "Well, we're Gong for X." That $600,000 deal was lost. Right? 

**Jason Lemkin** (00:59:00):
But, maybe that works in some space, right? And Gong's hard to sell today, too, don't get me wrong. But you see my point, it's just that simplicity, it's too complicated, right? And I know it's not the perfect heuristic, other than this technical thing, but the technical one, man, trust me. Too many B2B type reps melt in B2D. They just melt. They just, selling to VP of eng is a different... It's the same beast, but they don't suffer fools, do they? Not the VP of engineerings I'm close. They don't suffer fools.

**Lenny** (00:59:32):
I haven't heard this term, B2D before, business to developers. 

**Jason Lemkin** (00:59:35):
To developer, yeah. 

**Lenny** (00:59:36):
I imagine that what it stands for. Oh, interesting. Haven't heard that term before. Okay, let me zoom out a bit and I want to move on to a different topic. But before we do that, so we've talked about kind of these early days of hiring your first two reps, getting your VP of sales to help scale out the team. To give people a sense of where this goes, as the business scales, what does the org evolution look like? You have these two reps doing sales on their own, then you have a manager. What does it look like over the next couple phases of the sales org?

**Jason Lemkin** (01:00:03):
Look, to simplify, there's rules of eight. So in sales, I'm actually not sure what the tip of heuristic is in product, because product teams are leaner, at least I hope. I like it when they're leaner. But the thing about sales is there's no efficiency. If you have a sales-led motion, half your company is going to be in sales at 10 millionaire, half your company is going to be in sales at 50, half your company is going to be in sales at 100. 

**Jason Lemkin** (01:00:27):
One of the tough parts of the software model is that there's no, there's actually almost anti-efficiencies in sales. The public companies are actually less, they have the higher CACs than startups, believe it or not, because they have so much market penetration. So you're going to need a lot of people. And it's rules of eight, eight SDRs, outbound reps need one manager, eight AEs, eight sales execs need a director above them.

**Jason Lemkin** (01:00:51):
And then when you really scale, eight directors. You don't really ever going to have eight directors, but eight directors could have a VP or eight reports. And so there's rules of eight. If you want to build an organization on it, it's all rules of eight. For a while, for SDRs, thinking this was this high velocity entry-level position, people pushed it to 12. But the more folks I talk to today, the more I hear, everyone's reverted to eight. I'd rather have fewer SDRs, having better conversations. And so it's just, you can build your whole org with eights.

**Lenny** (01:01:21):
That is way too easy of an answer. So the idea there is you hire this VP of sales, then you hire six more sales reps, AEs. So what happens next? You hire another-

**Jason Lemkin** (01:01:32):
Got to start hiring managers.

**Lenny** (01:01:32):
Manager, got it. Yeah. 

**Jason Lemkin** (01:01:33):
Yeah. 

**Lenny** (01:01:34):
And then you scale eight more reps.

**Jason Lemkin** (01:01:35):
And once you have eight AEs scaled, you almost always are going to start, a VP's going to already... A good VP of sales already be hunting two directors.

**Lenny** (01:01:44):
Two directors. 

**Jason Lemkin** (01:01:45):
Whether they're just split up normally, whether they're east, west is a classic way. Another way, you might have smaller and larger customers. That's pretty standard, right? Commercial and enterprise. But a good VP of sales, once you have, she or he has eight reports, will be bringing in two directors, or maybe two VPs if they're an SVP. There's a lot of title-

**Jason Lemkin** (01:02:00):
... directors or maybe two VPs if they're an SVP. There's a lot of title inflation today, which doesn't bother me nearly as much as a lot of other things we've chatted about. I don't care what your title is if you'll actually do sales or do work, but yeah, we need to bring in managers at 8:00. And folks that can, also, a related point is, a lot of founders were, "When will my ... " I really love my head of sales, but she's a stretch. Most of them should be stretches.

**Jason Lemkin** (01:02:25):
If you hire the seasoned one, they're not going to work, so for 95 out of 100 of us, our first head of sales should be a stretch VP of Sales. And what that means is, usually, they were a director before that or a senior director or sort of a VP, okay? But you should be cautious about hiring your first VP of Sales for their first third VP of Sales gig. They're not willing to do the job anymore. You need a stretch and it's worth it for their career growth. It's worth it for their personal growth. It's worth it for the equity, right? So you're going to hire a stretch. That's an important tip right there. But then, you're going to be a little worried, how far can Lenny go? How far can ... I love Lenny, but he was a director for two years before this, and this is his first rule, and he did so good last year, but I'm worried Lenny's going to break. I'm worried Lenny's going to break.

**Jason Lemkin** (01:03:09):
And related to this quote, the simple answer is, Lenny will break if he can't source those directors under him. You can scale forever as a sales leader if you can hire better managers under you. And it's true of any functional area, but what you'll see in sales is, you'll see your stretch VP of Sales drown when they never can hire better managers under them, They'll end up doing some weird internal promotions, which you want, you want to do internal promotions. My rule is you want to promote 50% from within and hire 50% without for your managers, but if, really, the only people you can promote are just junior people on your team, you can't find leaders, you can't scale. I do see this happen too frequently is, a first time out of sales, does it, and then, they're like, "Here are my managers, Lorraine and Jason, but they really were just reps for two years and they don't know how to do it." So you have a whole bunch of people who don't know how to do it. That's when the organization cracks.

**Lenny** (01:04:06):
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**Lenny** (01:05:11):
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**Lenny** (01:05:26):
There's these terms that we throw, around account executive reps. It might be helpful just to give people a sense of what does this actually mean when someone's an account executive. Is it that they're just sitting there calling and doing sales directly? Whatever the key terms are for the roles or the title might be helpful to quickly summarize.

**Jason Lemkin** (01:05:42):
Yeah, I think the two ones that are probably less obvious for folks that are new are SDR, Sales development representative, and AE, account executive.

**Lenny** (01:05:42):
Okay, great.

**Jason Lemkin** (01:05:49):
And SDR is, generally, although I wish it wasn't, it is generally an entry level position, often fresh out of school, generally paid on the order of 60K to 80K, OTE and SaaS and US-based SaaS, and their job is to email for dollars, dial for dollars, and in many cases, to screen, to screen inbound leads. It's entry level, and their job is to pass on a lead to a sales executive, an account executive, an AE. Not all companies have SDRs. It's a longer topic, but pretty much all the best teams have some type of SDR function now because we've learned we want to specialize. We want openers opening and we want closers closing, so that's the rough idea.

**Jason Lemkin** (01:06:34):
SDRs are openers in some cases, and account executives are the more seasoned folks closing. And it varies, compensation from account executives that are US-based could be anywhere from like 90K to 200K, depending on, that's with base and bonus split at 50/50, depending on whether they're hunting tiny deals or big ones. That's the way an account executive works. And I think what doesn't happen today ... Those are the acronyms, SDR and AE. I think people can figure out VP of Sales, that's vice president of sales.

**Jason Lemkin** (01:07:03):
I think a thing that's gone on in the industry that founders expect now because there's so much discussion of it on LinkedIn and social media, but I haven't found work, is that an account executive will magically be full stack. They will do outbound themselves, they'll go use ZoomInfo and Apollo and develop a list themselves, they'll manage that outbound list themselves. If they don't have enough inbound, in their free time, you know what they'll do, Lenny, in their free time? They'll just do more outbound. You know what sales reps do in their free time? Today, they sell real estate or courses.

**Jason Lemkin** (01:07:36):
If you want a tip, for founders, I know you want this full stack AE, and they do exist when they're micromanaged in certain high performance sales organizations, but they don't exist in real life. They're specialized. Most AEs, they want to be closers, they want to be handed a lead, a contact me, a lead. They want to work the lead and close the lead, and you hire some SDRs to help generate more demand. Those are the basic acronyms. What's hard for most founders, here's the tough part, most founders have enough stamina to manage a couple of these AEs, these account executives, these folks who work the leads. Most founders do not have the stamina to manage 10 kids fresh out of college that need to be micromanaged on an hourly basis to call leads. Very, very few founders themselves can manage a team of SDRs, but if you can, it's like a superpower.

**Lenny** (01:08:28):
That was really helpful. I did not know what exactly these terms represented, and when you're hiring those first two reps, they should be at the account executive level.

**Jason Lemkin** (01:08:37):
Yes. And they'll do some outbound and they'll do some of this SDR work. It's just, ultimately, people end up specializing.

**Lenny** (01:08:43):
What is the title for someone above the account executive? Is it Director of Sales?

**Jason Lemkin** (01:08:46):
Yeah. There's really no ... It's a meta topic. I wish there were another career path for these ICs. This does exist in product, it does exist in engineering. It should exist in sales, but the truth is it doesn't. There is no senior SDR for this outbound. And for this AE, this person closing, yes, you can go more enterprise and make more money, but there isn't really a senior AE track, but there should be. It's unfortunate. It should be.

**Jason Lemkin** (01:09:15):
Too many of these folks are looking for, they look to go into management for promotion when what they really should look for is to be a super IC, and it's a shame that more folks don't try to implement a super IC role in sales, but that's basically what the career path is in management. And that's why another tip, another mistake that founders often make is, they hire the number one account executive at a hot company to be their head of sales. It's not the same skills. Most sales executives should not be managers. They don't want to be managers. They want to be individuals. They want to be highly paid individual contributors, these days, that mostly work from home and make a lot of money working 30 hours a week. That does not naturally breed great management skills.

**Lenny** (01:10:02):
I want to shift directions a little bit and talk about product and sales. A lot of listeners of this podcast are product managers or founders building products. How involved should product managers be in sales. And vice versa, how involved should sales be in the roadmap and what's being built?

**Jason Lemkin** (01:10:19):
I think, in the best run B2B organizations that I've worked with, and my own as well, the great, at least the head of product, the VP of Products, are deeply involved in sales. Deeply involved in sales. Deeply involved in sales. A company I invested in that's just crossing $60 million, they just had a meeting with who will be their largest customer. And the head of product who was there at the board meeting is like, "Thank God I was there because what they want to do with our product, we sort of do. We sort of." Now, the product does do it, but what they want to basically do is deconstruct the product. They don't actually want to use the backend and analytics and some other pieces, but they don't really need the front end of the product. If the head of product hadn't been there, that deal would've been lost. It would've been lost.

**Jason Lemkin** (01:11:11):
An AE doesn't know that. Even if an AE did know that, a sales rep did know that even. If the VP of Sales didn't know ... A VP of Sales would not be empowered enough to stand up in a meeting and say, "I know what you want. We can launch tomorrow on this and, in 90 days, we'll tweak the UI so it does exactly what you want." You want that in your big deals. If you have that magical VP of Product that truly owns the roadmap, owns it and has the gravitas for that meeting, they become like the mini CEO in these meetings. A lot of CEOs, at scale, I've talked to, I was just talking with Daniel Chait at Greenhouse, he does this, they're over $200 million, when they have that head of product, they divide and conquer with the big customers. And the CEO can do some and the VP of product can do others.

**Jason Lemkin** (01:11:52):
And sometimes, the VP of Eng, if they're very producty, can also do that role too. Sometimes, you get these three weapons. In the old days, sometimes, your VP of Customer Success could do this, but unfortunately, and I know this is going to trigger some people, I don't see customer success stepping up for this pseudo product role anymore and I see them shrinking more and more into process. But that magical VP product, they give you that scale, that CEO level scale.

**Jason Lemkin** (01:12:17):
Can a product manager or a director do that? In my experience, the best ones, absolutely. The mid-pack ones that are working on the color of the pixel, I'd rather not have them in the deal. You have to be utterly fluent in the product, the entire product. You have to be the one that knows how to connect the pieces that don't ought to always connect themselves, and you have to have the gravitas to work with a large customer, to make commitments. That deep knowledge and commitment, it's hard below the VP level, but if it does exist, I love it. They become one of the greatest strategic weapons of a B2B company is bringing the product team, because the VP of Sales never quite goes that deep and can't make the commits, and the CEO can't be everywhere.

**Lenny** (01:13:04):
The bane of many product managers' existence is the flip side of sales is involvement in product. Do you have any advice for how you've seen the best SaaS companies handle requests from sales going into product, how product teams should think about requests from sales and making that work?

**Jason Lemkin** (01:13:21):
I think this one is so simple and I'm surprised people make it so complicated. I know we all release 28 times a day, but the reality is, software still goes on quarterly release cycles no matter what we say, okay? Your customers cannot process 88 releases a day, okay? The maximum a customer can process is two big releases a year and maybe quarterly, so let's simplify. That's how customers think. Forget about how we build software.

**Jason Lemkin** (01:13:46):
Every quarter, give your head of sales a certain budget, whether it's story points or 10% of the pie chart, however you do it, give them a budget. And when you do this, things will radically change then. They radically change because even the best VPs of sales, they change the wind. On Monday, what they really need is the HubSpot integration. And they're like, "Oh, my god. We weren't going to do that for two years, but I guess we could change everything on Monday," but then, on Wednesday, a new prospect comes in and, "We need SAP, but we just spent two days spec-ing out." You need SAP. And then, on Monday, it's Salesforce, right? And it's not that sales isn't honest, it's just, the big deals, the big ones always, the tail's wagging the dog and it burns out the organization. Even with the best sales leaders, I find it burns out, especially because the stressful deals, they overreact, and as good as they are, they're not product people, so they don't really know how to prioritize and force rank. If you say, "Okay, Lenny, my VP of Sales, great. HubSpot, SAP, whatever you want, you've got 10% of the budget, you've got a hundred story points, whatever metric or heuristic you use, but you've got to decide now each quarter." And if you want to change during the course of the quarter, if you want to disrupt our whole engineering product team, you can do it, but understand there's a high cost, and the later you do it in the quarter, the less successful it's going to be because we already started the HubSpot integration. Not only will it demoralize the team, but we're going to run out of ... You already used your budget.

**Jason Lemkin** (01:15:10):
I just don't see enough product leaders being objective here, saying, "Listen, here's your budget. You get it, period. No one can take it from you. I guess the CO can steal it if she really wants to, but it's yours, VP of Sales." And they will do the low balancing across their team. They will do it. They will listen and they'll say, "Maybe I don't need that HubSpot integration after all. Maybe." Right?

**Jason Lemkin** (01:15:32):
Because what a VP of Sales should be doing is taking ... The CO has to look five years into the future, product has to look about two years into the future, and you really can't ask a VP of Sales to look more than 12 months into the future because that's where they're next on the line, but a good one will load balance feature requests across the year and they will listen to all the ... Because they'll actually be in sales, they'll actually be in deals, and they'll be listening and they'll realize, look, even though we're going to lose this deal to HubSpot, I've gotten four SAP requests the last ... I'm going to take that bet. Even if I'm wrong, I'm going to take that bet. You got to give them that ... And you got to talk about it every week and say, "Look, here's your budget, Jane. Here's your story points, your whatever, your 10%. This is what we're building now for you, objectively, and this is what we're currently planning for the next two quarters. Would you like to change the ones for the next two quarters? Because we haven't committed any code yet, and if you really want to change what's in process, you can. Otherwise, it's going to be hyper disruptive," but you got to at least pretend to be objective, for sales.

**Jason Lemkin** (01:16:29):
If you're too emotional about it, you break the organization. You've got to say, "Listen, I know we've already written 80% of this upside integration. If you really need us to put us on the shelf and drop everything for a million dollar deal, we're a startup, we'll do it. But understand, just, is this really really what you want?" And if you involve them every week as a stakeholder, magic happens. But I don't see the VPs of Product that I work with, I know I'm not in all the staff meetings, but the meetings I'm in and the board meetings, I don't see this back and forth happening.

**Lenny** (01:16:58):
This might be the same exact answer you just gave, but say you're a PM and a salesperson comes to you, "Hey, we're about to close this deal. We just need this one feature. Can we just get this on the roadmap?," what's the best way to help that sales person understand why it's not happening, help them feel like, "Okay, I get it"? Is it exactly what just said? "Okay, we could change everything. Here's the story points you have this quarter."

**Jason Lemkin** (01:17:18):
Well, are you assuming that PM is the head of product or empowered enough to make the decisions?

**Lenny** (01:17:24):
I'd say, yeah, that PM could put something on the roadmap if they think it's important.

**Jason Lemkin** (01:17:29):
The reason I'm a little confused, just because I want to get it right, is what I'm hearing, which maybe isn't what you said. What I'm hearing is, a junior IC rep is talking to a more mid-level product person about the roadmap, and you want to have that. That's one of the reasons we actually go to the office. Because those discussions don't happen on Zoom, Lenny. They don't happen. And you want those discussions to happen, but you don't actually want them empowered. You want them to say, "Listen, I will talk to my boss." That's the right answer. It's too confusing.

**Jason Lemkin** (01:17:58):
This stress between product and sales is a good thing. It's a sign of a well-run B2B company when there is stress between product and sales. It's a good sign. If there's no stress, you're not in enough deals. You're not in enough deals if there's no stress. But the stress has to have enough process, even in a startup, that it doesn't break because your question alludes to the fact it can break organizations again and again. People get people resented on either side.

**Jason Lemkin** (01:18:27):
This is one thing where the answer has to be, "Look, we can chat about it over lunch. We can chat about this, and actually, you've got a good idea. You're an individual contributor. I'm going to tell my boss, on Monday, that I think we should do that. I'm going to use a little social capital and say we should do this," but you got to push the decisions up. That's as far as you're going to make recommendations. Otherwise, instead of that one stressful conversation with the VP, you've got a hundred, you've got 10 times a hundred, and again, they're fun lunch conversations, but they'll wreck you.

**Lenny** (01:18:54):
So the advice there is, basically, "Talk to your manager, I'll talk to my manager. They need to hash this out. It's not my call."

**Jason Lemkin** (01:19:00):
I think you got to push it up and I think you got to force the VP of Sales and the VP of Product to have this weekly meeting about the budget, and that will force them to have enough, just enough organization on their team so that it can all be surfaced up. Because that means they each have to have a meeting with their team for 30 minutes each week and say, "I'm going to put up on the whiteboard guys, so my sales team's going to say ... And I've been to many of these meetings where you force the sales team to force rank what they want and you end up, I tell you, Lenny, 100% of the time in the sales meeting, when you do a whiteboard force rank, you end up walking out with very different outputs than when you start the meeting in sales 100% of the time.

**Jason Lemkin** (01:19:36):
On the product side, you need to do the converse. You just need to say, "We owe sales 10% of our points, so in our team meeting, we're going to put in 15 minutes of our team meeting and we're going to make sure we have the priorities right from the sales team. Let's go over what I got from Linda. This is what she says. Is she wrong? Should we do something? How can we help the sales team be even more successful? And what inputs is the team hearing?" And they can raise their hand and say, "Look, I just had this all the way conversation with Bobby, but honestly, Lenny, I can build a HubSpot integration in a day." "What? I thought it was really hard." "No, I did it my last company. And listen, actually, we can actually outsource it to Bob and Linda at this agency I know for 20 grand. They'll build it next week." That's a magical moment. That's a magical moment. But you've got to each have these parallel meetings. The good thing, that tension can become debilitating. We've all seen those fights, those almost fights that break out, and they come from a place of passion, but you got to have structure.

**Lenny** (01:20:30):
Great advice. Another trend I've been noticing is that product teams are taking on P&L responsibilities and revenue goals. What's your sense of, is that good? Is that bad? How do you think about that for product teams?

**Jason Lemkin** (01:20:42):
You want everyone to be aligned on the big picture revenue goal for the end of the year. Is the question, you mean having financial bonuses and stuff tied to hitting the plan?

**Lenny** (01:20:51):
More that their KPIs and their OKRs are essentially this much revenue, you need to drive this much revenue from your experiments, from your product launches.

**Jason Lemkin** (01:21:00):
As you can see, I have a lot of opinions on things here that are data-driven and they're live experience. This one, I need another year in the oven, and I'll tell you why. I'll tell you why. What happened in the last 18 months is, in customer success, which is, in the old days, it was related to product. Now, it's less so, which I think is unfortunate. In customer success, in the last 18 months, this happened all across the industry. 24 months ago, customer success's goal was happy customers, measured in retention, sometimes NRR, net retention with upsells, sometimes with GRR, just logos, how many logos. That was your only job, happy customers. Happy customers.

**Jason Lemkin** (01:21:35):
Then, things got harder in many areas of B2B, and every customer success team last year, their goal was revenue. You need to bring in more revenue from existing base, and it destroyed customer relationships. There is a leading public company, I will not name them, I'm a positive guy. I will tell you, a company we all have used for years and loved, and they did this to their customer success team. And they came to us last year, we paid $299 a month for this product for our team. They asked us for $50,000 upfront or said they would turn it off that day. That week. Maybe it was that week. $50,000. That's what happens when you weaponize ...

**Jason Lemkin** (01:22:10):
So customer success, and I know this is going to trigger some people, but it is true, when I talk to leaders, customer success got weaponized and I don't like it, okay? I don't like it as a product. All founders are product people, I think. I don't like it as a product because I think it's bad for customers. That experience I got was horrific, wasn't it? That $50,000, being told. And it got walked back. And the fact that it got walked back is almost even worse because it wasn't true. It was a threat. So do I want to weaponize the product team? I want the product team aligned with revenue. These are the tough decisions for founders. But I think we're going to regret, to some extent, weaponizing teams that we don't need to weaponize.

**Jason Lemkin** (01:22:50):
Having said that, I will tell you, and again, no knock on Adobe, but when I was a VP at Adobe, and it was a long time ago, when product was in another building, in some cases, that was not great. And I love Scott and the whole team, it was a great experience, but I can just tell you, what I learned is, that did not work for me as an agile guy, having groups that would do this work in isolation.

**Jason Lemkin** (01:23:08):
And the problem was they would never talk to a customer. So I know I want product constantly talking to the customer. I know I want the VP of product with a lot of equity, wanting to hit the bookings number for the year. Do I want your average PM weaponized and forced to bring in revenue from an experimental feature when everyone agreed it's the test we're going to run? We only can run so many. You say, should they have revenue for new feature launches, for new product expansions, for new things? Yes, but everyone's got to make that decision. There aren't that many of us that have 1,000 extra engineers sitting around with nothing to do.

**Jason Lemkin** (01:23:44):
Even the smallest experiment, no matter what it says on the internet, again, that we release 100 a day, I would argue bringing any product extension to market's very expensive and risky because you're taking away from something else. These are expensive decisions, and if you punish people for the mistakes in product, this is something that big companies are actually better at than startups, which is, they don't fire you if the new initiative fails. Because then, no one at a big company would ever join the new initiative. There are exceptions, but no one at Adobe or Google would work on the new product if you got fired if it didn't hit $100 million its first year.

**Jason Lemkin** (01:24:21):
One thing I saw that was very, and I don't know how to bring to startups in my big company experience, was it's liberating to some extent to let people fail, up to a point. It's liberating. Because the cost of failure in a startup is so high. It's just so high. I know that's not what the exact question, and ask me in a year, but my advice is, just be careful that you don't weaponize functions.

**Lenny** (01:24:44):
I probably should have clarified. I think this is mostly true in PLG companies where most of the growth is coming from product, but I imagine the advice is still the same, as be careful putting too much pressure on the product team to focus on revenue.

**Jason Lemkin** (01:24:55):
Well, that's how you get dark stuff. Back in the day, I remember my sister worked at Vistaprint for online business cards, and her entire job was to force people to buy upsell of additional products on the checkout page they didn't want to buy. That was her whole job. And she was paid a large variable comp to get people to buy third party products without realizing in their checkout, and it worked. That's all she did for three years was figure out dark stuff, I mean, darkish, grayish stuff so that when I bought my business card, I also bought GoDaddy and 11 other things that I didn't want. It works, but it doesn't create high NPS, does it?

**Lenny** (01:25:34):
Mm-hmm. No.

**Jason Lemkin** (01:25:34):
The thing is, here's the thing that you have to think about. What's crazy, Lenny, now, and I talk to a lot of these folks is, there's so many SaaS companies that are a billion or more in revenue. I just wrote up, there's just Pimcore, which is SaaS for [inaudible 01:25:49]. I just wrote up, it just crossed a billion in revenue. So many folks are at a billion. CloudFlare just crossed a billion. And the only way you can do that is if your revenue compounds.

**Jason Lemkin** (01:25:59):
And in fact, just myself, I just interviewed Matt Mullen from WordPress, 20 years he's been doing this, and he said the number one thing he didn't understand at WordPress, at Automatic, was the power of compounding. When you do these things that are customer hostile, and a lot of us were under stress this last 12 days, to hit the numbers, right? This is your job as founders, is you got to load balance the fact that the cash has to last and my investors are mad with me, but nothing matters in B2B that it compounds. And some of this dark stuff you're talking about on PLG, it anti-compounds, right?

**Jason Lemkin** (01:26:30):
And I would say, let me flip it around. The magical thing at the high level that it compounds, if you have a self-service model, a PLG model, I actually think the number one most important metric is churn. It's going to this churn, churn, churn, churn. And when I meet any startup for investing or anything, if the churn is not top decile for a low end product, I'm always out. It's almost unsolvable. It's almost unsolvable. This 3% to 4% a month churn rate that a lot of SMB stuff, it's almost unsolvable. I think that's where the energy should go, is relentlessly bringing down churn so that, 20 years from now, we all have billion dollar AR companies. That's what we want.

**Lenny** (01:27:11):
I have a rapid fire set of questions before, actually, the lightning round. We'll see if we have time for all these things. I just have a bunch of random questions that I'm going to fire at you and and see what comes up. Sounds good? Okay, cool. I got a thumbs up. Let's do it. Okay.

**Lenny** (01:27:23):
What is just one thing a founder or product leader can do to become better at sales?

**Jason Lemkin** (01:27:28):
The number one thing they can do, and this is very tactical, at the end of each meeting, because again, both of them are good middlers, the head of product and the founders are good demos, they're good at talking shop, they're good at talking in the industry, they're good at talking workflows. Learn. This is the only school you have to learn. Even if you're not comfortable asking for money, learn to ask for what's the next step, Lenny. This is what all great salespeople have learned to do. Actually, mediocre salespeople are terrible at this. The best salespeople never leave a meeting without a next step. The next step does not have to be a check. Don't break the relationship, but what's the next meeting? The next meeting might be, "Lenny, is there someone else at your company that I could do a demo for?" People are lazy, they don't want to do multiple demos.

**Jason Lemkin** (01:28:06):
But in 2024, we all have to demo multiple stakeholders. In 2021, it was one stakeholder. Now, it's a four-stakeholder sale. If you don't know what to ask at the end of the call, ask, "Who else? Lenny, is there someone else? I know you're excited to buy our product, but who else? Is there someone else in the organization? Can I help you? Can I just do a demo?" A demo is not threatening. Whatever the next step is, "What's the next step we can get on our books?" And listen. The best salespeople march deals step by step by step by step. And then, they put in the time. They've done the demos, they've done the pilot if they need to, they've helped them get going, they've helped them see the value. And then, if it takes six steps to say, "Okay, Lenny, now, we've got five folks at the organization using it. They've seen, here's the deal. They love it. What can we do, Lenny, to get going? Can we get a contract signed so we can get going on February 1st?" It's just very natural.

**Jason Lemkin** (01:28:56):
And founders just end the Zoom. They end the zoom, and they wait. They wait in their inbox for the next step to come, but it doesn't, it rarely comes on its own. That's the number one tip is, make sure yourself and you log it in your CRM that you always have a next step after. You don't have to be great at sales. As long as you're moving the ball down the field, then, get it into the red zone, and then, figure out how to ask for the money. But you don't have to ask for the money until it's in the red zone.

**Lenny** (01:29:21):
Amazing. While we're on this topic, is there a book or a course or anything that you'd just recommend folks go to get better at sales? Just, is there some tactical, "Go read this thing, it'll help you?" Or is it just, it's hard, you're not going to figure it out reading a book?

**Jason Lemkin** (01:29:35):
I am a voracious reader. If I only wanted to plug myself, and I usually wouldn't, two things that we have, we have something called SaaStr.University. SaaStr dot University. It's free. There's a pretty good course on learning about sales. It's not perfect, but it's pretty good. It just organizes a lot of the stuff we're talking about and it's free. I think it's pretty helpful. We actually wrote a book called From Impossible To Inevitable that sold about 100,000 copies.

**Lenny** (01:29:59):
Oh, wow.

**Jason Lemkin** (01:29:59):
I did almost none of the work, although a lot of is my content. Aaron Ross did it. It's actually really good, and it interviews a lot of top sales leaders and goes through a lot of stuff we did. I don't usually hype it, but for 15 bucks, I think this ... And actually, I've never made one dollar. I've never even gotten a check for these 100,000 copies, but I do think it's actually ... The second edition's pretty good. I think, if you like what we're talking about, the SaaS University and the others. I just hyped my own course, which is free. I don't think I'm selling something if it's free. The book, I don't make a dollar on. I'd be very cautious, and this is tough, maybe this is something we can work on together. There's so many courses and so many people selling stuff the last two years. Just be cautious. Just be cautious.

**Jason Lemkin** (01:30:39):
The last thing I will say is, there is another community called Pavilion, and I do think it's great, Pavilion. It does have a lot of networking and stuff for sales and connecting sales with founders, and I am a super fan of what they're doing. That one's worth investing. But be careful about Bob and John's sales courses on the internet. There's too many.

**Lenny** (01:31:00):
Awesome. All right. We'll link to all those things in the show notes.

**Lenny** (01:31:03):
Next question, what is the ideal trial length for a free trial for sales team? Is it 14 days, a month, years? What do you recommend?

**Jason Lemkin** (01:31:11):
Tomas Tsangaris did a whole SaaStr presentation just on free trials and free trial legs. It was really good, but the one thing you didn't know, which I know just being an old timer is, one of the reasons we have 14-day trials on the internet is, in the old days, the Salesforce sales team, when they were SMB focused, wanted to close deals that month, so they forced Salesforce to move from 30 to 14 days. There was no evidence it was better for the customer. There was no evidence that it got usage going or people ... They just wanted to close deals the same month.

**Jason Lemkin** (01:31:41):
That's something to be cautious about, these metrics. And be cautious, since we're talking to product folks, be cautious that they're customer centric. Salesforce is very enterprise now, but in the old days, it pushed it to 14 days so the sales reps could close deals faster. What we learned from Slack and Zoom until recently was infinite trials work pretty good. So does Canva. Slack and Canva, and until recently, Zoom, were okay waiting four years to convert. And those are epic companies. Those are epic companies.

**Jason Lemkin** (01:32:12):
And these are one of the things that only founders can make these decisions. I wrote this post years ago saying, "Who's your VP of Free?" Because who's got a VP of Free? Do you know anybody that has a VP of Free? I got a VP of Growth, I got a VP of Product, but if you have this massive free base, who's your VP of Free? I know almost no one that has a VP of Free. And usually, it's some kind of collaboration between product and the CEO. They're the VP of Free, but even then, you need a VP of Free, and we don't have one. There's no perfect answers, but in a world where we're tightening nooses everywhere on trials, we're tightening nooses everywhere, be careful the advice you get, be careful the short-term advice.

**Jason Lemkin** (01:32:49):
For example, another related terrible piece of advice VCs give you is switch to annual contracts. This is terrible advice. Terrible advice, Lenny. Terrible advice from people who've never built products and aren't in the field. Going to annual contracts, on a spreadsheet, looks great. On a spreadsheet-

**Jason Lemkin** (01:33:00):
Going to annual contracts on a spreadsheet looks great. On a spreadsheet, it looks great. You know what's better? Letting customers pay what they want to pay. If it's you or me buying for ourselves, Lenny, we're still going to put it on a credit card, aren't we? If it's our own personal... And we want to pay monthly, usually, we don't know. We usually want to just try it monthly. Big companies want to pay annually because they have procurement departments. Why force people to go the way they don't want to go? So, just start, this is a product audience. Product needs to be the voice of the customer. It's not so much customer success as it used to be. It's not so much sales. You've got to be the voice of the customer together with the founders and push these things.

Think about, I would do the longest possible trial that is still customer-centric and understand there's tension. Understand there's tension, right? I say anytime I'm in a board meeting where some VC says, "Let's do annual for an SMB." I mean, I'm like, "No. Show me the money. Show me the evidence that this is better for the customer." Right? How many times have you gone to buy something for yourself and it's 19 bucks a month? And that's annoying because it's an [inaudible 01:34:05] credit card charge. But do you want to pay 240 up front? I mean, maybe you do it for a Riverside or something you use all the time, but some random product you just discovered, you're just going to bounce.

**Lenny** (01:34:14):
Yeah.

**Jason Lemkin** (01:34:14):
So, who's going to show the data in the bounce, right? A related point for like in sales. No one does enough. Everyone does these win-loss meetings, but no one talks enough about the deals they lost. Everyone talks about the deals they won. All the lore, the tribal lore in sales is how we won the deal. People should be spending more time talking about deals they lost than the deals they won, right? And same thing for this PLG motion. If you make your... Yes, I give everyone a pass for 2023, everyone whose growth plummeted and did things they shouldn't. Did price increases they didn't earn, cut the free trials, hid their free editions. I'm going to give you, look, you've got to give your team a little stress relief, right? So, I'm giving every person I work with, every portfolio, everyone a one-year pass, but the past is in the past. We got to get back to being customer-centric and building businesses that build to 1 billion ARR on their own because they're wonderful businesses that are product-centric, right? That recur. And every time you rip a customer off, you lose, the relationship's damaged, isn't it?

And a controversial thing I say to the product team, but I think this is the right thing if you're going long, is look, everyone raised prices last year, as you know, right? Everyone raised prices. The question I ask folks is, "Did you earn it?" What feature did you [inaudible 01:35:36]? If you raised prices 8% last year, did you add 30% more value to your product? In the old days pre these crazy years, the answer usually was yes. People would wait up, they would wait a couple years, right? And the whatever product from four years ago was so much better. It really was. Really had earned 1995 instead of... Last year, no one earned their price increases. No one came out with an amazing new edition, an amazing new functionality that earned it. They just sent you an email saying, "You're paying more." So we get one pass, but earn it. Earn your price increase.

**Jason Lemkin** (01:36:12):
Have the longest free trial possible. Own it. And this VP of free thing, it's got to be founders working with product because no one else cares about the free. You can't expect the sales team to care about free, can you? You can't expect the marketing team to care about free. Unless they can immediately monetize them, right? Unless they can ram them into conversion. People abuse their long tail in the last 18 months, but your long tail is... These are your... Even if they don't convert that much, they're your advocates. I mean, how many people read your newsletter? 500,000 people, right?

**Lenny** (01:36:42):
Almost 600,000.

**Jason Lemkin** (01:36:42):
Okay. 600,000. Now, not all of them convert, do they?

**Lenny** (01:36:46):
No, no. That's exactly how I look at it. It's kind of this Brian Belfort is this... Yeah.

**Jason Lemkin** (01:36:49):
What about the 590,000 of them that love Lenny that don't convert, right?

**Lenny** (01:36:49):
Yeah.

**Jason Lemkin** (01:36:53):
If you over monetize them, then Lenny's community doesn't exist if you over monetize them. But when you bring in a VP of sales for Lenny and a VP of growth and... You can't expect them to care as much about that long tail as you, right?

**Lenny** (01:37:10):
I love your passion around this. So, what I'm hearing partly is you're a big fan of freemium, essentially, right? And the benefits of product-led growth where there's something you could just use indefinitely, or do you still think that's a trial where it needs to end at some point? Or is it just like, "No, freemium is great in a lot of cases."

**Jason Lemkin** (01:37:26):
Listen, I'm a big fan of long tails when they work. They don't always work for enterprise products. I'm a big fan of delivering more value than you take out for your customers. And I am a big fan overall of free, because, and I know you'll agree with me this, every founder figures this out at some point, free products are better software. Products that cannot offer a free edition cut corners because they don't have to be good at onboarding. The products that do not have a free edition have human beings that bridge the gap between buying and deployment. And that's okay at ServiceNow, and that's okay in certain products, but the best products invest the engineering cycles to have a free edition. Whether that's truly free, whether it's an extended free trial, whether it's a long tail or a medium tail, the amount of... All of your product is better if people can actually, if you have a free edition, all your customers benefit, even your enterprise customers benefit, they all benefit.

**Lenny** (01:38:28):
This idea of VP of free, I think technically it's probably the head of growth for a PLG company or the person leading the self-serve part of the business.

**Jason Lemkin** (01:38:36):
But even in the organizations I work with that have those structures, that person is focused on monetizing that tail. Who stands up for the 590,000 other readers of Lenny? Who's their champion?

**Lenny** (01:38:50):
Yeah, and you're 100% right. That's exactly how I think about the newsletter. There's this free audience and ideas over time, they get convinced, "This is worth my money and let's give it a shot."

**Jason Lemkin** (01:38:58):
Yeah.

**Lenny** (01:38:59):
So, yeah.

**Jason Lemkin** (01:39:00):
And your growth person, let's say 1% converts. I'm just making that up. Going from 1 to 1.1 actually is a big deal at that scale or 1.2, right? That's okay. But you've got to nurture the 490,000, not spend all the calories just ramming the paid edition through, right? But only it's a fun thing, because once you hit a little bit of scale, this is part of your job as founders and product leaders is who's going to be this VP of free? It's not growth. I don't think it's the growth team or something. They got to hit their goal when their goal is to get another half million out of the newsletter this year. And that's their bonuses side to it. You think they're going to work on the free ones? No chance.

**Lenny** (01:39:43):
Makes sense. Oh man, there's so many things I can go into and want to go into. I also want to let you go. So with that, is there anything else, Jason, that you wanted to share before we get to our very exciting lightning round? Anything you want to leave listeners with?

**Jason Lemkin** (01:39:59):
I think just make this the year of the customer guys. And I really think that sales has been under so much pressure the last 18 months. Customer success has changed. Product teams have to step up. This is your time to do great things this year as product people. Be the voice of the customer. Be the VP of free, be the whatever. Be the champion. Stick your neck out. Work harder, folks. Work harder. The funnest part of anything in startups is working hard, but in product it's extra fun. By working harder, you have the more fun conversations. Find the delight in... The delight in product is making customers happy. It's not worth it otherwise. Otherwise, it's drudgery and it's endless PRDs and Gantt charts and mindless discussions. Make your customers happier this year, you will be happy. This is the year of product and we all get a pass for whatever bad deeds we did or mediocre deeds we did in 2023, get back and be the vanguard in your companies.

**Jason Lemkin** (01:40:53):
Startups need, and bigger companies, they need someone to inject energy again, in many cases. Energy has been lost. And it would be great if product can do it and say, "Listen, let's do something, even if we're not exactly sure how to get more leads, okay? Even if we're not exactly sure what to do, there's one thing under our control is the code we write and the product we ship. Let's do three great things this year. Let's just do them not good, great. Let's do three great." And what you'll find is the whole company will respond, including sales, including marketing, including... If we start shipping great thing. Few things inspire teams more than when you're shipping great products. So, be... If you weren't the leaders the last two years, if you were being forced to weaponize your customer base somehow ship three great things this year. That's my challenge. Ship three great things.

**Lenny** (01:41:39):
I love this. This is the year of product, that might be the title of this podcast.

**Jason Lemkin** (01:41:42):
Yes.

**Lenny** (01:41:44):
Jason, we've reached our very exciting lightning round. I've got six questions for you. Are you ready?

**Jason Lemkin** (01:41:50):
Okay. I'm worried about the first one, but go for it.

**Lenny** (01:41:52):
They're very non-surprising, because they're always the same. What are two or three books you've recommended most to other people?

**Jason Lemkin** (01:42:00):
That's the one I just want to take a pass on. I know they're the same. I read more than anybody else in the planet, but I wish I had those two great, great books that have changed my mind in the last 12 months, but hopefully I've added some value. But this one, I think I got to take a mulligan on it [inaudible 01:42:14] perfect answer.

**Lenny** (01:42:15):
Acceptable answer. Is there a favorite movie or TV show that you really enjoy?

**Jason Lemkin** (01:42:20):
Oh, the Terminal List.

**Lenny** (01:42:22):
The Terminal List?

**Jason Lemkin** (01:42:24):
The Terminal List, yeah. I'm not into the Chris Pratt. I'm not into that goofy superhero movies he does, and I know people like it, but The Terminal List, it's pretty, it's I got it, it's written by this ex Navy SEAL that I think tech people don't read the... Everyone, but outside of tech reads this author, but Amazon did this thing with The Terminal List and I think the reason it gripped me, it's like this post, it's like a lot of tech is right now is we're trying to do the right things, but are we doing the right things? It's just confusing. So this one, I couldn't put down, The Terminal List. That was my favorite show. The other one, the one I liked that I couldn't believe. You want the second one? Even though as product people, Maverick, and I'll tell you why. This movie Maverick, okay, you wouldn't think I'd be a Maverick guy, but the quality of this product, it was so high.

**Jason Lemkin** (01:43:18):
And what's interesting, you remember Maverick was supposed to come out before COVID, right? And Tom Cruise said no. He's like, "This is such a good product, we're going to wait years for this thing to come out." I'm like, " I'm not going to like this. I mean, cocktail was pretty good, but I'm not going to..." But as a product, when... I've watched this movie three times from a product perspective and just like software, when something is done that well, no wasted calories, no... You see everything tied together. That's the kind of software we love, right? It just delights us when it works better than we expected. So, those are my two.

**Lenny** (01:43:53):
You also did all his own stunts, which I think is a good metaphor for the VP of product, maybe being involved in sales?

**Jason Lemkin** (01:43:57):
Actually do the work.

**Lenny** (01:43:59):
Yeah, exactly. Oh man, I'm stretching it. Okay, next question. You answered a few of these already, but do you have a favorite interview question that you like to ask when you're interviewing salespeople, especially?

**Jason Lemkin** (01:44:09):
I answered it, but I'll bring it up again in case you do it again, is, "What do you want to do your first 14 days to head of product or head of sales? What do you want to do your first 14 days?" It's not a technical 30-day plan, you don't have to do 28 slides. It's what I call a Colombo question. Not that I really watch Colombo, but Colombo is this old detective on TV who, I don't know if you ever, he would ask these dumb questions, right? And then the murderer would thought Colombo was so dumb, but by the end of the episode he'd always incriminate himself because the question. So, when I interview, I always do Colombo questions. They're never tough ones, right? They're always nice. I don't know if I'm nice, I'm honest, but the interview [inaudible 01:44:42]. But Lenny, what would you do your first 14 days as VP of product here? And they don't want to visit customers. Don't hire them for either all sales or product. Just don't hire them.

**Lenny** (01:44:51):
I've got the answer now. That's exactly what I was going to say. Next question, what is a favorite product that you have recently discovered that you really like?

**Jason Lemkin** (01:45:00):
My two aren't really amazing, but I'll answer them. I love this Opus Clip app for making clips from video. I know you have a whole team, so you probably don't need it, but I produce a lot. I mean, a lot of us do content marketing, right? Whoever does it. I produce a lot of content. I don't have enough time to do this. So, the only AI tool that I've found helpful so far is Opus Clip, which takes all of the incredible amount of YouTube content we have, force ranks it and turns it into 59 second clips. And I'm a case study on the website, I tried the earlier products. They were almost there, right? But it's not, is it as good as a clip team? Right? Is it? No.

**Jason Lemkin** (01:45:41):
But literally in 60 seconds, what's interesting is it can do something, I don't have time to, I could never make YouTube clips. I could never make Instagram shorts. I could never make... It just would never happen, right? It would never happen. And the fact that one product enables you to do something at a minimum viable quality you couldn't do before, I'm a super fan. It's pretty interesting. So, that's my favorite. And my other one is this one. This is the folding phone that's finally as good as a normal phone. See, there's The Terminal List that I've got folded up and it just works.

**Lenny** (01:46:11):
That's wild. [inaudible 01:46:12].

**Jason Lemkin** (01:46:12):
Weighs the same as an iPhone.

**Lenny** (01:46:13):
Wow.

**Jason Lemkin** (01:46:13):
And so the reason it's cool is this has changed the way I do productivity. One Plus, it's the One Plus. One Plus, yeah, it's the first one that's the same form factor, same weight, same... So there's really no, there's no loss, right? And I can use my normal phone here like I'm using, it's a little funky, but it's the same... And then to be able to do content, to be able to check, to be able to do full email, full account, full everything like an iPad.

**Lenny** (01:46:39):
We see your Tesla where it's going, that gets parked. That's very cool. So, it's basically like an iPad plus a phone in one.

**Jason Lemkin** (01:46:47):
Yeah, I probably use this 50% of the time of the iPad.

**Lenny** (01:46:50):
Wow.

**Jason Lemkin** (01:46:50):
So, that's it. That is it. The other ones that-

**Lenny** (01:46:52):
And that's an Android?

**Jason Lemkin** (01:46:53):
... Make sense to me. Yeah, it's a Android, yeah.

**Lenny** (01:46:55):
Amazing. I had not seen that. That is extremely cool.

**Jason Lemkin** (01:46:55):
Very cool.

**Lenny** (01:46:58):
Two more questions. Do you have a favorite life motto that you often come back to share with friends or family, find useful in work or in life?

**Jason Lemkin** (01:47:07):
It's, "Be kind." When I reflect on my career, right? Mistakes, I don't think I'm mean. I actually think I care more than most people on the planet. I like to think I've helped a lot of people, but it's not taking the time to be kind at times and not taking time to be kind when things don't work out. Not taking time to be kind when you leave a situation. Not taking time to be kind when maybe someone's done the wrong thing, right? Not taking time to be kind to a customer that leaves, right? I remember in the early days of Adobe Sign of EchoSign, we had a top tech customer, and I killed myself to close this customer, right? Famous, huge, huge tech company today. And then our champion changed, right? Champion changed is the big issue in software and product and came in and brought in DocuSign instead of us.

**Jason Lemkin** (01:47:56):
And man, I was just so mad. I just tore into this guy. This guy had actually had dinner at my house, right? And he actually never, he just did it because it was good for his own career, right? And I was so mad, but I broke the relationship forever, right? That's just one random example. And I just think there's so much advice to fire fast and to be relentless and do all these things, right? But you've interviewed so many people, Lenny, right? I mean so many great leaders. I haven't done what you've done, but I've talked to almost all the top CEOs in SaaS, okay? And they're kind. It doesn't mean that they're soft, they're not soft, okay? They make tough decisions every day, but they're kind. And so in this world where everyone feels like they're getting ripped off or they're not paid enough, or I want founder pay for founder work, this motto, really it's troubling.

**Jason Lemkin** (01:48:51):
Everyone is founder pay for founder... "You want me to work more than 30 hours, Lenny? I need 30% of the company. I need founder pay for founder work." It's like, I hear you. But you'll find that by being totally committed, going to 110% and also finding a way to be kind, that will endure across your whole career. So, I know it's... Because I think I'm good, but not always kind. It's how I challenge myself all the time. Be kinder in that conversation. And the big epiphany, we already talked about it, it shouldn't have taken me this long to realize that anytime an employee fails, it's your fault, be kind. You hired them. They never knew what they were getting themselves into. If the job's too hard, if they lack the skills, if there's not enough budget, if there's not enough people, if it requires too many hours to work, you should have known that. Not them. Be kind. Be kind to every single person on your team that doesn't work out. That's not just putting together an email spreadsheet and sending it out. Try, try harder. Be there for them. Be kind.

**Lenny** (01:49:44):
Be kind. I love this. I know that you actually, your username on Twitter includes, "Be kind." In your actual-

**Jason Lemkin** (01:49:51):
It's a reminder.

**Lenny** (01:49:52):
Yeah.

**Jason Lemkin** (01:49:52):
It's a reminder.

**Lenny** (01:49:52):
So, you really back this up. Amazing. Okay, final question. You're on a giant conference, SaaStr.

**Jason Lemkin** (01:49:58):
Yes.

**Lenny** (01:49:59):
First of all, just talk about what it is so people know and explore it and check it out. But the actual question is what would surprise people most about running a conference like this? Things that they know, think about it goes into running a conference like this.

**Jason Lemkin** (01:50:13):
Any business, no matter how weird it is, that's at scale, the best one in it, it's always interesting, I think for case studies, right? The best, pick anything, the best cup maker, whoever it is, whoever makes the best... This is not an interesting business, the Starbucks cup on its own, but the best person, I pretty much guarantee you there's a good story there, right? SaaStr is a community as well like you have. We started this really early in 2011, 2012 for B2B founders back when that seemed like a weird thing to do. Now it doesn't seem so weird. So, we built a lot of content and we started doing events and meetups. I never did a meetup. You do a lot of meetups, but when I did meetups, I'd never actually even been to one. So, I did meetups in 2012 and 2013, but 1,000 people came to the meetups.

**Jason Lemkin** (01:50:56):
That will make sense to you today, but a decade ago for B2B, it was pretty, people were like, "Why are all these people here? We thought there was six of us that cared about B2B." So, then we did a big event. We did a one-day SaaStr annual in 2015, and 3000 people came including the evening. And so we just kept going. So, we get about 10 to 12,000 people together a year. What that means is change. It's no longer novel. I'm not the only person producing B2B content anymore. I'm not the only founder that sold their company like in the old days. There's plenty much more successful founders and better, I certainly, I don't do very many podcasts, because I'm not a hundredth as good as you. I do one a quarter, for example.

**Jason Lemkin** (01:51:34):
But the events have become this meeting place for founders that have hit scale, that are somewhere between 1 million and 20 million. And especially folks that aren't in Silicon Valley, especially folks that aren't sitting in Hayes Valley or wherever, that don't have a community. And that surprised me in the beginning how many international folks would fly all the way to the Bay Area. I remember this founder came from Perth, Australia for the first afternoon. I'm like, "Thanks for coming, but this is a lot of work." He is like, "I didn't come for you. I came because there's no one like me in Perth." So, it's taken on a life of its own. There's a lot of learnings. I think, I didn't intend to do mass scale events. I intended to just do meetups, because I had a community like you do meetups. So, it was absolutely an accident. The learnings are, it's extremely expensive and it has this weird curve. Okay, that's this weird curve where, and this is more because I think actually all founders, all startups should be doing events for their own customers, at least steak dinners I've written about. You should get your customers, if you get your customers and prospects together, you sell more. Get your customers and... It does not need to be a million dollar or 10 million event. It can be Roots Chrises, it can be whatever. It does not even need to be fancy. Just get them together.

**Lenny** (01:52:39):
It has to be steak though.

**Jason Lemkin** (01:52:41):
Yeah, well, no, but steak does work. I mean, I'm not a big steak guy. It could be any, just something nice where they want to go. It, just where they want to go. Your customers will sell your prospects for you. Your customers will do the selling for you [inaudible 01:52:53] bring them together.

**Jason Lemkin** (01:52:55):
When you do it really small, it's cheap, because how much does that back room cost at the steakhouse, right? It's worth it if you get one big deal out of it, right? And then you graduate and then you'll do a meetup and you'll do it at someone's office. Some tech company, they'll give it to you for free, they have an extra office. That costs nothing, right? And then you're like, "Well, that went well. We got 100, 200 people came to Lenny's thing at Digital Ocean." And then you say, "I'm gonna do an event." And then you rent a theater, a disuse. And these theaters are actually kind of cheap because they're dark during the day. There's not a lot going on and a lot of... Not movie theaters but music. So, those are cheap. But then you're like, "Well, there's only one stage and it smells like beer."

So, then the next year you do a hotel and a hotel's pretty expensive. It's like half a million, 800,000 and I hated hotels. But you know what I learned from the business model? It's turnkey landing. So, your marketing manager can call up the Hilton in any city and in two weeks spool up some mediocre AV, some mediocre chicken wings and drinks, okay? And you and I don't like to go to the Hilton that much. The ballroom C, we don't like to go to events [inaudible 01:54:00], but it turns out your customers don't care. They want to be together and talk about your app, right? And then if you do community like you do, you could probably charge a couple $100 for tickets and get some sponsors and bring in some revenue, right? But then after Hilton Ballroom B, it costs $2 million to turn the lights on.

**Jason Lemkin** (01:54:16):
And SaaStr annual... So, our Europa event in London's in early June, June fourth to fifth, please come if you want. That costs $2 million to turn the lights on in London. To turn the lights on in the Bay Area for 12,000 people in September in the Bay Area, which is the most expensive place, right? It's $10 million to turn the lights on.

**Lenny** (01:54:32):
Holy shit.

**Jason Lemkin** (01:54:34):
It's $10 million to turn the lights on. Now, I'm being a little expansive in what I mean by turn the lights on, but this is what it's going to cost you. It's going to cost you about $1000 per attendee in the Bay Area or Las Vegas to have a mass scale event. Not smaller, not one day in Grand Ballroom B, but a multi-day event, right? The AV is 1.5 million. The food and beverage is 1.5 million. The tenting is $800,000. I can keep going. So, once you get to 20, 30, 40 million in revenue, quietly, there's public companies out there.

**Jason Lemkin** (01:55:04):
They have software like Margins at that scale, but before that, it's brutal, right? And when I started doing these events, I interviewed the head of it back before March 2020 when tech companies did their own events, they've pretty much given up, which is an interesting side topic. Big ones like big, not small ones, but big ones. Biden View, the head of events at everyone, right? At Twilio and Marketo and dah, dah. They all be like, "Well, how'd the event go?" It'd all be the same. Well, we brought in about 1.5 million revenue. " Oh, okay." "And we lost 2 million." Everyone lost 2 million. Every single one lost 2 million. And you could see in the financial state, even Zoom at that scale just announced how much money they lose at Zoomtopia. So, it's a terrible business. What I like to say is it has diseconomies of scale. The Lenny's little free meetup costs zero, right? And then the one at Cloudflare's office costs zero.

**Jason Lemkin** (01:55:50):
And the one at Hilton B maybe costs $200, but then it costs $1000 per person at scale, right? Even more like Moscone like in San Francisco is probably the most expensive. You're approaching $2,000 per person, because you have to buy about 15 to 20 million of hotel nights to do an event in Moscone, right? So I don't know if that's... We could, I mean it's a side topic, Lenny's side podcast for people that care. But this diseconomies of scale. But there are a handful of public companies that do Lions, Money 2020, Shoptalk, a few others that are at SaaStr scale. And they cobbled together these very profitable public companies. But they all got to do at least 20 to 30 million, because you've got to get over a 10. And it's intimidating to get over a $10 million net, right?

**Jason Lemkin** (01:56:33):
And so what it means is we have to spend 14 to 16 months each year planning for SaaStr annual because it's not that fun if you fall six or $8 million behind. That's what happened in March 2020. We were the first major event to be canceled by COVID in March 2020. The first one, RSA happened for security the week before then we got canceled. So, we lost $10 million in one day and it took a long time to dig out of that hole, Lenny, especially since we monetize nothing, it took a long time, but they're worth it.

**Jason Lemkin** (01:57:04):
I know you don't do traditional events, but you do do meetups in your community, and I know they're worth it. I know they're worth it. Bringing your customers together, bringing your community together, and we talked about this offline before we started. Second tier, third tier events I do not think are worth it because they do not attract the best people. It's just a fact. Most people, it turns out from our founders are our core ICP. They go to two events a year on average, two events a year, okay? And they'll do other steak dinners. So, if you go to one of the two that they're at, you just might bump into Jeff Lawson or Eric Juan or whoever the next one. They might be there. That might be one of their two, but they don't go to number three through 10. They don't go to these. So, my role in general is people wonder, should I go to events? Is it worth my time? It's a waste of time.

**Jason Lemkin** (01:57:49):
And I know you've thought about it. And the irony is, even though we produce events, I used to think that. I didn't want to do events, I just wanted to do meetups, right? I was shy, I just wanted to do meetups. But then I learned over time that the best events bring the best vendors, the best executives, the best everything together. So, pick the one or two best events in your industry. And even if you're antisocial, try to go to one or two a year, go along. It pays off. It pays off, but maybe don't bother with the rest, even if they're ski slopes or they're fun that the subscale, subscale just doesn't work for meeting and marketing to people. It just doesn't work.

**Lenny** (01:58:25):
I said at the top of this podcast that there's a billion things that we could talk about, and I think that's exactly what happened. We can go in so many directions. And that alone was extremely interesting, because people want me to run a conference and I really don't. And I really enjoy the meetups that we have now. And we actually do have sponsors that cover drinks and food and things like that, and they're very chill, and I really love what happens at those things. But yes, this is really good information. Jason, I have two more questions for you that I ask everyone. I think we went through a couple of these things, but just real quick, where can folks find you if they want to reach out and maybe follow up on some of the stuff you talked about? And how can listeners be useful to you?

**Jason Lemkin** (01:59:03):
In general just go to this kooky, URL, SaaStr, saastr.com. That's a way to access a lot of what we're talking about. If they want to reach me to be transparent, I get a lot of impound, like I'm sure you do, to be honest, guys, it is hard to process the amount of inbound that folks get, right? But I'll give you an insider tip. I do see almost all of it. So, if you want to reach me, if you just want an hour of my time randomly, I'm sorry, honestly, I'm sorry, I don't have that hour of time, okay? But if you want to interact lightly, I mean, I'm on Twitter all most days @Jasonlk. I'm on LinkedIn a lot, just search for me, Jason Lemkin. LinkedIn's pretty cool. I have 260,000 followers. We have really good conversations. And I will tell you, if you have a question like you really need some help in your business or you want to do a pitch, if you send me the world's best email pitch, I will read a 100% of them.

**Jason Lemkin** (02:00:00):
Track it, put it in Mixmax, put it in HubSpot, put it... You'll see I'll open that email if the title's good. It doesn't mean I can respond. I'm sorry, no, Lenny can't respond, but realize it is going to be read. If you need a favor, like a help, if you ask the question super discreetly like, "Jason, here's the LinkedIn, I'm Lenny. I'm trying to hire this person. I'm at four millionaire doubling. Should I hire her?" There's a chance I'll look at that LinkedIn, I can tell you, or if it's one question, but if it's an hour or coffee, it isn't going to work, but it is going to get read. So, do that by email. Do it even by LinkedIn. I didn't used to read those LinkedIn emails, but if it's really good, I will read it and I probably respond to five or six of them a day.

**Jason Lemkin** (02:00:35):
I give to the extent that it's helpful, free answers, free advice. Not that I charge for anything, but make it good. Don't ask for coffee. So, many folks have written this, the most important people in the world read email. It's an open medium, right? And a subset of those folks actually read their social media. Even Elon Musk reads his, before he bought Twitter, he read it. You can access almost anyone in this industry. Just be super strategic about how you do it. Again, put yourself in your shoes. Would you answer this? Would you do this? But I would say half the stuff gets open to read. So, I know that's a rambly answer, but first, we talked about sales. You can reach anybody in the world via email. It's magical.

**Lenny** (02:01:11):
I'm sorry for the many emails you might get from this podcast. That is actually really good advice. Resonates deeply with me as well. Jason, thank you so much for being here.

**Jason Lemkin** (02:01:21):
Thank you for all the time, Lenny. I'm a super fan. It's great to be here and let me know anything I can do to support you or your community going forward.

**Lenny** (02:01:28):
I really appreciate that. And the same in reverse. Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast.com. See you in the next episode.

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## [2/15] The high-growth handbook: Molly Graham’s frameworks for leading through chaos, change, and scale

**Lenny Rachitsky** (00:00:00):
You've worked with many very high performing founder CEOs. Zuck, Cheryl Sandberg. Larry and Sergei at Google. Brett Taylor.

**Molly Graham** (00:00:07):
Google, when I was there, felt like two PhD students paradise. Facebook felt like 19-year-old hacker's dorm room. 80% of the culture of a company is literally defined by the personality of the founder. Our job as operators or as leaders is to help articulate the culture that they're creating.

**Lenny Rachitsky** (00:00:25):
When a lot of people think Molly Graham, a lot of people think of giving away your Legos.

**Molly Graham** (00:00:28):
You have to grow as fast as your company is growing if you really want to take advantage, both learning to give away what you've gotten good at and move on to the next shiny pile of Legos.

**Lenny Rachitsky** (00:00:39):
Sarah Caldwell. She told me that the framework that helped her most in her career is something that you call the J-curve versus stairs.

**Molly Graham** (00:00:46):
So Chamath, when he pitched me on this job, actually drew me a picture on a whiteboard. He said, the way a lot of people do careers is a set of stairs. Just walk up the stairs and you'll get promoted every two years. But that is boring. The much more fun careers are like jumping off cliffs and you do fall, but then you climb out way beyond where the stairs could ever get you.

**Lenny Rachitsky** (00:01:08):
Today, my guest is Molly Graham. Molly was an early employee at Google, also at Facebook, where she worked closely with Zuck on building the Chan Zuckerberg initiative. She also worked with Brett Taylor on scaling Quip, which he sold to Salesforce. She's also worked with hundreds of companies and founders helping them grow into the leaders that they want to become. Today, she leads Glue Club, which is a community for leaders operating in changing, growing environments who want to develop themselves as quickly as their companies. Molly is maybe most known for her advice to give away your Legos, which we chat about. Along with basically all of her favorite frameworks and mindsets and pieces of advice that she's developed and collected over time. For leaders who are going through rapid scale and growth and are just struggling to keep up. I think of this episode as a high growth handbook for leaders who are experiencing rapid scale.

**Molly Graham** (00:04:36):
Thanks, Lenny. I'm excited to be here.

**Lenny Rachitsky** (00:04:38):
I feel like this conversation was in an inevitability. I feel like you're the kind of guest where it's like, we will do this someday. I'm such a fan of your stuff. I've read all the stuff you've put out there over the years. We're going to be talking about the best frameworks and mindsets that you've developed over the years that have been really helpful to you, to founders, to companies that you've worked with to help them with growth and scale and change and all the stuff that comes with success. The way I think about this, I want to make this the greatest hits of Molly Graham.

**Molly Graham** (00:05:06):
Love it.

**Lenny Rachitsky** (00:05:08):
And so I sourced what I think are the greatest hits from a lot of colleagues that you've worked with, a lot of people you've worked with. We've chatted about the stuff that you find other people find most helpful. So we're going to be going through all that stuff. But let's help people understand why they should listen to this advice. What's kind of the backstory on these frameworks? Where did they come from? Where did you develop them? Tell us that story.

**Molly Graham** (00:05:29):
So first of all, Ami Vora, who you have had on your podcast, once said to me that all advice is just someone telling you what they did. And I always think about that. Because I really think that basically what I tell people is I've made every single mistake in the book. And then I got to the end of the book and I started inventing new mistakes. So mostly what I feel is that I like sharing my stories because I want to help people. I want to help people not make the same mistakes I did. And I also want to help people make sense of what they're experiencing. But I started in tech in 2007. I actually started at Google the week the iPhone launched and a lot of my scaling battle scars come from a couple of experiences. They come from a year and a half at Google, which is not very long.

**Molly Graham** (00:06:17):
And Google was pretty big when I was there. It has thousands of employees. But my department, which was the communications department, was 25 people when I joined and it grew in nine months to 125 people. And that was really my first experience with just all the sort of things that I still talk about today. In terms of what it feels like to grow really, really fast and sort of all the tools that I started developing from there. After Google, I left and followed Cheryl Sandberg and Elliot Schrage to Facebook. And I spent five years at Facebook. And I joined Facebook in 2008, and it's important context because it was 80 million users at the time. We were smaller than MySpace. It was 270 million in revenue, 500 employees. It did not feel inevitable. Most people thought we were going to sell it to Microsoft. When I told people I was going there, they were like, isn't that place just like a site for college kids? And so I was there for five years and it was a crazy five years.

**Molly Graham** (00:07:22):
When I left, it was 5,500 employees, five billion in revenue, over a billion users. So a huge amount of what I experienced, what I write about, what I talk about in Glue Club, which is the community that I run, comes from that rapid scale at Google and Facebook. But I also, I left Facebook right after we went public, about six months after we went public. And I only like doing jobs that I'm highly unqualified for. I like being on learning curves so steep that I'm scared I'm going to fall off. And so I left and I wanted to learn what it took to build something from nothing. And so I joined this little startup founded by Brett Taylor, a startup called Quip. I joined a couple of months before we launched and ran everything that wasn't product and engineering there for him. And that was such a valuable experience to me because the experience of building something from nothing is actually quite different than the experience of holding on for dear life while things are scaling so fast around you.

**Molly Graham** (00:08:27):
And it really taught me about all the tools and skills you need to go from zero to one and then from one to two and how lonely it can be to build something. And we eventually sold that company to Salesforce. And then again, only take jobs I'm highly unqualified for. But the last really chaotic scaling experience I had was actually helping Mark Zuckerberg and Priscilla Chan start their philanthropy, the Chan Zuckerberg Initiative. And I basically helped them for the first two years of its existence or its sort of like first full existence. And philanthropy sounds calm. You know what I mean? We're like, oh, giving money away. Must be so peaceful over there. And CZI grew from, I think the week I joined, it was 30 people and we bought two companies that week and it grew to 250 people that year. And it was like using every single tool in my toolkit that I had taken from every other job that I'd had.

**Molly Graham** (00:09:22):
So my advice and frameworks, like I said, come from having made a lot of mistakes. But I've also sort of made a personal study over the last 18 years, believe it or not. Essentially what does it take to thrive inside growing and changing companies, not just to hang on for dear life. What does it take to lead in the face of constant change? And really the other piece that I find truly fascinating is what genuinely makes the difference between a business that grows but then plateaus versus these generational businesses. The ones that go on forever. Sort of the difference between a Twitter or MySpace and a Facebook. Billions in revenue versus hundreds of billions in revenue. So what I like to do is take my experience and use it to help other leaders. I want to give people tools that work. And I also want to be honest about how hard all of this stuff really is.

**Lenny Rachitsky** (00:10:24):
Amazing. I say this a lot in this podcast. I just love the ROI that listeners of the podcast get. You spent 20 years toiling, struggling, working so hard, learning so much. And you're just here, here's all the answers that I've learned. And obviously not all the answers, but so many things that will help people avoid the pain and suffering that you've gone through.

**Molly Graham** (00:10:43):
That's the goal.

**Lenny Rachitsky** (00:10:45):
Also, a couple quick threads I want to follow here. One is Ami Vora, who you mentioned. She's now, I think, head of product at Anthropic.

**Molly Graham** (00:10:50):
Yes.

**Lenny Rachitsky** (00:10:50):
Amazing. Former podcast guest, also speaker at Lenny and Friends Summit two years ago. This other point you just made about how you've always gone to places that have been way beyond your... I forget how you phrased it, but just beyond your current capabilities almost. And were very difficult. I just had Matt McGinnis on the podcast. He's CEO at Rippling, now CPO at Rippling, and just recorded an episode with him. And he had this really powerful quote that if you're ever comfortable at work and feel like, oh, I got this, you're making a huge mistake. Something's going terribly wrong. That's not where you want to be.

**Molly Graham** (00:11:23):
Yeah. I always say I get bored really easily, which is both a strength and probably my greatest weakness. So I like being scared.

**Lenny Rachitsky** (00:11:30):
Okay. So let's actually dive into some of your greatest hits of frameworks. And the greatest of all greats, when a lot of people think Molly Graham, a lot of people think of giving away your Legos. Some people haven't heard of this, many people have, so let's cover this. What is this advice of giving away your Legos?

**Molly Graham** (00:11:47):
So this definitely started in my experience at Google. And then Facebook was a masterclass in giving away the Legos. But the way I like to talk about it is basically when I watch leaders and employees go through rapid scale, I like to think of somebody putting down a giant pile of Legos in front of a bunch of kindergartners and then just being like, build something. And that's sort of what it feels like when you start. It's like, well, there's so many Legos and it's so fun. There's a lot of opportunity, but it's also kind of scary and overwhelming. And you're like, there's so many Legos. What do I do? Isn't there an instruction manual hidden under this pile somewhere? But then you start building and you're like, oh, okay. You build something and then you take it apart and then you put it back together.

**Molly Graham** (00:12:33):
And then eventually you start to get momentum and you're like, okay, it's like I'm building a house. I got this. It's a house. All right, great. And then you're like, I'm good at building houses. I was put on earth to build houses. And almost assuredly inside of scaling companies, as soon as you're like, I feel good at this and I should do this forever. Somebody's going to show up and be like, okay, it's not a house. It's a neighborhood. And you need to take this house that's kind of half built and you're going to pass it off to this other person that we just hired. And you are going to go build dog parks and streets and other things that are entirely unhouse-like. And what happens when someone does that to you is you're like, wait a minute. First of all, I'm not done with this house. And I'm worried that this person's going to screw it up.

**Molly Graham** (00:13:18):
I'm also worried that building houses is actually the most fun thing and that I'm going to give the Legos to that person and they're going to have all the fun work and I'm going to hate building dog parks. Or that dog parks are irrelevant eventually and it's going to turn out we're in the house building business. So there's this incredible set of emotions that come territorialistic, paired with excitement. Fear paired with joy. But eventually you pass the house off and then you go work on neighborhoods and you're sort of like, okay, dog parks, I'm good at dog parks. I got this. And then again, you get to the like, I'm great. I was put on earth to build neighborhoods. And immediately someone shows up and says, it's not a neighborhood. It's a country or a city or a world. And it just goes on and on and on.

**Molly Graham** (00:14:03):
And for me, learning this muscle of both learning to give away what you've gotten good at and move on to the next shiny pile of Legos. And learning that the emotions associated with that are inevitable. I've been doing this for 18, 20 years, I still get attacked by these emotions all the time, but that doesn't mean that you shouldn't give them away and move on to the next thing.

**Molly Graham** (00:14:33):
That is both the torment of scaling companies, which is that the ground is moving under your feet. And as soon as you're comfortable, someone will make sure that you are uncomfortable, but it's also the opportunity, which is that you can go from being someone that's good at building houses to someone that knows how to build entire worlds. And that is where the Legos metaphor came from.

**Lenny Rachitsky** (00:14:55):
That is such a good metaphor. And if you've gone through this, you so understand what this is like and what... And also just the Legos is metaphor is so good for the different things you build.

**Molly Graham** (00:15:06):
I have a very weird brain that for some odd reason just always thinks in metaphors.

**Lenny Rachitsky** (00:15:11):
[inaudible 00:15:11].

**Molly Graham** (00:15:11):
So it showed up when I was... At Facebook in particular, I would find that every so often I would have to have what I called a Legos talk with someone where I would just see them start to ask these questions like, why are we hiring that person? Or what's that team even do? And I was like, okay, we need to have the chat about the Legos. And then eventually it turned into an article and a whole thing.

**Lenny Rachitsky** (00:15:33):
A whole thing. And just to be clear, the advice is give away your Legos, this is actually the path to a successful career.

**Molly Graham** (00:15:40):
I have watched a lot of people over many years struggle with feeling like they should hang on to the thing that they've been good at. And it almost always... Because, you know, essentially the nature of a scaling company is that the Lego pile is just getting bigger and bigger and bigger however fast that graph is going up into the right. I always say that's the graph of how fast your business is growing. It's the graph of how fast your company is expanding. And it's the graph of how fast your job is getting bigger. That means that if you actually just stay and build houses, eventually you're literally buried under a pile of Legos. Do you know what I mean? You held onto something that's down here and the opportunity is actually to stay on top of that pile and to learn to just give away your job every so often.

**Molly Graham** (00:16:27):
At Facebook, I got to a place where I was literally giving away my job every three weeks. I was constantly rehiring myself essentially because you have to sort of grow as fast as your company is growing if you really want to take advantage of the opportunity that comes with companies that are growing and changing quickly.

**Lenny Rachitsky** (00:16:45):
So people are hearing this, they're like, okay, my rational brain's like, I should give away my Legos. It'll help me. It'll be good for my career. In real life, it's very hard to actually do. To give away this empire that you've built, this team that you've built. This project that you're like, oh, this is going to be my thing. I know you have a really fun, useful tool to help people deal with that kind of irrational part of their brain. Talk about that.

**Molly Graham** (00:17:07):
So like I said, my brain works in weird metaphors. It's a weird brain. I was raised on The Muppets, and I like to think that this one came from, I guess, growing up watching weird animals. But basically, at some point I realized that this emotional rollercoaster that comes with scaling, with growing. With going through change, any kind of change. People feel that. Was never going to go away. And that no matter how good I got... Sometimes I think it gets worse the more senior you get, actually. Because you sort of feel like you're supposed to know what you're doing, and then you just get attacked by this monster that's like, who even gave you this job in the first place? So basically I externalized all these emotions that come with change into this little tiny monster. I named my monster, Bob. Your monster can be named whatever you want him to be named or her or them.

And Bob's job... I like to think his job is basically to make me the worst version of myself. He's the one that's like, oh, that person took all the fun Legos and you should go push them over and grab them back. Bob's job is... Bob's the one that wants to send the rage emails at 9:00PM and burn the house down. And the thing to learn about Bob is that, like I said, Bob never goes away. Bob is someone that you have to learn to deal with. But Bob's job is to make you the worst version of yourself. So your job is to let Bob do his thing, but not act on the emotions. Basically, all these emotions are normal and they are not useful. They are not the compass that should be telling you what to do.

But the other rule I have for managing Bob is a lot of people are like, oh, you're feeling off or tired or whatever. Go to bed and wake up tomorrow morning and you'll feel better. And the truth is that you're like, I want to send the rage email at 9:00PM. You still want to send it at 8:00AM. And a lot of these emotions just do not go away in 24 hours. So my rule of thumb from Facebook was give it two weeks. And the emotional, the sort of Bob... Bob is like these waves and they just roll through. So you made a new hire or somebody came in or you got layered or whatever. You'll have a set of reactions. And those reactions, again, they're normal, but they're not useful. They're not the ones that you should listen to. They are Bob.

**Molly Graham** (00:19:29):
And typically they go away in a couple of days, you get something new. Some new wave. But anything that lasts longer than two weeks is actually something you should pay attention to. It's something that if it's been around for two weeks, it's something you should go talk to someone about. Whether it's a manager or a friend or a coach or someone like that. That's the real stuff. Everything else is just Bob.

**Lenny Rachitsky** (00:19:50):
Is there a rule of thumb for when it actually, when you shouldn't give away your Legos? When it's like, okay, maybe you should fight back on this layering or whatever.

**Molly Graham** (00:20:00):
No rule of thumb. In general, I would actually say embracing change is far better than fighting it. And almost invariably, you cannot see what is around the corner, but it is almost always the thing to focus on. A lot of times I think inside of change, we get focused on the past, and one of the most valuable things you can do as a manager and a leader is help people focus on the future. I think... I'm sure there are times when people have done it and regretted it and it has led them somewhere.

**Molly Graham** (00:20:42):
I think being layered, for example, is one of the hardest things for people inside these experiences where someone brings in a manager above you. And I've also seen so many stories of that ending up being a great thing for someone. Even though they couldn't see it at the time. So in general, I would just say, step into the future and let the past go and see what you're going to learn. And sometimes you'll learn that it's time to leave or that this isn't the right pile of Legos for you. But it'll end up taking you somewhere that's worth exploring. Holding onto things almost always leads us to the worst version of ourselves.

**Lenny Rachitsky** (00:21:24):
It's a very Buddhist way of thinking too. Just don't cling.

**Molly Graham** (00:21:31):
There you go.

**Lenny Rachitsky** (00:21:31):
Yeah. And I think another part of this metaphor, I don't know if you think of it this way. Is the Legos aren't even your Legos, right? They're like the CEO's Legos, the shareholders' Legos. So you think they're your Legos, but no, you're not in charge.

**Molly Graham** (00:21:42):
Well, it is... I will say one of the hard-earned things is it can feel very emotional and it can feel very personal. It can feel like your work... I don't know, it can feel like your life is on the line sometimes. Just your work life. Oh, gosh, this matters so much. And one of the things that you learn as you get more senior and just have seen stuff is it's going to be okay. A friend of mine says, careers are long and nobody tells you that. But they're long. And this moment feels so dire and it feels so hard and it feels scary and it's going to be okay. So yeah, it is hard to know in the moment. And I think the story is going to be long and this is going to be one chapter or maybe even a part of a chapter, not a whole chapter. So embrace the length.

**Lenny Rachitsky** (00:22:37):
To build on that point, I've realized this is my fourth career doing what I do now. Whatever the hell this is. I was a engineer and then I was a founder. Then I was a product manager, and then what the hell I do now. Whatever this is, that's a whole different path.

**Molly Graham** (00:22:52):
You don't have a name for it yet, Lenny?

**Lenny Rachitsky** (00:22:55):
I don't. I hate all the terms people use for this world.

**Molly Graham** (00:22:58):
Somebody called me an influencer and I almost ripped their face off.

**Lenny Rachitsky** (00:23:00):
Yeah. [inaudible 00:23:02].

**Molly Graham** (00:23:02):
[inaudible 00:23:02].

**Lenny Rachitsky** (00:23:03):
Yeah.

**Molly Graham** (00:23:04):
Yeah, man. The most interesting careers are winding and they have starts and stops and failures and successes and control. Anybody that's been through a lot of this stuff, control is usually not the name of the game. It's usually just like, "Let's see what happens. We're going to try this and we're going to see what happens next."

**Lenny Rachitsky** (00:23:26):
This is a great segue to another framework that I've heard from folks you've worked with that have been really impactful on them. So, Sarah Caldwell, who's a big deal at OpenAI, she told me that the framework that helped her most in her career is something that you call the J-Curve versus Stairs career growth framework. Talk about what that's about.

**Molly Graham** (00:23:46):
I actually gave a TED Talk about this one a couple of years ago because I am so passionate about it, but you can listen to the very packaged eight-minute version of this, but I will tell you the real story because it's very relevant to a lot of folks that listen to your podcast. I was at Facebook for five years. Like I said, the first two years I was in HR and I was doing employment branding and culture work and I was ready to stay there. I think I had in my head I was going to stay there until we went public, that was my plan just because I wanted to help the company through that moment, again, in my head.

**Molly Graham** (00:24:21):
This guy that many people know, Chamath Palihapitiya, came to me and Chamath ran growth and mobile at the time. And he came to me and we had lunch and he said in his very Chamath way, "You're useless. What are you doing in HR? This is stupid. You should come work for me." And anybody that knows Chamath is like, "Yes, that is actually what he said." He managed to insult you and compliment you in one sentence.

**Molly Graham** (00:24:47):
He gave me all these options on his team. And then the last one he said to me was like, "I'm going to go build a mobile phone. Do you want to come do that with me?" And I had four simultaneous reactions. The first was like, " That is incredibly stupid. Why are we doing that?" And then it was like, "Is that actually a thing that we're doing?" And then it was like, "Whoa, I think that sounds kind of fun." And so I left the conversation at Chamath and I went and asked my boss, Lori Goler, who's the head of people at Facebook for a very long time, like, "Is this actually something we're doing?" And she was like, "I can't believe he offered you that, whatever."

**Molly Graham** (00:25:21):
And I basically just could not get it out of my head, but it didn't make any sense, A, that Chamath had asked me because I was in HR. Like, "What am I doing? I don't absolutely jack shit about mobile." But I had worked on a project with him and I guess he thought I was smart. And I talked to Cheryl and she was like, "Well, that project will be dead in two months, but you can do it because you'll still have a job here." My dad was like, "Well, don't do that. " And anyway, a lot of very wise people being like, "Don't do that."

**Molly Graham** (00:25:51):
But I kind of couldn't get out of my head. And my friend said to me, "You've proven you're really good at this sort of company-wide project management and HR. Why don't you go show yourself how actually good you are? Is this transferable?" So, I took the job and I spent the next six months feeling like an absolute idiot. I basically felt like a total jackass all the time. I was sitting in rooms with these brilliant people asking the dumbest questions of my life and at the end of the six months, Chamath, I think, took a lot of pride in giving me the lowest performance rating I've ever gotten in my life, and it just felt like falling off a cliff. Then, slowly, I remember I had been doing all these trips to Taiwan because we were actually working on hardware and I, at some point, came back from Taiwan and I drew on a whiteboard for him the layout of a mobile phone and trying to explain to him why something he wanted to do was not possible. I so vividly remember walking out of that meeting being like, "Oh, I actually know things." And slowly then, over the following three years, I became an expert in mobile. And I basically... The phone itself was a giant failure, massive, costly failure for Facebook, but it was not a failure for me. It was a huge job that taught me that I was capable of things that I never could have dreamed of if I had stayed in HR. It set me up to be capable of taking on things that I didn't know about.

**Molly Graham** (00:27:28):
Chamath, when he pitched me on this job, actually drew me a picture on a whiteboard. He said, "Look, you can stay..." The way a lot of people do careers is a set of stairs. "You can be boring." To use Chamath, "And stay on these stairs. Just walk up the stairs and you'll get promoted every two years and your title will change from manager to senior manager to director to senior director, whatever." And he was like, "But that is boring." And he's like, "The much more fun careers are like jumping off cliffs." Basically, that you jump off this thing and you do fall for a period of time. I always like to say it's about six to nine months, but then this thing happens where you climb out.

**Molly Graham** (00:28:05):
And the picture he drew had this J-curve sort of basically leading you to places that are way beyond where the stairs could ever get you. And to be totally honest, that has been my experience. That taking risks, accepting the sort of terrible fall and that experience of falling has been more than worth it. Part of the reason why Sarah mentions it is that I do give this sort of talk to people that are inside of really fast-growing companies, because it's such an important place to let go of Legos and jump off cliffs because there's so much opportunity. And it is a place where if you prove to people that you're actually good, if they believe that you are the kind of person that they can use to do lots of things, you can get these opportunities that you are just so deeply unqualified for, but they can take you to places that you could never have imagined.

**Molly Graham** (00:29:01):
You can come out of those companies with skills that no one would ever have reasonably hired you to do. But I ended my time at Facebook in product and did business development and hardware and a whole bunch of the stuff along the way. And again, nobody would've hired me to do that at the beginning, but it's just because I kept saying yes to things.

**Lenny Rachitsky** (00:29:22):
Molly, I got tingles listening to this story. Wow.

**Molly Graham** (00:29:25):
Does it sound familiar, Lenny?

**Lenny Rachitsky** (00:29:27):
It does. I want to ask, jumping off a cliff, sometimes you fall, really fall and you keep falling. Are there any kind of traits of like, "Okay, this is one that might be a J-Curve and worth the risk of falling, and this is when you should probably just not, let's not do this".

**Molly Graham** (00:29:46):
Yeah. I just think there are different kinds of fear. We talk a lot about this in Glue Club because one of the thing, there is a financial fear, right? Leaving a job and taking a job that has financial risk associated with it, or leaving a job and taking time off, which is something that I spend a lot of time talking to people about, you got to do the math and you got to... Sometimes there is a type of fear that is telling you like, "This is not the right time." Or, "I don't want to be financially anxious for months and months and months."

**Molly Graham** (00:30:21):
I use finances because it's the most concrete example of a type of fear that you should actually listen to. And sometimes you can do the math. I always counsel people through that. I'm like, "What is the number that you need to hit so that you're not constantly terrified financially?" And that number is wildly different for people based on their background and their life. "Can you do that? Can you consult, can you whatever in order to take this leap?" But a lot of times fear is just you saying, "I'm scared I can't do this. I'm scared I'm not capable of it. Yeah, I'm scared I'll fail."

**Molly Graham** (00:30:56):
And that's the kind of fear that I think of as a flashing green light because... And it sounds like Matt McGinnis said this too, where it's like, "That's the kind of fear that's saying, 'Why don't you go prove to yourself that you are actually capable of this?'" Or if you fail, like, "You'll have learned something, too." You know what I mean? You'll have learned, like, "I took this job in product at Facebook as my last chapter there, and let me tell you things that people should never fucking hire me to do." I was like, "I am not a good product manager." But I've got a great product mindset. I can sit in a bunch of chairs and hang with the product folks, but I'm not the person that cares about the button. Do you know what I mean?

**Molly Graham** (00:31:38):
And I would never have learned that. I wouldn't have known who I was if I hadn't taken that risk and failed or at least learned that it's not something I wanted to do again. So, there's many different lessons that come from facing down those fears and jumping off the cliff, but mostly what it is is knowing yourself better and knowing where you go next from there.

**Lenny Rachitsky** (00:32:03):
That is such helpful advice. I also love how you frame this of, "Prove it to yourself that you can do this." It's not, "I'm going to show them that I can do this." Because the way you describe this, usually it's an opportunity given to you. "Hey, can you do this thing? We want you to lead this new thing." And the fear is like, "I don't think I can do that." And what you're saying here is, "Prove it to yourself that you can." Or, I guess, it's also, "Okay, maybe I can't and then I'll learn that and then I'll know more about myself."

**Molly Graham** (00:32:27):
Yeah, exactly. I mean, one of the greatest gifts in a career is knowing yourself. And that's a lifelong journey because who you are and what you want changes, but that knowledge and that gift, nothing accelerates your self-knowledge faster than trying to do something that you don't know how to do and that you're scared of.

**Lenny Rachitsky** (00:32:50):
Probably the quote I use most on this podcast comes up again in my mind as you talk about this, this line that, "The cave you fear contains the treasure you seek."

**Molly Graham** (00:32:59):
Hell yes, exactly. Well said.

**Lenny Rachitsky** (00:33:03):
There it is.

**Molly Graham** (00:33:03):
I haven't heard that one from you, so clearly I need to listen more.

**Lenny Rachitsky** (00:33:04):
Okay, that's great. I'm glad I don't overuse it. It just feels like it comes up again and again, and I think your point about the runway and the finances is such an important one because that's a very real practical question. One thing I did when I took time off, I took a year off after I left my job. What helped me was I just created a runway goal for myself. I'm just like, "Okay, here's what it's going to cost me for six months or a year to live without any income. Am I comfortable just burning through these tens of thousands of dollars to explore and see something new emerge?" And so you just have to feel good. "Okay, yes, I'm going to burn all that money and that's part of it."

**Molly Graham** (00:33:37):
Yeah, that's exactly the exercise. You're saying "runway" I say "burn rate", so we both were raised inside of companies, incentive tech, but I think it is do the math, right? What can you afford? And it's both what can you afford and still feel safe? Because sometimes, I mean, again, I think that is different for everyone, but it is such an important set of math to do because, A, a lot of times that number is smaller than you think it is, then your brain makes it out to be if you have this sort of existential financial anxiety versus, I always say, "Specific financial anxiety is much more useful than existential financial anxiety." And some friends are leaving jobs and I'll be like, "Hey, your number is 5K or 10K a month. You have to believe that you can get a consulting gig that will pay you that. Do you believe that?" And it's like, "Either yes or no." And then, "Okay, either we're doing it or we're not.

**Lenny Rachitsky** (00:34:29):
The other part of this J- Curve that I think is really important to touch on is this idea of for the first six or nine months, you're going to be at the bottom of the J curve falling, still falling. And some projects don't last that long and then you're like, "Okay, total failure. I never emerged from this fall." So, is there any advice there? Just, how do you create that enough space to give you a chance to start to un-fall?

**Molly Graham** (00:34:49):
I mean, the most valuable thing that happens as you fall is learning. And even on the other side of failure, you've learned a shit ton. I always say, "The most important thing to do in the falling phase and the risk taking land is to learn to embrace being a professional idiot." Basically, being the one that shows up at the meeting and is like, "What are we talking about? What does that word mean?"

**Molly Graham** (00:35:18):
For a bunch of reasons. Number one, you can learn so much. And again, even in the face of failure, no one can take away your learning. Do you know what I mean? But the other thing is that it turns out that a lot of the questions in the world that, you're sitting in the meeting and you're like, "This is a dumb question. Everyone's going to think I'm an idiot." But then you get brave and you ask it and it turns out it wasn't a dumb question. Do you know what I mean? Turns out that everyone had that question in their mind, but no one was brave enough to ask it.

**Molly Graham** (00:35:48):
So, from a skills' perspective, again, regardless of outcome, being the person that sort of takes their learning in their own hands, learning no matter what and learning to ask those dumb questions, it's a superpower. I always say that, "Actually, my superpower is being a professional moron." Because I'm the one that shows up in a room and is like, "Do we have goals? What are we doing? Why are we talking about this? Why are we having this meeting?" And most of the time it's actually what I was hired to do, which is bring clarity.

**Lenny Rachitsky** (00:36:19):
It's so funny. I just recorded a podcast episode with a PM named Zevi who joined Wix and he had this thought, he's like a very young PM, just getting started and he's like, "Okay, I need to be a 10X PM because that's what they expect of me, that's what everyone that is really good, that's how I think of a 10X PM." And then he went into his first meeting and he just failed and he just felt so bad. He's like, "I guess I'm not that 10X PM. They're all going to see that. They think I'm terrible." And then he did another presentation a little bit later and people were so impressed with how he learned and evolved and improved. And he realized that he needs to be not a 10X PM, but a 10X learner, and that's what people actually expect from someone, especially a junior person.

**Molly Graham** (00:37:05):
Yeah. Well, I was having a conversation last night with a friend of mine who has a senior in high school and I was like, "What is the plan? What are we telling this senior in high school to think about relative to their career given everything that's going on with AI?" And we talked about it a bunch, but what we both circled back to was this idea of soft skills and that actually the only thing you can really anchor on right now is that teaching kids grit, teaching them hard work, teaching them learning, right? Learning how to learn, loving learning, being able to fall, in a world that's changing this fast. And I say this inside of companies too, right? I always say, like, "What you know today is way less valuable than what you can learn by tomorrow." If you're inside of a company where the growth curve is like this, what you know today is irrelevant.

**Molly Graham** (00:37:52):
Somebody once told... I'm sure this is faster now, but they rewrote the entire code base at Google every eight years, which means that if you're not learning, if you're not evolving, then you become irrelevant and extinct. It's actually the whole underlying point of the Legos stuff is that evolution is the way you stay on top, and I think that's more true today than it's ever been.

**Lenny Rachitsky** (00:38:14):
And luckily, AI is really good at helping us learn.

**Molly Graham** (00:38:16):
Totally.

**Lenny Rachitsky** (00:38:17):
So, that's good. Thank you, AI. And this actually comes up a bunch in the podcast. I ask a lot of AI-forward people what they're teaching their kids and curiosity is one of the main things people talk a lot about. Just like, "Help them develop curiosity about the world."

**Molly Graham** (00:38:31):
Yeah.

**Lenny Rachitsky** (00:38:31):
Yeah. Okay. I feel like I could be talking about this specific topic for a whole podcast episode, but I want to move on to a couple other frameworks that you've developed. One is something called a Waterline Model and another former colleague of here said, "This is the most impactful thing that they've learned from you on their career." So, talk about the Waterline Model.

**Molly Graham** (00:38:50):
Okay. Yeah. Well, first of all, the Waterline Model is not mine. It's from some business book somewhere, but I actually learned it. My first job out of college was leading wilderness trips. I led 75-day wilderness trips in Patagonia and Alaska for a school called NOLS, the National Outdoor Leadership School. NOLS basically teaches essentially leadership and communication skills to students.

**Molly Graham** (00:39:15):
I was mostly leading college age kids through wilderness expeditions. So, by having to lead a group of your peers that you don't know. Anyway, the Waterline Model is something that we taught on NOLS. It's a really, really helpful model for understanding how to diagnose when something is not working on a team, so I teach it inside of Glue Club and I'll just quickly explain it. Basically, the way to think about the Waterline Model is that a team is a boat and it's a boat on an ocean trying to get somewhere, getting somewhere is goals, right? "What are we trying to build or ship or do?" Essentially, that is going to be harder or easier based on whatever the shape of the ocean is, right? If it's really choppy, it's harder, if it's smooth and calm, it's going to be easy to get to your goals.

**Molly Graham** (00:40:02):
So, the Waterline basically asks the question like, "What is going on under the water? What is going on that's making it harder or easier to get to your goals?" And there's essentially four things underneath the water and they are in a descending order. The surface level is what's called structural things. Basically, structural things are like goal setting, vision, roles, expectations, kind of the structures you put in place to make a team and a company and a business make sense, that touch every single member of the team.

**Molly Graham** (00:40:37):
Right below that is something called dynamics, which is essentially how the team works together. It's culture, it's decision making, it's how we resolve conflict, all the sort of like interwoven pieces of how teams work together. And then below that is interpersonal, so basically relationships between two people and all the things that come with us being humans. And then the bottom is intrapersonal, meaning within one person, challenges and issues there.

**Molly Graham** (00:41:08):
The interesting thing about this model is that most people, when something's going wrong on a team, a lot of times we always go to the bottom. We go to the people. We're like, "The people aren't getting along, that person's having a rough moment." We go to the humans, but the rule with the Waterline Model, which is very memorable, is you snorkel before you scuba. So, 80% of problems on teams actually happen because of structural issues or dynamics issues. So, when there are problems on your team, where you start is at the top, you start structural issues.

**Molly Graham** (00:41:44):
And one of my biggest things that I say all the time over and over again inside of Glue Club is, "Your only goal as a manager, if you do nothing else, is clear roles and clear expectations. That's it." Because honestly, I've taken over a lot of teams in my life and almost always I show up and it turns out that no one knows what their job is and no one knows what success looks like. And if you can make those two things clear, which again is at the snorkel level, it will fix a huge percentage of other issues on a team. But the main thing is where you start and just always sort of starting at that structural level or the dynamics level and not sort of immediately going to the people and all that. Because yes, people cause all sorts of problems, but a lot of times the problems are happening because they're existing inside of a structure that's confusing.

**Lenny Rachitsky** (00:42:32):
Another very vivid metaphor and just, I love how it builds on it with the snorkeling. Okay. So, just to be super clear about this, the takeaway here is, you have a problem with your team, with the company, many people think it's, they jump to the people are the problem. "They're not good enough, they're not working hard enough." Really, what you're saying is, most often, the issue is not the person, it's the situation, whether it's the structure of how they're set up to work or the dynamics amongst the people. And specifically what you're saying is that the role maybe isn't clear or what success means for that role is not clear.

**Molly Graham** (00:43:08):
Every company I've worked with or advise, I often start with like, "What are the goals?" And usually what you get the hack is, "Uh, not clear." And that in and of itself is a structural issue, right? How can someone show up and decide what they're going to do with their day all day if the goals aren't clear, if they don't actually know what the priorities are? And then it goes to, okay, role, right? "Do I know what my job is? Do I know what number I was hired to own and drive?" And then, "Do I know what success looks like? How does my role tie to that overall goal that the company has?" Just literally right there. You got probably 80% of problems inside of companies because this is the hard work of company building. It's the stuff that's not intuitive. "How do you organize a group of people to know which direction to row?"

**Molly Graham** (00:43:52):
And that equation, again, I would say 80% of problems that I see, performance issues. I always start with, "Does this person actually know what you expect of them?" If not, go back to step one. Do you know what I mean? Clarify expectations, so the Waterline Model is just helpful for reminding us, like, "Start at the top."

**Lenny Rachitsky** (00:44:11):
So what would you do there? Say you're a manager, you're having an issue with a team member, would you go and ask, "Hey, let's just make sure we're aligned on goals and roles." Is that how'd you approach it or is there a different approach?

**Molly Graham** (00:44:24):
So a lot of times what I do is two-sided, right? So it's like, "Hey, here's what I'm seeing and tell me what's going on for you. Do you know X, Y, Z?" When I take over a team, when I'm doing my listening tour, part of what I'm asking is, "What do you think your job is? What number were you hired to drive?" Because what you'll find is often their picture is different than your picture. You think you've been clear, you described an elephant and they spat out a tiger and that coming back to like, "Okay, no, we're building an elephant. You're in charge of the trunk." Will, in some percentage of cases, actually make a huge difference to the person's work and time and performance. In plenty of cases it doesn't, but that's always where I would start because it so often is just a more fundamental problem that then would lead you to look at other things across the team.

**Molly Graham** (00:45:20):
But yeah, I would say two-way dialogue, but re-clarifying roles and expectations, re-describing the elephant over and over and over again, is one of the hardest parts about being a leader because you feel like a broken record, right? You feel like an idiot. You're like, "I've said this 45 times." Turns out no one heard you the first 43 and you have to. You have to re-describe it in order for people to hear you and to re-understand their sort of role in what they're doing.

**Lenny Rachitsky** (00:45:45):
I love how you reframed the way I approached it by starting with, "Here's what I'm seeing. What are you seeing? What do you think your role is? " The very non-violent communication oriented, which is a clear pattern on this podcast, just the power of that specific framework.

**Molly Graham** (00:45:58):
Yeah, totally. Well, like I said, work is about humans and it's...

**Molly Graham** (00:46:01):
Like I said, work is about humans and it's the art of organizing humans to get something done and build something that's greater than the sum of its parts and that is an art of the humanness in all of us, how do we get people to hear us, how do we get people aligned.

**Lenny Rachitsky** (00:46:17):
Work for a lifetime.

**Molly Graham** (00:46:19):
Totally.

**Lenny Rachitsky** (00:46:20):
**Molly Graham** (00:47:30):
Yeah, totally. I feel like there should be less than six but it's where we're at. I would say, at a high level, two things before I get to the six. One is that I definitely have a bone to pick with OKRs, I feel like it's obviously been a really helpful framework for Google and others and, a lot of times, when I show up inside a company or I'm talking to a leader and I'm like, "What are your goals?" what I get back is this spreadsheet that has 100 lines and feels like it's written in Greek and, when I look at it, I'm like, " This doesn't create clarity for anyone." And it brings me back to what is the point of goals, why do we have them and, at the end of the day, goals are a communication tool, that's what they are. They're a communication tool designed to create clarity, to help people know I'm going to show up at my desk, what should I work on, what's the most important thing and your 100-line spreadsheet doesn't help anybody.

**Molly Graham** (00:48:23):
And the second thing I would just say is you really have to ask the question what is right for me at this company and this stage, what is right for a seed stage company is not what is right for a company that's got an established business and a clear go-to-market machine. So, when I'm building in seed stage, I'm setting goals every two months in a very iterative way. When I have an established business, I can actually set annual goals but annual goals for early stage companies is just a waste of time. So, anyway, a lot of my goal setting stuff actually comes from Facebook which I think was very, very good at this.

**Molly Graham** (00:48:58):
So, the first role is that no company needs more than three company goals and the point of company goals is to help people know what the most important things are to success. So, Facebook basically had three goals for the entire time I was there, it was five years and we did six-month goal setting, I think we did annual goal setting that ended up getting reset every six months but whatever. So, the three goals were this, there was growth which was measured as monthly active users, that was the externally reported number eventually, MAUs. The second goal was engagement meaning how often do people come back and use the site and the third was revenue and we literally had three goals for the five years that I was there. If you can govern that business with three goals, you can govern literally any business with three goals. So, no company needs more than three goals.

**Molly Graham** (00:49:52):
The second thing is that one goal needs to win in a fight. So, if I'm sitting down and asking how do I prioritize my time on a given day I need to know what is the most important thing. At Facebook, we had growth and there's a lot of different ways you can add monthly active users to a social media site including you can go buy a whole bunch of bots in Indonesia and that would add to your MAU number but it would not add to your engagement number and it was very clear for the entire time that I was there that engagement was the most important thing. Acquiring users that were going to use the site all the time, that's what drives revenue, it's also what drove the heart of that site. So, if you had to prioritize something, you prioritized engagement, that goal won in the fight.

**Molly Graham** (00:50:35):
The third I'll say is, I call it the explain it to me like I'm five goal, but an intern that started on Monday should be able to look at your goals and understand them and, if they can't, then you are failing because they are not a communication tool that's effective. You have to be able to understand the goals, you have to explain the acronyms, you have to have numbers that make sense to average people, otherwise, again, it fails as a communication tool.

**Molly Graham** (00:51:03):
The fourth one is ... Actually, I stole a phrase from Claire Hughes Johnson who you've had on your podcast but wrote a book called Scaling People and in it she says this sentence that I love which is strategy should hurt. And my role used to be set non-goals, basically, make it as clear what you're not going to do as what you are going to do but strategy should hurt is a much better way to explain it to people which is, if you're not making trade-offs that are painful, you are not actually helping people prioritize their time. Because the nature of work is that people will show up every day and do something and either you are very clear with them about what the priorities are or they're going to prioritize for you because they're going to choose what they work on every day. And we see this so much with founders where they can't cut things off the list, they just have to have the 10 goals and I'm like, "Cool. Six of these goals are not going to get done so either you pick which four it is or other people are going to pick for you." So, strategy should hurt. If your goal setting process is not painful, then you're not prioritizing heavily enough.

**Molly Graham** (00:52:09):
Okay, ready? We're on number five. This is more of an organizational point but it's really important for the waterline model too which is that one goal has one owner. You have a number, that number has a name next to it. If you cannot do that work, you haven't done the most important work to actually make sure that these goals get accomplished. And it's organizational work and it's very painful because sometimes it feels like, oh, this person can own it or this, maybe they'll just own it together, two people owning a goal is no one owning a goal. One person owns the goal, who is it? It's not you as the CEO, it's someone that works for you so one goal, one owner. And then the last, which is the hardest, is that goals by themselves are not enough. I've spent a lot of time with founders that are like, "I did it. I set the goals. Why not working? I don't understand." And I'm like, "What did you do after you set the goals?" And they're like, "I don't know, I set the goals." Goals ...

**Molly Graham** (00:53:10):
James Clear who wrote Atomic Habits has this really lovely sentence which is winners and losers have the same goal. Goals by themself are not enough, you have to have a process by which you follow up on the goals and you hold people accountable to the goals and you learn from the goals because so much of goal setting, particularly if you're earlier in building your company, is about learning from trying to do something. You set a goal, can we do it, how hard is it to move this number, that is the ... You might be wrong all the time but you're learning what it takes to move the number. So, setting the goal by itself, not enough, you have to build a process in the system to actually learn from the goal.

**Lenny Rachitsky** (00:53:53):
Wow. This list is ... There's so much power in this list, it's such a succinct-

**Molly Graham** (00:53:57):
It's too long.

**Lenny Rachitsky** (00:53:58):
No, I don't think it is because each of these has so much depth and power to them that saves you so much headache and just wasted time and resources. Just the idea of one owner, one goal, something I've personally discovered to have such power because, and correct me if I'm missing something here but just, if you feel like someone else may be doing the thing or feels like it's not just fully your responsibility, there's so much less energy and just mental ... I don't know. You just don't care as much about hitting that goal. And if it's you-

**Molly Graham** (00:54:29):
Yeah, accountability.

**Lenny Rachitsky** (00:54:30):
Yeah. If it's like, "Lenny, this goal is your goal and, if you hit it, you've done it. If you don't, you've done a bad job," that'd be a such motivating, so motivating. If it's me and Molly, okay, well, we'll figure it out.

**Molly Graham** (00:54:41):
It creates a flood of clarity that seeps down from the person too. And to go back to the waterline model, I would say, so often you'll actually find companies that have set goals but no one owns the goals, everyone owns the goals, multiple people own the goal and you didn't actually get all the way to the answer. And I will say that the ownership thing is hard, it can feel painful but it's really important. There's only one owner and that means that that person, come hell or high water, owns that number.

**Lenny Rachitsky** (00:55:16):
Yeah. The way I described it [inaudible 00:55:18] was just someone's ass has to be on the line for this and that just works. That's such a powerful lever to drive things to have one person responsible.

**Molly Graham** (00:55:28):
Yeah.

**Lenny Rachitsky** (00:55:29):
The other's just this idea of strategy hurting, I love that. I love that phrase, I forgot Claire had that. So true.

**Molly Graham** (00:55:36):
So good.

**Lenny Rachitsky** (00:55:37):
Because the whole idea is you need to not do things, you need to decide what you're not ... The whole strategy, a big part of it is what we are not doing.

**Molly Graham** (00:55:44):
Yeah, absolutely. And if you're not making painful choices, then you're not actually doing it.

**Lenny Rachitsky** (00:55:49):
And this idea of three goals, so is it just ... So, do you go into a company and just go through a checklist essentially of here's the six things I look at to tell me where there's opportunity to improve?

**Molly Graham** (00:55:58):
When I work with founders and I see their goals, I use it as a way to get to know the business and I'm just going to be literally like, "What is this? What are you trying to explain?" And I can usually, through asking a lot of really dumb questions which, like I said, one of my superpowers, get them to explain to me the one sentence and the one number that they're actually trying to get across but it takes work and that's part of ... It's almost easier to write the 100-line spreadsheet than it is to say, "Wait, what are the three drivers of this business genuinely? What are they and how do they relate to each other?" And there can be things underneath them but there's three at the top that matter. So, yeah, I'm not a scientific person about it but a lot of it is just by asking people to explain their businesses to me, you can basically find the drivers.

**Lenny Rachitsky** (00:56:51):
And the story about Facebook having these same three goals for five years, considering their success, you may think they're not as complicated as your business but I am confident they're just as, if not, more complicated. They're a marketplace, a social network, they're a ad business, just they're ... There's a lot going on and, if they can work with three goals, you can do that too. And to your point, if it's not hurting, then you're doing something wrong.

**Molly Graham** (00:57:17):
Yeah, exactly.

**Lenny Rachitsky** (00:57:18):
I love how ... This is very much what I wanted this chat to be. It feels like every little segment is its own, could be its own podcast where we could talk about this for hours so I'm really excited how this is going. Moving on to another topic, you have not necessarily rules but rules of thumb that you find really helpful for people to have in their head as they're dealing with change and scale and growth and all that kind of stuff so let's just walk through that.

**Molly Graham** (00:57:45):
Yeah. So, for leaders that are leading through change and growth, the list is probably long but I always say to people don't come to me for management 101, I'm not the person to ask on how to run the most effective one-on-one with your people. What I think is not talked about enough is what it takes to manage and lead through change and that is a very particular set of feelings. And the first thing I learned, when someone makes you a manager or when you take a job as a leader inside a company, you really do feel like, "Oh, who gave me this job?" and you feel like you're supposed to know the answer to things. People come to you and ask questions and you're like, "I'm supposed to know. I'm a leader, I am supposed to have answers."

**Molly Graham** (00:58:35):
And I think, particularly inside of rapid change and scale and growth, it's really important to understand that your job as manager and a leader is not to have all the answers. It is not to have all the answers, it is to get good at finding them, it is to get good at bringing people together to find the answers. And that is hard because it requires saying, "I don't know, let's go figure it out," a whole bunch and it's scary as a leader to say I don't know because you think, "Oh, gosh, people are going to see through me." But again, the more you travel in life, the more you realize that the most experienced leaders are the ones that say, "No, no, no," all the time.

**Lenny Rachitsky** (00:59:13):
I think this is a good reminder of this Bob the monster concept because, hearing this, okay, I don't need to have all the answers as a leader. In real life, being in a meeting, people are like, "Hey, Molly, what do you think of this?" You're like, "Oh, I should have a good answer." And so, I think that's a good reminder of this idea of this Bob the monster is going to tell you, "Oh, you don't know anything, you're not ready for this. You suck at this, you're going to fail everyone that's [inaudible 00:59:34]."

**Molly Graham** (00:59:33):
They're regretting hiring you.

**Lenny Rachitsky** (00:59:35):
Yeah, exactly.

**Molly Graham** (00:59:36):
Everyone's going to see through you. Imposter, imposter, imposter.

**Lenny Rachitsky** (00:59:39):
Yeah, yeah.

**Molly Graham** (00:59:39):
Yeah, 100%.

**Lenny Rachitsky** (00:59:39):
But just remembering, there's going to be this part of your head and that's okay, it's there but it doesn't mean it's true.

**Molly Graham** (00:59:44):
Absolutely. And these things are muscles. Dealing with Bob as a muscle, learning to not react to all those things that attack you but also learning, oh, in this moment when someone asks me a question and I'm like, "Oh," actually I should be like, "I don't actually know, let's go ... Who should we ask? How can we learn this? How can we explore this together? What do you think?" Those are all actually very powerful questions and they're terrifying to, particularly earlier in your career, as a leader and a manager.

**Lenny Rachitsky** (01:00:11):
Awesome. So, yeah, so the lesson there is no one expects you to have all the answers as a leader.

**Molly Graham** (01:00:15):
No.

**Lenny Rachitsky** (01:00:16):
Awesome.

**Molly Graham** (01:00:17):
And particularly in this world, the one that's changing as fast as it is, nobody knows. Nobody knows what the answers are in a lot of cases, the war will be won by the people that are good exploring and figuring it out.

**Lenny Rachitsky** (01:00:29):
I love that phrase.

**Molly Graham** (01:00:31):
So, the second one is, and everyone that has learned this has learned it the hard way, do not promise things that you can't control. It's so tempting particularly when you're hiring people to be like, "Oh, yeah, your onboarding will be smooth and calm and everything's clear and we've figured it ... Let us tell you our vision and how obvious and clear and smart and blah, blah, blah." And then they show up and it's like, "Oh, shit," you know what I mean? There's no manual, no one knows what they're doing, it's all ambiguity and chaos. It's so easy when someone says I want to know that I'm going to be your CMO forever to be like, "Sure, you can be my CMO," you don't know that. Do you know what I mean? So, being really careful with promises of things that are out of your control like stability or titles or never hiring over someone is a flashing red light because there is literally no faster way to demoralize high performers than going back on a promise. Everyone that has been through it knows that feeling of like, "They told me this when I joined," and then they don't do it and you're like, "Well, fuck this place." So, no faster way to demoralize people or to hire the wrong people than promising things that are actually out of your control. Being honest and upfront about who you are as a company, about what you're able to promise, all of that is actually ... It's very hard work but it's so important because so much is out of your control and you need to hire people that are cool with that.

**Lenny Rachitsky** (01:02:01):
Love that. I learned this the hard way once, I had a ... One of my early projects, we were late and the head of product was just so pissed he's like, "Because I've been telling the CEO it's going to be on time because you've been telling me it's going to be on time and then it wasn't and why didn't you tell me that?" And I was just like, "Okay, it'll never happen again," and he's like, "You can't ... Don't tell me that because that's not true, that nay not ... You can't guarantee that." And so, that taught me that lesson of just, yeah, you're right, you want to say that, it feels so good. Okay, this will never happen again but you can't and they know you can't promise things like that.

**Molly Graham** (01:02:38):
Yeah. And sorry, I'm going to quote Claire Hughes Johnson again but she has this really fun phrase that she said in a talk at Glue Club that I've now latched onto and stolen from her which is, she was like, "Promises like that are like letter bombs that you mail yourself that are going to explode in your face in a year," and I was like, "That is the perfect metaphor." Because it's short-term pain, you want to make this person feel good right now so you promise them something but, in one year, you're going to make them feel terrible so don't do it.

**Lenny Rachitsky** (01:03:10):
Great advice. All right, keep going.

**Molly Graham** (01:03:11):
Yeah. So, again, could probably go on the topic of what it takes to manage and lead forever inside this stuff but I'll give you two more that I yell about a lot in Glue Club. The first is that we spend huge amounts of time talking about hiring. How do you get good at hiring? What's the right interview? How do I find the right people? Firing people is as important as hiring people. Getting good at identifying when someone does not belong or someone is not going to work out is actually a skill and being good at it as a company and as a leader is as important as identifying the right talent because, eventually, if you're not good at firing people, what you have is essentially barnacles on a ship. Really going forward with the ship metaphor, anyway.

**Lenny Rachitsky** (01:03:12):
It's true.

**Molly Graham** (01:04:00):
It's drag people that are sitting around not pushing the team forward. So, it's painful and it's horrible because it is humans but, when someone doesn't fit, you ... No one is right all the time when it comes to hiring, I actually say most people are wrong half the time. The best people in the world in hiring will tell you they have about a 50% average in terms of being right. That means half the hires don't work out. That means, half the time, you're going to need to fire the person. So, it's such an important skill to get good at particularly when you're going through a lot of change. And the last one is humans are messy and it's very emotional. And when you're a leader, particularly if you have any kind of anagram too or just if you like to make people happy and you want to be liked, it can be so hard to lead teams because you get tangled in the people. Firing people is a painful experience, reorganizing things, layering people, all these things are emotionally painful for the people and they're emotionally painful for you as a manager.

**Molly Graham** (01:05:05):
But my mantra that almost always leads in the best direction is serve the business, not the people meaning everyone is better off if this company is wildly successful. Everyone looks smart and makes lots of money or whatever if this company grows and does what we all dream it can. So, at the end of the day, the best decisions, the ones that are always going to be right are the ones that are like, "How do we do the right thing for this business?" And it also helps in political situations. Someone's acting weird or their Bob is raging all over the company, technically, everyone has the same goal. The goal is to build the biggest business possible, that's the answer. The answer is always what's the right thing for the business. And the people stuff can fall away when you actually focus on what's the right thing for the business.

**Lenny Rachitsky** (01:05:55):
A really useful tool to do that that I learned from my manager is to think about, when you're trying to decide whether to fire someone or change a project even though it's going to upset someone, is to say, "Okay, if there were no emotions involved, if this person had no negative reaction to this, what would I do?"

**Molly Graham** (01:06:15):
Totally.

**Lenny Rachitsky** (01:06:16):
And then that's the thing you should do and then you just do it. And then the question is how do I communicate this to them where their pain is lowest essentially.

**Molly Graham** (01:06:16):
In the kindest way possible.

**Lenny Rachitsky** (01:06:27):
In the kindest way possible. And because, to your point, if you optimize for the other thing of making people feel good, everything just falls apart, they're going to suffer even more down the road.

**Molly Graham** (01:06:38):
Yeah, absolutely. Direct is kind. And it feels kind or, really, honestly, easy to avoid these things or to work around them or to not but, at the end of the day, it's basically just a drag, the barnacle thing. It drags on your company, on your time, on your energy, et cetera.

**Lenny Rachitsky** (01:06:58):
Yeah. But again, very hard to do in real life to do the thing that's hard and cause someone to be sad and upset and frustrated and maybe leave.

**Molly Graham** (01:07:08):
It is so hard and all these things are muscles, you get better at ... They don't become easy, it's not like anybody who's like, "Oh, it's so ... I enjoy firing people," no, but you recognize it faster and you are like, "Oh, I need to go do this." And that is actually ... It's a practice and something that you need to practice to become the kind of leader that leads these long-enduring companies.

**Lenny Rachitsky** (01:07:32):
Yeah. And this tool of thinking, asking what would I do if there were no emotions involved and this person wouldn't be upset, it helps you realize, okay, I see, this actually doesn't make sense to just do it the easy way right now because it doesn't make sense.

**Molly Graham** (01:07:46):
Yeah, it strips away. It strips away the emotions.

**Lenny Rachitsky** (01:07:50):
Something else I wanted to make sure we spent a little time on is you have another tidbit along these lines which is around putting most of your energy into high performers versus spending all your time people that need help, talk about that.

**Molly Graham** (01:08:02):
As a leader, as a manager, you're running these teams and someone's struggling and it's very easy to get dragged into that and to end up spending a huge amount of energy on it but high performers are actually the future of your company. And if you think about it and if you've spent time on it, those are the folks where, if you invest your time and energy in them, you're going to get the 10x return that people talk about all the time in Silicon Valley. But what I've witnessed is that most people have a high performer and they just leave them alone, they're like, "That person's doing well so I'm just going to let them do their thing." And what I do when I have a high performer that's my favorite thing in the world is invest time and energy in them and basically build a whole system of working with them that is designed to draw out potential. And I would say there's two things here, one is it's really important to realize that our tendency-

**Molly Graham** (01:09:00):
There's two things here. One is it's really important to realize that our tendency is to actually spend time on low performers and it is not a good use of your time. See the point about firing people. But the other thing is that actively investing in and developing high performers is something that's important to get good at as a leader because that is how you create these little rocket ships that end up... You'll manage someone who's just like a project manager and all of a sudden they're running a whole function inside the company eventually, but it's because you took time and energy to invest in them. And my basic way of doing that, not to... I could spend a long time on this, but I would just say is I run experiments. I basically develop a theory about someone, "I think this person is capable of this kind of thing." And then piece by piece, it doesn't have to be a whole job or whole project. It can just be an incremental experiment, "I'm going to see if they can do this with less guidance or support from me."

**Molly Graham** (01:09:52):
I'm going to give them a bigger project. I'm going to give them something with more visibility. I'm going to manage them less, oversee them less, whatever. All of those are experiments to basically test your theory and deepen your theory in terms of this person's potential and their ability to help the company. And you're basically, for me, what I'm doing is deeply getting to know that person and then trying to pair them with company needs. What do we need? This person is great at zebra farming. Where do we need zebra farming? So how do I get them working on bigger and bigger and more and more critical things?

**Molly Graham** (01:10:27):
And to be honest, this is what people have done for me. At Facebook in particular, I've benefited from people being like, "Ooh, come help me with this thing." They saw potential in me and they asked me to help with something and it unlocked a huge amount for me. And so, it is such a powerful tool for getting more out of people that might be a little bit stuck if you leave them in this box. But if you start to expand the box, you can really unlock people.

**Lenny Rachitsky** (01:10:55):
So speaking of high performers, you've worked with many very high performing founder-CEOs. He worked really closely with Zuck, with Cheryl Sandberg, with Larry and Sergei at Google, with Brett Taylor, who I just... Just like you trying to read his resume, it takes three lines of things he's done over the course of his career. And so I just want to spend a little time on what are some things you've learned, maybe a few things you've learned from them, that group that you find yourself sharing with other people most.

**Molly Graham** (01:11:26):
That list is very long, but I'll give you a couple. The first one that I think is kind of counterintuitive is... So I said I worked at Facebook. I worked on culture, which is one of those words that doesn't really mean anything. So I define it as the way we do things around here. And I thought my job was to shape the culture. I thought it was to push the culture. And the most humbling lesson I learned is 80% of the culture of a company is literally defined by the personality of the founder. Facebook is Mark. Google is Larry and Sergei. Google, when I was there, it felt like a university. It's where ideas are more important in a lot of ways than what's shipped. And there's a campus and they basically wanted people to live there when I was there. It was designed to basically be a two PhD students' paradise.

**Molly Graham** (01:12:29):
Facebook felt like a 19-year-old hacker's dorm room when I was there. And it was shipping above all and all else. And it seeped with Mark's DNA. And I spent ages trying to create various changes inside the company or trying to push a point. And Mark would say literally one thing in an all hands, and it was like somebody threw a boulder into the pond. So our job as operators or as leaders around founders is to help articulate the culture that they're creating and to help extend it. My version of founder mode, which I know you've spent some time on on this podcast is your job is to build a company that would make a decision the way the founder would when they're not in the room. That is the work of building a company around a founder, but your job is not to shape culture.

**Molly Graham** (01:13:24):
That is mostly defined by the literal personality strengths and weaknesses of the person at the top. And that's been true of Mark and it's been true of Brett. Everywhere I go, that's who it is. You don't need a consulting firm to tell you, just go do a personality diagnosis on your founder. And the weaknesses thing is real. I've seen and watched friends try to shape a set of values at a company and it just doesn't match who the founder is. You say, "Move fast and break things," or whatever your version of that is. And your founder loves ambiguity and is perfectly happy with not making decisions. All that leads to is cultural dissonance. It leads to people being like, "Wait, what? I thought we said we care about moving fast and making aggressive decisions and it turns out..." So being really careful about what you say, because what people actually feel when it comes to culture is what you do and how you act every day.

**Molly Graham** (01:14:18):
You can never write anything down and you will still have a culture. It will be created through the actions and the decisions that you make and that your founder makes. So that would be a huge one.

**Lenny Rachitsky** (01:14:28):
Let me spend a little more time on this because this is so good. So all this advice on culture and it feels so true based on everything I've seen. So tip one there is just you can't really change the culture. Maybe there's a little bit on the edges you could adjust. It will come down and trickle down from the founder, CEO probably mostly, but just the founders in general.

**Molly Graham** (01:14:49):
And founder-CEO is probably the single biggest.

**Lenny Rachitsky** (01:14:49):
Founder-CEO.

**Molly Graham** (01:14:52):
Co-founders, it depends a lot on the company.

**Lenny Rachitsky** (01:14:56):
Awesome.

**Molly Graham** (01:14:57):
I think Stripe is probably very much like Patrick and John, but it's not every co-founder that has that level of power [inaudible 01:15:03].

**Lenny Rachitsky** (01:15:03):
Awesome. And then the way you describe culture, I think it's the way Seth Godin talks about it too, who's also been on the podcast. How cool is that?

**Molly Graham** (01:15:09):
So cool.

**Lenny Rachitsky** (01:15:10):
He said, "Culture is..." And what you said, "Culture is the way we do things around here. That's what culture is, is how we..." That's how people describe your culture is, "The way we do things around here."

**Molly Graham** (01:15:22):
I ran culture, whatever the hell that means, at Facebook for a hot second. I literally haven't done a values exercise since. And it sounds crazy, right? Because in theory, I know how to do this stuff. I don't really know how to do this stuff. But for me, the point is process and systems and how do we make decisions? That's where culture actually lives. It is what you do. It's how you hire. It's how you fire. It's who you don't hire. It's all of those decisions. That is culture. So whenever I'm working with a company or building a company, that's what I'm focused on, not on what's the shiny word that we're putting on the wall. You know what I mean?

**Lenny Rachitsky** (01:15:54):
Yeah. So the way you're describing is, as you said, it's what you do. It's not what you say?

**Molly Graham** (01:15:59):
Yeah.

**Lenny Rachitsky** (01:15:59):
Awesome. Keep going.

**Molly Graham** (01:16:04):
I'll give you two more that are helpful. This one is a Mark Zuckerberg classic, but he has this very strong feeling that people don't escalate enough. And he was very adamant about it at Facebook. And he brought it to CZI too where he was like, "Escalation is a tool." And he's like, "People get stuck. They get stuck with two people with equal power trying to solve a problem. You can spend so much time bashing heads, going back and forth. And actually what you just need to do is go up. You need to go." The problem is that we think of escalation as, "I'm A and B and I are disagreeing. And so, I'm going to go up to C and tell on B. I'm going to go tattle to the teacher." That is not what escalation is. What escalation is, "We disagree. Neither one of us has enough power to make this decision. Let's go to someone who does." My boss, my boss's boss, whoever it is.

**Molly Graham** (01:16:59):
As soon as you are stuck, escalate. Go together, go make your case to whoever it is, go together up. That is unlocking. It's saving you a whole bunch of time. And it's something that I've found as I've worked with companies and leaders in Glue Club. It's not a muscle that's very comfortable for people, but it's so smart. And Mark has a lot of these, but that one I really took away because again, I think so many people think of escalation as bad, a failure, like, "I failed, so I had to escalate." No, it's a tool. It's what management is for. They're there to unblock you. Let them unblock you. Stop arguing over something you can't decide.

**Lenny Rachitsky** (01:17:36):
And they'll be so happy knowing you did not waste a week debating this and then just arguing and just looking at data. It's like, "Okay, I can just tell you exactly what we should do. Let's go do that."

**Molly Graham** (01:17:44):
Exactly. You lack context or you lack power. And then the last one actually is from Cheryl Sandberg, who I learned an enormous amount from, huge, was like going to business school without going to business school working with her. But I say it a lot right now, so I'm going to say it on your podcast so maybe some people will hear me. Growing more than 100% every year is a bad idea. The happiest growth rate is 50%, 100% is manageable. Anything more than doubling and you are signing yourself up for a world of pain. And I have seen this over and over and over again. I had to scream way louder about it five years ago than I do now because we've been through collectively a lot of pain and a lot of layoffs. And obviously the combination of 2021 and then AI has led us to talk about unit economics and scaling with tools, not people.

**Molly Graham** (01:18:47):
But I still see companies and I'm still talking to founders that are like, "Yeah, we're 50 people and we're going to be 150 people next year." And I'm like, "Could you possibly do that with 100 people?" But here's what basically happens if you grow at more than 100%, which is you're growing too fast to de-dupe all the issues. So somebody posts this role, it actually turns out that that role is also being hired for on this other team. So you're hiring two people who more or less have the same job description and are assigned to the same number or the same problem, but nobody talked to each other. And those two people both show up and they're like, "I am doing this." And the person's like, "Wait, I thought I was doing that." Anyway, so then you've got all this... And think about all the time and all the energy and all the money that goes into de-duping that.

**Molly Graham** (01:19:32):
If you slow down, if you hire for quality and for real need versus the panic hiring, whatever your sales model spits out or whatever, you'll actually find leverage. You find, "Oh, I didn't need that person," or, "I didn't need this whole team," or, "I didn't need this whole function," or, "I can wait for that." So slow down. And again, these are all just guidelines in terms of the 50% is happy and 100% is manageable. But having seen enough of this, I can tell you these are good rules and you should pay attention to them. And sometimes you're like, "I have to double or I have to more than double or I have to triple," or whatever. I'm like, "Okay, just ask a whole lot of questions as you open roles. Ask a whole lot of questions as you hire because you will find duplication, you will find chaos coming in the front door."

**Molly Graham** (01:20:16):
More people does not actually make you faster. Do you know what I mean? We think it does. It does not. It makes it harder. It makes it harder to get work done. It makes it slower. So you should be scared of adding people, not like, "Oh, this is the answer to all my problems."

**Lenny Rachitsky** (01:20:27):
Amazing. And just to be clear, you're talking about the growth of the company. So doubling in a year, bad idea. It's possible, but you're saying it's going to be very hard and painful and probably a really bad idea.

**Molly Graham** (01:20:40):
Yeah. More than doubling head count growth.

**Lenny Rachitsky** (01:20:40):
More than doubling.

**Molly Graham** (01:20:40):
Great point.

**Lenny Rachitsky** (01:20:40):
Head count?

**Molly Graham** (01:20:42):
Yes, exactly.

**Lenny Rachitsky** (01:20:43):
Awesome. It's-

**Molly Graham** (01:20:44):
Please feel free to do whatever you want with your business.

**Lenny Rachitsky** (01:20:47):
Just advice. This is top of mind because I just had the interview with Matt McGinnis, but so much of what he talked about is this resonates with what you're talking about. He talked a lot about under-resourcing your team-

**Molly Graham** (01:20:59):
Totally.

**Lenny Rachitsky** (01:20:59):
Leads to much better outcomes because people don't work on the low priority stuff. They focus on only high priority stuff. And the other is this idea of escalating. He talked a lot about that. Just like, "Escalation is good. Tell me when there's something I can help with, please. I'm here waiting constantly-

**Molly Graham** (01:21:14):
There you go.

**Lenny Rachitsky** (01:21:14):
"To help."

**Molly Graham** (01:21:14):
Yeah, 100%.

**Lenny Rachitsky** (01:21:16):
Amazing. So maybe for a final question, one of your former colleagues, Eric Antonow, who's just this epic dude that few people know about-

**Molly Graham** (01:21:16):
Totally.

**Lenny Rachitsky** (01:21:24):
That I've chatted with over the last few months because he knows so many people that come on this podcast. He's a former Facebook person, now at OpenAI. I asked him what I should ask you about and he told me something really insightful about you. He said that you had this really massive growth spurt at Facebook, which you shared and talked about. And then after you leaving, you had this huge ambition to become COO, CEO, become this huge big deal boss person, just take over the world. And then he noticed your ambitions significantly pivot to working on community building and helping people with their careers. And you turned down really big C-level role opportunities. And the way he described it is you were a dog that once thought you were cat. And the other metaphor he used is you change from AC current to DC current, which I don't know exactly what that means. So does this resonate? And if so, just what happened there?

**Molly Graham** (01:22:22):
Eric is actually better at metaphors than I am, and I regularly rip his metaphors. But yes, Eric Antonow, the least well-known, but most brilliant person in my life. So I gave a talk at a company recently and somebody asked the question, "What's something you've changed your mind about?" And I was like, "Woof." But I actually talked about this because... So my brain is developing this model that is not done yet, but it's basically this idea that everybody has a proving phase to their career where you're proving to yourself and probably to your parents and some other people that you're good at stuff. You're like, "I'm going to prove." And it's an important phase because you need to learn. All the stuff we talked about. You need to learn what you're good at. You need to learn that you are good at things and that people should hire you for things and what are those things?

**Molly Graham** (01:23:22):
But part of that phase is also doing what you think matters, what you think you should do. Family programming or career books tell you this is what you should do, titles and all that stuff. And then, I think everybody has a moment and I think this moment varies wildly in terms of when it hits people, where you hit some sort of wall or I don't know what it is, speed bump, something, and the world forces you to say, "Okay, I've proven myself and I'm good at this thing. What do I want to do with it?" And for me, I spent 10 or 15 years proving to myself and to others that I was really good at this thing, basically working with brilliant founders to help bring their vision to life, "That's what you should hire me to do." That's what I was known for.

**Molly Graham** (01:24:20):
And it turned out that that wasn't what I love doing anymore. And it was really, really hard to walk away from because there was a lot of shoulds. It was like, "You should take this job with this fancy title. People are going to think you're so cool." And you get to... I call it a LinkedIn crush where you're really excited to post the job on LinkedIn, but you're deeply unexcited about doing the job. So you have all these LinkedIn crushes and you're like... And I vividly remember this one job that I turned down where I had to go for many walks. And what I was repeating over and over again to myself was, "What does this get you that you don't already have? What does this get you that you don't already have?" And I think, for me, it was this realization that these things that fed me early in my career just didn't feed me anymore, that I didn't get joy and excitement out of doing these jobs anymore, and I wasn't scared.

**Molly Graham** (01:25:20):
So it led me actually on a very long, windy journey, a founder journey, even though I have trouble with that title, just like the influencer title, to figure out what I wanted to build. And what I would've told you I wanted to build three years ago is actually not what I'm doing today, but through a lot of really fun experiments and a journey that never ends, what I've discovered is that what I love doing is building safe spaces for leaders to learn and grow, but also to find sanity and connection in a world that's kind of insane, whether it's working in a startup or some other kind of insanity, but that feeds me and there's nothing I love more than that, and I could not have told you that three years ago, but, to Eric's point, it really took a lot of work to switch currents or switch myself from a dog to a cat or whatever his metaphor is. And I think it's the work of it's ongoing work, but it's that thing of what do I want versus what do I think people expect of me?

**Lenny Rachitsky** (01:26:32):
There's so much depth there. This could be another entire podcast conversation talking through this journey, but I'm going to close with a note from your partner, Sarah. She told me that she has this sticker on her notebook with three pieces of advice that you gave her when she started at OpenAI. Get to know your customer, they have the answer; be patient because everything is going to change; and just keep trying. So just as a final question, is there anything along those lines that you think might be helpful for people to hear or is there anything else you want to share or leave listeners with?

**Molly Graham** (01:27:07):
Part of what I think is so important to realize inside of scaling and changing companies and the world is some things will always be true. And part of what I was saying to Sarah in the "get to know the customer, they have the answer" is, whatever bullshit is going on around you and whatever walls and ceiling are being rearranged this week, the customer is never going to change. That's a thing that will never change. And I think finding those immovable objects, those compasses in the face of a storm, which being inside of a scaling company in a startup feels like a tornado. And I think OpenAI is extra special on that front. You have to find these guiding lights that get you through that storm. And I think it's sort of the same thing as "Serve the business, not the people." What are the things that will always be true? We are here to do this. We are here to serve the customer.

**Molly Graham** (01:28:09):
And then the other piece of the three things that she wrote down is, I think that we, as humans, we seek stability. Our brains would like things to stop changing. We would like things to stay the same. And that is just not a reality inside of companies that are growing and changing as fast as OpenAI or a lot of the companies today that are being built. So actually, you need to start to expect instability. You need to start to just assume things are going to change. Assume you're going to have a new boss in six months. I talk about this a lot when I talk to folks at OpenAI, "You need to stop expecting that anything's going to be the same in six months or a year. You will have a different job. You will have a different boss."

**Molly Graham** (01:28:55):
How do you prepare for that? Do you know what I mean? How do you almost see the instability as stability because it's the only thing that is definitely going to be true. And part of that is to just keep going. You know what I mean? To just find these lights and these compasses or whatever metaphor sticks with you and focus on those because whatever is happening around you, you just got to keep moving forward and keep learning as much as you can because that's the real opportunity. Whatever happens to the company, however successful it is, all that you take away from it... I always say all that you take away from it is people that like working with you and want to work with you again and what you learned. That's it. You might hopefully take a bunch of money, but you might not. So people and what you learned, that's it. Focus on that.

**Lenny Rachitsky** (01:29:41):
It's all about the friends you made along the way. That old line is true. Oh, man. Molly, I feel like we've gone for so long and we've just scratched the surface. I'd love to have you back to go deeper on a lot of this stuff. I'm going to skip the lightning round because we've gone long and I want to keep people from having to listen to more. So I'm just going to end with, what should people know about what you're working on? Where can people go find you online? And how can listeners be useful to you?

**Molly Graham** (01:30:07):
You can find me on LinkedIn and you can find me on Substack. I have a Substack called Lessons that I'm slowly trying to turn into a community where we can talk about things, the real stuff. And you can find me at Glue Club, which, if you're a leader inside of one of these crazy companies that's changing all the time, we can be a great home for you.

**Lenny Rachitsky** (01:30:26):
What's the URL there just for folks to check out?

**Molly Graham** (01:30:27):
It's glueclub.com.

**Lenny Rachitsky** (01:30:28):
Glue, G-L-U-E?

**Molly Graham** (01:30:31):
G-L-U-E.

**Lenny Rachitsky** (01:30:32):
C-L-U-B.com. Great.

**Molly Graham** (01:30:34):
Yeah, exactly. And in terms of people, what people can do to be useful to me, I love helping leaders with problems. I really get a lot of energy out of unsticking people and helping people feel supported and seen and helping them grow. I do that through Glue Club. So if you're a leader that feels like you want some sanity and some support in the face of whatever tornado you're in, that's a great place to come. But the same is true of Substack. So if Glue Club isn't for you, come on over to Substack. I've opened up a bunch of channels to just talk about stuff, listen to people's problems, answer questions because I love helping people. And I think it's a complicated moment right now to be a leader and to figure out which way is up. So come on over.

**Lenny Rachitsky** (01:31:24):
Amazing. Molly, thank you so much for being here.

**Molly Graham** (01:31:27):
Thank you, Lenny. This was really fun.

**Lenny Rachitsky** (01:31:29):
Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [3/15] Aishwarya Naresh Reganti + Kiriti Badam

**Lenny Rachitsky** (00:00:00):
We worked on a guest post together. They had this really key insight that building AI products is very different from building non-AI products.

**Aishwarya Naresh Reganti** (00:00:08):
Most people tend to ignore the non-determinism. You don't know how the user might behave with your product, and you also don't know how the LLM might respond to that. The second difference is the agency control trade-off. Every time you hand over decision-making capabilities to agentic systems, you're kind of relinquishing some amount of control on your end.

**Lenny Rachitsky** (00:00:25):
This significantly changes the way you should be building product.

**Kiriti Badam** (00:00:28):
So we recommend building step-by-step. When you start small, it forces you to think about what is the problem that I'm going to solve. In all this advancements of the AI, one easy, slippery slope is to keep thinking about complexities of the solution and forget the problem that you're trying to solve.

**Aishwarya Naresh Reganti** (00:00:42):
It's not about being the first company to have an agent among your competitors. It's about have you built the right flywheels in place so that you can improve over time.

**Lenny Rachitsky** (00:00:50):
What kind of ways of working do you see in companies that build AI products successfully?

**Aishwarya Naresh Reganti** (00:00:55):
I used to work with the CEO of now Rackspace. He would have this block every day in the morning, which would say catching up with AI 4:00 to 6:00 AM. Leaders have to get back to being hands-on. You must be comfortable with the fact that your intuitions might not be right. And you probably are the dumbest person in the room and you want to learn from everyone.

**Lenny Rachitsky** (00:01:13):
What do you think the next year of AI is going to look like?

**Kiriti Badam** (00:01:16):
Persistence is extremely valuable. Successful companies right now building in any new area, they are going through the pain of learning this, implementing this and understanding what works and what doesn't work. Pain is the new moat.

**Lenny Rachitsky** (00:01:29):
Today, my guests are Aishwarya Reganti and Kiriti Badam. Kiriti works on Kodex at OpenAI and has spent the last decade building AI and ML infrastructure at Google and at Kumo. Ash was an early AI researcher at Alexa and Microsoft and has published over 35 research papers. Together, they've led and supported over 50 AI product deployments across companies like Amazon, Databricks, OpenAI, Google, and both startups and large enterprises. Together, they also teach the number one rated AI course on Maven, where they teach product leaders all of the key lessons they've learned about building successful AI products. The goal of this episode is to save you and your team a lot of pain and suffering and wasted time trying to build your AI product. Whether you are already struggling to make your product work or want to avoid that struggle, this episode is for you. If you enjoy this podcast, don't forget to subscribe and follow to your favorite podcasting app or YouTube.

**Aishwarya Naresh Reganti** (00:05:13):
Thank you, Lenny.

**Kiriti Badam** (00:05:14):
Thank you for having us. Super excited for this.

**Lenny Rachitsky** (00:05:16):
Let me set the stage for the conversation that we're going to have today. So you two have built a bunch of AI products yourself. You've gone deep with a lot of companies who have built AI products, have struggled to build AI products, build AI agents. You also teach a course on building AI products successfully and you're kind of on this mission to just reduce pain and suffering and failure that you constantly see people go through when they're building AI products. So to set a little just foundation for the conversation we're going to have, what are you seeing on the ground within companies trying to build AI products? What's going well? What's not going well?

**Aishwarya Naresh Reganti** (00:05:54):
I think 2025 has been significantly different than 2024. One, the skepticism has significantly reduced. There were tons of leaders last year who probably thought this would be yet another crypto wave and kind of skeptical to get started. And a lot of the use cases that I saw last year were more of slap chat on your data. And that was calling themselves an AI product. And this year, a ton of companies are really rethinking their user experiences and their workflows and all of that and really understanding that you need to deconstruct and reconstruct your processes in order to build successful AI products. And that's the good stuff. The bad stuff is the execution is still all over the place. Think of it. This is a three-year-old field. There are no playbooks, there are no textbooks. So you really need to figure out as you go. And the AI lifecycle, both pre-deployment and post-deployment is very different as compared to a traditional software lifecycle.

**Aishwarya Naresh Reganti** (00:06:57):
And so a lot of old contracts and handoffs between traditional roles, like say PMs and engineers and data folks has now been broken and people are really getting adapted to this new way of working together and kind of owning the same feedback loop in a way. Because previously, I feel like PMs and engineers and all of these folks had their own feedback loops to optimize. And now you need to be probably sitting in the same room. You're probably looking at agent traces together and deciding how your product should behave. So it's a tighter form of collaboration. So companies are still kind of figuring that out. That's kind of what I see in my consulting practice this year.

**Lenny Rachitsky** (00:07:37):
So let me follow that thread. We worked on a guest post together that came out a few months ago. And the thing that stood out to me most that stuck with me most after working on that post is this really key insight that building AI products is very different from building non-AI products. And the thing that you're big on getting across is there's two very big differences. Talk about those two differences.

**Aishwarya Naresh Reganti** (00:08:01):
Yes. And again, I want to make sure that we drive home the right point. There are tons of similarities of building AI systems and software systems as well, but then there are some things that kind of fundamentally change the way you build software systems versus AI systems. And one of them that most people tend to ignore is the non-determinism. You're pretty much working with a non-deterministic API as compared to traditional software. What does that mean and why does that have to affect us is in traditional software, you pretty much have a very well-mapped decision engine or workflow. Think of something like Booking.com. You have an intention that you want to make a booking in San Francisco for two nights, et cetera. The product has kind of been built so that your intention can be converted into a particular action and you kind of are clicking through a bunch of buttons, options, forms, and all of that, and you finally achieve your intention.

**Aishwarya Naresh Reganti** (00:08:59):
But now that layer in AI products has completely been replaced by a very fluid interface, which is mostly natural language, which means the user can literally come up with a ton of ways of saying or communicating their intentions. And that kind of changes a lot of things because now you don't know how your user's going to be here. That's on the input side. And the output is also that you're working with a non-deterministic probabilistic API, which is your LLM. And LLMs are pretty sensitive to prompt phrasings and they're pretty much black boxes. So you don't even know how the output surface will look like. So you don't know how the user might behave with your product, and you also don't know how the LLM might respond to that. So you're now working with an input, output, and a process. You don't understand all the three very well. You're trying to anticipate behavior and build for it.

**Aishwarya Naresh Reganti** (00:09:53):
And with agentic systems, this kind of gets even harder. And that's where we talk about the second difference, which is the agency control trade-off. What we mean by that, and I'm kind of shocked so many people don't talk about this. They're extremely obsessed with building autonomous systems, agents that can do work for you. But every time you hand over decision-making capabilities or autonomy to agentic systems, you're kind of relinquishing some amount of control on your end. And when you do that, you want to make sure that your agent has gained your trust or it is reliable enough that you can allow it to make decisions. And that's where we talk about this agency controlled trade-off, which is if you give your AI agent or your AI system, whatever it is, more agency, which is the ability to make decisions, you're also losing some control and you want to make sure that the agent or the AI system has earned that ability or has built up trust over time.

**Lenny Rachitsky** (00:10:49):
So just to summarize what you're sharing here, essentially, people have been building product, software products for a long time. We're now in a world where the software you're building is one, non-deterministic, can just do things differently. As you said, you go to booking.com, you find a hotel, it's going to be the same experience every time. You'll see different hotels, but it's a predictable experience. With AI, you can't predict that it's going to be the exact same thing, the thing that you plan it to be every time. And then the other is there's this trade-off between agency and control. How much will the AI do for you versus how much should the person still be in charge? And what I'm hearing is the big point here is this significantly changes the way you should be building product. And we're going to talk about the impact on how the product development lifecycle should change as a result.

**Lenny Rachitsky** (00:11:35):
Is there anything else you want to add there before we get into that?

**Kiriti Badam** (00:11:39):
Yeah, it's definitely one of the key points that this kind of distinction needs to exist in your mind when you're starting to build. For example, think about if your objective is to hike Half Dome in Yosemite. You don't start hiking it every day, but you start training yourself in minor parts and then you slowly improve and then you go to the end goal. I feel like that's extremely similar to what you want to build AI products in the sense that when you don't start with agents with all the tools and all the context that you have in the company in day one and expect it to work or even tinker at that level. You need to be deliberately starting in places where there is minimal impact and more human control so that you have a good grip of what are the current capabilities and what can I do with them and then slowly lean into the more agency and lesser control.

**Kiriti Badam** (00:12:29):
So this gives you that confidence that, okay, I can know that, okay, this is the particular problem that I'm facing and the AI can solve this extent of it. And then let me next think through what context I need to bring in, what kind of tools I need to add to this to improve the experience. So I feel like also it's a good and a bad thing in the sense that it's good that you don't have to see the complexity of the outside world of all of this fancy AI agents force and feel like I cannot do that. Everyone is starting from very minimalistic structures and then evolving. And the second part is the bad thing is that as you are trying to build this one click agents into your company, you don't have to be overwhelmed with this complexity. You can slowly graduate.

**Kiriti Badam** (00:13:16):
So that's extremely important. And we see this as a repeating pattern over and over.

**Lenny Rachitsky** (00:13:20):
Okay. So let's actually follow that because that's a really important component of how you recommend people build AI stuff, AI products, AI agents, all the AI things. So give us an example of what you're talking about here, this idea of starting slow with agency and control and then moving up this rung.

**Kiriti Badam** (00:13:38):
Yeah. For example, a very important or very prevalent application of AI agents is customer support. Imagine you are a company who has a lot of customer support tickets and why even imagine OpenAI is the exact same thing when we were launching products and there was a huge spike of support volume as we launched successful products like Image or GPT-5 and things like that. The kind of questions you get is different. The kind of problems that the customers bring to you is different. So it's not about just dumping all the list of help center articles that you have into the AI agent. You kind of understand what are the things that you can build. And so initially the first step of it would be something like you have your support agents, the human support agents, but you will be suggesting in terms of, okay, this is what the AI thinks that is the right thing to do.

**Kiriti Badam** (00:14:33):
And then you get that feedback loop from the humans that, okay, this is actually a good suggestion for me in this particular case and this is a bad suggestion. And then you can go back and understand, okay, this is what the drawbacks are or this is where the blind spots are, and then how do I fix that? And once you get that, you can increase the autonomy to say that, okay, I don't need to suggest to the human. I'll actually show the answer directly to the customer. And then we can actually add more complexity in terms of, okay, I was only answering questions based on health center articles, but now let me add new functionality. I can actually issue refunds to the customers. I can actually raise feature requests with the engineering team and all of these things. So if you start with all of this on day one, it's incredibly hard to control the complexity.

**Kiriti Badam** (00:15:19):
So we recommend building step by step and then increasing it.

**Lenny Rachitsky** (00:15:23):
Awesome. And you have a visual actually that we'll share of what this looks like. But just to kind of mirror back what you're describing, this idea of start with high control, low agency, the example you gave is the support agents just kind of giving suggestions, is not able to do anything, the user is in charge. And then as that becomes useful and you are confident it's doing the right sort of work, you give it a little more agency and you kind of pull back on the control the user has. And then if that's starting to go well, then you give it more agency and the user needs less control to control it. Awesome.

**Aishwarya Naresh Reganti** (00:16:02):
I think the higher level idea here is with AI systems, it's all about behavior calibration. It's incredibly impossible to predict upfront how your system behaves. Now, what do you do about it? You make sure that you don't ruin your customer experience or your end user experience. You keep that as is, but then remove the amount of control that the human has. And there is no single right way of doing it. You can decide how to constrain that autonomy. I mean, a different example of how you could constrain autonomy is pre-authorization use cases. Insurance pre-authorization is a very ripe use case for AI because clinicians spend a lot of time pre-authorizing things like blood tests, MRIs and things like that. And there are some cases which are more of low hanging fruits. For instance, MRIs and block tests, because as soon as you know patient's information, it's easier to approve that and AI could do that versus something like an invasive surgery, et cetera, is more high risk. You don't want to be doing that autonomously.

**Aishwarya Naresh Reganti** (00:17:11):
So you can kind of determine which of these use cases should go through that human and the loop layer versus which of the use cases AI can conveniently handle. And then all through this process, you're also logging what the human is doing because you want to build a flywheel that you could use in order to improve your system. So you're essentially not showing the user experience, not eroding trust, at the same time logging what humans would otherwise do so that you can continuously improve your system.

**Lenny Rachitsky** (00:17:41):
So let me give you a few more examples of this kind of progression that you recommend. And the reason I'm spending so much time here is this is a really key part of your recommendation to help people build more successful AI products. This idea of start slow with high control and low agency and then build up over time once you've built confidence that it's doing the right sort of work. So a few more examples that you shared in your post that I'll just read. So say you're building a coding assistant, V1 would be just suggest inline completion and boilerplate snippets. V2 would be generate larger blocks like tests or refactors for humans to review. And then V3 is just apply the changes and open PRs autonomously. And then another example is a marketing assistant. So V1 would be draft emails or social copy, just like here's what I would do.

**Lenny Rachitsky** (00:18:26):
V2 is build a multi-step campaign and run the campaign. And V3 is just launch it A/B tested auto-optimize campaigns across channels. Awesome. Yeah. And again, just to summarize where we're at, just to give people the advice we've shared so far. One is just important to understand AI products are different. They're non-deterministic. And you pointed out, and I forgot to actually mirror back this point, both on the input and the output. The user experience is non-deterministic.People will see different things, different outputs, different chat conversations, different maybe UI if it's designing the UI for you. And also the output obviously is going to be non-terministic. So that's a problem and a challenge. And then-

**Aishwarya Naresh Reganti** (00:19:08):
I mean, if you think of it's also the most beautiful part of AI, which is, I mean, we are all much more comfortable talking than following a bunch of buttons and all of that. So the bar to using AI products is much lower because you can be as natural as you would be with humans, but that's also the problem, which is there are tons of ways we communicate and you want to make sure that that intent is rightly communicated and the right actions are taken because most of your systems are deterministic and you want to achieve a deterministic outcome, but with non-deterministic technology and that's where it gets a little messy.

**Lenny Rachitsky** (00:19:44):
Awesome. Okay. I love the optimistic version of why this is good. Okay. And then the other piece is this idea of this trade-off of autonomy versus control when you're designing a thing. And I imagine what you're seeing is people try to jump to the ideal, like the V3 immediately and that's when they get into trouble both. It's probably a lot harder to build that and it just doesn't work. And then they're just like, "Okay, this is a failure. What are we even doing?"

**Kiriti Badam** (00:20:08):
Exactly. I feel there's a bunch of things that you actually have to get confidence in before you get to V3. And it's easy to get overwhelmed that, oh, my AI agent is doing these things wrong in a hundred different ways and you're not going to actually tabulate all of them and fix it. Even though you've learned how do you deal with the evaluation practices and stuff like that, if you're starting on the wrong spot, you are actually going to have a hard time correcting things from there. And when you start small and when you start with building a very minimalistic version with high human control and low agency, it also forces you to think about what is the problem that I'm going to solve. We use this term called problem first. And to me, it was obvious in the sense that that I do need to think about the problem, but it's incredible how well it resonates with the people that in all this advancements of the AI that we are seeing, one easy, slippery slope is to just keep thinking about complexities of the solution and forget the problem that you're trying to solve.

**Kiriti Badam** (00:21:10):
So when you're trying to start at a smaller scale of autonomy, you start to really think about what is the problem that I'm trying to solve and how do I break it down into levels of autonomy that I can build later? So that is incredibly useful and we keep repeating this part and over and over with everyone we talk to.

**Lenny Rachitsky** (00:21:31):
And there's so many other benefits to limiting autonomy because there's just danger also of the thing doing too much for you and just messing up your, I don't know, your database, sending out all these emails you never expected. And there's like so many reasons this is a good idea.

**Aishwarya Naresh Reganti** (00:21:45):
Yep. I recently read this paper from a bunch of folks at UC Berkeley. Basically Matei Zaharia, [inaudible 00:21:54] and the folks at Databricks and it said about 74% or 75% of the enterprises that they had spoken to, their biggest problem was reliability. And that's also why they weren't comfortable deploying products to their end users or building customer facing products because they just weren't sure or they just weren't comfortable doing that and exposing their users to a bunch of these risks. And that's also why they think a lot of AI products today have to do with productivity because it's much low autonomy versus end-to-end agents that would replace workflows. And yeah, I love their work otherwise as well, but I think that's very in line with what at least we are seeing at my startup as well.

**Lenny Rachitsky** (00:22:38):
Okay. Very interesting. There's an episode that'll come out before this conversation where we go deep into another problem that this avoids, which is around prompt injection and jailbreaking and just how big of a risk that is for AI products where it's essentially an unsolved and unsolvable problem potentially. I'm not going to go down that track, but that's a pretty scary conversation we had that'll be out before this conversation.

**Aishwarya Naresh Reganti** (00:23:02):
I think that will be a huge problem once systems go mainstream. We're still so busy building AI products that we're not worried about security, but it will be such a huge problem to kind of, especially with this non-deterministic API again. So you're kind of stuck because there are tons of instructions that you could inject within your prompt and then it's going really bad.

**Lenny Rachitsky** (00:23:28):
Okay. Let's actually spend a little time here because it's actually really interesting to me and no one's talking about this stuff, which is like the conversation we had is just it's pretty easy to get AI to do stuff it shouldn't do. And there's all these guardrail systems people put in place, but turns out these guardrails aren't actually very good and you can always get around them. And to your point, as agents become more autonomous and robots, it gets pretty scary that you could get AI to do things you shouldn't do.

**Kiriti Badam** (00:23:54):
I think this is definitely a problem, but I feel in the current spectrum of customers adopting AI, the extent to which companies can actually get advantage of AI or improve their processes or streamline the existing processes that they have, I feel it's still in the very early stage. 2025 has been an extremely busy year for AI agents and customers trying to adopt AI, but I feel the penetration is still not as much as you would actually get advantage out of it. So with the right sort of human in the loop points in here, I feel we can actually avoid a bunch of these things and focus more towards streamlining the processes. And I am more on the optimist side in the sense that you need to try and adopt this before actually trying to be only for highlighting the negative aspects of what could go wrong.

**Kiriti Badam** (00:24:47):
So I feel like strongly that companies has this adopt this, they definitely ... No company at OpenAI we talk to has never had been the case that, oh, AI cannot help me in this case. It has always been that, oh, there is this set of things that it can optimize for me and then let me see how I can adopt it.

**Lenny Rachitsky** (00:25:06):
Sweet. I always like the optimistic perspective. I'm excited for you to listen to this and see what you think because it's really interesting. And to your point, there's a lot of things to focus on. It's one of many things to worry about and think about. Okay, let's get back on track here. So we've shared a bunch of pro-tips and important piece of advice. Let me ask, what other patterns and kind of ways of working do you see in companies that do this well and teams that build AI products successfully? And then just what are the most common pitfalls people fall into? So we could just maybe start with, what are other ways that companies do this well, build AI products successfully?

**Aishwarya Naresh Reganti** (00:25:43):
I almost think of it as like a success triangle with three dimensions that's never always technical. Every technology problem is a people problem first. And with companies that we have worked with, it's these three dimensions, like great leaders, good culture and technical prowess. With leaders itself, we work with a lot of companies for their AI transformation, training, strategy and stuff like that. And I feel like a lot of companies, the leaders have built intuitions over 10 or 15 years and they're kind of highly regarded for those intuitions. But now with AI in the picture, those intuitions will have to be relearned and leaders have to be vulnerable to do that. I used to work with the CEO of now Rackspace, Gagan. So he would have this block every day in the morning, which would say catching up with AI 4:00 to 6:00 AM, and he would not have any meetings or anything like that.

**Aishwarya Naresh Reganti** (00:26:42):
And that was just his time to pick up on the latest AI podcast or information and all of that. And he would have weekend vibe coding sessions and stuff like that. So I think leaders have to get back to being hands-on. And that's not because they have to be implementing these things, but more of rebuilding their intuitions because you must be comfortable with the fact that your intuitions might not be right and you probably are the dumbest person in the room and you want to learn from everyone. And that I've seen that being a very distinguishing factor of companies that build products which are successful because you're kind of bringing in that top-down approach. It's almost always impossible for it to be bottom-up. You can't have a bunch of engineers go and get buy-in from the leader if they just don't trust in the technology or if they have misaligned expectations about the technology.

**Aishwarya Naresh Reganti** (00:27:34):
I've heard from so many folks who are building that our leaders just don't understand the extent to which AI can solve a particular problem or they just vibe code something and assume it's easy to take it to production and you really need to understand the range of what AI can solve today so that you can guide decisions within the company. The second one is the culture itself. And again, I work with enterprises where AI is not their main thing and they need to bring in AI into their processes just because a competitor is doing it. And just because it does make sense because there are use cases that are very ripe. Then along the way, I feel a lot of companies have this culture of FOMO and you will be replaced and those kind of things and people get really afraid. Subject matter experts are such a huge part of building AI products that work because you really need to consult them to understand how your AI is behaving or what the ideal behavior should be.

**Aishwarya Naresh Reganti** (00:28:27):
But then I've spoken to a bunch of companies where the subject matter experts just don't want to talk to you because they think their job is being replaced. So I mean, again, this comes from the leader itself. You want to build a culture of empowerment, of augmenting AI into your own workflows so that you can 10X at what you're doing instead of saying that probably you'll be replaced if you don't adopt AI and stuff like that. So that kind of an empowering culture always helps. You want to make your entire organization be in it together and make AI work for you instead of trying to guard their own jobs, et cetera. And with AI, it's also true that it opens up a lot more opportunities than before. So you could have your employees doing a lot more things than before and 10x their productivity. And the third one is the technical part which we talk about.

**Aishwarya Naresh Reganti** (00:29:18):
I think folks that are successful are incredibly obsessed about understanding their workflows very well and augmenting parts that could be ripe for AI versus the ones that might need human in the loop somewhere, et cetera. Whenever you're trying to automate some part of a workflow, it's never the case that you could use an AI agent and that will solve your problems. It's always, you probably have a machine learning model that's going to do some part of the job. You have deterministic code doing some part of the job. So you really need to be obsessed with understanding that workflow so you can choose the right tool for the problem instead of being obsessed with the technology itself. And another pattern I see is also folks really understand this idea of working with a non-deterministic API, which is your LLM. And what that means is they also understand the AI development lifecycle looks very different and they iterate pretty quickly, which is can I build something iterate quickly in a way that it doesn't ruin my customer experience at the same time gives me enough amount of data so that I can estimate behavior.

**Aishwarya Naresh Reganti** (00:30:31):
So they build that flywheel very quickly. As of today, it's not about being the first company to have an agent among your competitors. It's about, have you built the right flywheels in place so that you can improve over time? When someone comes up to me and says, "We have this one click agent, it's going to be deployed in your system." And then in two or three days, it'll start showing you significant gains. I would almost be skeptical because it's just not possible. And that's not because the models aren't there, but because enterprise data and infrastructure is very messy and you need a bit to ... Even the agent needs a bit to understand how these systems work. There are very messy taxonomies everywhere. People tend to do things like get customer data, we want, get customer data, we do, and these kind of things. And all those functions exist and they're being called and basically there's a lot of tech debt that you need to deal with.

**Aishwarya Naresh Reganti** (00:31:23):
So most of the times, if you're obsessed with the problem itself and you understand your workflows very well, you will know how to improve your agents over time instead of just slapping an agent and assuming that it'll work from day one. I probably will go as far to say that if someone's selling you one click-agents, it's pure marketing. You don't want to buy into that. I would rather go with a company that says, "We're going to build this pipeline for you," and that will learn over time and build a flywheel to improve than something that's going to work out of the box. To replace any critical workflow or to build something that can give you significant ROI, it easily takes four to six months of work, even if you have the best data layer and infrastructure layer.

**Lenny Rachitsky** (00:32:05):
Amazing. There's a lot there that resonates so deeply with other conversations I've been having on this podcast. One is just for a company to be successful at seeing a lot of impact from AI, the founder-CEO has to be deep into it. I had Dan Shipper on the podcast and they work with a bunch of companies helping them adopt AI. And he said that's the number one predictor of success. Is the CEO chatting with ChatGPT, Claude, whatever, many times a day. I love this example you gave with the Rackspace CEO has catch up on AI news in the morning every day. I was imagining he'd be chatting with the chatbot versus reading news.

**Aishwarya Naresh Reganti** (00:32:42):
With the kind of information you have as of today, you could just ... I mean, you want to choose the right channels as well because everybody has an opinion. So whose opinion do you want to bank on? I feel like having that good quality set of people that you're listening to really makes sense. So he just has a list of two or three sources that he always looks at. And then he comes back with a bunch of questions and bounces it around with a bunch of AI experts to see what they think about it. And I was part of that group, so I kind of know-

**Lenny Rachitsky** (00:33:11):
I love that.

**Aishwarya Naresh Reganti** (00:33:13):
... about the questions that he comes up with.

**Lenny Rachitsky** (00:33:13):
That's cool.

**Aishwarya Naresh Reganti** (00:33:15):
It's pretty cool. I was like, "Why are you doing so much?" And then he says, "It trickles down into a bunch of decisions that we take."

**Lenny Rachitsky** (00:33:21):
Okay. Let me talk about another topic that's very ... It's been a hot topic on this podcast. It was a hot topic on Twitter for a while, evals. A lot of people are obsessed with evals, think they're the solution to a lot of problems in AI. A lot of people think they're overrated that you don't need evals. You can just feel the vibes and you'll be all right. What's your take on evals? How far does that take people in solving a lot of the problems that you talk about?

**Kiriti Badam** (00:33:47):
In terms of what is going on in the community, I feel there's just this false dichotomy of this either evals is going to solve everything or online monitoring or production monitoring is going to solve everything. And I find no reason to trust one of the extremes in the sense that I will entirely bank my application on this or like that to solve the thing. So if you take a step back, think of what are evals. Evals are basically your trusted product thinking or your knowledge about the product that is going into this set of data sets that you're going to build in the sense that this is what matters to me. This is the kind of problems that my agent should not do and let me build a list of datasets so that I'm going to do well on those. And in terms of production monitoring, what you're doing there is you're deploying your application and then you're having some sort of key metrics that actually communicate back to you on how customers are using your product.

**Kiriti Badam** (00:34:47):
You could be deploying any agent and if the customer is giving a thumbs up for your interaction, you better want to know that. So that is what production monitoring is going to do. And this production monitoring has existed for products for a long time, just that now with the AI agents, you need to be monitoring a lot more granularity. It's not just the customer always giving you explicit feedback, but there is many implicit feedback that you can get. For example, in ChatGPT, if you are liking the answer, you can actually give a thumbs up. Or if you don't like the answer, sometimes customers don't give you thumbs down, but actually regenerate the answer. So that is a clear indication that the initial answer that regenerator is not meeting the customer's expectation. So these are the kind of implicit signals you always need to think about.

**Kiriti Badam** (00:35:35):
And that spectrum has been increasing in terms of production monitoring. Now let's come back to the initial topic of like, okay, is it evals or is it production monitoring? What does it matter? So I feel, again, we go back to this problem first approach of what is it that you're trying to build. You're trying to build a reliable application for your customers that's not going to do a bad thing. It's always going to do the right thing. Or if it is doing a wrong thing, you're basically alerted very quickly. So I break this down into two parts. One is nobody goes into deploying an application without actually just testing that. This testing could be wipes or this testing could be, "Okay, I have this 10 questions that it should not go wrong no matter what changes I make, and let me build this and let's call this an evaluation dataset." Now, let's say you build this, you deployed this, and then you figured, "Okay, now I need to understand whether it's doing the right thing or not."

**Kiriti Badam** (00:36:32):
So if you're a high throughput or a high transaction customer, you cannot practically sit and evaluate all the traces. You need some indication to understand what are the things that I should look at. And this is where production monitoring comes into the picture that you cannot predict the base in which your agent could be doing wrong, but all of these other implicit signals and explicit signals, those are going to communicate back to you what are the traces that you need to look at. And that is where production monitoring helps. And once you get this kind of traces, you need to examine what are the failure patterns that you're seeing in these different types of interactions. And is there something that I really care about that should not happen? And if that kind of failure modes are happening, then I need to think about building an evaluation dataset for it.

**Kiriti Badam** (00:37:20):
And okay, let's say I built an evaluation dataset for my agent trying to offer refunds where explicitly I have configured it not to. So I built this evaluation dataset and then I made my changes in tools or prompts or whatever, and then I deployed the second version of the product. Now there is no guarantee that this is the only problem that you're going to see. You still need production monitoring to actually catch different kinds of problems that you might encounter. So I feel evals are important, production monitoring is important, but this notion of only one of them is going to solve things for you that is completely dismissible in my opinion.

**Lenny Rachitsky** (00:37:58):
All right. A very reasonable answer. And the point here isn't, it's not just as simple as do both. It's more that there are different things to catch and one approach won't catch all the things you need to be paying attention to.

**Aishwarya Naresh Reganti** (00:38:11):
Exactly.

**Lenny Rachitsky** (00:38:12):
Awesome.

**Aishwarya Naresh Reganti** (00:38:13):
I want to take two steps back and kind of talk about how much weight the term evals has had to take in the second half of 2025 because you go meet a data labeling company and they tell you our experts are writing evals and then you have all of these folks saying that PMs should be writing evals, they're the new PRDs. And then you have folks saying that evals is pretty much everything, which is the feedback loop you're supposed to be building to improve your products. Now, step back as a beginner and kind of think what are evals? Why is everyone saying evals? And these are actually different parts of the process and nobody is wrong in the sense that yes, these are evals, but when a data labeling company is telling you that our experts are writing evals, they're actually referring to error analysis or experts just leading notes on what should be right.

**Aishwarya Naresh Reganti** (00:39:02):
Lawyers and doctors write evals, that doesn't mean they're building LLM judges or they're building this entire feedback loop. And when you say that a PM should be writing evals, doesn't mean they have to write an LLM judge that's good enough for production. I think there are also very prescriptive ways of doing this and plus one to KD, which is you cannot predict upfront if you need to be building an LLM judge versus you need to be using implicit signals from production monitoring, et cetera. I think Martin Fowler at some point had this term called semantic diffusion back in the 2000s, which kind of means that someone comes up with a term, everybody starts butchering it with their own definitions and then you kind of lose the actual definition of it. That is what is happening to evals or agents or any word in AI as of today, everybody kind of sees a different side to it, I guess.

**Aishwarya Naresh Reganti** (00:39:54):
But if you make a bunch of practitioners sit together and ask them, "Is it important to build an actionable feedback loop for AI products?" I think all of them will agree. Now, how you do that really depends on your application itself. When you go to complex use cases, it's incredibly hard to build LLM judges because you see a lot of emerging patterns. If you built a judge that would test for verbosity or something like that, it turns out that you're seeing newer patterns that your LLM judge is not able to catch, and then you just end up building too many evals. And at that point, it just makes sense to look at your user signals, fix them, check if you have regressed and move on instead of actually building these judges. So it all depends. I think one statement that every ML practitioner will tell you is it really depends on the context. Don't be obsessed with prescriptions they're going to change.

**Lenny Rachitsky** (00:40:45):
That's such an important point, this idea that, especially that evals just means many things to different people now. It's just a term for so many things. And it's complicated to just talk about evals when you see it as the stuff data labeling companies are giving you and things PMR, right? And there's also benchmarks. People call benchmarks a little bit evals. It's like-

**Aishwarya Naresh Reganti** (00:41:03):
I recently spoke to a client who told me, "We do evals." And I was like, "Okay, can you show me your dataset?" And said, "No, we just checked LM Arena and Artificial Analysis. These are independent benchmarks and we know that this model is the right one for our use case." And I'm like, "You're not doing evals. That's not evals. Those are model evals."

**Lenny Rachitsky** (00:41:20):
But it makes sense. The word, it could be used in that context. I get why people think that, but yeah, now it's just confusing it even more.

**Aishwarya Naresh Reganti** (00:41:26):
Yep.

**Lenny Rachitsky** (00:41:27):
Just one more line of questioning here that I think that's on my mind is the reason this became kind of a big debate is Cloud Code. The head of Cloud Code, Boris, was like, "Nah, we don't do evals on Cloud Code. It's all vibes." What can you share, Kiriti, on Kodex and the Kodex team, how you approach evals?

**Kiriti Badam** (00:41:44):
So Kodex, we have this balanced approach of you need to have evals and you need to definitely listen to your customers. And I think Alex has been on your podcast recently and he's been talking about how you're extremely focused on building the right product. And a big part of it is basically listening to your customers. And coding agents are extremely unique compared to agents for other domains in the sense that these are actually built for customizability and these are built for engineers. So coding agent is not a product which is going to solve these top five workflows or top six workflows or whatever. It's meant to be customizable in multi different ways. And the implication of that is that your product is going to be used in different integrations and different kinds of tools and different kinds of things. So it gets really hard to build an evaluation dataset for all kinds of interactions that your customers are going to use your product for.

**Kiriti Badam** (00:42:38):
With that said, you also need to understand that, okay, if I'm going to make a change, it's at least not going to damage something that is really core to the product. So we have evaluations for doing that, butt the same time we take extreme care on understanding how the customers are using it. For example, we built this code review product recently and it has been gaining extreme amount of traction. And I feel like many, many bugs in OpenAI as well as even our external customers are getting caught with this. And now let's say if I'm making a model change to the code review or a different kinds of RL mechanism that I trained with it, and now if I'm going to deploy it, I definitely do want A/B test and identify whether it's actually finding the right mistakes and how are users reacting to it? And sometimes if users do get annoyed by your incorrect code regis, they go to the extent of just switching off the product.

**Kiriti Badam** (00:43:36):
So those are the signals that you want to look at and make sure that your new changes are doing the right thing. And it's extremely hard for us to think of these kind of scenarios beforehand and develop evaluation data sets for it. So I feel like there's a bit of both. There's a lot of vibes and there's a lot of customer feedback and we are super active on the social media to understand if anybody's having certain types of problems and quickly fix that. So I feel it's a ... How do I put this? It's like a domain of things that you do here.

**Lenny Rachitsky** (00:44:08):
That makes so much sense. Okay. What I'm hearing, Codex, pro evals, but it's not enough.

**Kiriti Badam** (00:44:13):
Yes.

**Lenny Rachitsky** (00:44:13):
But also just watch customer behavior and feedback. And also there's some vibes just like, is this feeling good? As I'm using it, generating great code that I'm excited about that I think is great.

**Kiriti Badam** (00:44:24):
I don't think if anybody's coming and seeing that I have this concrete set of evals that I can bet my life on and then I don't need to think about anything else, it's not going to work. And every new model that you're going to launch, we get together as a team and test different things. Each person is concentrating on something else. And we have this list of hard problems that we have and we throw that to the model and see how well they're progressing. So it's like custom evals for each engineer, you would say, and just understand what the product is doing in its new model.

**Lenny Rachitsky** (00:44:58):
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**Lenny Rachitsky** (00:45:43):
We've been talking for almost an hour already, and we haven't even covered your extremely powerful software development workflow for building AI products that you two developed that you teach in your course, that you basically combined all the stuff we've been talking about into a step-by-step approach to building AI products. You call it the continuous calibration, continuous development framework. Let's pull up a visual to show people what the heck we're talking about, and then just walk us through what this is, how this works, how teams can shift the way they build their AI products to this approach to help them avoid a lot of pain and suffering.

**Aishwarya Naresh Reganti** (00:46:18):
Before we go about explaining the life cycle, a quick story on why Kiriti and I came up with this is because there are tons of companies that we keep talking to that have the pressure from their competitors because they're all building agents. We should be building agents that are entirely autonomous. And I did end up working with a few customers where we built these end-to-end agents. And turns out that because you start off at a place where you don't know how the user might interact with your system and what kind of responses or actions the AI might come up with, it's really hard to fix problems when you have this really huge workflow, which is taking four or five steps, making tons of decisions. You just end up debugging so much and then kind of hot fixing to the point where at a time we were building for a customer support use case, which is the example that we give in the newsletter as well.

**Aishwarya Naresh Reganti** (00:47:13):
And we had to shut down the product because we were doing so many hot fixes and there was no way we could count all the emerging problems that were coming up. And there's also quite some news online. Recently, I think Air Canada had this thing where one of their agents predicted or hallucinated a policy for a refund, which was not part of their original playbook, and they had to go by it because legal stuff. And there have been a ton of really scary incidents. And that's where the idea comes from. How can you build so that you don't lose customer trust and you don't end up, or your agent or AI system doesn't end up making decisions that are super dangerous to the company itself. At the same time, build a flywheel so that you can improve your product as you go. And that's where we came up with this idea of continuous calibration, continuous development.

**Aishwarya Naresh Reganti** (00:48:08):
The idea is pretty simple, which is we have this right side of the loop, which is continuous development, where you scope capability and curate data, essentially get a data set of what your expected inputs are and what your expected outputs should be looking at. This is a very good exercise before you start building any AI product because many times you figure out that a lot of the folks within the team are just not aligned on how the product should behave. And that's where your PMs can really give in a lot more information and your subject matter experts as well. So you have this data set that you know your AI product should be doing really well on. It's not comprehensive, but it lets you get started. And then you set up the application and then design the right kind of evaluation metrics. And I intentionally use the term evaluation metrics, although we say evals because I just want to be very specific in what it is because evaluation is a process, evaluation metrics are dimensions that you want to focus on during the process.

**Aishwarya Naresh Reganti** (00:49:07):
And then you go about deploying, run your evaluation metrics. And the second part is the continuous calibration, which is the part where you understand what behavior you hadn't expected in the beginning, right? Because when you start the development process, you have this data set that you're optimizing for, but more often than not, you realize that that data set is not comprehensive enough because users start behaving with your systems in ways that you did not predict. And that's where you want to do the calibration piece. I've deployed my system. Now I see that there are patterns that I did not really expect and your evaluation metrics should give you some insight into those patterns, but sometimes you figure out that those metrics were also not enough and you probably have new error patterns that you have not thought about. And that's where you analyze your behavior, spot error patterns.

**Aishwarya Naresh Reganti** (00:49:59):
You apply fixes for issues that you see, but you also design newer evaluation metrics to figure out that they are emerging patterns. And that doesn't mean you should always design evaluation metrics. There are some errors that you can just fix and not really come back to because they're very spot errors. For instance, there's a tool calling error just because your tool wasn't defined well and stuff like that. You can just fix it and move on. And this is pretty much how an AI product lifecycle would look like. But what we specifically also mention is while you're going through these iterations, try to think of lower agency iterations in the beginning and higher control iterations. What that means is constrain the number of decisions your AI systems can make and make sure that they're humans in the loop and then increase that over time because you're kind of building a flywheel of behavior and you're understanding what kind of use cases are coming in or how your users are using the system.

**Aishwarya Naresh Reganti** (00:50:59):
And one example I think we give in the newsletter itself is the customer support. This is a nice image that kind of shows how you can think of agency and control as two dimensions. And each of your versions keep on increasing the agency or the ability of your AI system to make decisions and lower the control as you go. And one example that we give is that of the customer support agent, where you can break it down into three versions. The first version is just routing, which is your agent able to classify and route a particular ticket to the right department? And sometimes when you read this, you probably think, is it so hard to just do routing? Why can't an agent easily do that? And when you go to enterprises, routing itself can be a super complex problem. Any retail company, any popular retail company that you can think of has hierarchical taxonomies.

**Aishwarya Naresh Reganti** (00:51:52):
Most of the times the taxonomies are incredibly messy. I have worked in use cases where you probably have taxonomy that says some kind of hierarchy and then that says shoes and then women's shoes and men's shoes all at the same layer where idea you should be having shoes and then women's shoes and men's shoes should be subclasses. And then you're like, okay, fine. I could just merge that. And you go further and you see that there's also another section on the shoes that says for women and for men, and it's just not aggregated. It's not fixed for some reason. So if an agent kind of sees this kind of a taxonomy, what is it supposed to do? Where is it supposed to route? And a lot of the times we are not aware of these problems until you actually go about building something and understanding it.

**Aishwarya Naresh Reganti** (00:52:39):
And when these kind of problems, real human agents see these kind of problems, they know what to check next. Maybe they realize that the node that says for women and for men that's under shoes was last updated in 2019, which means that it's just a dead node that's lying there and not being used. So they kind of know that, okay, we're supposed to be looking at a different node and stuff like that. And I'm not saying agents cannot understand this or models are not capable enough to understand this, but there are really weird rules within enterprises that are not documented anywhere. And you want to make sure that the agents have all of that context instead of just throwing the problem at that.

**Aishwarya Naresh Reganti** (00:53:17):
Yeah. Coming back to the versions we had, routing was one where you have really high control because even if your agent routes to the wrong department, humans can take control and undo those actions. And along the way, you also figure out that you probably are dealing with a ton of data issues that you need to fix and make sure that your data layer is good enough for the agent to function. We do is what we said of a Copilot, which is now that you've figured out routing works fine after a few iterations and you've fixed all of your data issues, you could go to the next step, which is, can my agent provide suggestions based on some standard operating procedures that we have for the customer support agent? And it could just generate a draft that the human can make changes to. And when you do this, you're also logging human behavior, which means that how much of this draft was used by the customer support agent or what was omitted. So you're actually getting error analysis for free when you do this because you're literally logging everything that the user is doing that you could then build back into your flywheel.

**Aishwarya Naresh Reganti** (00:54:22):
And then we say, post that, once you've figured out that those drafts look good and most of the times maybe humans are not making too many changes, they're using these drafts as is. That's when you want to go to your end-to-end resolution assistant that could draft a resolution that could solve the ticket as well. And those are the stages of agency where you start with low agency and then you go up high. We also have this really nice table that we put together, which is what do you do at each version and what you learn that can enable you to go to the next step and what information do you get that you can feed into the loop, right? When you're just doing your routing, you have better quality routing data, you also know what kind of prompts you need to be building to improve the routing system.

**Aishwarya Naresh Reganti** (00:55:09):
Essentially, you're figuring out your structure for context engineering and building that flywheel that you want. And while I go through this, I want to also be very clear that two things. One is when you build with CCCD in mind, it doesn't mean that you've fixed the problem all for one. It's possible that you've probably gone through V3 and you see a new distribution of data that you never previously imagined, but this is just one way to lower your risk, which is you get enough information about how users behave with your system before going to a point of complete autonomy. And the second thing is you're also kind of building this implicit logging system. A lot of people come and tell us that, "Oh, wait, there are evals. Why do you need something like this? " The issue with just building a bunch of evaluation metrics and then having them in production is evaluation metrics catch only the errors that you're already aware of, but there can be a lot of emerging patterns that you understand only after you put things in production.

**Aishwarya Naresh Reganti** (00:56:17):
So for those emerging patterns, you're kind of creating a low risk kind of a framework so that you could understand user behavior and not really be in a position where there are tons of errors and you're trying to fix all of them at once. And this is not the only way to do it. There are tons of different ways. You want to decide how you constrain your autonomy. It could be based on the number of actions that the agent is taking, which is what we do in this example. It could be based on topic. There's just some domains where it's pretty high risk to make a system completely autonomous for certain decisions, but for some other topics, it's okay to make them completely autonomous and depending on the complexity of the problem. And that's where you really want your product managers, your engineers and subject matter experts to align on how to build this system and continuously improve it.

**Aishwarya Naresh Reganti** (00:57:10):
The idea is just behavior calibration and not losing user trust as you do that behavior calibration, I guess.

**Lenny Rachitsky** (00:57:17):
We'll link folks to this actual post if they want to go really deep. You basically go through all of these steps by step, a bunch of examples. And the idea here is, as you said, that the reason, everything about what you're describing here is about making it continuous and iterative and kind of moving along this progression of higher autonomy, less control. And this idea of even calling continuous calibration, continuous development is communicating it's this kind of iterative process. And just to be clear, this naming is kind of ode to CI/CD, continuous integration, continuous deployment suite. And the idea here is that this is the version of that for AI where instead of just integrating into unit tests and deploying constantly, it's running evals, looking at results, iterating on the metrics you're watching, figuring out where it's breaking and iterating on that. Awesome. Okay.

**Lenny Rachitsky** (00:58:08):
So again, we'll point people to this post if they want to go deeper. That was a great overview. Is there anything else before we go into different topic around this framework specifically that you think is important for people to know?

**Aishwarya Naresh Reganti** (00:58:18):
I think one of the most common questions we get is, how do I know if I need to go to the next stage or if this is calibrated enough? There's not really a rule book you can follow, but it's all about minimizing surprise, which means let's say you're calibrating every one or two days and you figure out that you're not seeing new data distribution patterns, your users have been pretty consistent with how they're behaving with the system. Then the amount of information you gain is kind of very low and that's when you know you can actually go to the next stage. And it's all about the wipes at that point, do you know you're ready, you're not receiving any new information. But also it really helps to understand that sometimes there are events that could completely mess up the calibration of your system. An example is GPT-4o doesn't exist anymore, or it's going to be deprecated in APIs as well.

**Aishwarya Naresh Reganti** (00:59:16):
So most companies that were using 4o should switch to 5 and 5 has very different properties. So that's where your calibration's off again. You want to go back and do this process again. Sometimes users start behaving with systems also differently over time or user behavior evolves. Even with consumer products, you don't talk to ChatGPT the same way you were talking, say, two years ago, just because you know the capabilities have increased so much. And also just people get excited when these systems can solve one task, they want to try it out on other tasks as well. We built this system for underwriters at some point. Underwriting is a painful task. There are agreements that are like loan applications are like 30 or 40 pages, and the idea for this bank was to build a system that could help underwriters pick policies and information about the bank so that they could approve loans.

**Aishwarya Naresh Reganti** (01:00:15):
And for a good three or four months, everybody was pretty impressed with the system. We had underwriters actually report gains in terms of how much time they were spending, et cetera. And first three months, we realized that they were so excited with the product that they started asking very deep questions that we never anticipated. They would just throw the entire application document at the system and go, "For a case that looks like this, what did previous underwriters do? " And for a user, that just seems like a natural extension of what they were doing, but the building behind it should significantly change. Now, you need to understand what does for a case like this mean in the context of the loan itself? Is it referring to people of a particular income range or is it referring to people in a particular geo and stuff like that?

**Aishwarya Naresh Reganti** (01:00:58):
And then you need to pick up historical documents, analyze those documents, and then tell them, "Okay, this is what it looks like," versus just saying that there's a policy X, Y, and Z, and you want to look up that policy. So something that might seem very natural to an end user might be very hard to build as a product builder, and you see that user behavior also evolves over time, and that's when you know that you want to go back and recalibrate.

**Lenny Rachitsky** (01:01:24):
What do you think is overhyped in the AI space right now? And even more importantly, what do you think is under-hyped?

**Kiriti Badam** (01:01:34):
As I said, super optimistic in different things that are going in AI. So I wouldn't say overhyped, but I feel kind of misunderstood is the concept of multi-agents. People have this notion of, "I have this incredibly complex problem. Now I'm going to break it down into, hey, you are this agent. Take care of this. You're this agent. Take care of this." And now if I somehow connect all of these agents, they think they're the agent utopia and it's never the case that there are incredibly successful multi-agent systems that are built. There's no doubt about that. But I feel a lot of it comes in terms of how are you limiting the ways in which the system can go off tracks. And for example, if you're building a supervisor agent and there are subagents that actually do the work for the super agent, supervisor agent, that is a very successful pattern.

**Kiriti Badam** (01:02:24):
But coming with this notion of I'm going to divide the responsibilities based on functionality and somehow expect all of that to work together in some sort of gossip protocol, that is extremely misunderstood that you could do that. I don't think current ways of building and current model capabilities are right there in terms of building those kind of applications. I feel that is kind of misunderstood than overrated. Underrated, I feel it's hard to probably believe, but I still feel coding agents are underrated in the sense that I feel like you can go on Twitter and you can go on Reddit and you see a lot of chatter about coding agents, but talking to an engineer in any random company, especially outside of Bay Area, you can see the amount of impact this coding agents can create and the penetration is very low. So I feel like 2025 and 2026 is going to be an incredible year for optimizing all of these processes.

**Kiriti Badam** (01:03:25):
And I feel that is going to be creating a lot of value with AI.

**Lenny Rachitsky** (01:03:28):
That's really interesting on that first point. So the idea there is you'll probably be more successful building and using an agent that is able to do its own sub-agent splitting of work versus a bunch of, say, Codex agents. Will you do this task, you do that task?

**Kiriti Badam** (01:03:44):
You can have agents to do these things and you as a human can orchestrate it or you can have one larger agent that is going to orchestrate all of these things, but letting the agents communicate in terms of peer-to-peer kind of protocol, and then especially doing this in a customer support kind of use case is incredibly hard to control what kind of agent is replying to your customer because you need to shift your guardrails everywhere and things like that.

**Lenny Rachitsky** (01:04:08):
Yeah. Okay. Great picks. Okay. Ash, what do you got?

**Aishwarya Naresh Reganti** (01:04:12):
Can I say evals? Will I be canceled?

**Lenny Rachitsky** (01:04:15):
In which category? Which bucket do they go?

**Aishwarya Naresh Reganti** (01:04:18):
Overrated.

**Lenny Rachitsky** (01:04:20):
Overrated. Okay, go for it. We won't let you get canceled.

**Aishwarya Naresh Reganti** (01:04:22):
Just kidding. I think evals are misunderstood. They are important, folks. I'm not saying they're not important, but I think just this, I'm going to keep jumping across tools and going to pick up and learn if new tool is overrated. I still am old school and feel like you would really need to be obsessed with the business problem you're trying to solve. AI is only a tool. I try to think of it that way. Of course, you need to be learning about the latest and greatest, but don't be so obsessed with just building so quickly. Building is really cheap today. Design is more expensive, really thinking about your product, what you're going to build. Is it going to really solve a pain point? Is what is way more valuable today? And it will only become more true in the near future. So really obsessing about your problem and design is underrated and just rote building is overrated, I guess.

**Lenny Rachitsky** (01:05:15):
Awesome. Okay. Similar sort of question. From a product point of view, what do you think the next year of AI is going to look like? Give us a vision of where you think things are going to go by, say by the end of 2026.

**Kiriti Badam** (01:05:30):
Yeah, I feel there's a lot of promise in terms of this background agents are proactive agents who is ... They're going to basically understand your workflow even more. If you think of where is AI failing to create value today, it's mainly about not understanding the context. And the reason that it's not understanding the context is it's not plugged into the right places where actual work is happening. And as you do more of this, you can give the agent more of context and then it start to see the world around you and understand what are the set of metrics that you're optimizing for or what are the kind of activities that you're trying to do. It is a very easy extension from there to actually gain more out of it and then let the agent prompt you back. We already do this in terms of ChatGPT pulse, which kind of gives you this daily update of things you might care about.

**Kiriti Badam** (01:06:20):
And it's very nice to actually have that jog your brain up in terms of, "Oh, this is something that I haven't thought about. Maybe this is good." And now when you extend this to more complex tasks, like a coding agent, which says that, "Okay, I have fixed five of your linear tickets and here are the patches. Just to review them at the start of your day." So I feel that is going to be extremely useful. And I see that as a strong direction in which products are going to build in 2026.

**Lenny Rachitsky** (01:06:44):
That's so cool. So essentially agents anticipating what you want to do and getting ahead of you and I've solved these problems for you or I think this is going to crash your site. Maybe you should fix this thing right here or I see the spike here and let's refactor our database. Amazing. What a world. Okay. Ash, what do you got?

**Aishwarya Naresh Reganti** (01:07:04):
I'm all in for multimodal experiences in 2026. I think we have done quite some progress in 2025, and not just in terms of generation, but also understanding. Until now, I think LLMs have been our most commonly used modules, but as humans, we are multimodal creatures, I would say. Language is probably one of our last forms of evolution. As the three of us are talking, I think we're constantly getting so many signals. I'm like, "Oh, Lenny's nodding his head, so probably I would go in this direction or Lenny's bored, so let me stop talking." So there's a chain of thought behind your chain of thought and you're constantly altering it with language that dimension of expression is not explored as well. So if we could build better multimodal experiences that would get us closer to human-like conversation richness. And you will also, just given the kind of models, there's a bunch of boring tasks as well, which are ripe for AI.

**Aishwarya Naresh Reganti** (01:08:04):
If multimodal understanding gets better, there are so many handwritten documents and really messy PDFs that cannot be passed even by the best of the models as of today. And if it's possible, there'll be so much data that we can tap into.

**Lenny Rachitsky** (01:08:21):
Awesome. I just saw Demis from DeepMind, AI, Google, whatever they call the whole org, talking about this where he thinks that's going to be a big part of where they're going, combining the image model work, the LLM, and also their world model stuff, Genie, I think is what it's called. Yes. So that's going to be a wild, wild time. Okay. Last question. If someone wants to just get better at building AI products, what's just maybe one skill or maybe two skills that you think they should lean into and develop?

**Aishwarya Naresh Reganti** (01:08:52):
I think we did cover a bunch of best practices for AI products, which is start small, try to get your iteration going well and build a flywheel and all of that. But again, if you kind of look at it at a 10,000 feet level for anybody building today, like I was saying, implementation is going to be ridiculously cheap in the next few years. So really nail down your design, your judgment, your taste and all of that. And in general, if you're building a career as well, I feel for the past few years, your former years, say the first two, three years of building your career is always focused on execution, mechanics and all of that. And now we have AI that could help you ramp pretty quickly and post that. I mean, after a few years, I think everybody's job becomes about your taste, your judgment and kind of what is uniquely you.

**Aishwarya Naresh Reganti** (01:09:49):
I think nail down on that part and try to figure out how you can bring in that kind of a perspective. It doesn't have to mean that you should be significantly old, have years of experience. We recently hired someone and we use this very popular app for tracking our tasks and we've been using it for years and we pay a high subscription fee for it. And this guy just came with his own vibe coded app to the meeting. He onboarded us to all of it and he's like, "Okay, let's start using this." And I think that kind of agency and that kind of ownership to really rethink experiences is what will set people apart. And I'm not being blind to the fact that vibe coded apps have high maintenance costs. And maybe as we scale as a company, we have to replace it or we have to think of better approaches.

**Aishwarya Naresh Reganti** (01:10:36):
But given that we are a small size company now and just ... I was really shocked because I never thought of it. If you've been used to working in a certain way, you associate a cost with building. And I feel like folks who grew up in this age have a much lower cost associated in their mind. They just don't mind building something and going ahead with it. And they're also very enthusiastic to try out new tools. That's also probably why AI products have this retention problem because everybody's so excited about trying out these new tools and all of that. But essentially having the agency and ownership, and I think it's also the going to be the end of the busy work era. You can't be sitting in a corner doing something that doesn't move the needle for a company. You really need to be thinking about end-to-end workflows, how you can bring in more impact.

**Aishwarya Naresh Reganti** (01:11:26):
I think all of that will be super important.

**Lenny Rachitsky** (01:11:28):
That reminds me, I just had Jason Lemkit on the podcast. He's very smart on sales, go to market, run Saster, and he replaced his whole sales team with agents. He had 10 salespeople and then he was 1.2 and 20 agents. And one of the agents, it was just tracking everyone's updates to Salesforce and kind of updating it automatically for them based on their calls. And one of the salespeople was like, "Okay, I quit." And it turned out he wasn't really doing anything. He was just sitting around and he's like, "Okay, this will catch me. I got to get out of here. So to your point about, it'll be harder to sit around and twiddle your thumbs, I think is really right.

**Kiriti Badam** (01:12:07):
Yeah. I think to add on to that, I feel like persistence is also something that is extremely valuable, especially given that anybody who wants to build something, the information is at your fingertips even more than the past decade. You can learn anything overnight and become that sort of Ironman kind of approach. So I feel like having that persistence and going through the pain of learning this, implementing this and understanding what works and what doesn't work. And as you are going through this pain of developing multiple approaches and then solving the problem, I feel that is going to be the real moat as an individual. I like to call it pain is the new moat, but I feel that is exactly super useful to actually have this in, especially in building these AI products.

**Lenny Rachitsky** (01:12:56):
Say more about this. I love this concept. Pain is the new moat. Is there more there?

**Kiriti Badam** (01:13:00):
Yeah, I feel as a company, I mean, successful companies right now building in any new area, they are successful not because they're first to the market or they have this fancy feature that more customers are liking it. They went through the pain of understanding what are the set of non-negotiable things and trade them off exactly with what are the features or what are the model capabilities that they can use to solve that problem. This is not a straightforward process. There's no textbook to do this or there's no straightforward way or a known credit path to be here. So a lot of this pain I was talking about is just going through this iteration of like, "Okay, let's try this and if this doesn't work, let's try this." And that kind of knowledge that you built across the organization or across your own lived experiences, I feel that pain is what translates into the moat of the company. This could be a product of evals or something that you built. And I feel that is going to be the game changer.

**Lenny Rachitsky** (01:13:59):
That is awesome. It's like turning a coal into diamond.

**Kiriti Badam** (01:14:03):
Yes.

**Lenny Rachitsky** (01:14:04):
Okay. I feel like we've done a great job helping people avoid some of the biggest issues people consistently run into building AI products. We covered so many of the pitfalls and the ways to actually do it correctly. Before we get to our very exciting lightning round, is there anything else that you wanted to share? Anything else you want to leave listeners with?

**Aishwarya Naresh Reganti** (01:14:25):
Be obsessed with your customers. Be obsessed with the problem. AI is just a tool and try to make sure that you're really understanding your workflows. 80% of so called AI engineers, AIPMs spend their time actually understanding their workflows very well. They're not building the fanciest and the most cool models or workflows around it. They're actually in the weeds understanding their customer's behavior and data. And whenever a software engineer who's never done AI before, here's the term, look at your data. I think it's a huge revelation to them, but it's always been the case. You need to go there, look at your data, understand your users, and that's going to be a huge differentiator.

**Lenny Rachitsky** (01:15:09):
That's a great way to close it. The AI isn't the answer. It's a tool to solve the problem. With that, we have reached our very exciting lightning round. I've got five questions for both of you. Are you ready?

**Aishwarya Naresh Reganti** (01:15:22):
Yay. Yes.

**Lenny Rachitsky** (01:15:24):
All right. So you can both answer them. You can pick one which you want to answer. Either way, up to you. What are two or three books you find yourself recommending most to other people?

**Aishwarya Naresh Reganti** (01:15:32):
For me, it's this book called When Breath Becomes Air, Lenny. It was written by Paul Kalanithi. I think he was an Indian original neurosurgeon who was diagnosed with lung cancer at 31 or 32. And the whole book is his memoir and just is written after he was diagnosed. And it's really beautiful, especially because I read it during COVID and all we ever wanted to do during COVID is stay alive. There are a bunch of really nice quotes within the book as well, but I remember one of them, he was kind of arguing against a very popular quote by Socrates, which is, "The unexamined life is not worth living," or something like that, which means you really need to be thinking about your choices, you need to understand your values, your mission and all of that. And Paul says, "If the unexamined life is not worth living, was the unlived life worth examining?" Which means are you spending so much time just understanding your mission and purpose that you've forgotten to live?

**Aishwarya Naresh Reganti** (01:16:32):
And I think everybody who's staying in the AI era and building and continuously going through the space of reinventing themselves need to take a pause and live for a bit, I guess. They need to stop evaling life too much.

**Lenny Rachitsky** (01:16:46):
I was going to say that. That's where my mind went. You got to write some evals for your life. Oh my God, we've gone too far.

**Aishwarya Naresh Reganti** (01:16:52):
Yep. Yeah.

**Lenny Rachitsky** (01:16:53):
Beautiful.

**Aishwarya Naresh Reganti** (01:16:53):
That's my favorite book.

**Kiriti Badam** (01:16:55):
I like more of science fiction books. So I really like this 3 Body problem series. It's like a three book series. It has elements of grander than science fiction, life outside earth and how it impacts human decision making process. And it also has elements of geopolitics and how much important or valuable abstract science is to human progress. And then when that gets stopped, it's not noticeable in everyday life, but it can cause devastating effects. So I feel like AI helping in these areas, for example, is going to be extremely crucial. And that book is a nice example of what could happen otherwise.

**Lenny Rachitsky** (01:17:35):
Completely agree. Absolutely. Love. Might be my favorite sci-fi book except, or series even, and it's three. I have to read of all three, by the way. I find that it only got really good about one and a half books in. So if anyone's tried it and like, "What the heck is going on here?" Just keep reading and get to the middle of the second one and then it gets mind-blowing.

**Kiriti Badam** (01:17:52):
Yes.

**Lenny Rachitsky** (01:17:54):
If you love sci-fi and you're in AI, you got to read this book called A Fire Upon the Deep by Vernon Vinge. Check it out. It's incredible. I saw Noah Smith on his newsletter recommend this book and there's sequels to it, but this is the one that's so incredible. And it's actually, it turns out it's about AGI and super intelligence and all these things, and it's just so epic. And no one's heard of it.

**Kiriti Badam** (01:18:19):
Thank you.

**Lenny Rachitsky** (01:18:20):
There you go. I'm giving you one back. Okay, next question. What's a favorite recent movie or TV show that you've really enjoyed?

**Aishwarya Naresh Reganti** (01:18:26):
I started rewatching Silicon Valley and I think it's so true. It's so timeless. Everything is repeating all over again. Anybody who's watched it a few years ago should start rewatching it and you'll see that it's eerily similar to everything that's happening right now with the AI wave.

**Lenny Rachitsky** (01:18:41):
That's a good idea to rewatch it. I love that their whole business was like an algorithm to compress, like a compression algorithm. It's like maybe a precursor to LLMs in some small way. No, I get it. All right, Kiriti, what you got?

**Kiriti Badam** (01:18:54):
I'm going to drag this and say lot a movie or a TV show, but there's this game I picked up recently called Expedition 33. It has nothing to do with AI, but it's an incredibly well-made game in terms of the gameplay or the movie and the story and the music. It's been amazing.

**Lenny Rachitsky** (01:19:10):
I love that you have time to play games. That's a great sign. I love that. Someone OpenAI, I'm just imagining you're ... There's nothing else going on except just coding and having meetings.

**Kiriti Badam** (01:19:20):
Yeah, it has been incredibly hard to find time for that.

**Lenny Rachitsky** (01:19:22):
That's good. That's a good sign. I'm happy to hear this. Okay. What's a favorite product that you've recently discovered that you really love?

**Aishwarya Naresh Reganti** (01:19:28):
For me, it's Whisper Flow. I think I've been using it quite a bit and I didn't know I needed it so much. The best part is it's a conceptual transcription tool, which means if you go to Codex and start using Whisper Flow, it starts identifying variables and all of that. And it's so seamless in terms of transcription to instruction. You could say something like, "I'm so excited today. Add three exclamation marks," and it seamlessly switches. It adds those three exclamation marks instead of writing add three exclamation marks. And I think it's pretty cool. If you're not using it, you should try it.

**Lenny Rachitsky** (01:20:03):
I'll do a plug. Get Whisper Flow for free for an entire year for a year for free by becoming an annual subscriber of my newsletter.

**Aishwarya Naresh Reganti** (01:20:12):
That's how I got access to it, Lenny.

**Lenny Rachitsky** (01:20:14):
There we go. I think I pitched this deal. I think people don't truly understand how incredible this is. They're like, "No way this is real. It's real." And 18 other products, lennysproductpass.com, check it out. Moving on. Kiriti.

**Kiriti Badam** (01:20:28):
Awesome. I actually am a stickler for productivity. I keep experimenting new CLI tools and things which can make me faster. So I feel like a Raycast has been amazing. I've discovered all this new shortcuts that you can use to open different things, type in shortcut commands and things like that. And Caffeinate is another thing that I've recently discovered from my teammates. It helps you prevent Mac from sleeping so you can run this really long Codex task for four or five hours locally, let it build the thing and then you can wake up and be like, "Okay, this is good. I like this."

**Lenny Rachitsky** (01:21:02):
That's hilarious, that combo. Codex and Caffeinate. You guys need to use it, build that yourself, an OpenAI version of that, or the Codex agent should just keep your Mac from sleeping. That's so funny. By the way, Raycast, also part of Lenny's product pass. One year for your Raycast. Amazing. Yeah.

**Aishwarya Naresh Reganti** (01:21:20):
Lenny didn't tell us these folks. Yes. These are actually our favorite products.

**Lenny Rachitsky** (01:21:25):
These are just two of 19 products. No Caffeinate though. I don't know if that's even paid. Okay, let's keep going. Do you have a favorite life motto that you find yourself coming back to in work or in life?

**Aishwarya Naresh Reganti** (01:21:35):
For me, I think this is one my dad told me when I was a kid and it's always stuck, which is they told it couldn't be done, but the fool didn't know it, so he did it anyway. I think be foolish enough to believe that you can do anything if you put your heart to it, especially now because you have so much data at your hand that could be pointing towards the fact that you probably will be unsuccessful. How many podcasts made it to more than a thousand subscribers or how many companies hit more than one million ARR? And there's always data to show you that you won't be successful, but sometimes just be foolish and go ahead with it.

**Lenny Rachitsky** (01:22:12):
That's great. Yeah.

**Kiriti Badam** (01:22:13):
For me, I am more of an overthinker. So I really like this quote from Steve Jobs that you can only connect the dots looking backwards. So a lot of the times there are numerous choices and you don't really know the optimal one to pick, but life works in ways that you can actually see back and be like, "Oh, these are actually beautiful in terms of how our transition." So I feel like that is extremely useful in keep moving forward, keep experimenting.

**Lenny Rachitsky** (01:22:39):
Final question. Whenever I have two guests on the podcast at once, I like to ask this question. What's something that you admire about the other person?

**Aishwarya Naresh Reganti** (01:22:48):
I think with Kiriti, he's pretty calm and very grounded and he's always been my sounding board. I can throw a ton of ideas at him and he always comes up with, he's able to anticipate the kind of issues that might land into. And he's extremely kind and lets his work speak instead of actually doing a lot of talking, I guess. But if I had to pick one, I think he's the most incredible husband.

**Lenny Rachitsky** (01:23:20):
Reveal. Little did people know.

**Aishwarya Naresh Reganti** (01:23:25):
We've been married for four years and been the most beautiful four years of my life.

**Lenny Rachitsky** (01:23:31):
Wow. Okay. How do you follow that?

**Kiriti Badam** (01:23:34):
Yeah, it's super hard to follow that. I would say I am extremely privileged in terms of working with really smart people in great companies in the Silicon Valley. And I feel the unique thing that stands with Aishwarya across like any other smart folks I've worked on is she has this really amazing knack of teaching and explaining something in a very understandable and easy to comprehend way. And that combined with persistence is super useful, especially in this fast-moving AI world that we are in the sense that there's so many new things coming up. It feels overwhelming, but when I hear her talk about, this is how you make sense of this entire thing, this is where it plugs in. I feel like, oh, that is so simple. I can also do that. So she empowers a lot of people by simplifying things and explaining things in the most understandable way.

**Kiriti Badam** (01:24:25):
So I feel that is an incredible quality.

**Lenny Rachitsky** (01:24:27):
Amazing. How sweet. I got to do this all the time. I need more guest to do it. That was great. Okay. Final questions. Where can folks find stuff that you're working on, find you online, share your course link, and then just how can listeners be useful to you?

**Aishwarya Naresh Reganti** (01:24:41):
I write a lot on LinkedIn. So if you want to listen to pragmatists who've been in the weeds, working on AI products and what they're seeing, you can follow my work. We also have a GitHub repository with about 20K stars, and that repository is all about good resources for learning AI. It's completely free. And if you like what we spoke today, we also run a super popular course. We leave a link to it on building enterprise AI products. And the course is a lot about unlearning mindsets and following a problem-first approach instead of a tool-first or a hype-first approach. So you can check that out as well. And if you don't want to do the course, we write a lot, we give out a lot of free resources, we have free sessions, so make sure you follow our work.

**Kiriti Badam** (01:25:27):
Yeah, I would also add that you can also find me on LinkedIn. I don't write a lot, I guess, but I'm super all excited to just talk to any complex product that you're building. And if you have thoughts on how you can use coding agents to make your life better or however the problems that you're seeing, always my DMs are open and we can have a great discussion.

**Lenny Rachitsky** (01:25:47):
Awesome. Well, Kiriti and Ash, thank you so much for being here.

**Kiriti Badam** (01:25:52):
Thank you so much.

**Aishwarya Naresh Reganti** (01:25:53):
Thank you, Lenny. This was so much fun.

**Lenny Rachitsky** (01:25:54):
So much fun. Bye, everyone.

**Lenny Rachitsky** (01:25:58):
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [4/15] The power user’s guide to Codex: parallelizing workflows, planning techniques, advanced context engineering tips, automating code reviews, and more | Alexander Embiricos

**Lenny Rachitsky** (00:00:00):
You lead work on Codex.

**Alexander Embiricos** (00:00:01):
Codex is OpenAI's coding agent. We think of Codex as just the beginning of a software engineering teammate. It's a bit like this really smart intern that refuses to read Slack, doesn't check Datadog unless you ask it to.

**Lenny Rachitsky** (00:00:12):
I remember Karpathy tweeted the gnarliest bugs that he runs into that he just spends hours trying to figure out nothing else has solved, he gives it to Codex, lets it run for an hour and it solves it.

**Alexander Embiricos** (00:00:21):
Starting to see glimpses of the future where we're actually starting to have Codex be on call for its own training. Codex writes a lot of the code that helps manage its training run, the key infrastructure. So we have a Codex code review that's catching a lot of mistakes. It's actually caught some pretty interesting configuration mistakes. One of the most mind-blowing examples of acceleration, the Sora Android app, like a fully new app, we built it in 18 days and then 10 days later, so 28 days total, we went to the public.

**Lenny Rachitsky** (00:00:45):
How do you think you win in this space?

**Alexander Embiricos** (00:00:47):
One of our major goals with Codex is to get to proactivity. If we're going to build a super system, has to be able to do things. One of the learnings over the past year is that for models to do stuff, they're much more effective when they can use a computer. It turns out the best way for models to use computers is simply to write code. And so we're kind of getting to this idea where if you want to build any agent, maybe you should be building a coding agent.

**Lenny Rachitsky** (00:01:04):
When you think about progress on Codex, I imagine you have a bunch of evals and there's all these public benchmarks.

**Alexander Embiricos** (00:01:10):
A few of us are constantly on Reddit. There's praise up there and there's a lot of complaints. What we can do is as a product team just try to always think about how are we building a tool so that it feels like we're maximally accelerating people rather than building a tool that makes it more unclear what you should do as the human?

**Lenny Rachitsky** (00:01:24):
Being at OpenAI, I can't not ask about how far you think we are from AGI.

**Alexander Embiricos** (00:01:28):
The current underappreciated limiting factor is literally human typing speed or human multitasking speed.

**Lenny Rachitsky** (00:01:35):
Today, my guest is Alexander Embiricos, product lead for Codex, OpenAI's incredibly popular and powerful coding agent. In the words of Nick Turley, head of ChatGPT and former podcast guest, "Alex is one of my all time favorite humans I've ever worked with, and bringing him and his company into OpenAI ended up being one of the best decisions we've ever made." Similarly, Kevin Weil, OpenAI's CPO, said, "Alex is simply the best."

**Lenny Rachitsky** (00:01:59):
In our conversation, we chat about what it's truly like to build product at OpenAI, how Codex allowed the Sora team to ship the Sora app, which became the number one app in the app store in under one month. Also, the 20x growth Codex is seeing right now and what they did to make it so good at coding, why his team is now focused on making it easier to review code, not just write code, his AGI timelines, his thoughts on when AI agents will actually be really useful, and so much more. A huge thank you to Ed Bayes, Nick Turley, and Dennis Yang for suggesting topics for this conversation. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. And if you become an annual subscriber of my newsletter, you get a year free of 19 incredible products, including a year free of Devin, Lovable, Replit, Bolt, n8n, Linear, Superhuman, Descript, Wispr Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD, Mobbin, PostHog, and Stripe Atlas. Head on over to lennysnewsletter.com and click Product Pass.

**Alexander Embiricos** (00:05:18):
Thank you so much. I've been following for ages and I'm excited to be here.

**Lenny Rachitsky** (00:05:21):
I'm even more excited. I really appreciate that. I want to start with your time at OpenAI. So you joined OpenAI about a year ago. Before that, you had your own startup for about five years. Before that, you were a product manager at Dropbox. I imagine OpenAI is very different from every other place you've worked. Let me just ask you this, what is most different about how OpenAI operates and what's something that you've learned there that you think you're going to take with you wherever you go, assuming you ever leave?

**Alexander Embiricos** (00:05:49):
By far, I would say the speed and ambition of working at OpenAI are just dramatically more than what I can imagine. And I guess it's kind of an embarrassing thing to say because everyone who's a startup founder thinks like, "Oh yeah, my startup moves super fast and the talent bar is super high and we're super ambitious." But I have to say, working in OpenAI just made me reimagine what that even means.

**Lenny Rachitsky** (00:06:11):
We hear this a lot about feels like every AI company is just like, "Oh my God, I can't believe how fast they're moving." Is there an example of just like, "Wow, that wouldn't have happened this quickly anywhere else"?

**Alexander Embiricos** (00:06:20):
The most obvious thing that comes to mind is just the explosive growth of Codex itself. I think it's a while since we bumped our external number, but it's like the 10x-ing of Codex's scale was just super fast in a matter of months and it's well more since then. And once you've lived through that, or at least speaking for myself, having lived through that now, I feel like anytime I'm going to spend my time on building tech product, there's that speed and scale that I now need to meet.

**Alexander Embiricos** (00:06:52):
If I think of what I was doing in my startup, it moved way slower and there's always this balance with startups of how much do you commit to an idea that you have versus find out that it's not working and then pivot. But I think one thing I've realized at OpenAI is the amount of impact that we can have and, in fact, need to have to do a good job is so high that I have to be way more ruthless with how I spend my time now.

**Lenny Rachitsky** (00:07:15):
Before we get to Codex, is there a way that they've structured the org or, I don't know, the way that OpenAI operates that allows the team to move this quickly? Because everyone wants to move super fast. I imagine there's a structural approach to allowing this to happen.

**Alexander Embiricos** (00:07:29):
I mean, so one thing is just the technology that we're building with has just transformed so many things from both how we build, but also what kinds of things we can enable for users. And we spend most of our time talking about the sort of improvements within the foundation models, but I believe that even if we had no more progress today with models, which is absolutely not the case, but even if we had no more progress, we are way behind on product. There's so much more product to build. So I think just the moment is ripe, if that makes sense.

**Alexander Embiricos** (00:08:01):
But I think there's a lot of counterintuitive things that surprised me when I arrived as far as how things are structured. One example that comes to mind is when I was working on my startup and before that, when I was at Dropbox, it was very important, especially as a PM to always rally the ship and it was like make sure you're pointed in the right direction and then you can accelerate in that direction. But here, I think because we don't exactly know what capabilities will even come up soon and we don't know what's going to work technically, and then we also don't know what's going to land even if it works technically, it's much more important for us to be very humble and learn a lot more empirically and just try things quickly. And the org is set up in that way to be incredibly bottoms up.

**Alexander Embiricos** (00:08:45):
This is, again, one of those things that, as you were saying, everyone wants to move fast. I think everyone likes to say that they're bottoms up, or at least a lot of people do, but OpenAI is truly, truly bottoms up. And that's been a learning experience for me that now it'll be interesting if I ever work at... I don't think it'll even make sense to work at a non-AI company in the future. I don't even know what that means. But if I were to imagine it or go back in time, I think I would run things totally new.

**Lenny Rachitsky** (00:09:10):
What I'm hearing is this ready, fire, aim is the approach more than ready, aim, fire. And there's something, and as you process that, because that may not come across well, but I actually have heard this a lot at AI companies is because you don't know, and Nick Turley shared I think the same sentiment, because you don't know how people will use it it doesn't make sense to spend a lot of time making it perfect. It's better to just get it out there in a primordial way, see how people use it, and then go big on that use case.

**Alexander Embiricos** (00:09:39):
Yeah. Okay, to use this analogy a little bit, I feel like there is an aim component, but the aim component is much fuzzier. It's kind of like, roughly what do we think can happen? Someone I've learned a ton from working here is a research lead, and he likes to say that at OpenAI, we can have really good conversations about something that's a year plus from now, and there's a lot of ambiguity in what will happen, but that's a right sort of timeline. And then we can have really good conversations about what's happening in low months or weeks. But there's this awkward middle ground, which was as you start approaching a year, but you're not at a year where it's very difficult to reason about, right?

**Alexander Embiricos** (00:10:18):
And so as far as aiming, I think we want to know, "Okay, what are some of the futures that we're trying to build towards?" And a lot of the problems we're dealing with in AI, such as alignment are problems you need to be thinking out really far out into the future. So we're kind of aiming fuzzily there, but when it comes down to the more tactically like, "Oh yeah, what product will we build and therefore how will people use that product?" That's the place where we're much more like, "Let's find out empirically."

**Lenny Rachitsky** (00:10:41):
That's a good way of putting it. Something else that when people hear this, people sometimes hear companies like yours saying, "Okay, we're going to be bottoms up. We're going to try a bunch of stuff. We're not going to have exactly a plan of where it's going in the next few months." The key is you all hire the best people in the world. And so that feels like a really key ingredient in order to be this successful at bottoms up work.

**Alexander Embiricos** (00:11:02):
It just super resonates with me. I was just, again, surprised or even shocked when I arrived at the level of individual drive and autonomy that everyone here has. So I think the way that OpenAI runs, you can't read this or listen to a podcast and be like, "I'm just going to deploy this to my company." Maybe this is a harsh thing to say, but I think very few companies have the talent caliber to be able to do that. So it might need to be adjusted if you were going to implement this.

**Lenny Rachitsky** (00:11:34):
Okay. So let's talk Codex. You lead work on Codex. How's Codex going? What numbers can you share? Is there anything you can share there? Also, just not everyone knows exactly what Codex is, explain what Codex is.

**Alexander Embiricos** (00:11:45):
Totally, yeah. So I had the very lucky job of living in the future and leading products on Codex. And Codex is OpenAI's coding agent. So super concretely, that means it's an IDE extension, a VS code extension that you can install or a terminal tool that you can install. And when you do so, you can then basically pair with Codex to answer questions about code, write code, run tests, execute code, and do a bunch of the work in that thick middle section of the software development lifecycle, which is all about writing code that you're going to get into production.

**Alexander Embiricos** (00:12:21):
More broadly, we think of Codex as what it currently is just the beginning of a software engineering teammate. So when we use a big word like teammate, some of the things we're imagining are that it's not only able to write code, but actually it participates early on in the ideation and planning phases of writing software and then further downstream in terms of validation, deploying and maintaining code.

**Alexander Embiricos** (00:12:46):
To make that a little more fun, one thing I like to imagine is if you think of what Codex is today, it's a bit like this really smart intern that refuses to read Slack and doesn't check Datadog or Century unless you ask it to. And so no matter how smart it is, how much are you going to trust it to write code without you also working with it? So that's how people use it mostly today is they pair with it. But we want to get to the point where it can work just like a new intern that you hire, you don't only ask them to write code, but you ask them to participate across the cycle. So you know that even if they don't get something right the first try, they're eventually going to be able to iterate their rate there.

**Lenny Rachitsky** (00:13:21):
I thought the point about not reading Slack and Datadog was it's just not distracted, it's just constantly focused and is always in flow. But I get what you're saying there is it doesn't have all the context on everything that's going on.

**Alexander Embiricos** (00:13:31):
Yeah. And that's not only true when it's performing a task, but again, if you think of the best team and teammates, you don't tell them what to do. Maybe when you first hire them, you have a couple meetings and you're like, "Hey," you learn, "Okay, these prompts work for this teammate, these prompts don't. This is how to communicate with this person." Then eventually you give them some starter tasks, you delegate a few tasks. But then eventually you just say like, "Hey, great. Okay, you're working with this set of people in this area of the code base. Feel free to work with other people on other parts of the code base too, even. And yeah, you tell me what you think makes sense to be done." And so we think of this as proactivity and one of our major goals with Codex is to get to proactivity.

**Alexander Embiricos** (00:14:09):
I think this is critically important to achieve the mission of OpenAI, which is to deliver the benefits of AGI to all humanity. I like to joke today that AI products, and it's a half joke, they're actually really hard to use because you have to be very thoughtful about when it could help you. And if you're not prompting a model to help you, it's probably not helping you at that time. And if you think of how many times the average user is prompting AI today, it's probably tens of times. But if you think of how many times people could actually get benefit from a really intelligent entity, it's thousands of times per day. And so a large part of our goal with Codex is to figure out what is the shape of an actual teammate agent that is helpful by default.

**Lenny Rachitsky** (00:14:54):
When people think about Cursor and even Cloud Code, it's like a IDE that helps you code and auto completes code and maybe does some agentic work. What I'm hearing here is the vision is different, which is it's a teammate. It's like a remote teammate, a building code for you that you talk to and ask to do things. And that also does IDE, auto complete and things like that. Is that a kind of a differentiator in the way you think about Codex?

**Alexander Embiricos** (00:15:18):
It's basically this idea that if you're a developer and you're trying to get something done, we want you to just feel like you have superpowers and you're able to move much, much faster. But we don't think that in order for you to reap those benefits, you need to be sitting there constantly thinking about, "How can I invoke AI at this point to do this thing?" We want you to be able to plug it in to the way that you work and have it just start to do stuff without you having to think about it.

**Lenny Rachitsky** (00:15:44):
Okay. I have a lot of questions along those lines, but just how's it going? Is there any stats, any numbers you can share about how Codex is doing?

**Alexander Embiricos** (00:15:49):
Yeah, Codex has been growing absolutely explosively since the launch of GPT-5 back in August. There's definitely some interesting product insights to talk about as to how we unlock that growth, if you're interested. But again, the last stat we shared there was we were well over 10x since August. In fact, it's been 20x since then. Also, the Codex models are serving many trillions of tokens a week now, and it's basically our most served coding model. One of the really cool things that we've seen is that the way that we decided to set up the Codex team was to build a really tightly integrated product and research team that are iterating on the model and the harness together. And it turns out that lets you just do a lot more and try many more experiments as to how these things will work together.

**Alexander Embiricos** (00:16:35):
And so we were just training these models for use in our first party harness that we were very opinionated about. And then what we've started to see more recently actually is that other major API coding customers are now starting to adopt these models as well. And so we've reached a point where actually the Codex model is the most served coding model in the API as well.

**Lenny Rachitsky** (00:16:55):
You hinted at this, what unlocked this growth, I'm extremely interested in hearing that. It felt like before, I don't know, maybe this was before you joined the team, it just felt like Cloud Code was killing it. Just everyone was sitting on top of Cloud Code. It was by far the best way to code. And then all of a sudden Codex comes around. I remember Karpathy tweeted that he just has never seen a model like this. I think the tweet was the gnarliest bugs that he runs into that he just spends hours trying to figure out nothing else has solved, he gives it to Codex, lets it run for an hour and it solves it. What'd you guys do?

**Alexander Embiricos** (00:17:30):
We have this strong sort of mission here at OpenAI basically to build AGI. And so we think a lot about how can we shape the product so that it can scale. Earlier I was mentioning like, "Hey, if you're an engineer, you should be getting help from AI thousands of times per day," and so we thought a lot about the primitives for that when we launched our first version of Codex, which was Codex Cloud. And that was basically a product that had its own computer, lived in the cloud, you could delegate to it. And the coolest part about that is you could run many, many tasks in parallel. But some of the challenges that we saw are that it's a little bit harder to set that up, both in terms of environment configuration, like giving the model the tools it needs to validate its changes and to learn how to prompt in that way.

**Alexander Embiricos** (00:18:20):
My analogy for this is, going back to this teammate analogy, it's like if you hired a teammate, but you're never allowed to get on a call with them and you can only go back and forth asynchronously over time. That works for some teammates and eventually that's actually how you want to spend most of your time. So that's still the future, but it's hard to initially adopt. And so we still have that vision of like, that's what we're trying to get you to, a teammate that you delegate to and then is proactive, and we're seeing that growing. But the key unlock is actually first you need to land with users in a way that's much more intuitive and trivial to get value from.

**Alexander Embiricos** (00:18:54):
So the way that most people discover, the vast majority of users discover Codex today is either they download an IDE extension or they run it in their CLI and the agent works there with you on your computer interactively. And it works within a sandbox, which is actually a really cool piece of tech to help that be safe and secure, but it has access to all those dependencies. So if the agent needs to do something, it needs to run a command, it can do so within the sandbox. We don't have to set up any environment. And if it's a command that doesn't work in the sandbox, it can just ask you. And so you can get into this really strong feedback loop using the model. And then over time, our team's job is to help turn that feedback loop into you as a byproduct of using the product, configuring it so that you can then be delegating to it down the line.

**Alexander Embiricos** (00:19:38):
And again, analogy, keep coming back to it, but if you hire a teammate and you ask them to do work, but you just give them a fresh computer from the store, it's going to be hard for them to do their job. But if as you work with them side by side, you could be like, "Oh, you don't have a password for this service we use, here's the password for this service. Yeah, don't worry, feel free to run this command," then it's much easier for them to then go off and do work for hours without you.

**Lenny Rachitsky** (00:20:01):
So what I'm hearing is the initial version of Codex was almost too far in the future. It's like a remote in the cloud agent that's coding for you asynchronously. And what you did is, "Okay, let's actually come back a little bit, let's integrate into the way engineers already integrate into IDs and locally and help them on ramp to this new world,"

**Alexander Embiricos** (00:20:21):
Totally. And it was quite interesting because we dogfood product a ton at OpenAI. So dogfood as in we use our own product. And so Codex has been accelerating OpenAI over the course of the entire year, and the cloud product was a massive accelerant to the company as well. It just turns out that this was one of those places where the signal we got from dogfooding is a little bit different from the signal you get from the general market because at OpenAI, we train reasoning models all day and so we're very used to this kind of prompting and think upfront, run things massively in parallel and it would take some time and then come back to it later asynchronously. And so now when we build, we still get a ton of signal from dogfooding internally, but we're also very cognizant of the different ways that different audiences use the product.

**Lenny Rachitsky** (00:21:12):
That's really funny. It's like live in the future, but maybe not too far in the future. And I could see how everyone at OpenAI is living very far in the future, and sometimes that won't work for everyone.

**Alexander Embiricos** (00:21:23):
Yeah.

**Lenny Rachitsky** (00:21:23):
What about just intelligence training data? I don't know, is there something else that helped Codex accelerate its ability to actually code? Is it better, cleaner data? Is it more just models advancing? Is there anything else that really helped accelerate?

**Alexander Embiricos** (00:21:38):
Yeah, so there's a few components here. I guess you were mentioning models and the models have improved a ton. In fact, just last Wednesday, we shipped GPT-5.1-Codex-Max, a very accurately named model, that is awesome. It is awesome both because it is for any given task that you were using GPT-5.1-Codex for, it's roughly 30% faster at accomplishing that task. But also it unlocks a ton of intelligence. So if you use it at our higher reasoning levels, it's just even smarter. And that tweet you were saying Karpathy made about, "Hey, give this your gnarliest bugs," obviously there's a ton going on in the market right now, but Codex-Max is definitely carrying that mantle of us tackling the hardest bugs. So that is super cool.

**Alexander Embiricos** (00:22:28):
But I will say it's like some of how we're thinking about this is evolving a little bit from being like, "Yeah, we're just going to think about the model and let's just train the best model," to really thinking about what is an agent actually overall? And I'm not going to try to define agent exactly, but at least the stack that we think of it as having is it's like you have this model, really smart reasoning model that knows how to do a specific kind of task really well, so we can talk about how we make that possible. But then actually we need to serve that model through an API into a harness, and both of those things also have a really big role here.

**Alexander Embiricos** (00:23:02):
So for instance, one of the things that we're really proud of is you can have GPT-5.1-Codex-Max work for really long periods of time. That's not normal, but you can set it up to do that or that might happen. But now routinely we'll hear about people saying, "Yeah, it ran overnight or it ran for 24 hours." And so for a model to work continuously for that amount of time, it's going to exceed its context window. And so we have a solution for that, which we call compaction.

**Alexander Embiricos** (00:23:28):
But compaction is actually a feature that uses all three layers of that stack. So you need to have a model that has a concept of compaction and knows like, "Okay, as I start to approach this context window, I might be asked to prepare to be run in a new context window." And then at the API layer, you need an API that understands this concept and has an endpoint that you can hit to do this change. And at the harness layer, you need a harness that can prepare the payload for this to be done. So shipping this compaction feature that now just made this behavior possible to anyone using Codex actually meant working across all three things. And I think that's increasingly going to be true.

**Alexander Embiricos** (00:24:02):
Another maybe underappreciated version of this is if you think about all the different coding products out there, they all have very different tool harnesses with very different opinions on how the model should work. So if you want to train a model to be good at all the different ways it could work, maybe you have a strong opinion that it should work using semantic search. Maybe you have a strong opinion that it should call bespoke tools or maybe you have, in our case, a strong opinion that it should just use the shell and work in the terminal, you can move much faster if you're just optimizing for one of those worlds. So the way that we built Codex is that it just uses the shell, but in order to make that safer and secure, we have a sandbox that the model is used to operating in.

**Alexander Embiricos** (00:24:45):
So I think one of the biggest accelerants, to go all the way back to answer to your question, is just we're building all three things in parallel and tuning each one and constantly experimenting with how those things work with a tightly integrated product and research team.

**Lenny Rachitsky** (00:24:59):
Do you think you win in this space? Do you think it'll always be this kind of race with other models constantly leapfrogging each other? Do you think there's a world where someone just runs away with it and no one else can ever catch up? Is there a path to just, "We win"?

**Alexander Embiricos** (00:25:15):
Again, comes back to this idea of building a teammate, and not just a teammate that participates in team planning and prioritization, not just a teammate that really tests its code and helps you maintain and deploy it. But even a teammate... If you think, again, an engineering teammate, they can also schedule a calendar invite or move standup or do whatever, right? And so in my mind, if we just imagine that every day or every week some crazy new capability is just going to be deployed by a research lab, it's just impossible for us as humans to keep up and use all this technology. So I think we need to get to this world where you kind of just have an AI teammate or super assistant that you just talk to and it just knows how to be helpful on its own. So you don't have to be reading the latest tips for how to use it, you've plugged it in and it just provides help.

**Alexander Embiricos** (00:26:10):
So that's kind of the shape of what I think we're building. And I think that will be a very sticky winning product if we can do so. So the shape that in my head, at least I have, is that we build... Maybe a fun topic is like, "Is Chat the right interface for AI?" I actually think Chat is a very good interface when you don't know what you're supposed to use it for. In the same way that if I think of I'm on MS Teams or in Slack with a teammate, Chat is pretty good. I can ask for whatever I want. It's kind of the common denominator for everything. So you can chat with a super assistant about whatever topic you want, whether it be coding or not. And then if you are a functional expert in a specific domain such as coding, there's a GUI that you can pull up to go really deep and look at the code and work with the code.

**Alexander Embiricos** (00:26:54):
So I think what we need to build as OpenAI is basically this idea of you have Chat, ChatGPT and not as a tool that's ubiquitously available to everyone, you start using it even outside of work to just help you. You become very comfortable with the idea of being accelerated with AI. So then you get to work and you just can naturally just, "Yeah, I'm just going to ask it for this and I don't need to know about all the connectors or all the different features. I'm just going to ask it for help and it'll surface to me the best way that it can help at this point in time and maybe even chime in when I didn't ask it for help." So in my mind, if we can get to that, I think that's how we really build the winning product.

**Lenny Rachitsky** (00:27:32):
This is so interesting because with my chat with Nick Turley, the head of ChatGPT, I think he shared that the original name for ChatGPT was Super Assistant or something like that. And it's interesting that there's that approach to the super assistant and then there's this Codex approach. It's almost like the B2C version and the B2B version. And what I'm hearing is the idea here is, okay, you start with coding and building and then it's doing all this other stuff for you, scheduling meetings, I don't know, probably posting in Slack, I don't know, shipping designs. I don't know, is the idea that this is the business version of ChatGPT in a sense, or is there something else there?

**Alexander Embiricos** (00:28:08):
Yeah. So we're getting to the one-year time horizon conversation. A lot of this might happen sooner, but in terms of fuzziness, I think we're at the one year. So I'll give you a contention and a plausible way we get there, but as for how it happens, who knows? So basically, if we're going to build a super assistant, it has to be able to do things. So we're going to have a model and it's going to be able to do stuff affecting your world. And one of the learnings I think we've seen over the past year or so is that for models to do stuff, they're much more effective when they can use a computer.

**Alexander Embiricos** (00:28:41):
Right, okay, so now we're like, okay, we need the super assistant that can use a computer, or many computers. And now the question is, okay, well, how should it use the computer? And there's lots of ways to use a computer. You could try to hack the OS and use accessibility APIs, maybe a bit easier as you could point and click. That's a little slow and unpredictable sometimes. And another way, it turns out the best way for models to use computers is simply to write code. So we're kind of getting to this idea where, well, if you want to build any agent, maybe you should be building a coding agent and maybe to the user, a non-technical user, they won't even know they're using a coding agent, the same way that no one thinks about are they using the internet or not, which is they're more just like, "Is WiFi on?"

**Alexander Embiricos** (00:29:23):
So I think that what we're doing with Codex is we're building a software engineering teammate, and as part of that, we're kind of building an agent that can use a computer by writing code. And so we're already seeing some pull for this. It's quite early, but we're starting to see people who are using Codex for coding adjacent product purposes. And so as that develops, I think we'll just naturally see that, oh, it turns out we should just always have the agent write code if there is a coding way to solve a problem instead of... Even if you're doing a financial analysis, maybe write some code for that.

**Alexander Embiricos** (00:29:55):
So basically like you were like, "Hey, is this the two ends of this product for the super assistant of ChatGPT?" In my mind, just coding is a core competency of any agent including ChatGPT. And so really what we think we're building is that competency. So here's the really cool thing about agents writing code is that you can import code. Code is composable, interoperable. Because one very reductive view we could have for an agent is it's just going to be given a computer and it's just going to point and click and go around. But that is the future. And then how we get there is difficult to chart a path because a lot of the questions around building agents aren't like, "Can the agent do it?" But it's more about, "Well, how can we help the agent understand the context that it's working in?" And the team that's using it probably has a way that they like to do things. They have guidelines. They probably want certain deterministic guarantees about what the agent can or cannot do. Or they want to know that the agent understands this detail.

**Alexander Embiricos** (00:30:57):
An example would be if we're looking at a crash reporting tool, hitting a connector for it, every sub-team probably has a different meta prompt for how they want the crashes to be analyzed. And so we start to get to this thing where, yeah, we have this agent sitting in front of a computer, but we need to make that configurable for the team or for the user and let them... Stuff that the agent does often, we probably just want to build in as a competency that this agent has that it can do.

**Alexander Embiricos** (00:31:24):
So I think we end up with this generalizable thing, that you were saying, of an agent that can just write its own scripts for whatever it wants to do. But I think that the really key part here is can we make it so that everything that the agent has to do often or that it does well, we can just remember and store so that the agent doesn't have to write a script for that again? Or maybe if I just joined a team and you are already on the same team as me, I can just use all those scripts that the agents had written already.

**Lenny Rachitsky** (00:31:53):
Yeah, it's like if this is our teammate, they can share things that it's learned from working with other people at the company. It just makes sense as a metaphor.

**Alexander Embiricos** (00:32:01):
Right. Yeah.

**Lenny Rachitsky** (00:32:02):
It feels like you're in the Karpathy camp of, "Agents today are not that great and mostly slop and maybe in the future they'll be awesome." Does that resonate?

**Alexander Embiricos** (00:32:11):
So I think coding agents are pretty great. I think we're seeing a ton of value there.

**Lenny Rachitsky** (00:32:11):
Yeah, that feels right. That feels right, yeah.

**Alexander Embiricos** (00:32:17):
And then I think agents outside of coding, it's still very early. And this is just my opinion, but I think they're going to get a whole lot better once they can use coding too in a composable way. It's kind of the fun part of when you're building for software engineers, at my startup, we were building for software engineers too for a lot of that journey, and they're just such a fun audience to build for because they also like building for themselves and are often even more creative than we are in thinking about how to use the technology. So by building for software engineers, you get to just observe a ton of emergent behaviors and things that you should do and build into the product.

**Lenny Rachitsky** (00:32:54):
I love how you say that because a lot of people building for engineers get really annoyed because the engineers they're just always complaining about stuff. They're like, "Ah, that sucks. Why'd you build it this way?" I love that you enjoy it, but I think it's probably because you're building such an amazing tool for engineers that can actually solve problems and just code for them.

**Lenny Rachitsky** (00:33:12):
Kind of along those lines, there's always this talk of what will happen with jobs, engineers, coding, do you have to learn coding? All these things. Clearly the way you're describing it is it's a teammate, it's going to work with you, make you more superhuman, it's not going to replace you. What's the way you just think about the impact on the field of engineering, having this super intelligent engineering teammate?

**Alexander Embiricos** (00:33:33):
I think there's two sides to it, but the one we were just talking about is this idea that maybe every agent should actually use code and be a coding agent. And in my mind, that's just a small part of this broader idea that, hey, as we make code even more ubiquitous... I mean, you could probably claim it's ubiquitous today, even pre AI, right? But as we make code even more ubiquitous, it's actually just going to be used for many more purposes. And so there's just going to be a ton more need for humans with this competency.

**Alexander Embiricos** (00:34:01):
So that's my view. I think this is quite a complex topic. So it's something we talk about a lot and we have to see how it pans out. But I think what we can do basically as a product team building in the space is just try to always think about how are we building a tool so that it feels like we're maximally accelerating people rather than building a tool that makes it more unclear what you should do as the human?

**Alexander Embiricos** (00:34:27):
I think, to give an example right now, nowadays when you work with a coding agent, it writes a ton of code, but it turns out writing code is actually one of the most fun parts of software engineering for many software engineers. So then you end up reviewing AI code. And that's often a less fun part of the job for many software engineers. So I actually think we see that this plays out all the time in a ton of micro decisions. So we as a product team, we're always thinking about, "Okay, how do we make this more fun? How do we make you feel more empowered? Where is this not working?" And I would argue that reviewing agent written code is a place that today is less fun.

**Alexander Embiricos** (00:35:04):
So then I think, "Okay, what can we do about that?" Well, we can ship a code review feature that helps you build confidence in the AI written code. Okay, cool. Another thing we could do is we can make it so that the agent's better able to validate its work. And it gets all the way down into micro decisions. If you're going to have an agent capability to validate work, and let's say you have... I'm thinking of Codex Web right now, you have a pain that sort of reflects the work the agent did, what do you see first? Do you see the diff or do you see the image preview of the code it wrote? And I think if you're thinking about this from perspective, "How do I empower the human? How do I make them feel as accelerated as possible?" You obviously see the image first. You shouldn't be reviewing the code unless first you've seen the image, unless maybe it's been reviewed by an AI and now it's time for you to take a look.

**Lenny Rachitsky** (00:35:49):
When I had Michael Truell, the CEO of Cursor on the podcast, he had this kind of vision of us moving to something beyond code. And I've seen this rise of something called spec-driven development where you just write the spec and then the AI writes code for you. So you start working at this higher abstraction level. Is that something you see where we're going, just like engineers not having to actually write code or look at code and there's going to be this higher level of abstraction that we focus on?

**Alexander Embiricos** (00:36:16):
Yeah. I mean, I think there's constantly these levels of abstraction and they're actually already played out today. Today, coding agents, mostly it's prompt to patch. We're starting to see people doing spec-driven development or planned and driven development. That's actually one of the ways when people ask, "Hey, how do you run Codex on a really long task?" Well, it's like often collaborate with it first to write a plan.md, like a markdown file that's your plan. And once you're happy with that, then you ask it to go off and do work. And if that plan has verifiable steps, it'll work for much longer. So we're totally seeing that.

**Alexander Embiricos** (00:36:50):
I think spec-driven development is an interesting idea. It's not clear to me that it'll work out that way because a lot of people don't like writing specs either, but it seems plausible that some people will work that way. A bit of a joke idea though is if you think of the way that many teams work today, they often don't necessarily have specs, but the team is just really self-driven and so stuff just gets done. And so almost that it's like, I'm coming up with this on the spot, so it's not a good name, but chatter-driven development where it's just like stuff is happening on social media and in your team communications tools. And then as a result, code gets written and deployed.

**Alexander Embiricos** (00:37:29):
So yeah, I think I'm a little bit more oriented in that way of I don't even necessarily want to have to write a spec. Sometimes I want to, only if I like writing specs. Other times I might just want to say like, "Hey, here's the customer service channel and tell me what's interesting to know, but if it's a small bug, just fix it." I don't want to have to write a spec for that, right?

**Alexander Embiricos** (00:37:51):
I have this sort of hypothetical future that I like to share sometimes with people as a provocation, which is in a world where we have truly amazing agents, what does it look like to be a solopreneur? And one terrible idea for how it could look is that actually there's a mobile app and every idea that the agent has to do is just vertical video on your phone and then you can swipe left if you think it's a bad idea and you can swipe right if it's a good idea. And you can press and hold and speak to your phone if you want to give feedback on the idea before you swipe. And in this world, basically what your job is is just to plug in this app into every single signal system or system of record, and then you just sit back and swipe. I don't know.

**Lenny Rachitsky** (00:38:39):
I love this. So this is like Tinder meets TikTok meets Codex.

**Alexander Embiricos** (00:38:42):
It's pretty terrible.

**Lenny Rachitsky** (00:38:43):
No, this is great. So the idea here is this agent is watching and listening to you, paying attention to the market, your users, and it's like, "Cool, here's something I should do." It's like a proactive engineer just like, "Here, we should build this feature, fix this thing."

**Alexander Embiricos** (00:38:56):
Exactly. Exactly.

**Lenny Rachitsky** (00:38:58):
I think it's a really good idea.

**Alexander Embiricos** (00:39:00):
Communicating with you in the lowest effort way for your consumers.

**Lenny Rachitsky** (00:39:02):
Yeah, yeah, the modern way we communicate, swipe left to right and vertical feed. And then the Sora video, okay, so I see how this all connects now. I see.

**Alexander Embiricos** (00:39:11):
Yeah. To be clear, we're not building that, but it's a fun idea. I mean, in this example though, one of the things that it's doing is it's consuming external signals, right? I think the other really interesting thing is if we think about what is the most successful AI product to date, I would argue, it's funny actually not to confuse things at all, but the first time we used the brand Codex at OpenAI was actually the model powering GitHub Copilot. This is way back in the day, years ago. And so we decided to reuse that brand recently because it's just so good, Codex, code execution.

**Alexander Embiricos** (00:39:46):
But I think actually auto completion and IDEs is one of the most successful AI products today. And part of what's so magical about it is that when it can surface ideas for helping you really rapidly, when it's right, you're accelerated. When it's wrong, it's not that annoying. It can be annoying, but it's not that annoying. So you can create this mixed initiative system that's contextually responding to what you're attempting to do.So in my mind, this is a really interesting thing for us as OpenAI as we're building.

**Alexander Embiricos** (00:40:22):
So for instance, when I think about launching a browser, which we did with Atlas, in my mind, one of the really interesting things we can then do is we can then contextually surface ways that we can help you as you're going about your day. And so we break out of this, we're just looking at code or we're just in your terminal into this idea that, "Hey, a real teammate is dealing with a lot more than just code. They're dealing with a lot of things that are web content. So how can we help you with that?"

**Lenny Rachitsky** (00:40:51):
Man, there's so much there. I love this. Okay, so auto complete on web with the browser. That's so interesting. Just like, "Here's all the things that we can help you with as you're browsing and going about your day."

**Lenny Rachitsky** (00:41:01):
I want to talk about Atlas. I'll come back to that. Codex, code execution, did not know that. That's really clever. I get it now. Okay, and then this chatter, what is a chatter-driven development? No, this is a really good idea, but it reminds me, I had Dhanji on the podcast, CTO of Block, and they have this product called Goose, which is their own internal agent thing. And he talked about an engineer at Block just has Goose watch him with his screen and listens to every meeting and proactively does work that he should probably want to do. So ships to PR, sends an email, drafts a Slack message. So he's doing exactly what you're describing in kind of a very early way.

**Alexander Embiricos** (00:41:45):
Yeah, that's super interesting. And I bet you, so if we went and asked them what the bottleneck to that productivity is, did they share what it is?

**Lenny Rachitsky** (00:41:54):
Probably looking at it and just making sure this is the right thing to do, yeah.

**Alexander Embiricos** (00:41:58):
Yeah. So we see this now. We have a Slack integration for Codex. People love if there's something that you need to do quickly, people will just @ mention Codex, "Why do you think this bug is happening?" It doesn't have to be an engineer. Even maybe data scientists often here are using Codex a ton to just answer questions like, "Why do you think this metric moved? What happened?" So questions, you get the answer right back in Slack. It's amazing, super useful. But as for when it's writing code, then you have to go back and look at the code.

**Alexander Embiricos** (00:42:25):
So the real, I think, bottleneck right now is validating that the code worked and writing code review. So in my mind, if we wanted to get to something like the friend you were talking about's world, I think we really need to figure out how to get people to configure their coding agents to be much more autonomous on those later stages of the work.

**Lenny Rachitsky** (00:42:46):
It makes sense. Like you said, writing code, I used to be an engineer, I was an engineer for 10 years, really fun to write code, really fun to just get in the flow, build architect, test. Not so fun to look at everyone else's code and just have to go through and be on the hook if it's doing something dumb that's going to take down production. And now that building has become easier, what I've always heard from companies that are really at the cutting edge of this is the bottleneck is now figuring out what to build. And then it's at the end of like, "Okay, we have all this, all 100 PRs to review. Who's going to go through all that?"

**Alexander Embiricos** (00:43:14):
Right.

**Lenny Rachitsky** (00:43:15):
Yeah.

**Alexander Embiricos** (00:44:24):
Yeah, I mean, I think mostly I just feel much more empowered. I've always been sort of more technical leaning PM, and especially when I'm working on products for engineers, I feel like it's necessary to dogfood the product. But even beyond that, I just feel like I can do much, much more as a PM. And Scott Belsky talks about this idea of compressing the talent stack. I'm not sure if I've phrased that right. But it's basically this idea that maybe the boundaries between these roles are a little bit less needed than before because people can just do much more. And every time someone can do more, you can skip one communication boundary and make the team that much more efficient.

**Alexander Embiricos** (00:45:03):
So I think we see it in a bunch of functions now, but I guess since you asked about products specifically, now answering questions much, much easier. You can just ask Codex for thoughts on that. A lot of PM type work, understanding what's changing. Again, just ask Codex for help with that. Prototyping is often faster than writing specs. This is something that a lot of people have talked about.

**Alexander Embiricos** (00:45:29):
I think something that, I don't think it's super surprising, but something that's slightly surprising is we see... We're mostly building Codex to write code that's going to be deployed to production, but actually we see a lot of throwaway code written with Codex now. It's kind of going back to this idea of ubiquitous code. So you'll see someone wants to do an analysis. If I want to understand something, it's like, okay, just give Codex a bunch of data, but then ask it to build an interactive data viewer for this data. That's just too annoying to do in the past, but now it's just totally worth the time of just getting an agent to go do something.

**Alexander Embiricos** (00:46:02):
Similarly, I've seen some pretty cool prototypes on our design team about if you want to... Well, a designer basically wanted to build an animation, and this is the Coin Animation Codex, and it was like normally it'd be too annoying to program this animation. So they just vibe coded a animation editor and then they use the animation editor to build the animation, which they then check into their repo.

**Alexander Embiricos** (00:46:24):
Actually, our designers, there's a ton of acceleration there. And speaking of compressing the talent stack, I think our designers are very PME. So they do a ton of product work and they actually have an entire vibe coded side prototype of the Codex app. And so a lot of how we talk about things is we'll have a really quick jam because there's 10,000 things going on, and then the designer will go think about how this should work. But instead of talking about it again, they'll just vibe code a prototype of that in their standalone prototype. We'll play with it. If we like it, they'll vibe engineer that prototype into an actual PR to land. And then depending on their comfort with the code base, like Codex utilizing Rust is a little harder, maybe they'll land it themselves or they'll get close and then an engineer can help them land the PR.

**Alexander Embiricos** (00:47:11):
We recently shipped the Sora Android app, and that was one of the most mind-blowing examples of acceleration, actually, because usage of Codex internally at OpenAI is obviously really, really high, but it's been growing over the course of the year, both in terms of now it's basically all technical staff use it, but even the intensity and know how of how to make the most of coding agents has gone up by a ton. And so the Sora Android app, a fully new app, we built it in 18 days. It went from zero to launch to employees, and then 10 days later, so 28 days total, we went to just GA, to the public, and that was done just with the help of Codex. So pretty insane velocity.

**Alexander Embiricos** (00:47:55):
I would say it was a little bit... I don't want to say easy mode, but there is one thing that Codex is really good at if you're a company that's building software on multiple platforms, so you've already figured out some of the underlying APIs or systems, asking Codex to port things over is really effective because it has something you can go look at. And so the engineers on that team were basically having Codex go look at the iOS app, produce plans of work that needed to be done, and then go implement those. And it was looking at iOS and Android at the same time. And so basically it was two weeks to launch to employees, four weeks total. Insanely fast.

**Lenny Rachitsky** (00:48:31):
What makes that even more insane is it became the number one app in the app store. This just boggles the mind. Okay, so 28 days?

**Alexander Embiricos** (00:48:39):
Yeah, so imagine number one app in the app store with a handful of engineers. I think it was two or three possibly in a handful of weeks.

**Lenny Rachitsky** (00:48:51):
Yeah, this is absurd. Wow.

**Alexander Embiricos** (00:48:56):
Yeah, so that's a really fun example of acceleration. And then Atlas was the other one that I think Ben did a podcast, the engine lead on Atlas, sharing a little bit about how we built there. Atlas is actually... I mean, it's a browser, and building a browser is really hard. So we had to build a lot of difficult systems in order to do that. And basically we got to the point where that team has a ton of power users of Codex right now, and it got to the point they where basically... We were talking to them about it, because a lot of those engineers are people I used to work with before at my startup. And so they'd say, "Before this would've taken us two to three weeks for two to three engineers, and now it's like one engineer, one week." So massive acceleration there as well.

**Alexander Embiricos** (00:49:49):
And what's quite cool is that we shipped Atlas on Mac first, but now we're working on the Windows version. So the team now is ramping up on Windows and they're helping us make Codex better on Windows too, which is admittedly earlier, just the model we shipped last week is the first model that natively understands PowerShell. So PowerShell being the native Shell language on Windows. So yeah, it's been really awesome to see the whole company getting accelerated by Codex from... And most obviously, also research and improving how quickly we train models and how well we do it. And then even design, as we talked about, and marketing. Actually, we're at this point now where my product marketer is often also making string changes just directly from Slack or updating docs directly from Slack.

**Lenny Rachitsky** (00:50:37):
These are amazing examples. You guys are living at the bleeding edge of what is possible, and this is how other companies are going to work. Just shipping, again, what became the number one app in the app store and just beloved all over the... It just took over, I don't know, the world for at least a week. Built, you said, in 28 days and I don't know, 10 days, 18 days just to get the core of it working.

**Alexander Embiricos** (00:51:00):
Yeah, so it was like 18 days we had a thing that employees were playing with, and then 10 days later we were out.

**Lenny Rachitsky** (00:51:05):
And you said just a couple engineers.

**Alexander Embiricos** (00:51:07):
Yeah.

**Lenny Rachitsky** (00:51:07):
Two or three. Okay. And then Atlas you said took a week to build?

**Alexander Embiricos** (00:51:12):
No, no, no. So Atlas, not the whole week, but Atlas was a really meaty project. And so I was talking to one of the engineers on Atlas about just what they use Codex for. And it's basically like, "We use Codex for absolutely everything." And I was like, "Okay, well, how would you measure the acceleration?" And so basically the answer I got back was, "Previously would've taken two to three weeks for two to three engineers, and now it's like one engineer, one week."

**Lenny Rachitsky** (00:51:36):
Do you think this eventually moves to non-engineers doing this sort of thing? Does it have to be an engineer building this thing? Could Sora have been built by, I don't know, a PM or designer?

**Alexander Embiricos** (00:51:45):
I think we will very much get to the point, well, basically where the boundaries are a little bit blurred. I think you're going to want someone who understands the details of what they're building, but what details those are will evolve. Kind of like how now if you're writing Swift, you don't have to speak assembly. There's a handful of people in the world, and it's really important that they exist and speak assembly, maybe more than a handful, but that's a specialized function that most companies don't need to have.

**Alexander Embiricos** (00:52:14):
So I think we're just going to naturally see an increase in layers of abstraction. And then the cool thing is now we're entering the language layer of abstraction, like natural language, and then natural language itself is really flexible. You could have engineers talking about a plan and then you could have engineers talking about a spec, and then you could have engineers talking about just a product or an idea. So I think we can also start moving up those layers of abstraction as well.

**Alexander Embiricos** (00:52:39):
But I do think this is going to be gradual. I don't think it's going to go off to all of a sudden nobody ever writes anything, any code and it's just specs. I think it's going to be much more like, "Okay, we've set up our coding agent to be really good at previewing the build or at running tests," maybe that's the first part that most people have set up. And it's like, "Okay, now we've set it up so they can execute the build and it can see the results of its own changes, but we haven't yet built a good integration harness so that it can," in the case of Atlas... By the way, I don't know if they've done any of this or not. I think they've done a lot of this. But maybe the next stage is enable it to load a few sample pages to see how well those work. So then, okay, now we're going to set it up to do that.

**Alexander Embiricos** (00:53:18):
And I think for some time at least, we're going to have humans curating which of these connectors or systems or components that the agent needs to be good at talking to. And then in the future, there will be an even greater unlock where Codex tells you how to set it up or maybe sets itself up in a repo.

**Lenny Rachitsky** (00:53:34):
What a wild time to be alive. Wow. I'm curious just the second order effects of this sort of thing, just how quickly it is to build stuff. What does that do? Does that mean distribution becomes much, much more important? Does it mean ideas are just worth a lot more? It's interesting to think about how quick how that changes.

**Alexander Embiricos** (00:53:51):
I'm curious what you think. I still don't think ideas are worth as much as maybe a lot of people think. I still think execution is really hard. You can build something fast, but you still need to execute well on it, still needs to make sense and be a coherent thing overall, yeah, and distribution is massive.

**Lenny Rachitsky** (00:54:08):
Yeah. Just feels like everything else is now more important. Everything that isn't the building piece, which is coming up with an idea, getting to market, profit, all that kind of stuff.

**Alexander Embiricos** (00:54:18):
Yeah. I think we might've been in this weird temporary phase where, for a while, it was so hard to build product that you mostly just had to be really good at building product and it maybe didn't matter if you had an intimate understanding of a specific customer. But now I think we're getting to this point where actually if I could only choose one thing to understand, it would be really meaningful understanding of the problems that a certain customer has. If I could only go in with one core competency.

**Alexander Embiricos** (00:54:52):
So I think that's ultimately still what's going to matter most. If you're starting a new company today and you have a really good understanding and network of customers that are currently underserved by AI tools, I think you're set. Whereas if you're good at building websites, but you don't have any specific customer to build for, I think you're in for a much harder time.

**Lenny Rachitsky** (00:55:14):
Bullish on vertical AI startups is what I'm hearing. Yeah, I completely agree. There's the general thing that can solve a lot of problems and then there's like, "We're going to solve presentations incredibly well and we're going to understand the presentation problem better than anyone and we're going to plug into your workflows and all these other things that matter for a very specific problem." Okay, incredible.

**Lenny Rachitsky** (00:55:36):
When you think about progress on Codex, I imagine you have a bunch of evals and there's all these public benchmarks. What's something you look at to tell you, "Okay, we're making really good progress," I imagine it's not going to be the one thing, but what do you focus on? What's something you're trying to push? What's a KPI or two?

**Alexander Embiricos** (00:55:51):
One of the things that I'm constantly reminding myself of is that a tool like Codex naturally is a tool that you would become a power user of. So we can accidentally spend a lot of our time thinking about features that are very deep in the user adoption journey. And so we can kind of end up oversolving for that. And so I think it's just critically important to go look at your D7 retention. Just go try the product, sign up from scratch again. I have a few too many ChatGPT Pro accounts that I've, in order to maximally correctly dogfood, signed up for on my Gmail and they charge me 200 bucks a month. I need to expense those. But I think just the feeling of being end user and the early retention stats are still super important for us because as much as this category is taking off, I think we're still in the very early days of people using them.

**Alexander Embiricos** (00:56:44):
Another thing that we do that I think we might be the most user feedback/social media pilled team out there in this space is like a few of us are constantly on Reddit and Twitter, and there's praise up there and there's a lot of complaints, but we take the complaints very seriously and look at them. And I think that, again, because you can use a coding agent for so many different things, it often is kind of broken in many sort of ways for specific behaviors. So we actually monitor a lot just what the vibes are on social media pretty often, especially I think for Twitter/X, it's a little bit more hypey and then Reddit is a little more negative but real actually. So I've started increasingly paying attention to how people are talking about using Codex on Reddit actually.

**Lenny Rachitsky** (00:57:39):
This is important for people to know. Which of the subreddits do you check most? Is there like an r/Codex or?

**Alexander Embiricos** (00:57:44):
I mean, the algorithm's pretty good at surfacing stuff, but r/Codex is there.

**Lenny Rachitsky** (00:57:48):
Okay. I'll take. Very interesting. And then if people tag you on Twitter, you still see that, but maybe not as powerful as seeing it on Reddit.

**Alexander Embiricos** (00:57:56):
Well, yeah. Well, the thing with Twitter is it's a little bit more one-to-one, even if it's in public. Whereas with Reddit, those are really good upvoting mechanics and maybe most people are still not bots, unclear. So you get good signal on what matters and what other people think.

**Lenny Rachitsky** (00:58:09):
So interestingly, Atlas, I want to talk about that briefly. You guys launched Atlas. I tweeted actually that I tried Atlas and then I don't love the AI only search experience. I was just like, "I just want Google sometimes," or whatever. Just waiting for AI to give me an answer, I'm like, "I don't want to... " And there was no way to switch. I just tweeted, "Hey, I'm switching back. It's not great." And I feel like I made some PMs at OpenAI sad. And I saw someone tweet, "Okay, we have Atlas now," which I imagine was always part of the plan. It's probably an example of just, "We got to ship stuff, see how people use it and then we figure it out." So I guess one is that, I don't know, is there anything there? And two, I'm just curious, why are you guys building a web browser?

**Alexander Embiricos** (00:58:48):
So I worked on Atlas for a bit. I don't work on it now. But a bit of the narrative here for me just to tell my story a bit was I was working on this screen sharing, pair programming startup, and then we joined OpenAI. And so the idea was really to build a contextual desktop assistant. And the reason I believe that's so important is because I think that it's really annoying to have to give all your context to an assistant and then to figure out how it can help you. So if it could just understand what you are trying to do, then it could maximally accelerate you. So I still think of Codex actually as a contextual assistant from a little bit of a different angle, starting with coding tasks.

**Alexander Embiricos** (00:59:30):
But some of the thinking, at least for me personally, I can't speak for the whole project, was that a lot of work is done in the web. And if we could build a browser, then we could be contextual for you, but in a much more first class way. We weren't hacking other desktop software which have very varied support for what content they're rendering to the accessibility tree. We wouldn't be relying on screenshots, which are a little bit slower and unreliable. Instead, we could be In the rendering engine and extract whatever we needed to help you. And also I like to think of video games, I don't know if you've played, I don't know, say Halo, you walk up to an object, I mean, this is true for many games, you press... Man, it's been a long time, this is embarrassing. Press X and it just does the right thing. And I was one of those guys who always read the instruction manual for every video game that I bought.

**Alexander Embiricos** (01:00:24):
And I remember the first time I read about a contextual action and I just thought it was this really cool idea. And the thing about a contextual action is we need to know what you are attempting to do. We have a little bit of context and then we can help. And I think this is critically important because imagine this world that we reach where we have agents that are helping you thousands of times per day.

**Alexander Embiricos** (01:00:49):
Imagine if the only way we could tell you that we helped you was if we could push notify you. So you get a thousand push notifications a day of an AI saying like, "Hey, I did this thing. Do you like it? " It'd be super annoying, right? Whereas imagine, going back to software engineering, I was looking at a dashboard and I noticed some key metric had gone down. And at that point in time, an AI could maybe go take a look and then surface the fact that it has an opinion on why this metric went down and maybe a fix right there right when I'm looking at the dashboard. That would much more keep me in flow and enable the agent to take action on many more things.

**Alexander Embiricos** (01:01:26):
So in my mind, part of why I'm excited for us to have a browser is that I think we have then much more context around what we should help with. Users have much more control over what they want us to look at. It's like, "Hey, if you want us to take action on something, you can open it in your AI browser. If you don't, then you can open it in your other browser." So really clear control and boundaries. And then we have the ability to build UX that's mixed initiatives so that we can surface contextual actions to you at the time that they're helpful as opposed to just randomly notifying you.

**Lenny Rachitsky** (01:01:58):
Hearing the vision for Codex being the super assistant, it's not just there to code for you. It's trying to do a lot for you as a teammate, as this kind of super teammate, and that makes you awesome at work. So I get this. Speaking of that, are there other non-engineering common use cases for Codex? Just ways that non-engineers... We talked about designers prototyping and building stuff, are there any fun or unexpected ways people are using Codex that aren't engineers?

**Alexander Embiricos** (01:02:24):
I mean, there's a load of unexpected ways, but I think most of where we're seeing real traction with people using things are still for now very, I would say, coding adjacent or tech-oriented, places where there's a mature ecosystem or maybe you're doing data analysis or something like that. I personally am expecting that we're going to see a lot more of that over time. But for now, we're keeping the team very focused on just coding for now because there's so much more work to do.

**Lenny Rachitsky** (01:02:54):
For people that are thinking about trying out Codex, does it work for all kinds of code bases? What code does it support? If you're like, I don't know, SAP, can you add Codex and start building things? What's the sweet spot? Where does it start to not be amazing yet?

**Alexander Embiricos** (01:03:11):
I'm really glad you asked this question actually because the best way to try Codex is to give it your hardest tasks, which is a little different than some of the other coding agents. Some tools you might think, "Okay, let me start easy or just vibe code something random and decide if I like the tool." Whereas we're really building Codex to be the professional tool that you can give your hardest problems to. And that writes high quality code in your enormous code base that is in fact not perfect right now. So yeah, I think if you're going to try Codex, you want to try it on a real task that you have and not necessarily dumb that task down to something that's trivial, but actually a good one would be you have a hard bug and you don't know what's causing that bug and you ask Codex to help figure that out or to implement that the fix.

**Lenny Rachitsky** (01:04:00):
I love that answer. Just give it to your hardest problem.

**Alexander Embiricos** (01:04:03):
I will say if you're like, "Hey, okay, well, the hardest problem I have is that I need to build a new unicorn business," obviously that's not going to work. Not yet. So I think it's like give it the hardest problem, but something that is still one question or one task to start. That's if you're testing and then over time you can learn how to use it for bigger things.

**Lenny Rachitsky** (01:04:25):
Yeah. What languages does it support?

**Alexander Embiricos** (01:04:27):
Basically, the way we've trained Codex is there's a distribution of languages that we support and it's fairly aligned with the frequency of these languages in the world. So unless you're writing some very esoteric language or some private language, it should do fine in your language.

**Lenny Rachitsky** (01:04:41):
If someone was just getting started, is there a tip you could share to help them be successful? If you could just whisper a little tip into someone just setting up Codex for the first time to help them have a really good time, what's something you would whisper?

**Alexander Embiricos** (01:04:54):
I might say try a few things in parallel. So you could try giving it a hard task, maybe ask it to understand the code base, formulate a plan with it around an idea that you have and kind of build your way up from there. And the meta idea here is, again, it's like you're building trust with a new teammate. And so you wouldn't go to a new teammate and just give them like, "Hey, do this thing. Here's zero context." You would start by first making sure they understand the code base and then you would maybe align on an approach and then you would have them go off and do bit by bit. And I think if you use Codex in that way, you'll just naturally start to understand the different ways of prompting it because it's a super powerful agent and model, but it is a little bit different to prompt Codex than other models.

**Lenny Rachitsky** (01:05:38):
Just a couple more questions. One, we touched on this a little bit, as AI does more and more coding, there's always this question of, "Should I learn to code and why should I spend time doing this sort of thing?" For people that are trying to figure out what to do with their career, especially if they're into software engineering computer science, do you think there's specific elements of computer science that are more and more important to lean into, maybe things they don't need to worry about? What do you think people should be leaning into skill-wise as this becomes more and more of a thing in our workplace?

**Alexander Embiricos** (01:06:11):
I think there's a couple angles you could go at this from. Well, the easiest one to think of at least is just be a doer of things. I think that with coding agents getting better and better over time, it's just what you can do as even someone in college or a new grad is just so much more than what that was before. And so I think you just want to be taking advantage of that. And definitely when I'm looking at hiring folks who are earlier career, it's definitely something that I think about is how productive are they using the latest tools? They should be super productive. And if you think of it in that way, they actually have less of a handicap than before versus a more senior career person because the divide is actually getting smaller because they've got these amazing coding agents now. So that's one thing, which is, I guess the advice is just learn about whatever you want, but just make sure you spend time doing things, not just fulfilling homework assignments, I guess.

**Alexander Embiricos** (01:07:11):
I think the other side of it though is that it's still deeply worth understanding what makes a good overall software system. So I still think that skills, like really strong systems engineering skills, or even really effective communication and collaboration with your team, skills like that I think are important or are going to continue to matter for quite some time. I don't think it's going to be all of a sudden the AI coding agents are just able to build perfect systems without your help. I think it's going to look much more gradual where it's like, okay, we have these AI coding agents, they're able to validate their work. It's still important.

**Alexander Embiricos** (01:07:51):
For example, I'm thinking of an engineer who was working on Atlas, since we were talking about it, he set up Codex so that it can verify its own work, which is a little bit non-trivial because of the nature of the Atlas project. So the way that he did that was he actually prompted Codex like, "Hey, why can't you verify your work? Fix it," and did that on a loop. And so you still, at various phases, are going to want a human in the loop to help configure the coding agent to be effective. So I think you still want to be able to reason about that. So maybe it's less important that you can type really fast and you understand exactly how to write... Not that anyone writes a 4H loop or something, or you don't need to know how to implement a specific algorithm. But I think you need to be able to reason about the different systems and what makes a software engineering team effective. So I think that's the other really important thing.

**Alexander Embiricos** (01:08:40):
Then maybe the last angle that you could take is, I think if you're on the frontier of knowledge for a given thing, I still think that's deeply interesting to go down, partially because that knowledge is still going to be... Agents aren't going to be as good at that, but also partially because I think that by trying to advance the frontier of a specific thing, you'll actually end up being forced to take advantage of coding agents and using them to accelerate your own workflow as you go.

**Lenny Rachitsky** (01:09:09):
What's an example that when you talk about being at the frontier of something?

**Alexander Embiricos** (01:09:12):
Codex writes a lot of the code that helps manage its training runs, the key infrastructure. We move pretty fast and so we have a Codex code review is catching a lot of mistakes. It's actually cause some pretty interesting configuration mistakes. And we're starting to see glimpses of the future where we're actually starting to have Codex even be on call for its own training, which is pretty interesting. So there's lots there.

**Lenny Rachitsky** (01:09:38):
Wait, what does that mean to be on call for its own training? So it's running, it's training and it's like, "Oh, something broke, someone needs..." And does it alert people or it's like, "Here, I'm going to fix the problem and restart"?

**Alexander Embiricos** (01:09:47):
This is an early idea that we're figuring out. But the basic idea is that during a training run, there's a bunch of graphs that today humans are looking at and it's really important to look at those. We call this babysitting.

**Lenny Rachitsky** (01:09:59):
Because it's very expensive to train, I imagine, and very important to move fast and-

**Alexander Embiricos** (01:10:03):
Exactly. And there's a lot of systems underlying the training run. And so a system could go down or there could be an error somewhere that gets introduced. And so we might need to fix it or pause things or, I don't know, there's lots of actions we might need to take. And so basically having Codex run on a loop to evaluate how those charts are moving over time is this idea that we have to how to enable us to train way more efficiently.

**Lenny Rachitsky** (01:10:26):
I love that. And this is very much along the lines of this is the future of agents. Codex isn't just for building code, it's a lot more than that.

**Alexander Embiricos** (01:10:34):
Yeah.

**Lenny Rachitsky** (01:10:36):
Okay, last question. Being at OpenAI, I can't not ask about your AGI timeline and how far you think we are from AGI. I know this isn't what you work on, but there's a lot of opinions, a lot of, I don't know, timelines. How far do you think we are from a humanly human version of AI, whatever that means to you?

**Alexander Embiricos** (01:10:56):
For me, I think that it's a little bit about when do we see the acceleration curves go like this? Or I don't know which way I'm mirrored here. When do we see the hockey stick? And I think that the current limiting factor, I mean, there's many, but I think a current underappreciated limiting factor is literally human typing speed or human multitasking speed on writing prompts. And like you were talking about, it's like you can have an agent watch all the work you're doing, but if you don't have the agent also validating its work, then you're still bottlenecked on can you go review all that code?

**Alexander Embiricos** (01:11:29):
So my view is that we need to unblock those productivity loops from humans having to prompt and humans having to manually validate all the work. So if we can rebuild systems to let the agent be default useful, we'll start unlocking hockey sticks. Unfortunately, I don't think that's going to be binary. I think it's going to be very dependent on what you're building. So I would imagine that next year, if you're a startup and you're building new pieces, like some new app or something, it'll be possible for you to set it up on a stack where agents are much more self-sufficient than not. But now let's say, I don't know, you mentioned SAP, let's say you work in SAP, they have many complex systems and they're not going to be able to just get the agent to be self-sufficient overnight in those systems. So they're going to have to slowly maybe replace systems or update systems to allow the agent to handle more of the work end to end.

**Alexander Embiricos** (01:12:22):
So basically my long answer to your question, maybe boring answer is that I think starting next year, we're going to see early adopters starting to hockey stick their productivity. And then over the years that follow, we're going to see larger and larger companies like hockey stick that productivity. And then somewhere in that fuzzy middle is when that hockey sticking will be flowing back into the AI labs and that's when we'll basically be at the AGI tier.

**Lenny Rachitsky** (01:12:48):
I love this answer. It's very practical and it's something that comes up a lot on this podcast. Just like the time to reveal all the things AI is doing is really annoying and a big bottleneck. I love that you're working on this because it's one thing to just make coding much more efficient and do that for people. It's another to take care of that final step of, "Okay, is this actually great?" And that's so interesting that your sense is that's the limiting factor. It comes back to your earlier point of even if AI did not advance anymore, we have so much more potential to unlock as we learn to use it more effectively. So that is a really unique answer. I haven't heard that perspective on what is the big unlock. Human typing speed to review basically what AI is doing for us. So good.

**Lenny Rachitsky** (01:13:31):
Okay, Alexander, we covered a lot of ground. Is there anything that we haven't covered? Is there anything you wanted to share, maybe double down on before we get to our very exciting lightning round?

**Alexander Embiricos** (01:13:43):
I think one thing is that the Codex team is growing. And as I was just saying, we're still somewhat limited by human thinking speed and human typing speed. We're working on it. So if you're an engineer or a salesperson, or I'm hiring a product person, please hit us up. I'm not sure the best way to give contact info, but I guess you can go to our jobs page, or do they have contact for you actually? Do listeners have contact for you?

**Lenny Rachitsky** (01:14:10):
Where they send me like, "Hey, I want to apply to Codex"? I do have a contact form at lennyrachitsky.com. I'm afraid of all the amazing people that are going to ping me. But there we go, we could try that. Let's see how that goes.

**Alexander Embiricos** (01:14:20):
Okay. Or maybe an easier version, we can edit all that out or up to you. Yeah, or I would just say you can drop us a DM. For example, I'm Embirico on Twitter, and hit me up if you're interested in joining the team.

**Lenny Rachitsky** (01:14:33):
What a dream job for so many people. What's a sign they... I don't know, what's a way to filter people a little bit so they're not flooding your inbox?

**Alexander Embiricos** (01:14:42):
So specifically if you want to join the Codex team, then you need to be a technical person who uses these tools. And I think I would just ask yourself the question, "Hey, let's say I were to join OpenAI and work on Codex over the next six months and crush it, what does the life of a software engineer look like then?" And I think if you have an opinion on that, you should apply. And if you don't have an opinion on that and have to think about it first, depending on how long you have to think about it, I guess that would be the filter. I think there's a lot of people thinking about this space. So we're very interested in folks who have already been thinking about what the future should look like with agents. And we don't have to agree on where we're going, but I think we want people who are very passionate about the topic, I guess.

**Lenny Rachitsky** (01:15:28):
It's very rare to be working on a product that has this much impact and is at such a bleeding edge of where it's possible. What a cool role for the right person. So it's awesome that you have an opening and this audience is a really good fit potentially for that role. So I hope we find someone, that would be incredible. With that, we've reached our very exciting lightning round. I've got five questions for you, Alexander. Are you ready?

**Alexander Embiricos** (01:15:54):
I don't know what these are, but I'm excited. Let's do it.

**Lenny Rachitsky** (01:15:57):
They're the same questions I ask everyone except for the last one. So probably not a surprise. I should probably make them more often a surprise. Okay, first question, what are a couple of books that you recommend most to other people, two or three books that come to mind?

**Alexander Embiricos** (01:16:12):
I have been reading a lot of science fiction recently, and I'm sure this has been recommended before, but The Culture, I think it's Ian Banks is the name of the author. Part of why I love it is because it's basically relatively recent writing about a future with AI, but it's an optimistic future with AI. And I think a lot of sci-fi is fairly dystopian. But the joke, at least on The Culture subreddit is that, let me see if I can get this right, it is a space communist utopia, or I think it's a gay space communist utopia. And I just think it's really fun to think about, to use The Culture as a way to think about what kind of world can we usher in and what decisions can we make today to help usher in that world.

**Lenny Rachitsky** (01:17:02):
Wow. I don't think anyone's recommended that. I know you're reading, you've mentioned before I start recording, Lord of the Rings right now. If you want another AI-ish sci-fi book, have you read Fire Upon the Deep?

**Alexander Embiricos** (01:17:15):
No, I haven't.

**Lenny Rachitsky** (01:17:15):
Okay, it's incredibly good. It's like a sci-fi space opera sort of epic tale with super intelligence.

**Alexander Embiricos** (01:17:25):
Cool.

**Lenny Rachitsky** (01:17:25):
Yeah. Mostly not optimistic, but somewhat optimistic.

**Lenny Rachitsky** (01:17:30):
Okay, next question. Is there a favorite recent movie or TV show that you've really enjoyed?

**Alexander Embiricos** (01:17:36):
Yeah, there's an anime called Jujutsu Kaisen, which I really like. Again, it's got a slightly dark topic of demons. But what I love about it is that the hero is really nice. And I think there's this new wave of anime and cartoons where the protagonists are really friendly and people who care about the world rather than being sort of, if you look at some older anime that started the genre, there's Evangelion or Akita and those characters, the protagonists are deeply flawed, quite unhappy. They didn't start the genre, but it was a trend for a while to poke fun at the idea that in these cartoons the protagonist was very young, but being given a ridiculous amount of responsibility to save the world. So there was kind of a wave of content that was critiquing this by making the character basically go through serious mental issues in the middle of the show. And I'm not saying this is better, but at least it's quite fun to have these really positive protagonists are just trying to help everyone around them.

**Lenny Rachitsky** (01:18:43):
I love how much we're learning about your personality hearing these recommendations. Nice protagonists, optimistic futures. I like the [inaudible 01:18:53].

**Alexander Embiricos** (01:18:53):
I think if you don't believe it, you can't will it into existence. So you need a balance.

**Lenny Rachitsky** (01:18:57):
This is your training data.

**Lenny Rachitsky** (01:18:59):
Is there a product you recently discovered you really love? Could be an app, could be some clothing, could be some kitchen gadget, tech gadget, a hat.

**Alexander Embiricos** (01:19:09):
Yeah, so I have been quite into combustion engines and cars. Actually, the reason I came to America initially was because I wanted to work on US aircraft, but now I work in software. And so for the longest time, I've basically only had quite old sports cars, old just because they were more affordable. And then recently we got a Tesla instead. And I have to say that I find the Tesla software quite inspiring. In particular, it has the self-driving feature. And I've mentioned a few times today, I think it's really interesting to think about how to build mixed initiative software that makes you feel maximally empowered as a human, maximally in control, but yet you're getting a lot of help. And I think they did a really good job with enabling the car to drive itself, but all these different ways that you can adjust what it's doing without turning off the self-driving. So you can accelerate, it'll listen to that. You can turn a knob to change its speed. You can steer slightly. I think it's actually a masterclass in building an agent that still leaves the human in control.

**Lenny Rachitsky** (01:20:21):
This reminds me Nick Turley's whole mantra is, "Are we maximally accelerated?"

**Alexander Embiricos** (01:20:26):
Yeah.

**Lenny Rachitsky** (01:20:26):
Feels like it's completely infiltrated everything at OpenAI, which makes sense, that tracks.

**Lenny Rachitsky** (01:20:32):
Two more questions. Do you have a life motto that you often think about and come back to in work or in life that's been helpful?

**Alexander Embiricos** (01:20:39):
I don't know if I have a life motto, but maybe I can tell you about the number one company value from my startup.

**Lenny Rachitsky** (01:20:45):
Love it.

**Alexander Embiricos** (01:20:46):
Which is still something that sticks with me, which is to be kind and candid.

**Lenny Rachitsky** (01:20:51):
That tracks. Kind and candid. Wow, that's a great combo.

**Alexander Embiricos** (01:20:54):
Yeah. And we had to put them together because we, as founders, realized that we often would be nice and it wasn't actually the right thing to do. We would delay the difficult conversations and we were not candid. And so every time we would remind ourselves of this motto and then we would become more candid. And then six months later, we would realize that we were in fact not candid six months ago and we needed to be even more candid. So then the question is like, "Okay, how should we be candid?" It's like, "Okay, well, let's think of being candid as an act of kindness," but also think of that both in terms of doing it and willing ourselves to do it, but also in terms of how we frame it as people.

**Lenny Rachitsky** (01:21:32):
That is a beautiful way of summarizing how to lead well. What's the book about challenge directly but care deeply? Radical Candor.

**Alexander Embiricos** (01:21:43):
Yeah, yeah.

**Lenny Rachitsky** (01:21:44):
So it's like another way of thinking about Radical Candor.

**Lenny Rachitsky** (01:21:46):
Okay, last question. I was looking up your last name just like, "Hey, what's the story here?" So your last name is Embiricos, and I was talking at ChatGPT and it told me the most famous individuals with the surname are the influential Greek poet and psychoanalyst Andreas Embiricos and his relative, the wealthy shipping magnate and art collector, George Embiricos. So the question is, which of these two do you most identify with, the Greek poet and psychoanalyst or the wealthy shipping magnate and art collector?

**Alexander Embiricos** (01:22:19):
I think it's going to have to be the poet because he loved the island that our family's from.

**Lenny Rachitsky** (01:22:27):
Wait, you know those people? Okay, this is not news to you. Okay.

**Alexander Embiricos** (01:22:30):
Well, I mean, it's an enormous family. But it's like Greek, so these big families, everyone's your uncle.

**Lenny Rachitsky** (01:22:30):
Love this. Okay.

**Alexander Embiricos** (01:22:36):
You know what I mean? My mother's Malaysian and also everyone is my uncle or aunt in Malaysia too, if that makes sense.

**Lenny Rachitsky** (01:22:42):
Yeah.

**Alexander Embiricos** (01:22:43):
But yeah, he loved this island that the family initiated from. I believe, I don't actually know where that's shipping magnate lived, I think it was New York or something. But anyway, we all came from this island called Andros, which is a really beautiful place. And it's like there's more livestock there than humans. Not too many tourists go there. But I think part of what I think is really cool is he published a lot and a lot of his writing is about the beauty of that island, which I think is super cool.

**Lenny Rachitsky** (01:23:12):
Wow, that was an amazing answer.

**Lenny Rachitsky** (01:23:14):
Two more questions, where can folks find you if they want to follow you online and maybe reach out? And then how can listeners be useful to you?

**Alexander Embiricos** (01:23:20):
I'm one of those people who has social media only for the purposes of having work. My phone turns black and white at 9:00 PM at night. But yeah, so Twitter or X, @Embirico. And yeah, if you post in r/Codex, I'll probably see it. So you can go there.

**Alexander Embiricos** (01:23:40):
How can listeners be useful? I would say please try Codex, please share feedback, let us know what to improve. We pay a ton of attention to feedback. I think, honestly, the growth has been amazing, but it's still very early times, so we still pay a lot of attention and hope to do so forever. And also, I would say if you're interested in working on the future of coding agents and then agents generally, then please apply to our job site and/or message me in those social media places.

**Lenny Rachitsky** (01:24:10):
Alexander, this was awesome. I always love meeting people working on AI because it always feels like this very, I don't know, sterile, scary, mysterious thing. And then you meet the people building these tools and they're always just so awesome, and you especially, just so nice. And like the examples you shared, optimism and kindness, this is what we want to be. These are the kinds of people we want to be building these tools that are going to drive the future. So I'm really thankful that you did this. I'm grateful to have met you, and thank you so much for being here.

**Alexander Embiricos** (01:24:45):
Yeah, thanks so much for having me. This was fun.

**Lenny Rachitsky** (01:24:48):
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [5/15] How to show up in any room with a low heart rate: Silicon Valley’s missing etiquette playbook | Sam Lessin

**Sam Lessin** (00:00:00):
I just feel like no one's being honest in teaching founders this. Be early. Don't order the most expensive thing on the menu. For a video call, an appropriate background. Don't smell like shit. Tell me why you decided to spend time teaching people proper etiquette. You have a lot of really young people.

**Sam Lessin** (00:00:14):
They've been holed up in a room coding. And they show up encouraged by Silicon Valley to be in some way abrasive on purpose. You want to be able to show up in a way where people are like, "Okay, this is someone I can work with and trust." Etiquette is a skill for how to show up in a room with a low heart rate. You're at the Kleiner Perkins holiday party.

**Sam Lessin** (00:00:29):
You have all the venture capitalists in the world and all the CEOs. You're at your first company. You're like, "Oh my God, this is my shot, but I need to convince this person of that and make this connection." It becomes very transactional. If you show up like a little energizer bunny, you're going to scare one off.

**Sam Lessin** (00:00:44):
You're going to project totally the wrong vibe. This isn't your one shot. You'll have other opportunities. You kind of want to show up with the self-confidence and the calm of abundance. This is part of the story. This is not the entire story.

**Lenny Rachitsky** (00:00:58):
Today, my guest is Sam Lessin, partner at Slow Ventures, previous VP of Product at Facebook and two-time founder. This is an unconventional episode that may surprise you in how interesting and useful it is to your life. I asked Sam to come on the pod and talk about proper etiquette.

**Lenny Rachitsky** (00:01:15):
You'll hear the backstory of how Sam got into this stuff, but this is turning into a big thing for him. He's teaching classes around the world. He published a book on proper etiquette. I love his framing for why etiquette matters, that the goal of learning good etiquette is to show up in a room with a low heart rate. And we cover all kinds of social interactions like introductions, small talk, meals, meetings, and basically all of the most important things you need to know when it comes to etiquette.

**Lenny Rachitsky** (00:01:40):
I personally found these tips really, really useful and I learned a lot from this conversation and from his book. Sam is also hilarious and so fun. And I hope you enjoy this very unique episode. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. And if you become an annual subscriber of my newsletter, you get a year free of 19 incredible products, including a year free of Lovable, Replit, Bolt, Gamma, n8n, and Linear, Devin, PostHog, Superhuman, Descript, Wispr Flow, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD, Mobbin and Stripe Atlas.

**Sam Lessin** (00:04:26):
Blessed to be here. I'm excited to have the conversation.

**Lenny Rachitsky** (00:04:29):
This is going to be a very different kind of conversation. I suspect this is actually going to be really, really useful and really, really interesting to a lot of people.

**Sam Lessin** (00:04:39):
Useful and interesting. That's so unlikely.

**Lenny Rachitsky** (00:04:41):
That's the Venn diagram that we aim for. I also think it's going to just be a lot of fun. So I really appreciate you doing this. I want to give you the opportunity to set the stage for why we're chatting through this. Just tell me why you decided to spend time on teaching people proper etiquette and why should people pay attention to this? Why is this important?

**Sam Lessin** (00:05:01):
I really enjoy things at the intersection of hilarious and useful. You kind of need both. And hilarious just because you should have fun in life. We should be working on things that are fun and interesting. Also, candidly, if we're being more honest about it's very hard to cut through the noise these days. So you need humor is a great way to cut through it, but humor just for the sake of being funny is not that useful.

**Sam Lessin** (00:05:25):
There has to be a deeper truth to it. And so with this etiquette thing that we've gotten into, it started as many things to do with a tweet. It got escalated into an event. It's gotten escalated into a book and a bunch of other stuff. I kind of believe that you should always ... There you go. You got it with you. I've got my coffee too.

**Sam Lessin** (00:05:42):
There's a rule, you always want to just double down in life. And so I'd say why etiquette? Look, there's a serious real narrative to why etiquette matters in 2025 for founders, almost 2026. One, we talk about software getting commoditized. We talk about all this fearmongering and scared people feel about Silicon Valley and AI and all the things that are going on.

**Sam Lessin** (00:06:06):
The net is if you want to do business and you want to do business and build great partnerships with team members, with companies you want to do business with, almost like with anyone, the reality is etiquette ironically matters a lot. There is a deep truth to this. Especially when you're asking people to trust you with their data, trust you with their business. And when technology is no longer some cute sideshow, but it's a major deal, people are worried about losing their jobs.

**Sam Lessin** (00:06:29):
Understanding how to meet people, where they're at, build trust, mirror kind of expected behaviors. These are all tools. And so that's the deep truth. The shallow truth is it's kind of funny to teach Silicon Valley people etiquette. The whole narrative for so long has been, none of this matters, just focus on your product.

**Sam Lessin** (00:06:48):
Saying, "Well, actually it does matter." And I'm wearing a T-shirt. I'm not exactly known as the most high etiquette personal, but I do know the rules, and I think it's fun and funny as well. And I think those things are both important.

**Lenny Rachitsky** (00:07:02):
I want to get into the actual rules, but just to follow that thread, you had a really great line somewhere that etiquette is almost a skill for how to show up in a room with a low heart rate.

**Sam Lessin** (00:07:10):
Yeah, this is the thing I think about a lot. Again, again, you and I are now old people, right? But you're young.

**Lenny Rachitsky** (00:07:11):
I feel that.

**Sam Lessin** (00:07:18):
You feel it in your bones. It's like you're young, right? You're at the Kleiner Perkins holiday party. You have all the venture capitalists in the world and all the CEOs. You're at your first company, you're young, maybe you're from a different country. You're like, "Oh my God, this is my shot. I have all these people I need to talk to and I need to convince this person of that and make this connection."

**Sam Lessin** (00:07:39):
It becomes very transactional. If you show up like a little energizer bunny, you're going to scare one off. You're going to project totally the wrong vibe, but I can understand why you'd be a high intensity moment in a lot of ways. I think understanding how to show up, take a beat, come in with a mindset, not of scarcity, but of abundance, understand how to give more than you take, understand how to build a relationship, not collect business cards.

**Sam Lessin** (00:08:05):
These are things that actually serve you massively well. And I just feel like no one's being honest and teaching founders this. Instead, they're saying, "Oh no, all that matters is your product." I'm like, "The product does matter a lot, but if understanding these rules can be the difference between doing really well and missing a business deal, if you show up with too high a heart rate and you burn a bunch of mild relationship opportunities, I don't know why you wouldn't want these skills."

**Lenny Rachitsky** (00:08:30):
Yeah. Is one way to think about it. You can be successful not doing any of this, not knowing any of this. You're hurting yourself, you're making it harder.

**Sam Lessin** (00:08:39):
Yeah, I think you're creating an unnecessary uphill battery for yourself. I'd also say that, look, this goes back to technical differentiation where things are at. It is true that if you bring manna from heaven, you invent something that is literally the next Google or whatever out of pure thought that sometimes none of this stuff matters, that's it. It's literally like that happens. It happens very, very rarely, but it does happen.

**Sam Lessin** (00:09:07):
It is not what 99.9% of startups are doing. But if you have that, yeah, you basically can get away with anything. It is true. That doesn't mean you should. You shouldn't be a jerk. And candidly, over the course of history, you need to work with great people and you'll be more successful if you show up with good etiquette and rules and context. But there are ways in which that can trump all. It's just candidly not the experience of 99.9% of startups.

**Lenny Rachitsky** (00:09:30):
Awesome. Okay. So let's get into it. You divided this book into about 10 categories, 10 social situations is one way to describe it. So let's just go through each one and just give us some pieces of advice.

**Sam Lessin** (00:09:41):
Sure.

**Lenny Rachitsky** (00:09:41):
And something I didn't mention, the reason I'm excited to do this, the reason I reached out to you to do this, this wasn't you pitching me, "Hey, let's talk about this in your podcast." Is I was like, "Wow, this is really interesting. I did not know these things."

**Sam Lessin** (00:09:51):
Cheers. Again, I think the thing for us is I'm kind of a ship early, ship often guy. So V1, you should buy it now and study it because it's good and it's going to be a limited edition. The funny part about doing this is people come back with a bunch of other things we should cover. So I suspect that eventually this will evolve beyond it, but I think we're starting with some good stuff.

**Lenny Rachitsky** (00:10:10):
Let's do it. Okay. So introductions and entering a room.

**Sam Lessin** (00:10:13):
Yeah. Be early. That's the first one. Again, I say this as someone who, I'll be honest, again, hypocrisy ... I live with hypocrisy. I'm frequently not early, but you should be early. And you don't need to be half an hour early. That's a little weird, but making sure that you have some buffer time so that, again, think about low heart rate. If you come in racing in the room five minutes late, your heart rate is up.

**Sam Lessin** (00:10:37):
If you come in, you had a second to take a beat in the waiting room. They kept you waiting. That's the dynamic I think you want to cultivate. Now, if you're not late, I'm sorry, if you're not early, just apologize. It doesn't need to be like a 511, again, it goes back to this heart rate thing. You could just apologize simply and move on. I've seen people screw this up so many times when they come and flustered and all over the place, you're like, "It's okay. We understand."

**Sam Lessin** (00:11:02):
So I think that's another really kind of obvious one, but an important one. Something I've seen, I'll go for a few others that we talk a bunch about in kind of is, look, you want to have a strong handshake, firm, don't crush the person's hand. Again, this is not practice on your friends. You want to repeat names back is a really, really valuable thing to think about when you're meeting someone and say, "Hey, Lenny, it's great to meet you." Why? It shows that you're actually trying to remember the person's name.

**Sam Lessin** (00:11:31):
A lot of times people meet a lot of people. If it's like, nice to meet you, you move on. First, it's going to be harder for you to remember the person's name. Second, it actually shows you're meeting them and making an effort to actually connect and say, "Okay, I'm trying to focus on you. You're not just a number to me." You're not just a potential check for whatever it's going to be. So there's a bunch of things like that. I'm kind of curious. We can go through a bunch more, but those are some of the ones I would think about.

**Lenny Rachitsky** (00:11:52):
One that I loved was if somebody else is late, do not make them feel bad and do the opposite of what you're doing, of what you do.

**Sam Lessin** (00:11:59):
100%. And I think this is like, I've seen this with entrepreneurs. And I get I'm a VC. I do get scheduled in 30 minute chunks back to back all the time, especially on Zoom. Guess what? I am frequently late. I don't feel good about it, but it happens. And a lot of times, founders, most of the time I'd say founders know that if I'm late, I will always apologize. I'll try to email them ahead, et cetera, but then it is what it is and we kind of get right into it.

**Sam Lessin** (00:12:26):
Every once in a while, you'll have some founder who is super indignant about it. It's fine if you feel that way, but it's really not very productive to make a big deal out of it. If this is a deal breaker for you that I was a few minutes late, then now I feel like I'm wasting the next 25 minutes of my meeting time because this is going to be the wrong dynamic, and so I just think there's like, don't harp on it. It's okay.

**Lenny Rachitsky** (00:12:50):
Yeah, and also some advice on eye contact. Share that one.

**Sam Lessin** (00:12:54):
It's really important. Again, I think the thing I keep in mind is especially in an age where everyone's used to being in front of computer screens and looking at six different windows at the same time. Again, people are taking their time to meet with you or at a party, they're taking their time to listen to you. And it's just a matter of respect to be like, "I'm actually here in this conversation. I'm not off on my screen. I'm not glancing around the room."

**Sam Lessin** (00:13:17):
Now look, there are some people, we all know this who are literally quite neurodivergent and that's very hard for them. That happens, and a lot of founders have neurodivergence in some ways. So there is grace in this to a point, but it's a thing you should make at least an effort. I think one of the most important things about all this stuff is what matters in some ways is the signaling of the effort as much as the actual thing. I think it's a really big overarching theory. So it's like, "Look, if you have trouble with this, but you're really trying, that goes a long way." Versus just being like, whatever.

**Lenny Rachitsky** (00:13:48):
Maybe a final tip there is around partners, introducing the partner, saying hi to their partner, share that one.

**Sam Lessin** (00:13:53):
Yeah. So look, this happens all the time. And again, in the spirit of, I'm not permitted at this either, if you're with your partner, introduce them first. Bring them into the conversation. One great trick we talk about in the book, which I really unfortunately use all the time is there's this whole thing. Let's pretend you're with your partner or with someone who's a friend. It doesn't have to be romantic partner, whoever you're with. You're going to forget someone's name, and what you really should do is-

**Lenny Rachitsky** (00:14:18):
All the time. All the time.

**Sam Lessin** (00:14:19):
You get the friend's name and that's what you're supposed to do. The etiquette is you say, "Lenny, please meet my wife, Jessica." And that kind of thing. Now here's the thing, this is where you start betting rules. What if I don't remember your name? If you have your partner with you, you can flip it around and say, "Jessica, I want to introduce you." And then you can kind of figure out how to frame it up so that you then Lenny extends your hand and say, "It's really nice to meet you, Jessica." And you get to pick up the name again or things like that. So there are some-

**Lenny Rachitsky** (00:14:46):
You let it hang, that's the craziest-

**Sam Lessin** (00:14:47):
... which by the way-

**Lenny Rachitsky** (00:14:48):
I love that.

**Sam Lessin** (00:14:48):
It's a great example of the fact that if you're really in tune socially, you kind of know what's going on. You know what I mean? You're not an idiot. You know, "Oh, what is proper? What this person is doing?" There's a gap between it. There's a reason. The reason is, but it's at least enough plausible deniability of semi-bad etiquette that leverages the social situation to be a better etiquette, that it's a useful thing to think about as a small queue.

**Lenny Rachitsky** (00:15:14):
I'm so bad at remembering names. I think I have a medical issue, so I just can't remember names. So this tip alone is so good. And just to reinforce it, so there's almost two ways to do this is what you're describing. Either it's like my wife's name's Michelle, so it's like, "Hey, Michelle. I meet my wife, Michelle." And then they're like, "Oh, I'm Bob. Nice to meet you, Michelle." Or make it a little more awkward of just, "Michelle, meet and then let it hang." Is that the thing?

**Sam Lessin** (00:15:41):
"Michelle, I want to introduce you." Or something like that.

**Lenny Rachitsky** (00:15:43):
I want to introduce you. Okay.

**Sam Lessin** (00:15:44):
Something like that, or I want to introduce you to Michelle. You look at them in the eye, and then your wife would be like, "Hi, I'm Michelle. Remind me your name or it's nice to meet you." Or whatever it ends up being. Look, I also, for what it's worth, I actually have such a clinical problem on name face recognition that actually runs in our family. I have this whole backstory, which is part ... and I worked at Facebook for a long time, was really into it early on.

**Sam Lessin** (00:16:06):
No kidding. I think part of my early attraction to the platform was it was the first time you walk around college and you're like, "I know these people, I just don't remember their names." You're like, "Oh my God, there's a resource I can study." And this was a very valuable social thing for me. So I'm with you. I have the same problem I think a lot of people do.

**Lenny Rachitsky** (00:16:21):
Okay. Let's move on to conversations. Give us some tips.

**Sam Lessin** (00:16:25):
So I mean, on the conversation front, I think the key again is to welcome people into the conversation. Consider it, especially you see this happen sometimes, especially when there's weird power dynamics at play. You'll see some famous VC or founder walk into the room, and then some young startup person will waylay them and almost flock them off and they're really excited to talk to this person, but you're like, there's a bunch of people around and the more you can be inclusive and low heart rate, it's not a scarcity mindset, it's an abundance mindset.

**Sam Lessin** (00:16:58):
I think that's kind of the tone to think about in terms of what a conversation is and how to show up in a room and meet with people. Another really big one we harp on a lot in the book in a bunch of the panels is ask questions, but there's a limit. So asking questions is great.

**Sam Lessin** (00:17:16):
You're coming in and says, "Hey, it's nice to meet you. Let me give you my four-minute startup spiel and everything I'm into da-da-da." So self-centered. It kind of misses the point that a conversation is a give and a get and it should be an exchange. And so when you go in with a mindset of I should ask questions, that's great.

**Sam Lessin** (00:17:32):
There is doing it too much, which is when it's done in a forced way, sometimes I feel this, you'll meet someone and you feel like it's the inquisition or all they're trying to do is extract information to you and giving you nothing in return. This happens sometimes. And so I think, again, this is about balance, this is about low heart rate. I do think questions are a great tool to engage someone, but don't make it six questions in a row and make sure there's always in some ways a give to get.

**Sam Lessin** (00:18:00):
If you can come in, the best conversations are coming, someone comes in and gives you an idea or has a point or sparks something, then it's like a game of ping pong, then you can kind of react to and it goes back and forth where there's openness and they're playing with you, not playing single player is almost the way I would think about it in a conversation.

**Lenny Rachitsky** (00:18:17):
So the tip here. So it's basically indexed towards asking questions, but not 100% you asking questions?

**Sam Lessin** (00:18:22):
Yeah, consider almost, put it this way, imagine you're playing ping pong or tennis or whatever you want. Hit the ball back, right? That's the question and they'll hit it back to you and then you hit it back to them. That's kind of what the flow should be. If you hit 10 balls at them in a row, you know what I mean? Or that's kind of not the vibe you want to go for.

**Lenny Rachitsky** (00:18:41):
Yep. Awesome.

**Sam Lessin** (00:18:42):
Look, we talk a lot about matching vocabulary. You're going to meet a lot of different people. You want to make people feel good and welcome. I'm not saying that you should walk into a room and start talking and jibe, but I am saying if you're speaking to a university professor versus a 12-year-old, if they're using a certain level of vocabulary words, again, the point is to meet people where they're at in a way that makes them feel relaxed and good, not try to mirror them, if that makes sense. But there is a subtlety that I think really matters to it.

**Lenny Rachitsky** (00:19:16):
Cool. There's a few more I'll point out real quick. Connecting to this idea of asking questions, not trying to give your whole spiel constantly, this idea of leaving them wanting more.

**Sam Lessin** (00:19:25):
Yes. This is important. I think in the end of the day, most interactions, let's put it you meet someone you're really interested in or whatever. If we're being transactional about it, what's the real goal? The real goal is to leave people in a position where they're like, "Wow, that was a really interesting person. I'd love to hear from them again or meet with them again." Or maybe even better, every once in a while this will happen.

**Sam Lessin** (00:19:45):
It's like, "Wow, that's a really interesting person or idea." The person walks across the room someone else is like, "Hey, you really should talk to Lenny. Shout to Sam." You want to leave them being like, "That's interesting and I like to continue this or expand it." Not, "I just heard this entire person's life story, I never need to talk to them again." And so I do think there's, again, leave them wanting more, I think is important. And that is partially about knowing when to excuse yourself gracefully as much as it is about when to enter.

**Lenny Rachitsky** (00:20:12):
And again, this comes back to this idea of abundance. This isn't your one shot. You'll have other opportunities. People don't want to feel like you're just on them just trying to-

**Sam Lessin** (00:20:20):
I've had this conversation with so many, and I think it's a uniquely American and honestly a uniquely Silicon Valley thing. I'll go so far to say that, which is, look, we're used to, especially if you're young and these are big opportunities or big moments, people are kind of used to this environment of scarcity. It reminds me of the Eminem song when he talks about, I have one shot, one opportunity.

**Lenny Rachitsky** (00:20:43):
I think it's ... Oh, Eminem. Yeah, yeah, yeah.

**Sam Lessin** (00:20:44):
It's a great song. It's a great song. It's great beat. Every once in a while before a big presentation, you got to listen to it and pump yourself up. But actually, again, in terms of putting people at ease and building relationships and etiquette, even if in your heart of hearts, you're like, "This really is my one shot."

**Sam Lessin** (00:21:00):
You kind of want to show up with the self-confidence and the calm of abundance, being like, "This is not going to be my only opportunity. This is an opportunity. I'm excited to be here. I'm engaged. This is part of the story. This is not the entire story." And I think if you kind of remind yourself of that, you remind yourself that it's okay to not know everything, you keep focusing on low heart rate, engagement, eye contact, you get so much of the way there.

**Lenny Rachitsky** (00:21:26):
You also have a tip about how to handle famous people that you might meet.

**Sam Lessin** (00:21:29):
There's so many ways. There's a bunch of tips about, I think, generally famous people, but I think there's this thing which is not being sycophantic is what I basically say, but also not being ridiculous is almost the way I would frame it in this conversation, which the ridiculous is if you go up to Mark Zuckerberg and everyone knows what he looks like and who he is and you're like, "Hi, I'm Sam. And who are you?" You're like, "What are you doing?" It's ridiculous.

**Sam Lessin** (00:21:55):
On the other hand, going up and being like, "You're the most important person I've ever met." Is wrong. And so there's a way to, again, it's about grace as much as anything else and recognizing that they're people. And again, you're playing an iterative game and the best thing you can do is say, as much as it might actually be, this is not my only opportunity to meet Mark.

**Sam Lessin** (00:22:14):
And in an ideal world when I walk away, I'm like, "That was a pretty nice person. Maybe I want to talk to them again." Now going up and being like, "I need your email address and phone number." It's like, no, let him offer it. That type of stuff I think matters.

**Lenny Rachitsky** (00:22:28):
Maybe one last tip is you actually start with this one of this line of great to see you when you meet someone versus nice to see you again.

**Sam Lessin** (00:22:34):
Well, again, Lenny, you and I probably use this all the time, I bet, because I honestly, again, we go by name, face, whatever. It's really difficult social situation to put someone in. It just think about from their perspective. If you go up to someone and say, "Hey, it's really great. It's great to meet you. " And you're like, "We've met five times."

**Sam Lessin** (00:22:51):
It's quite embarrassing and for them, for you, for everyone. And so the more I love ... In fact, my wife of many, many years who have data since college has a really funny story about this, which is the first time right before we started dating, I went up to her and I basically did a nice to see you line and I very clearly couldn't remember if we had met before and we had and she remembered. And so for me, this is an important one to keep in mind.

**Lenny Rachitsky** (00:23:17):
My wife is constantly making fun of me of saying like, "Oh." And not knowing if I've met someone before not she's like, how can you ... I don't know what to do with you. So that's a great one. So the line there is great to see you because it works whether you meet them or not.

**Sam Lessin** (00:23:30):
Yeah. And again, it's one of those things where here's the thing. People aren't dumb. If you go around saying nice to see you to everyone, they're like, there's a small percent chance this person doesn't remind me who I ... remember who I am. And there are other ways or cute, but that's okay. That's part of the etiquette dance to some degree is like, that's fine. What's not fine is it's so nice to meet you and we've met six times, right?

**Lenny Rachitsky** (00:23:54):
Yep. Okay. Let's talk hygiene. There's a couple there that stood out to me. Tell me if I'm missing any that you think are really important. One is just subtle fragrance.

**Sam Lessin** (00:24:04):
Yeah, don't smell like shit. It's like don't overpower it. You shouldn't smell like you just doused yourself in perfume or cologne or whatever it is, but you also shouldn't smell bad. And it's again, your scent should not be noticeable is almost the way I would put it in any direction. There's no advantage to that is basically what I would say.

**Lenny Rachitsky** (00:24:27):
By the way, this is a good question. Does this advice apply both equally to men and women? Is there anything that as maybe as we go through it?

**Sam Lessin** (00:24:33):
So it's an interesting question. I think broadly in this book, the answer is broadly yes. I will say that there is probably in January or February going to be a, what we've internally been calling the fem etiquette course because my wife and other women have said, "This is good, but there's a bunch of other stuff that women need to know." And so I can't speak to that yet. I think the fragrance one I would say, I don't think you want your fragrance to be memorable for anyone no matter what your gender is.

**Lenny Rachitsky** (00:24:59):
Awesome. This is good. Let me take two tangents here real quick. One is, you told a story on your podcast about your kids and the impact this has had on them. Maybe share a story there.

**Sam Lessin** (00:25:09):
Well, look, here's the funny thing. I literally have realized in doing this, I love my children. They have terrible manners and there's certain things they're not bad at. But broadly speaking, I have an eight-year-old, a six-year-old, and a four-year-old. And I'm like, "Wow, you guys eat like animals. You don't know how to use a forklift knife properly." Again, it's not like at four or six, it's not like stopping them well in life. I'm like, "I can't be the etiquette guy if you guys are eating like." Some of them has been really cute.

**Sam Lessin** (00:25:37):
Others have been really funny. My six-year-old has started standing whenever my wife comes to the table, which is kind of arcane from an etiquette perspective. You can argue about whether it's actually even really etiquette anymore or not. But if you're being really formal when a woman comes to the table, you stand. And it's very funny to have the six-year-old do that. So in our household-

**Lenny Rachitsky** (00:25:56):
That is so funny.

**Sam Lessin** (00:25:56):
Don't judge me yet, but in a year you can judge me on my children's etiquette. Then it might have to be a children's etiquette book.

**Lenny Rachitsky** (00:26:01):
So good. I think actually at the end you say that whenever anyone joins you for meal, whether it's a man or woman, you stand up as a modern way of thinking about that.

**Sam Lessin** (00:26:09):
That might be better is what I would say. I will say that it's an ongoing, somewhat hilarious debate at our dinner table. I'm just trying to get them to not use a fork and knife like animals right now, but we're working on it. We're working on it.

**Lenny Rachitsky** (00:26:22):
I was just speaking of that. I was just listening to Tyler Cowen had Alison Gopnik on this podcast and she studies kids and her whole thing is how kids learn scientists and she has a whole thing about how they figure out how to use a fork by just experimenting until something works.

**Sam Lessin** (00:26:36):
Right. To be clear, they're able to feed themselves. It works, but it's like you look at it and it's like, what are you doing?

**Lenny Rachitsky** (00:26:43):
Yeah. Okay. The other tangent is you didn't share the class you actually taught to founders already. So maybe share a little bit about this class you taught.

**Sam Lessin** (00:26:49):
Yeah. So before the book, we did a class specifically initially for YC founders, right? Partially because YC, Garry Tan got very mad about this. So I'm like, well, now I have to do it because that's very funny. But yeah, we basically, we gave them all certificates of completion, but we did a class. We hosted, it was at the Four Seasons. We did some stuff that was fun and a little bit irreverent.

**Sam Lessin** (00:27:10):
We had some very fancy wokes basher people come in with models and show a talk about dress at different types of events and things like that, which was kind of tongue in cheek, but a lot of fun. We also did caviar and wine tasting type stuff. But then we also spent a lot of time focusing on the actual meat of the matter, which is things like how to show up with a low heart rate, how to have an abundance mindset, basic skills, like look people with the eye, shaking hands, how to eat that shows that you're being respectful, things like that.

**Lenny Rachitsky** (00:27:40):
Why do you think Garry Tan was so mad at it? Is it because he's like, "This is a waste of time, not worth it." Versus just build a thing that's successful?

**Sam Lessin** (00:27:46):
I don't know. I don't really understand what makes Garry Tan mad and it's fine. But from my perspective, I think he's just like, in some ways, again, to be clear, it's a little tongue in cheek. We're a little bit making fun of the fact that YC Founders do come out a little bit like animals. Having met with many of them, I guess it's not their fault.

**Sam Lessin** (00:28:01):
They're like young kids, they've been holed up in a room coding and that's all they've been thinking about for months or whatever. And so when they show up at your office to pitch you and they get a coffee or something and then they leave it on the table and don't ask you where to put it, it's a subtle sign of not being aware of your broader environment that you may or may not know, but I think it's valuable. Maybe they're like, "This is the wrong thing to focus on." I just think it's funny as much as anything else.

**Lenny Rachitsky** (00:28:29):
I love it. Okay. So on the hygiene thing, is there anything else?

**Sam Lessin** (00:28:30):
I think the hygiene stuff, you should get the book. I think it's fairly obvious hygiene stuff. Don't be covered in schmutz, you know what I mean? Show respect, try to anticipate how the room is going to be dressed and don't massively overdress or underdress. It's like if you show up to a business casual thing in a tuxedo, you're kind of trying to stand out. Don't be memorable from that perspective, but you also don't want to be memorable in the other direction. It's like, "Wow, that person really has no respect for the room."

**Lenny Rachitsky** (00:28:59):
This actually, you're getting to the next category, which is dress, which I'm excited about.

**Sam Lessin** (00:29:02):
Yeah. Look, I got to say, again, in terms of know the rules, but don't always follow them. My first job out of college, I was an associate at a bathing company. This is a consulting firm and there was a business casual and I kind of came up with the snarky realization that there was a minimum dress code, but there wasn't a maximum dress code. So I started in the office as somewhat of a mini rebellion in the consulting firm, what we call tuxedo Tuesdays, where all the associates would wear tuxedos to work, which then meant we didn't have to go to the client meetings because they would never take us to client meetings and tuxedos.

**Sam Lessin** (00:29:32):
So it was like, again, you know the rules to break them, it was fun. But I do think from a dress perspective, again, I think the real thing is look put together, look like you cared, look like you made some effort, but you don't overdo it is basically the upshot of the most simple way to dress, unless you're trying to very intentionally break a rule, which maybe you are, but I think you should do that with a lot of cultural understanding. Let's put it that way.

**Lenny Rachitsky** (00:29:57):
So here's a couple tips that I love. So one is just dress one Level up as a really simple tip.

**Sam Lessin** (00:30:01):
It's an easy way to win. Not two, not three, but one.

**Lenny Rachitsky** (00:30:05):
And oftentimes you can reduce that. If you have a suit on, you could take off the jacket and you're a little less formal.

**Sam Lessin** (00:30:11):
Sure. 100%.

**Lenny Rachitsky** (00:30:12):
And then you talk about fit of the item versus the brand.

**Sam Lessin** (00:30:16):
100%. At the end of the day, fit is everything. And I say this, again, you guys, all your listeners have to understand. Part of my joy at doing this is there's some level of hypocrisy in it, which is great. You got to have a little bit of that in your life in terms of how I myself behave sometimes. But look, in the end of the day, a well-fitting $20 shirt is way better than a misfitting $500 shirt.

**Sam Lessin** (00:30:41):
And candidly, it's the same thing. It's like if you're a startup founder, you do want to dress to the level of the room, but you kind of shouldn't show up and you shouldn't have a Rolex. It's very classless, if that makes sense, to show up as a startup founder with a Rolex. Again, it goes back to heart rates, trying too hard. You're not going to trick anyone is the upshot.

**Lenny Rachitsky** (00:31:06):
Yeah. This bit of our brand and just expensiveness of the item is such a big one that I think is ... I think people don't realize just it could be a pretty cheap thing that if you get tailored in some small way, it just looks so much better even if it's not the highest quality item.

**Sam Lessin** (00:31:20):
A hundred percent. Again, I would put differently, think about the average person, not every person in the world, but the average person in the world can look at a suit and be like intuitively that seems like it fits the person who doesn't. Most people can have no idea what things cost. And so in some ways, it's this weird thing where it's like if you show up at a super misfitting but very expensive item, you're like, "What signal are you setting?"

**Sam Lessin** (00:31:47):
It's like, well, you're not very aware culturally. You're not matching the room. You're not showing a lot of sensitivity to the situation and what people actually can prioritize. And it's like, are you trying to impress me because you have a fancy outfit? What are we talking about?

**Lenny Rachitsky** (00:32:01):
One other tip you have is if you're not sure the level of dress, just ask.

**Sam Lessin** (00:32:04):
Yeah, this is a big thing in general, which is I think people are afraid to ask in all sorts of situations down to which forks should I use? Or what's the expect? It is absolutely fine to ask. In fact, if anything, it shows a level of confidence and calm and humility to ask if you don't know. So I actually think this is a great example. There's absolutely nothing wrong with asking about dress, about etiquette, about expectations. Again, it goes back to this whole give to get.

**Sam Lessin** (00:32:35):
If you get someone on the phone and you ask them a hundred questions about etiquette, at a certain point you're like, that's not a pit game of ping pong. But it's totally fine to call and be like, "Hey, what's this going to look like?" And by the way, it's important because in New York versus San Francisco, there's different expectations. People do things differently and your job, you're not expected to know every nuance of every culture you might enter.

**Lenny Rachitsky** (00:32:58):
So maybe as a final question in dress, do you have any just, I don't know, tips for dressing well? I know this is a big question that our professionals spend time teaching and charging for.

**Sam Lessin** (00:33:10):
I think the answer is find someone in your universe who you think dresses well. And again, ask them for help and what makes sense from that perspective. Again, the well-tailored, great. That makes a lot of sense. The basics I can say, yeah, have jeans that are clean and fit you, things like that. But again, when my wife listens to this podcast episode and hears me being asked about specifics of dress, she's going to be chuckling.

**Lenny Rachitsky** (00:33:39):
Great. That's a win. Okay. Let's talk about dining. Give us some advice for etiquette during dining.

**Sam Lessin** (00:33:49):
Tip well. Don't not tip, don't tip badly. Don't be super stingy about, "Okay, who ordered the flambe?" Split bills evenly, make things easy for waiters. In general, it's don't order the most expensive thing on the menu. Does it really matter? Especially to an investor, do they really care? No, they don't really care, but they do notice. And you're like, ah, you are the type of person that is truly insensitive to what things cost, even if it doesn't actually matter, and so I think there are things like that.

**Sam Lessin** (00:34:25):
Same with wine. And then I think, look, in the terms of asking, ideally, don't order first because I think if you see how someone ... Are we doing starters? How long is this meal going to be? There's a lot of times in dining situations you don't know. And the more you let someone else set the tone and then match that tone, the better. You kind of want to go middle of the pack to last, if that makes sense. Yeah.

**Lenny Rachitsky** (00:34:49):
So this isn't a situation if someone takes you out to dinner, like it's a VC, another found, someone invites you to dinner.

**Sam Lessin** (00:34:50):
Or a partner or whatever it is.

**Lenny Rachitsky** (00:34:50):
Yeah. Awesome.

**Sam Lessin** (00:34:58):
And look, I think I would say in terms of this, which I always think is important is like, look, within reason, always offer to pay. Now, you should be turned down. If you go out with a VC and you put a card down, 99% of the time they'll be like, "I got this. Please, don't worry about it." And that's the right vibe on it. You do not need to do this if it's a $10,000 dinner, if they've ordered a super expensive bottle of wine.

**Sam Lessin** (00:35:25):
There are limits to this, but if you go out to a normal dinner in a normal situation, you don't offer to split it, you just offer to pay for it. And then you should be declined on that, but there is a little bit of a risk there because someone might not decline you and then you kind of are on the hook for it, but that is, I think, the polite thing and the polite way to approach it.

**Lenny Rachitsky** (00:35:44):
What if they're just a billionaire? I just had dinner with a very successful VC and I did not feel like offering to pay made sense, would you still-

**Sam Lessin** (00:35:52):
Yeah, I actually would. I think it depends what the dynamic is. If the billionaire ordered a $10,000 bottle of wine, you don't need to offer. If you had a normal meal, I actually think it's great to offer. And they'll almost certainly be like, "Of course not." But I will say, I'll tell you a funny story, which is when you go out with dinner to really fancy people or someone who's like ... There are two interesting dynamics.

**Sam Lessin** (00:36:19):
One is it's actually, I think, especially nice to offer and even sometimes pay because the reality is, if you think about it, they obviously don't care about the money, but no one does that. They're like, "Well, clearly you should pay." And so the more you're like, "Oh no, I'm treating us like this is a conversation and equals and I'd love to offer or just pay as big."

**Sam Lessin** (00:36:38):
The second thing, which is important is if you're, especially if you're out to dinner with someone who's very, very well known, you have to tip like crazy because the problem is, this is one thing, this is not in the book, this is a 201 course. But if you go out and start with someone that are like, okay, they're either known or relatively known known and you're making the gesture of buying it, not because they obviously don't care about the money.

**Sam Lessin** (00:37:01):
It's more like the gesture that's nice that you would offer that. You kind of have to tip the way they would tip and they're going to tip 100% of the bill because it's just the right thing to do. And so I do think if you're going to do that, you really have to tip well.

**Lenny Rachitsky** (00:37:16):
Got it. Speaking of tipping, my God, I hate tipping so much as a concept. Obviously, people deserve to be paid well and I love that they make more money, but it's just so convoluted and just like, what the hell do I do? I never know.

**Sam Lessin** (00:37:30):
Tip a lot.

**Lenny Rachitsky** (00:37:31):
Tip a lot. Okay.

**Sam Lessin** (00:37:32):
Just tip a lot. I think 10, 20% is the minimum. If you're out in a situation, I think you kind of want to tip in my mind to the level of no one you're effectively paying for would bat an eye that you're being stingy is the way I would think about it. 20% feels like the minimum. 30% sometimes, more seems a little bit silly, but it is a squirrely topic. And again, let me put it this way. I don't think you want your tip to be memorable is almost way to put it.

**Sam Lessin** (00:38:01):
This is not a thing to focus on. When the person that go out to dinner with thinks back on the dinner a month later, they want to think about the content of the conversation or what the ideas were or the business opportunity, they want to think about, "Oh my God, that guy tipped an incredible amount. What was that?"

**Sam Lessin** (00:38:17):
And by the way, I have stories in my own life where I've been out with people and they've tipped so much that a decade later I remember it. And I'm like, "It's fine. They can afford it." It's cool that they did that for the server, but honestly, the only thing I remember from the night is how much the person tips.

**Lenny Rachitsky** (00:38:34):
Okay. But I think if you're extremely rich, you do it. Don't even tell anyone basically, but feel free to do it, obviously.

**Sam Lessin** (00:38:40):
Sure. Yeah. The tip is not a point. The tip is like everyone should feel good about it. And again, it's about, again, putting people in a sense of ease and comfort and you might not like it and it might not be fair in the world, but people are like, everyone's being taken care of. I can be relaxed about this is kind of what you're going for.

**Lenny Rachitsky** (00:38:58):
I recommend your next book after this FamiKit edition is a tipping guide.

**Sam Lessin** (00:39:05):
A tipping guide. Well, you know what would be funny is there's a great episode of Seinfeld that's all about the tipping calculator. I don't know if you're a Seinfeld guy.

**Lenny Rachitsky** (00:39:12):
I love Seinfeld. I don't remember that.

**Sam Lessin** (00:39:15):
It's a great tipping episode. I feel like you could honestly have a very funny modern LLM app that is only about tipping. Imagine a tipping app where instead of a tipping calculator, you take a picture of the bill, it geolocates where you are, you're like, "This is the situation." And it just tells you what to tip.

**Lenny Rachitsky** (00:39:35):
Well, a restaurant is one thing, but then it's like the garbage person, a gardener, the person that-

**Sam Lessin** (00:39:42):
Especially this time, end of year, at the end of year-

**Lenny Rachitsky** (00:39:45):
Yeah, exactly.

**Sam Lessin** (00:39:45):
I think no one knows how to tip anymore.

**Lenny Rachitsky** (00:39:48):
Yeah, some guy that installs the shades in our house. Do I tip?

**Sam Lessin** (00:39:52):
I don't think you tip that person. Do you tip that person?

**Lenny Rachitsky** (00:39:55):
No, I did not because you already paid a bunch of money for this thing.

**Sam Lessin** (00:39:58):
Yeah, no, I feel like that is weird. I wouldn't tip people like that.

**Lenny Rachitsky** (00:40:04):
That's why we need this book. You're just going to be a huge hit. What does [inaudible 00:40:08]?

**Sam Lessin** (00:40:08):
Why are you going to fund it? You want to fund it with me? We should fund an app. Talk about YC companies. If someone pitched me in the terms of funny but real, if someone pitched me the 2026 AI-driven tipping calculator, which gives the social situation and the details and all the things and it's like, this is how much to tip, that is hilarious and probably quite useful.

**Lenny Rachitsky** (00:40:28):
And probably not venture scale, but I don't care.

**Sam Lessin** (00:40:31):
You know what? It's 2026, it might be. Everyone has the problem. Everyone globally has the same problem, which is tipping.

**Lenny Rachitsky** (00:40:38):
Yeah, just the tokens cost of that.

**Sam Lessin** (00:40:40):
It actually reminds me, I'm reading one of my sons right now, the third book of Hitchhiker's Guide to the Galaxy.

**Lenny Rachitsky** (00:40:45):
Mm-hmm.

**Sam Lessin** (00:40:46):
And I don't know if you've read this series.

**Lenny Rachitsky** (00:40:47):
Yes.

**Sam Lessin** (00:40:48):
It's important stuff. Do you remember about the, what's Bristomatics Mathics?

**Lenny Rachitsky** (00:40:48):
No.

**Sam Lessin** (00:40:53):
So basically the idea is that after the improbability drive, the way they're able to move across the universe very quickly is the most complicated math in the universe, which is the math of your bill at a restaurant. So I think we're onto something. It's in science fiction.

**Lenny Rachitsky** (00:41:10):
Oh, man. What other tip that I just thought of as around dining is you have this tip about sommeliers, give them a sip of your wine if you order something really well.

**Sam Lessin** (00:41:17):
Yeah. If you want to say nice, that I'm going to be very clear again in terms of, I don't order a very nice wine. No sommelier really cares what I'm drinking. But if you are doing that or you're into it, which is great, again, think about being generous. If they're like, "Oh, I would love to taste that." Have a taste. It's great.

**Lenny Rachitsky** (00:41:33):
I love that. I would love to do that. That sounds really nice.

**Sam Lessin** (00:41:36):
Yeah.

**Lenny Rachitsky** (00:41:37):
Yeah. Okay. And one other tip here I wrote down, B for bread, D for drinks. So explain that.

**Sam Lessin** (00:41:42):
Oh, BD. You just got to look at your hands, right? Bs and Ds for which was your bread plate is kind of the way to think about it. And look, people get this wrong all the time. You sit down at a big table and you're like, ah, which is mine? And you're kind of waiting for someone to pick it up and do the math. I'm like, "Okay, that's my bread." But the B and D is useful. Also, look, forks and knives, just knowing what side they go on, knowing one thing I always drive with me nuts that is related is the knife blade goes in.

**Sam Lessin** (00:42:11):
People, when they put their knives down, the knife blade goes in because you don't want to stab your partner next to you, but it was really funny. I had an entrepreneur, this is not yet a product. People have started sending me videos of them dining or in situations and asked for feedback. And I recently had to give feedback to an entrepreneur who was like, "You did a very nice job eating your soup. Good job. Your napkin should have been in your lap and your knife is pointed the wrong way."

**Lenny Rachitsky** (00:42:38):
Okay. Napkin actually. Okay. So napkin in lap.

**Sam Lessin** (00:42:40):
Napkin in lap. Not in your neck, not off to the side. Napkin in lap.

**Lenny Rachitsky** (00:42:46):
I saw someone once had a napkin on just one leg versus both legs. Any opinion there?

**Sam Lessin** (00:42:51):
I don't think you want your napkin placement to be memorable. My biggest thing is the point about etiquette is that it gets out of the way. It shouldn't be memorable.

**Lenny Rachitsky** (00:42:59):
It's like the Kindle. You don't want to think about the technology.

**Sam Lessin** (00:43:02):
No, let the conversation flow.

**Lenny Rachitsky** (00:43:04):
Yeah. Okay. Amazing. Okay. Speaking of conversation, small talk and humor, give us some tips.

**Sam Lessin** (00:43:10):
Here's the thing. Humor's great. I love humor. It can be overdone. And again, it shouldn't be the point of things. And also, I'd say humor is quite conditional and subtle to the audience. So in the room you're in, dirty jokes. You don't want to tell a dirty joke in the wrong room. But I think the thing about humor is there's this interesting subtlety to why it's so useful in social settings, which is one, it kind of shows you the ultimate mastery of a social situation.

**Sam Lessin** (00:43:40):
If you're able to tell a joke, which is right up to the line or even pushes at one degree to show your own comfort in the space. So the ultimate demonstration of comfort in a space is to tell a joke that's a little over the line or a little off color, but not too off color. It's like the ultimate thing. So if you're really in it and feeling good, using humor is great.

**Sam Lessin** (00:44:02):
You should not be remembered as only the comedian. And again, the level of jokes that you're playing with is a very subtle thing. So you don't want to tell a knock-knock joke with adults, but the off-color sex joke that is hilarious, you better be pretty confident before you tell it in space. The last thing you want, a joke that everyone laughs at is great.

**Sam Lessin** (00:44:28):
A joke that no one laughs at, it's like a huge risk maneuver you failed. It's not the point. So it's a great tool. I love it. I think everyone should have their file of jokes. I do. I don't know about you, but I have a Evernote. It's not Evernote anymore. It's like a bear of my favorite jokes. And they're loosely ranked and from least offensive to most offensive because I forget the jokes, but you kind of want to use humor sparingly and smartly.

**Lenny Rachitsky** (00:44:54):
This is your next book, this list of jokes.

**Sam Lessin** (00:44:56):
List of jokes ranked by social situation and level of extremeness.

**Lenny Rachitsky** (00:45:01):
Everyone's going to be telling you same jokes. Oh, man.

**Sam Lessin** (00:45:05):
Well, there is a funny joke about that, which is the whole, the prisoners. I won't even tell it. It's funny. There's a funny joke about people who know all the jokes. I'll leave it at that.

**Lenny Rachitsky** (00:45:14):
I'll leave you hanging and wanting more for our next podcast conversation. Abundance. Okay. You also recommend self-deprecating as a-

**Sam Lessin** (00:45:22):
Make fun of yourself, not other people.

**Lenny Rachitsky** (00:45:24):
Yeah.

**Sam Lessin** (00:45:25):
It's just like you can make fun of yourself as much as you want. Again, making fun of other people shows an incredible level of familiarity. And if you're there with your business partner and you're really feeling the vibe, and again, it can be quite effective, but the second it feels disparaging or people aren't on the same wavelength, it's a very high risk maneuver, making fun of yourself is always fun.

**Lenny Rachitsky** (00:45:50):
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**Lenny Rachitsky** (00:46:39):
WorkOS allows you to build like the best with delightful APIs, comprehensive docs, and a smooth developer experience. Go to workows.com to make your app enterprise ready today. Also, so you said you had this kind of list of jokes that you ... Because I can't remember. I have zero jokes in my head that I'm like, "Okay, here I'm going to get one, so I should make a list."

**Sam Lessin** (00:46:57):
Make a list. I got some good ones for you.

**Lenny Rachitsky** (00:47:00):
Okay. I love to borrow some. You also recommend having some stories, please read kind of stories to tell.

**Sam Lessin** (00:47:05):
Yeah, you want to have some easy stories that. Again, it shouldn't be 10 minutes. But again, I think the thing about it is imagine that the whole game in these social settings, again, is putting other people at ease, making them feel like you understand them and the room and you're a trustworthy person and on the same wavelength. And in some ways think about it as who's carrying the conversation.

**Sam Lessin** (00:47:25):
It shouldn't be your monologue. You shouldn't force them to monologue either. And that's where questions come in and back and forth. Having a fun build on story, they tell a story, you have a story to tell. Again, I really think of it as a conversation or these social interactions as a ping pong game and you kind of want to have a few of those in your arsenal.

**Lenny Rachitsky** (00:47:43):
Your last tip in that section was how to wind down a conversation, just the importance of realizing it's time to wind down any tips for how to do that.

**Sam Lessin** (00:47:49):
Yeah. Just like when the conversation is over, gracefully leave and basically the upshot. The worst is the conversation ends and the person just stands there and you're like, "I'm going to go get a drink." Or do you what I mean? And then whatever once in a while the person will be like, if someone says to you in a conversation they wind it down, it's like, "I'm going to go grab a drink."

**Sam Lessin** (00:48:09):
Most of the time, that is not an invitation saying, "I'd love one too and follow them to the bar." So I just think you have to recognize the signs effectively when the moment is passing or it's time to move on, et cetera, and then respect it is what I would say.

**Lenny Rachitsky** (00:48:27):
I use that one all the time. Is there any other ways you find useful to wind down in conversation just to get out of a conversation?

**Sam Lessin** (00:48:32):
Well, I think the other thing people do frequently, which again is totally fine if done respectfully, is bring someone else into the conversation. In some ways, give them their next partner and be like, "Oh, I've enjoyed this conversation. It's super cool. By the way, have you met Steve? Let's go meet Steve. Go talk to Steve for a bit and I'll pick you back on it." And like, "Okay, I'm going to go say hi to my wife."

**Sam Lessin** (00:48:55):
There's ways to handle it. Again, the key though is subtlety on these things, I think in all things. You want to basically let people feel respected as much as anything else. So if you're too overt about it, even if everyone kind of knows what's going on, the key is to give them plausible deniability to themselves and the community effectively that we've wound down this conversation. So you have to look for the signs.

**Lenny Rachitsky** (00:49:21):
Awesome. Okay. There's four more sections. The next one is scheduling etiquette. Give us some advice.

**Sam Lessin** (00:49:28):
Oh, well, I think I'd like to say that I'm fairly famous for hating Calendly. I actually, I think I am not overstating it when saying there was a period where calendar where I personally had driven most of their growth for the month. I got messages from the board because I went on this diatribe about how much I hate Calendly and how disrespectful it is. And apparently, this was such an internet fervor. I got millions of engagements that meaningfully drove their month. So they thanked me for it.

**Lenny Rachitsky** (00:49:53):
Wow. No bad press.

**Sam Lessin** (00:49:54):
No bad press and no bad tweets. Yeah. So I think, look, there's a few obvious ones. Make sure you have availability. If you ask someone to schedule with you, it's not always wrong to be like, "Here's my availability or here's a link." But make it real. You have to give them real options. I really strongly believe the default should not be Calendly.

**Sam Lessin** (00:50:13):
The default in most situations, especially if you recognize the power hierarchy or the busyness hierarchy. If you're the less senior person, if you are the less busy person, you should let the other person tell you when they're free and then make it work on your end, is what I would say. And it's fine if the first thought doesn't work, but one of the first three really needs to.

**Sam Lessin** (00:50:33):
So I think that in some ways that it's important to respect that. It's better to ask what they can do and then move your schedule. If you really can't and you're going to use a scheduling agent or something, it just needs to actually have real options is basically, I think that comes up all the time. Look, rescheduling happens when you do it, give notice as much as possible.

**Sam Lessin** (00:50:55):
Once you're asking for rescheduling, you need to be even more accepting of what the other person can do, I think is really important. If you're asking to reschedule, you basically within reason need to make it work for them, is what I would say. And then look, I think there's obvious stuff that people should know and sometimes forget. Time zones are really tricky. People screw them up all the time. Check.

It's worth the extra check to make sure you're not both scheduling, you're getting the numbers right, but then also really importantly, scheduling at reasonable times. Sometimes people are like, "I want to meet at this time." You're on EST, you're aware, that's like 4:00 in the morning for me. And so I think being respectful of that and just asking, I think is super important.

**Sam Lessin** (00:51:34):
It's not rocket science because it's important. Last point I make, which we make in the book is you really need to respect EAs and PAs and the whole people. The number one way to look extremely classless is to not respect people who are helping the other person. This is the number one thing. Now you don't need to be so over the top exuberant.

**Sam Lessin** (00:51:57):
You don't want to overdo it, but there should be this deep well of respect for anyone who's helping you, whether that's a server or a PA or an EA or whatever. This needs to come with an extra gesture of respect. That means saying thank you when they schedule and follow up with them and things like that.

**Lenny Rachitsky** (00:52:14):
I'll tell a story when we were selling our company to Airbnb, we had this guy helping us sell the company and he made it a big point to build a good relationship with the EA and office manager at Airbnb because if they like you, it helps. It helps you get the thing.

**Sam Lessin** (00:52:31):
The general story of make the gatekeepers happy and like you is true. I do think there are ways to overdo that for what it's worth. It becomes almost too transactional. If you show up with flowers for the EA, you better be damn confident in what you're doing, if that makes sense. But just like the small things go a long way.

**Sam Lessin** (00:52:50):
You just make eye contact with them, thank them, respect them. If they bring you a coffee cup, ask them where to put it when you're done. Don't treat people who you might feel like the team or the staff feel that way, make them feel like part of the team it equals.

**Lenny Rachitsky** (00:53:08):
The Calendly stuff, I feel like that's its own separate book of Calendly Etiquette.

**Sam Lessin** (00:53:15):
Calendly is one of my favorite episodes of going hyperviral on something hilarious.

**Lenny Rachitsky** (00:53:20):
So one thing that Calendly does is you can embed. I don't do this yet, but I should. It feels like just embedding your times in the email feels like a good-

**Sam Lessin** (00:53:27):
Look, I'll be honest, I go the other way. I don't use any of that stuff. And look, I think scheduling is very complicated. This is part of it. I always think of these, it's like, when are you free? It totally depends on who's asking. If Barack Obama, or I don't know, I won't say Donald Trump, someone wants to meet with me and it's like 4:00 in the morning my time, or I'm totally, "You know what? I'm going to make it work."

**Sam Lessin** (00:53:55):
And so I do think I'm actually kind of against the flat hierarchy, all meetings are the same, da-da-da-da. That does mean, honestly, I think that you probably knocked me. My bet is that my calendar moves more than most people's, and I'm sure that feels disrespectful to some, and it is. I want to be really clear, but it's also the reality of trying to balance these things.

**Lenny Rachitsky** (00:54:16):
The other flaw with Calendly, I've realized someone once figured out my Calendly URL and just booked a meeting with me, like a founder wanting to pitch me, and it was on my calendar. I'm like, "Who is that?"

**Sam Lessin** (00:54:26):
The funny one I've had is there are people whose names are very similar to other people I know. And every once in a while, I've ended up accepting a cold meeting and showing up and be like, "You are not the person I expected." Because I was like, "Oh, your name is off by one letter." That's cool. That's not a thing to call out, by the way, from an etiquette perspective. Once you're committed to the meeting, you're doing the meeting, even if it's the wrong person.

**Lenny Rachitsky** (00:54:49):
Oh, man. Okay. Well, one last question with Calendly. Something I try to do, curious to get your take is like, okay, someone want a founder, I'm meeting with a founder and the way I approach it is like, okay, do you have a Calendly or something I could use to book a meeting with you? If not, in parentheses, here's my calendar in case that might make it easier.

**Sam Lessin** (00:55:08):
Yeah, I think that's perfectly fine in terms of the way to do it. I just think the key is to make it easy for the person you're trying to do business with.

**Lenny Rachitsky** (00:55:15):
And not make them feel like they have to do the work if they don't want to.

**Sam Lessin** (00:55:21):
No, you do the work. Basically, you're asking for something, you do the work is the upshot.

**Lenny Rachitsky** (00:55:23):
Okay, sweet. Moving on to the next topic, communication.

**Sam Lessin** (00:55:27):
Don't use emojis, try to proofread your stuff, get to the point quickly, assume the person you're reading is busy. Again, I think these are all the types of things that none of this is rocket science from my perspective, but people, it's just good to remember and be on top of it. I do think people have different things on this. I personally do think that on things like email, you kind of do have an SLA to respond.

**Sam Lessin** (00:55:53):
There's some people I know who are like, "Email does not mean I have to respond. You send me an email, you may or may not get a response. It's completely up to me. I have no contract to respond to your email." I personally go the other way, which I feel like from an etiquette perspective, I don't owe you a 12-page essay, but I do owe at least an acknowledgement quickly of what you've sent.

**Sam Lessin** (00:56:13):
You don't want to leave people hanging. But again, I just think it's like if you read an email, I'm sorry, if you write an email, imagine you're receiving it. How does it feel? Does it feel like you're asking a ton of the person you send it to? If you send them 10 paragraphs, it's annoying. You're like, "Okay, I have to read all this. What am I going to find time?"

**Sam Lessin** (00:56:33):
This is like you're asking a lot. It's kind of like a monologue and a conversation. You've just said, "I'm going to spend 10 minutes talking at you." And so I do think keeping it short and to the point, not being silly, not using emoji, trying to make it readable. These are all important things.

**Lenny Rachitsky** (00:56:47):
Say more about the emoji piece. Is your advice just know emojis if it's a business?

**Sam Lessin** (00:56:52):
I think emoji from my perspective, it's quite a step of familiarity, if that makes sense. From a business context, look, if someone sends you a smiley face, you can respond with it. You can kind of match. Again, it goes back to this matching vocabulary and language. I'm not saying you should be totally cut and dry, but it's kind of, I would say emojis almost feel like jokes to me, which is like, tell them at your own risk and they're probably not worth it.

**Lenny Rachitsky** (00:57:20):
Awesome. Okay. I do use emojis. I use the thank you hands one a lot.

**Sam Lessin** (00:57:25):
Look, I think text is different. I think people might have slightly different takes on this. And so I wouldn't say anything I'm saying here is dogmatic, but I'm just saying that, again, emojis are not highly legible to most people. They can mean lots of different things.

**Sam Lessin** (00:57:39):
They usually have cultural connotation to them. And I would say that they're kind of harder to read than just a well-worded, simple to the point email, and I think you just want to come across as a literate to the point, simple, clear person.

**Lenny Rachitsky** (00:57:54):
And emoji sometimes kind of implies used AI to generate this thing because ChatGPT loves emojis.

**Sam Lessin** (00:57:59):
Totally. And I also say, look, there's a whole ... We want to get spicy for a second.

**Lenny Rachitsky** (00:58:04):
Yes.

**Sam Lessin** (00:58:05):
I don't know how I feel about people who have invested too much in their emojis. So you get people who have changed the colors of their emojis from the default or whatever. I'm not saying not to do it. I'm just saying it's quite a statement is my view that you've invested in your emoji pack or using special emojis people haven't seen before or it's like, again, it's a subtlety and you got to understand the room and the culture and what you're responding to, but I do think that people read more into that than people want to be read into it.

**Lenny Rachitsky** (00:58:36):
You know what's crazy now? I don't know if you've seen this, you can create your own emojis now in iOS and emojis are so ... It's like a whole new world, just infinite emojis.

**Sam Lessin** (00:58:45):
But again, it's like if you choose to use those, you are going way out on a limb that people are going to be receptive to that and not be like, "This is a person who spent a lot of their time customizing their emoji pack when they probably should be doing something more interesting."

**Lenny Rachitsky** (00:58:58):
They should be finding product market fit.

**Sam Lessin** (00:59:00):
Yeah, or learning etiquette.

**Lenny Rachitsky** (00:59:03):
The other tip you had that I loved, which is think about the order of the emails when you're emailing somebody.

**Sam Lessin** (00:59:09):
Yeah, I do think you kind of want to think about ... There is a connotation to who you send it to and who you CC and the order in which people. Now, I don't want to overstate this, but put differently, it's like if you're sending an email and the first person on the email is the assistant and the fourth person on the email is the CEO, you've probably done it wrong.

**Lenny Rachitsky** (00:59:30):
And because the implication there is who you think is most important comes first. Who's first to mind?

**Sam Lessin** (00:59:35):
Well, who are you really sending this to? You know what I mean? It's almost the way I think and who's kind of included. So if I look at an email and I'm the first person in the two, candidly, I mildly pay more attention to it than when I'm the fifth, right? Because in my head I'm like, "Okay, well, this is really to Kevin and I'm on it."

**Sam Lessin** (00:59:53):
If you see an email sent to many, many, many people, almost by definition, it's not that important is almost the way I would put And so you have to be really careful with managing that. I think even the who do you send it to and who do you CC. There's a language to that from an etiquette perspective to understand. And I do think people sometimes miss that. The CC line is very, very valuable.

**Sam Lessin** (01:00:13):
It means, "Hey, you should have a copy of this. This is not really to you. I'm not expecting an immediate response." I even think there's even a subtlety who responds then. If you send an email to 10 people CC'd to the whole nine yards, there is a subtle etiquette to when you respond.

**Sam Lessin** (01:00:30):
If you are the fifth CC on an email, you're not expected to be the first response. Again, you can break this rule. There are times to break it. There's a subtlety to it, but it wasn't really sent to you. And so there's a whole language to who you send to and who you CC in the order that, again, it's very subtle, but is worth understanding.

**Lenny Rachitsky** (01:00:51):
So true. Man, and you would think nobody sees all these little things in the Gmail thread, but you do. They're just right there and you're like-

**Sam Lessin** (01:00:59):
This is the whole thing about etiquette is it's all this invisible stuff that you don't need to spend all ... In some ways, the whole story of doing this well is it should not occupy 80% of your brain. What you're saying is, "I've got this. We're on the same wavelength. My heart rate is low. I'm doing it properly and I'm doing it with intuitively almost." Which is a hard ask because what we're basically saying is these are unknown things, but intuitively you should just know them. And that is actually what you're signaling is you can trust me because intuitively, there's this well of knowledge and cultural connection and whatever that we can share effectively.

**Lenny Rachitsky** (01:01:33):
And if your BCC definitely do not reply all.

**Sam Lessin** (01:01:36):
Yeah. I have some unbelievably funny faux pas from my history with CC/BCC To lines. One of the worst etiquette/mistakes I ever made, never, ever, ever put someone you're talking about who's not on an email in the To line to check the spelling of their name and then hit send. That's a bad idea. So whatever you're doing, that's not even an etiquette thing. That's just being smart.

**Lenny Rachitsky** (01:02:04):
Proofreading thing.

**Sam Lessin** (01:02:04):
Proofread. Proofread. And don't send emails to people about them that they're not supposed to see.

**Lenny Rachitsky** (01:02:10):
And it's like Gmail makes it too easy to do that because it adds them automatically if you have their name.

**Sam Lessin** (01:02:15):
No, for me it was more just like the way to check the spelling of someone's name is not to put them in the To field, ever.

**Lenny Rachitsky** (01:02:20):
Yeah. Okay. Oh man. Well, you're still kicking, so it wasn't so bad.

**Sam Lessin** (01:02:27):
I'm still kicking.

**Lenny Rachitsky** (01:02:28):
Okay, great. Okay. Two more. Meeting etiquette.

**Sam Lessin** (01:02:33):
Yeah. Again, we've talked about arriving a little bit early. You should do that. Don't arrive up too, too early. Again, if you're an hour early, walk around the block. It's fine to walk around the block. You don't want to sit in someone's office, because then all of a sudden it feels like this person's been here a long time.

**Sam Lessin** (01:02:49):
It feels like even though they're not scheduled, we're leaving them hanging. You're six coffees deep with the receptionist. You don't want that. So I think you want to be 10 to 15 minutes early. You do not want to be much earlier than that. We talked about meeting other convenience. I do think it's fine to start with a little bit of small talk.

**Sam Lessin** (01:03:10):
There are times it's not or times people are running behind, but the pleasantry of the weather is nice or how is your weekend? Or something that kind of cuts the air a little bit and then you flip into business is a good thing. Even though it feels transparent, it's still useful is what I would say. And again, it's almost a signal of, I am here for business, but I am a normal person and I'm willing to have signaling like, "Oh, I know that we should start with a normal conversation." If that makes sense.

**Lenny Rachitsky** (01:03:41):
I don't know if you saw this on Twitter. Someone described small talk as the TCP/IP act handshake.

**Sam Lessin** (01:03:45):
I love it. It's a great description. I always describe it as like, imagine the modem crash from when we were kids on a 424. That old modem crash. That's what small talk is. It's a modem crash. We're trying to hit the wavelengths, et cetera, is the way-

**Lenny Rachitsky** (01:04:02):
And so that you're ready to talk, so they ready to really communicate.

**Sam Lessin** (01:04:04):
Yeah. For meetings, virtual ones, camera on. And again, I say this as someone who sometimes violates this. I violate it knowingly. I violate it knowing what it costs me, but you really should have your camera on. And again, you should dress appropriately for a video call. You should have an appropriate background. If you have your bed in your background, it should be nicely made. You know what I mean?

**Sam Lessin** (01:04:28):
In some ways it's like doing the easy stuff is the key. And I go a step further. I actually, this is less a hard rule. I actually really don't love virtual backgrounds for the same reason. I'm like, "Look, I'm not going to judge you if you're in your bedroom, if you're a startup founder. It's fine, but I would like to see that your bed is made." Or I'll give you another one that's classic that I see with founders all the time.

**Sam Lessin** (01:04:55):
Close your closet. People will get on Zoom calls and you'll be on a call and their closet is open and I'm like, "It's not a big deal, but do you see your own self picture here? Can you just close your closet? I don't want to see your shirts." That type of stuff I think goes further than people realize.

**Lenny Rachitsky** (01:05:15):
Awesome. One other tip you had was clean up after yourself if you're in a real meeting.

**Sam Lessin** (01:05:20):
The easiest way, and this is by the way, it's for my partner, Kevin Colleran, But the easiest way to come off badly is to not offer to put your coffee cup in the kitchen. And honestly, we do this because if you think about it, we work for LPs, limited partners. That's who we raise money from and then deploy it from.

**Sam Lessin** (01:05:40):
And my partner, Kevin, even more than me, has this thing, which is he is maniacal about this, which is no matter who's in the room, if we're with an LP, you take the coffee, you take the Diet Coke. Even if you know full well, someone's going to come in and clean up after you and you make a point of asking where we can put it or putting it on the side table, et cetera, and acknowledging that there is a mess.

**Lenny Rachitsky** (01:05:59):
I feel that. Final topic is exiting and leaving. What are some tips?

**Sam Lessin** (01:06:05):
You should stand where people leave the table, right? Not ridiculously. By the way, you should go stand with the table. Stand to shake hands. Don't be sitting when you're shaking your hands. It's just what you do. And it shows that you're aware of it.

**Sam Lessin** (01:06:16):
Follow up with gratitude. You should send people thank you notes. They shouldn't be long. They shouldn't be ridiculous, but we met. I got something out of it. Thank you for taking your time is always appreciated is what I would say. Obviously there's stuff like don't take calls, et cetera. This is the kind of obvious stuff in terms of exiting and how you think about it.

**Sam Lessin** (01:06:40):
Even if someone rings you and the meeting's over and you're overtime, you're like, "I got to pick up this call." You hit the button that says I'm calling you right back and then walk away. Don't just pick up the phone and wave, is what I would say. Yeah, and I think there's stuff like that that I would just keep in mind.

**Lenny Rachitsky** (01:06:57):
Also, don't make a production if you're exiting, just exit.

**Sam Lessin** (01:07:03):
Yes, I think that's really true. There's even a point to like, there are lots of scenarios where I think an Irish goodbye is the best goodbye where you just kind of disappear. Any large group setting I think is great. Maybe you thank one person on the way out of the host. But the I am leaving now, right? Let me kind of say goodbye to everyone and hug everyone. It's too much. It's too much.

**Lenny Rachitsky** (01:07:25):
I love that. Okay. We got through everything. Is there anything else that you think might be important to share?

**Sam Lessin** (01:07:31):
There's so much other stuff. Again, I go back to this whole thing, which is like, these are all fun tips. I love the cartoons. We iterated them a bunch. We have more to do. We're having a lot of fun with this and I think it is providing a lot of value to people, which is great. That's kind of my goal is to both have fun and actually provide value and help people. That intersection is great.

**Sam Lessin** (01:07:49):
There's a thousand other tips. And so for the biggest thing for me is when you have more or think of them, send them to us because there'll be a second version of the book and then a third. And I actually really want to cite the people who contribute to it. The book is what? 50, 60 pages. It will be a few hundred eventually. And I think there's a lot more to come and we're going to be doing classes next year all over the country and actually world. We're going to do one in Tel Aviv.

**Lenny Rachitsky** (01:08:13):
Wow.

**Sam Lessin** (01:08:15):
We're going to do one. We're certainly doing one in New York and a few other places and it'll be fun.

**Lenny Rachitsky** (01:08:19):
Oh my God. You got a whole new life forming here.

**Sam Lessin** (01:08:23):
The funny thing is this stuff, the etiquette story is obviously pretty fun. And so people like Morning Brew just keep making videos about this and there's this whole etiquette thing going on. And I'm like, "Oh my God, I've done some pretty good investments in my life, built some cool products. Am I going to be ultimately remembered as the etiquette guy? That's kind of hilarious."

**Lenny Rachitsky** (01:08:41):
That's what we're doing here.

**Sam Lessin** (01:08:42):
I'm into it though. I'm into it. I'm fine with that.

**Lenny Rachitsky** (01:08:45):
So you have the TLDR at the beginning. I'll just read this real quick and add anything that we're missing.

**Sam Lessin** (01:08:49):
Yeah, sure.

**Lenny Rachitsky** (01:08:50):
So this is just like what to do if you do nothing else. Remember the goal of all etiquette is essentially building trust and project genuine confidence. Always maintain an abundance mindset. Remember that you are worthy and have nothing to prove and that it's okay to ask questions and keep your heart rate low.

**Sam Lessin** (01:09:06):
That's the points. If nothing else, if you remember those points, we will be served well.

**Lenny Rachitsky** (01:09:12):
There we go. Okay. I'm going to take us to close out and make this more of a regular episode. I'm going to take us to two recurring segments on the podcast, AI corner and Contrarian corner. I don't know if I told you I was going to ask you these questions, but I'm going to go for it.

**Sam Lessin** (01:09:23):
You lay it on me. I love Contrarian corner. An AI corner depends what you mean by that.

**Lenny Rachitsky** (01:09:28):
So the question in AI corner is just what's one way you've found AI useful in your worker life that might be helpful for people to hear?

**Sam Lessin** (01:09:34):
So look, I'm by default pretty skeptical of most AI applications. I will say the thing that I've had the most fun with with AI and I find great is it actually is partially where the cartoons for this came from is I built a little personal news aggregator called Letter Meme that basically takes all the newsletters I don't have time to read and turns them into daily cartoons.

**Sam Lessin** (01:09:56):
So I have a grid of what's going on in the world as a front page in cartoons. And I actually love it. It's like the best way I get an overview because there's all these smart newsletters, you don't have time to read any of them. So I piped them all in and-

**Lenny Rachitsky** (01:10:07):
Except Lenny's Newsletter, but keep going.

**Sam Lessin** (01:10:07):
And what? Except Lenny's Newsletter. Sure, of course. That one I don't put in the aggregator, although of course I don't do that, but it's great. So I'm super into it.

**Lenny Rachitsky** (01:10:19):
And that's lettermeme.com. I'm checking it out. I love this. So this is AI generated, aggregates all the important newsletters and creates a little summary and a cartoon.

**Sam Lessin** (01:10:28):
Yeah, and you can make your own. So for me, I actually, I'm looking at this now, there's actually one thing that got messed up on this we need to change. But yeah, the idea is you basically get an email digest once a day and it's continuously updated to what's going on in the world and you pipe your own newsletters into it. So it's like whatever you actually trust and pay attention to.

**Lenny Rachitsky** (01:10:45):
Genius. And how did you build this? Was it vibe coded? Did you have an engineer help you?

**Sam Lessin** (01:10:49):
Well, both actually. I actually, the vibe coding thing, this is exactly what vibe coding is good for is like, this is like cursor and digital ocean and Cloudflare will get you a long way in terms of just building this stuff on the fly. And so I built the first version of this myself end to end, but then vibe coding also doesn't really scale. And so the reality is after a certain point, I had some friends who are great engineers just take it and up level in a few ways that I honestly ran out of time to work on.

**Lenny Rachitsky** (01:11:15):
Well, let's go to Contrarian corner. This could be an entire podcast conversation with you, I suspect, but just like what's something you want to share that you believe that most other people don't believe?

**Sam Lessin** (01:11:25):
I think that the venture capital, the seed venture capitalists who invest in companies that are branded as AI companies are going to lose an impossibly large amount of money in the coming years. And that doesn't mean that I don't think you should be using AI to build things. I actually think you absolutely should.

**Sam Lessin** (01:11:44):
It's kind of like not using AI in your startup is the equivalent of not using the cloud in 2010 or not using the internet in 2000. It would be insane. Of course we're going to use these tools, but there's a difference between a great business that you're using AI to supercharge or make better or just as a piece of infrastructure. That's not an AI business.

**Sam Lessin** (01:12:03):
This is a business and I'm very into those versus all of these companies that come out that say we are the AI blank, I think they're all going to zero. Even my kind of argument this from a seed perspective is like, look, is OpenAI a good investment or not? It's a terrible seed investment, right? The way the number's baked out, even at a $500 billion market cap when all said and done, the seed investors have made something like 25 times their money. That's insane if you think about it. That's basically the worst.

**Sam Lessin** (01:12:33):
It's like a middling at best seed investment for the company that is defining the moment. And the reason is because these things are so capital consumptive. So if you're trying to deploy $100 billion, the market is fragmented, people want to dream a dream, people want religion, they want belief. There's a bunch of reasons why you can squint and justify it. You know what? If the storytelling of Elon allows SpaceX, which by the way, I love, I think SpaceX is an awesome company, but if all of a sudden that actually can be worth $1.4 trillion to the public market, guess what?

**Sam Lessin** (01:13:06):
The money plowers are going to do great with all this narrative driven religion is what I would put it, but if you're a disciplined seed investor, I guess my contrary intake would be run away from things that are AI companies because even if you look smart for the moment, you're playing a dangerous game of get out before the narrative collapses.

**Lenny Rachitsky** (01:13:26):
Wow. I love this. Okay. I want to follow this through a little bit. So you're saying because of the dilution that goes along with these companies.

**Sam Lessin** (01:13:31):
It's like they're too capital. If they work, they're too capital intensive. Seed investing does not work in highly capital intensive businesses, so that's not going to happen. Two, they're fundamentally commoditizing in all sorts of ways. It's very unclear what the lock-in or value is on any of these things. And so it's just like the whole dynamic is off, and the thing is people are desperate right now for things to believe in.

**Sam Lessin** (01:13:54):
If you think about the history, we've done so well as a country with Terra Nova. The US was amazing for so many years because we had the West, and if you were going to work hard, you could go west and you'd do great. And there was all this opportunity, land of opportunity. We've had reverberated, my generation, your generation, we were blessed because we had the internet. The internet was digital Terra Nova and we got to build fortunes and do amazing stuff and new work be in this new world that was created, but it was effectively the same thing as the West all over again.

**Sam Lessin** (01:14:23):
And ever since then, whether it's mobile, which again, if you look at the math on it, everyone wants it to be disruptive in Terra Nova, not really. It's just more internet or like crypto, which by the way, I think crypto is amazing. I think it's the closest thing to Terra Nova, but to now the AI God narratives, every generation is desperate for their Terra Nova story with good reason, right? But the story is it has to be real. And I think unfortunately this time, this is a classic example of AI is a powerful tool. It's incredibly powerful for existing businesses and existing structures. It's not a great startup opportunity.

**Lenny Rachitsky** (01:14:58):
So what is it you look for? What do you look for when you're investing in AI startup?

**Sam Lessin** (01:15:03):
Well, again, I won't invest anything that I would consider an AI startup. I'll invest in things that use AI. For me, I think I'm really interested in the cultural implications of AI or the new businesses that need to exist because it is a force in the world. So we've done a lot, whether it's Sublime Security or Outtake, things like that that are basically all around the theme of the Voight-Kampff test from Great Blade Runner, which was the test they used randomly, are you a real or are you a bot?

**Lenny Rachitsky** (01:15:28):
The turtle.

**Sam Lessin** (01:15:28):
That's a huge problem.

**Lenny Rachitsky** (01:15:29):
The turtle on their back?

**Sam Lessin** (01:15:30):
Yeah, so there's a whole set of companies that are implications of AI and how do you manage it and handle it as a society. I think there's a lot to do there. And there's a lot to do in cultural shifts from AI. There's all sorts of interesting trends to follow there. There's all sorts of businesses that will be disrupted. They're not AI businesses, they're businesses that'll be disrupted in interesting ways.

**Sam Lessin** (01:15:52):
So I think there's a lot of opportunity, but again, I think there's a ... I at least draw a distinction between if you're trying to be ... certainly a foundation model company, but any of these things that are like, we're going to win because AI, I'm like, "No, you're going to win because of something else, and AI is going to be a propellant to it."

**Lenny Rachitsky** (01:16:08):
Sam, you're a fascinating human. I feel like we could talk for hours. Is there anything before we get to our very exciting lightning round, anything else you wanted to share?

**Sam Lessin** (01:16:23):
Look, I'm happy to be here. I love your work. It's good to be on your podcast. Happy to bullshit whenever. But no, we're good.

**Lenny Rachitsky** (01:16:25):
Okay. Well, with that, we've reached a very exciting lightning round. I've got five questions for you.

**Sam Lessin** (01:16:28):
Five questions. I'm ready.

**Lenny Rachitsky** (01:16:30):
Five questions. What are two or three books that you find yourself recommending most to other people?

**Sam Lessin** (01:16:35):
Ooh, okay. So let me pull up my list because I got to pull up my Kindle for this. One is I'm reading right now. I got to admit, I like to make fun of Marc Andreessen a lot, but he recommended a book called The Ancient City, which is fascinating. And so I'm in the middle of that right now. I'm really enjoying it. Man's Search for Meaning is great. I'm just going through. What have I read recently? You know what's great?

**Area 51, An Uncensored History**:
The Top Secret Military Base. Not very Erudite, great book. And then I honestly think the one serious one I'll make a recommendation on is Lessons From History is one of my favorite books ever. I would really, really, really recommend it. It's a short read, but Lessons From History by Ariel Durant is probably the most approachable, non-obvious book I love.

**Lenny Rachitsky** (01:17:28):
I did that one on Audible and you could just listen to it all in a couple hours.

**Sam Lessin** (01:17:32):
It's a short read. It's not a long book. I honestly think for me-

**Lenny Rachitsky** (01:17:34):
A few hours.

**Sam Lessin** (01:17:35):
... Hours of investment to intellectual return. My one real answer right now would be that I could give you a thousand others from ... There are things like The Banana King. Have you heard this one? Have you seen this one?

**Lenny Rachitsky** (01:17:35):
No.

**Sam Lessin** (01:17:48):
Oh my God, this is so good. What's it called? So The Fish that Ate the Whale. Incredible book. So is The Last Kings of Shanghai, if you know that one. These are all amazing books, but they're longer and there are more stories. Just everyone on the podcast should go read Will Durant.

**Lenny Rachitsky** (01:18:02):
Okay. I love it. Favorite recent movie or TV show that you've really enjoyed?

**Sam Lessin** (01:18:07):
Oh, I got to say, recent, I think Landman is fabulous. Have you watched Landman?

**Lenny Rachitsky** (01:18:12):
Yeah. Yeah. I've watched the latest episodes.

**Sam Lessin** (01:18:14):
I'm really into it. That would be my most recent take.

**Lenny Rachitsky** (01:18:17):
What's interesting about that show is now tech companies are all super into energy and data centers.

**Sam Lessin** (01:18:26):
Well, I'll tell you a funny story, which I talked to a founder who's in Midland, Texas, which is kind of part of the show, and it's so classic and typical. He's like, all these Silicon Valley people and now think they understand the energy industry because they watched Landman. It's wild, but I totally believe it because I know too many venture capitalists are like, "Oh, I now understand this because I watched Landman." And he's like, "It's totally a thing."

**Lenny Rachitsky** (01:18:52):
Yeah, I would feel that. Okay. Favorite product that you recently discovered that you love could be a gadget, could be an app, could be clothing.

**Sam Lessin** (01:19:02):
Okay, I'll pitch people on, this is a little self-serving, but I will pitch people on June date. So, okay, I actually don't use this product because I'm happily married, but this is cool. And here's the basic idea is if you think about so much of AI right now, this goes back to what businesses do you overhaul that are interesting that have AI implications, but are not AI. So everyone's got this whole virtual girlfriend, loneliness, you're going to chat with your friend, whatever, fine. These guys came out and what they do is they're like, look, if we're really trying to match up humans, call it Tinder 2.0, one of the best sources of information to do that is their ChatGPT histories.

**Sam Lessin** (01:19:42):
So this app is kind of built around the premise of distilling, you ask ChatGPT a structured prompt that they've designed, it pulls out an unbelievably good profile of who you are and then you basically match with people based on what you're actually asking ChatGPT about and the implications of who you are and things like that. And the fun part is I haven't obviously done dates on it, not for me, but I have pulled my profile from like, "Wow, this is shockingly good description of who I actually am." And so I think that's a really fun business.

**Lenny Rachitsky** (01:20:13):
It's like that prompt that the ChatGPT folks once had of just visualize my space based on everything you know about me. And it was like, holy moly.

**Sam Lessin** (01:20:22):
It's pretty good. It's pretty good.

**Lenny Rachitsky** (01:20:23):
And so in this dating app, I love this idea. It's so good. So do people read that? Are they able to see that as your profile or it's private?

**Sam Lessin** (01:20:30):
It's a little abstracted from that, but it's like the matching and the core thing is based on it. And again, I was just like, "Wow, this is good."

**Lenny Rachitsky** (01:20:39):
And it goes on dates for you. Okay. So it simulates what a date might be like between you two potentially.

**Sam Lessin** (01:20:44):
I guess.

**Lenny Rachitsky** (01:20:45):
Yeah. Your AI goes on dates for you, receives one high match, each one carefully. Wow, so fun.

**Sam Lessin** (01:20:51):
It's good. I get it if you're single. I tried with my newsletter at one point, I was like, "Oh, honestly, the people who read my newsletter are pretty weird and specific." And so, "Hey, why don't I just offer a matchmaking service?" Where I'm like, "Okay, if you read my newsletter really and you're here, tell me who you are. I will build a little LLM and try to match with people."

**Sam Lessin** (01:21:11):
Didn't work for me because candidly, you know what happened? Way too many qualified women and not enough men. I just don't have the liquidity. I have a hundred great women who wrote in being like, "Hey, this is what I'm looking for. This is who I am." And you're like, "You were an amazing person." And then the four guys who write in are like, "I want a 25-year-old hot girlfriend." This is not going to work, but June date might have the liquidity to make it

**Lenny Rachitsky** (01:21:34):
Work. By the way, your newsletter is awesome. Tell people where to find it while we're on that topic.

**Sam Lessin** (01:21:38):
And I actually can't. There's no way to sign up for it. If you send me an email.

**Lenny Rachitsky** (01:21:44):
Perfect.

**Sam Lessin** (01:21:45):
If you send me an email lessin@gmail, I'm fine. Then if I respond, you get automatically added. I basically just, that's how people I'm interacting with get added, or I'll just add you. But it's actually funny. There's not ... Oh, you know you can do actually, I'll get you on it, is if you go to wlessin.com, I have my little bot app up that includes a little LLM that kind of is trained on my writing just because I was having fun building it. If you enter your email address there and talk to it, it will add you. But no, there's not like a signup page for it.

**Lenny Rachitsky** (01:22:20):
Okay. I love this contrarian growth strategy. No, no, you can't really sign up. Okay, that's great. Okay, two more questions. Do you have a favorite life motto that you find yourself coming back to often in work or in life?

**Sam Lessin** (01:22:32):
No, but I will say that I have hanging in my gym, which I love from the Facebook Hackathon after the launch of Google Plus. This is going way back in history. They made great posters, which was Carthage Must Burn. And it was just this great moment in work time when it was like, "Okay, game on, Google's coming for us." And I had some of the most fun work experiences. We were working literally 24 hours a day. Past midnight every night was in that period and I love that poster. So Carthage Must Burn. How about that?

**Lenny Rachitsky** (01:23:08):
Great. Final question. You are a fellow podcaster. I really enjoy your podcast called More or Less. It's very clever and lead named because of Lessin and with the Morins, Dave Morin.

**Sam Lessin** (01:23:19):
We love our lessons. My wife and I are contrarians and hate everything by default and the Morins think everything's amazing. So it kind of works from a dynamic perspective.

**Lenny Rachitsky** (01:23:27):
I'd love it. And Dave Morin and Brit Morin, just to be clear, and your wife, she runs the information.

**Sam Lessin** (01:23:31):
Yeah. So she's good at prompting us with it's actually going around the world and then we just bullshit about it.

**Lenny Rachitsky** (01:23:36):
So what's something you've learned from that experience? I don't know. Any surprises about podcasting?

**Sam Lessin** (01:23:40):
So here's the thing, I think people ... It's kind of a weird podcast in some ways because honestly, I don't think any of us are highly filtered and it's kind of just like talking to your friends for an hour a week, which I want to do. I love the Morins. We don't live that close to them.

**Sam Lessin** (01:23:54):
So it's kind of fun to just have that time set aside and then cut it up and post on the internet pretty raw. Here's the funny thing about it. It serves our purposes way more than I actually expected it to, which is we enjoy doing it, which is the most important part. We would do it if no one was listening. And weirdly, a lot of our friends and people we care about in the industry seem to pick up the pieces they like from it and talk to us about it.

**Sam Lessin** (01:24:19):
And so it's like a great conversation starter. So it's like weird. It's not a huge podcast. It's big enough, but I actually think the thing that's been most surprising to me about it is that even though we don't really have growth strategies and we're not trying to blow it up and we don't get paid for it, it's like weirdly useful is what I would say. Even the fact that it's like ... I don't know.

**Sam Lessin** (01:24:41):
I'm not sure if that's your experience too, and I think your podcast is probably much larger than ours, but it's weirdly listened to and useful by the people that we care about and it's fun despite the fact that it's not like a ... I don't know, it's not all in or something yet. I don't think it ever will be. It's like way too niche, but we have fun.

**Sam Lessin** (01:25:05):
I would happily do it with my friends if no one was listening and then it's like, that's kind of wild that a bunch of people that we actually do care about find the pieces that are interesting. And it actually is helpful from a business perspective because you bullshit about something and then some entrepreneur shows up and is like, "Hey, by the way, here's a better idea." You're like, "That's great."

**Lenny Rachitsky** (01:25:23):
Sam, you were awesome. This was such a fun chat. Very different from my regular podcast. I think people will find this extremely interesting and useful, as I thought, and also fun. Two followup questions, where can folks find you online if they want to reach out, check out your find, what should people know?

**Sam Lessin** (01:25:39):
Look, our venture firm is called Slow Ventures, slow.co. I'm Sam Lessin. I'm kind of Lessin everywhere, whether it's like Twitter or Instagram or whatever you used or just lessin@gmail is my last name, is my email address and I do read it. So I don't know. I appreciate you having me on. It's always fun to see you. It's been too long and I don't know. Come hang out in the pool house sometime.

**Lenny Rachitsky** (01:26:02):
Okay. I love this. This is an excuse to hang out. Sam, thank you so much for being here.

**Sam Lessin** (01:26:07):
Hey, great to see you.

**Lenny Rachitsky** (01:26:09):
Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [6/15] The non-technical PM’s guide to building with Cursor | Zevi Arnovitz (Meta)

**Lenny Rachitsky** (00:00:00):
You are a product manager shipping product without knowing how to write code, barely knowing how to review code.

**Zevi Arnovitz** (00:00:06):
I have zero technical background, did music in high school ... when Sonnet 3.5 came out. I remember watching a YouTube video building apps using Bolt or Lovable. It basically felt like someone came up to me and said, "You have superpowers now."

**Lenny Rachitsky** (00:00:19):
These days, you're using Cursor with Claude Code.

**Zevi Arnovitz** (00:00:22):
If you're non-technical like me, code is terrifying, but AI just makes it so much possible. In the next coming years, I think everyone's going to become a builder. Titles are going to collapse and responsibilities are going to collapse.

**Lenny Rachitsky** (00:00:33):
The main challenge people have is reviewing the code that AI has written.

**Zevi Arnovitz** (00:00:36):
It's very difficult for me to catch mistakes. What I'll do is basically /review. This tells Claude to start reviewing its own code, but what's even cooler is I have Codex as well as Cursor open. I will have each of them review the code.

**Lenny Rachitsky** (00:00:52):
This comes back to this quote. I think everyone's always hearing. It's not that you will be replaced by AI. You'll be replaced by someone who's better at using AI than you.

**Zevi Arnovitz** (00:00:59):
It's the best time to be a junior, contrary to what a lot of people are saying, how there's no more junior roles out there. Yeah, that's true, but also when else in history could you get out of school and just build a startup on your own?

**Lenny Rachitsky** (00:01:11):
Today, my guest is Zevi Arnovitz. Zevi is a PM at Meta. Prior to that, he was a PM at Wix, and this is a truly remarkable conversation that every non-technical product person needs to hear. Zevi is super young and has no technical background, but as a smart, young, ambitious person, has learned how to use Cursor and Claude Code to build significant and real products completely on his own, and he's created his own very clever and effective workflow that everyone listening can copy.

**Lenny Rachitsky** (00:01:40):
To make that copying even easier, at the top of the show notes of this episode, you can download all of the prompts and /commands and start doing all of this yourself. Zevi shows you how to work with cursor to quickly add your ideas to Linear to explore your idea with AI, how to develop your plan, how to then build the thing, and then have different LLMs review your code and update your documentation, and then use all of this as a learning opportunity to develop your own sense of how things work.

**Lenny Rachitsky** (00:02:07):
I haven't stopped thinking about this conversation since we had it, and everyone needs to pay attention to what AI is unlocking for non-technical people. A huge thank you to Tal Raviv for encouraging me to meet Zevi. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

**Zevi Arnovitz** (00:04:56):
Thanks for having me, Lenny. I'm a huge fan of the show and tons of people that I've admired most and learned the most from. I've been on here, so it's a crazy moment for me. I'm really excited for this.

**Lenny Rachitsky** (00:05:06):
I really appreciate that. I want to start by reading actually a note I got about you from Tal Raviv, who is a previous podcast guest, many times newsletter collaborator. One of the most AI forward product managers that I know I've learned a ton from him. So here's what he said about you when he introduced us.

**Lenny Rachitsky** (00:05:23):
Zevi is the most hands-on vibe coding PM I know, and I've personally learned so much from him. His engineers at Meta ask him to teach them how to do what he does. Every time we get coffee, I repeatedly get this feeling of everyone needs to be hearing this.

**Zevi Arnovitz** (00:05:37):
That's so nice.

**Lenny Rachitsky** (00:05:38):
And so that's the goal. That's the goal of this conversation is to help more people hear what you figured out. We're going to get very hands-on. We're going to do a lot of show versus tell, showing people what you've figured out about how to be a PM, a non-technical PM building stuff. I want to give people a little bit of background on you because I think this is going to inspire a lot of listeners to feel like they can also do what we're about to show you. This is going to look very advanced, but just give people a little bit of sense of just your background.

**Zevi Arnovitz** (00:06:05):
I'm very non-technical. I have zero technical background. Did music in high school. A lot of Israelis do technology units in the Army. I was not in a tech unit. And basically a year ago, I was traveling with my wife for three months in Asia and we were in Japan and that was around when Sonnet 3.5 came out. And I remember watching a YouTube video. I think it was either Greg Isenberg or Riley Brown and they were basically building apps using, it was either Bolt or Lovable, just using AI.

**Zevi Arnovitz** (00:06:39):
And it was like a crazy moment for me because I was watching this and it basically felt like someone came up to me and said, "Hey Zevi, there's this cool new technology you should check out. You should really give it a try. Oh, and by the way, you have superpowers now." And the second I got home from Japan, I didn't even unpack my bags, ran to my computer, opened Bolt, opened an account, and for the past year I've been building.

**Zevi Arnovitz** (00:07:03):
And the last thing I'll say on that is we talked about this a bit before we started recording, but I was prepping with Claude for the episode and I was trying to clarify what my goal is for this episode. And Claude said, "If people walk away thinking how amazing you are, you failed. And if people walk away and open their computer and start building, you've succeeded." So I really hope that we can inspire some people to do the same.

**Lenny Rachitsky** (00:07:27):
I love that so much. I feel like that should be the goal for my podcast. If you're like, "I love that guest." It's less of a win. If it's just like, "Oh, I'm so inspired to do the thing that they figured out, that is the real win." I love, Claude is the best.

**Zevi Arnovitz** (00:07:41):
I agree.

**Lenny Rachitsky** (00:07:42):
Okay. So let's dive in and give people, let's start with kind of a high level overview of how you operate and you use AI in your job. What are the core tools and just what's kind of like the frame of reference for the workflow that you figured out and how you operate?

**Zevi Arnovitz** (00:07:56):
This all started where I was a project's power user. I love projects, GPT projects.

**Lenny Rachitsky** (00:08:03):
ChatGPT projects?

**Zevi Arnovitz** (00:08:03):
Yeah, exactly. GPT projects and Claude projects, which are basically a shared folder of chats which share both custom instructions and shared knowledge base. And I think it was around when GPT started using memory where I thought it was interesting, but it really annoyed me because I do a bunch of different things. I'm a terrible runner, I'm a PM, I was a student, psychology student, so I had all these different facets of life. And what happened was the memory feature was mixing stuff up.

**Zevi Arnovitz** (00:08:39):
So like I talked to GPT about running and it would say, "Oh yeah, after this 5K, you're going to crush all your next product reviews." And it's like, okay, I understand that you have that in your memory, but it's just not relevant. And projects basically allows you to compartmentalize and have things within the right context. So tracking back to the story I told when we came back from Japan, I started building this app.

**Zevi Arnovitz** (00:09:05):
The first thing I noticed was that these products were built in a way where, and when I say these products, I mean Bolt and Lovable, were built in a way where they were super eager to write code. So their system prompt was you're a coding agent. So when you'd write something, they'd straight away start coding. So at the beginning of a project, this was super fun and exciting because they just go and start building your app.

**Zevi Arnovitz** (00:09:27):
But later on when things got more complex, this created much more problems because planning is really important when you're implementing something technical and let's say you're implementing payments or something that's going to be a change to your database. If the coding agent is just like, "All right, I got it." And just starts writing code, this always results in terrible things, some really gnarly bugs that I had.

**Zevi Arnovitz** (00:09:51):
And to mitigate this, what I did was I created sort of a CTO. So again, I'm not technical. I have been in product for a while, but I know zero stuff about code. So basically what I did was I created a CTO with the custom prompt of it being the complete technical owner of the project. So I told it, "I own the problem. I own how we want the users to feel. You're the complete owner of how this is going to be built. I want you to challenge me. I don't want you to be a people pleaser."

**Zevi Arnovitz** (00:10:24):
All these things that kind of mitigate the regular ChatGPT-isms. I always think about this where for some reason, the easiest way for me to think about AI is to imagine it as people. And I think ChatGPT would probably be the worst CTO because it's such a people pleaser and it's so sycophantic where ... Just a short story I had a few weeks ago, I was trying to learn about Bun JavaScript, which was acquired by Anthropic and I was trying to understand what they do.

**Zevi Arnovitz** (00:10:54):
So I was talking to GPT and this wasn't within my co-founder CTO project and I asked it if it's similar to a different framework that I have in my app called Zustand, which nothing to do at all with what Bun JavaScript does. And basically GPT goes, "Oh yeah, it's exactly the same." And then it started talking about what it meant and I was like, "Wait, no, these are not the same at all." And it said the most terrifying and hilarious thing.

**Zevi Arnovitz** (00:11:22):
He goes, "Oh, I'm sorry. I thought you were just making this up and I was riffing with you." And I was like, "Oh no, no, no, this is terrible." So basically if regular ChatGPT was a CTO, that would be the CTO who goes along with your dumbest ideas. So creating the project allowed me to mitigate that.

**Lenny Rachitsky** (00:11:40):
So this is, just to be super clear, you have a ChatGPT project that you've given a prompt to be your CTO of your product and being a non-technical person, this is kind of like the thing you talk to when, and we'll get to what you're actually using to build when you have questions about architecture and decisions that are technical.

**Zevi Arnovitz** (00:12:00):
Yeah. So now I'll show my full workflow and I don't involve GPT anymore, but I definitely would recommend, even though the technology has gone ... So when I started this, there was no plan mode or ask mode. It was just build on these products on Lovable and Bolt, and they've progressed a ton. A lot of what I had as workflows have become ingrained in these products, which is really interesting.

**Zevi Arnovitz** (00:12:24):
I would still recommend start with a project for, first of all, the reason that I said, and also it kind of puts you in a place where you're in a chatbot and not writing code. So you take the time to converse and to learn, which I think is critical. And the second thing is if you're non-technical like me, code is terrifying. It's the scariest thing in the world to look at, and I look at it as kind of like exposure therapy.

**Zevi Arnovitz** (00:12:49):
I think if you see this where I'm working like in Claude or in Cursor, you might be excited to start using those, but I would really recommend starting slow with a GPT project, beautiful UI, super simple, then maybe graduate to like a Bolt or a Lovable, and then go to Cursor in light mode, slowly, slowly, gradually ease in until you open a terminal, go full dark mode, go full dev. So I would really recommend doing this gradually.

**Lenny Rachitsky** (00:13:19):
That is awesome advice. And so just to be clear, these days you're using Cursor with Claude Code powering it. And what I love about that is that you've never written code. The way you put it, you're afraid of even looking at code.

**Zevi Arnovitz** (00:13:33):
Yeah, 100%.

**Lenny Rachitsky** (00:13:33):
You can do exposure therapy, and I love that Cursor is useful to you. And what you're telling us is that graduating from a ChatGPT project that is kind of your technical co-founder kind of taught you enough to feel more comfortable going straight to cursor. You said that you actually went to Bolt and Lovable kind of in the interim and then you went just straight to Cursor. What's the reason to just go straight to Cursor? Is it just Cursor can do everything and once you get the hang of it, it's actually the most powerful tool?

**Zevi Arnovitz** (00:14:02):
Yeah, I think I graduated from each tool when I kind of outgrew it. So Bolt was awesome until I was trying to connect payments to my app and I kind of started losing it and then I graduated to Cursor and I've actually fallen in love with Claude. So I'm using Claude Code, but that also runs within Cursor, and I think this is Tal who told me this.

**Zevi Arnovitz** (00:14:22):
I'm not sure who he's quoting, but code is just words at the end of the day. So it's just files on your computer. So basically you can be working on the same project and carry it from app to app. And especially now, I can work with multiple models and apps on my project. So start slow, but definitely there's a lot of places you can graduate to.

**Lenny Rachitsky** (00:14:42):
Awesome. Okay. Should we dive into screen share showing how you operate?

**Zevi Arnovitz** (00:14:47):
Awesome. I pulled up cursor. Can you see it?

**Lenny Rachitsky** (00:14:49):
Mm-hmm.

**Zevi Arnovitz** (00:14:50):
Perfect. So within my code base, what you can see here on the left, these are all my code files. Here on the right is Cursor. So this is basically like having AI, which has access to all the code. And here in the middle, I have Claude Code running. And what you can see here, I'm going to close cursor for a second. What you can see here are all my /commands.

**Zevi Arnovitz** (00:15:10):
Basically what /commands are, they are reusable prompts that I save within the code base that I can run by writing / and then the name of the file. So here you can see Create Issue, which is the first command that I'm going to use. And basically what this tells Claude, it says the user is meant development and thought of a bug or a feature and improvement, capture it fast so they can keep working.

**Zevi Arnovitz** (00:15:32):
And then it basically says, this is the format that I want you to capture the linear issue in, and it explains a bunch of things what exactly Claude needs to do to get there. So the way I invoke this is basically I'll do /create issue and this injects this prompt into Claude. So it says, "I'm ready to help you to capture this issue, what's on your mind."

**Zevi Arnovitz** (00:15:56):
So basically when I'll do this is if I'm working on a big project and I suddenly come across a bug or have an idea that I don't want to work on right now, but I want to work on later, I'll do this really quick and Claude's main goal is to quickly capture what I'm thinking about. So quickly to run through my full workflow. So basically it starts with creating an issue. So this is the create issue /command, which basically tells Claude that I'm mid-development and it should quickly capture what I'm thinking about and create an issue within linear.

**Zevi Arnovitz** (00:16:26):
Then later on, when I want to pick this up, I have the exploration phase. Exploration phase is basically telling Claude, we're going to only explore what we want to solve here. It could either pull from linear or I can just speak freely to it. And what it will do is it will analyze and understand the issue and just ask clarifying questions. The next phase after we've done finished exploration phase is we're going to create a plan.

**Zevi Arnovitz** (00:16:50):
So you can see create plan. This basically has a template that I love for creating plans, and the output of this at the end of the day will be a markdown file with our plan that we can end up building along with code. After creating the plan, we have execute plan. After execution, we have review and then we have peer review, which is really cool and we'll get into later on, and at the end, we update the docs.

**Zevi Arnovitz** (00:17:14):
So this is updating documentation and everything so that agents can write better code later on. So I think what we'll do is we're going to build a feature live for my app, which I think is really cool. But first what I'd like to do is show you the app so you have some context. So this is StudyMate. It's a platform for students, which allows them to upload study materials and create interactive tests based on their own materials.

**Zevi Arnovitz** (00:17:40):
So here we can go to the top. Let's upload a PDF. We can decide what pages we want to be quizzed on. We can decide the number of questions, the difficulty level. And basically what happens behind the scenes is we send the information the user uploaded along with the system prompt and any other augmentations the users decided to Gemini and we create a quiz. These are challenging questions that are meant to assess comprehension. You even have some hints and once we do a few of these, we can submit ... I got them right?

**Lenny Rachitsky** (00:18:21):
Terrible results.

**Zevi Arnovitz** (00:18:22):
Yeah, lucky.

**Lenny Rachitsky** (00:18:23):
And so just to be really clear about this, this is like a side business that you have, an app that you built that's making money. That's like a thing you vibe coded having no technical experience.

**Zevi Arnovitz** (00:18:32):
Yeah, this is my weekend project. Yeah. This is what I do.

**Lenny Rachitsky** (00:18:35):
Amazing.

**Zevi Arnovitz** (00:18:35):
On weekends. Yeah. So you get basically deep explanations into why each question was wrong or each question was right. And at the moment, StudyMate only has multiple choice questions. And I was doing some competitor research over the last weekend and I saw competitors who had true or false questions and also fill in the blank questions, which I loved. So I think that'd be really cool if we could build that live. How's that sound?

**Lenny Rachitsky** (00:19:00):
I love it. Crossing your fingers, this all work. I just want to highlight the stuff you shared right before this in cursor. So this is a huge deal, what you describe here. This is essentially what you've figured out is a way as a person that has no idea how to write any code, how to build a product in Cursor as a product manager using this series of /commands that you've concocted that you're going to be sharing with listeners. They can download all these and just use them directly. They don't have to figure out all these prompts that you've figured out.

**Zevi Arnovitz** (00:19:31):
Yeah, 100%. Basically what happened was I formulated the backbone of this with the CTO, and it was basically within the system prompt of the CTO project that I had within GPT. So it said, step one, we do this. Step two, we do this. And now I'll keep building. And if I see something that happens over and over again, I'll just create a /command and then it will be automated within the workflow.

**Lenny Rachitsky** (00:19:54):
Amazing. So just to summarize the /command. So one is create an issue in Linear, which I love. Linear is awesome. Shout out Linear.

**Zevi Arnovitz** (00:20:02):
That's also from the product pass.

**Lenny Rachitsky** (00:20:04):
From the product pass. Oh my God, what a value? Okay. So step one is create the issue in linear. So it's a command. So this prompt /command you've created, just create issue. Then it's explore, which is explore the idea, help me ideate on what this could be. And this is Claude helping you think through the feature and product. And then it's actually create the plan.

**Lenny Rachitsky** (00:20:26):
And so it's like the AI helping you build the plan to build the product. Then it's actually execute, which is just build the thing. And then there's this review, peer review step, which is awesome that you'll share. And then there's document, update documentation based on the new feature that we're adding. Sweet.

**Zevi Arnovitz** (00:20:44):
Yeah. Cool. So let's go ahead and start building. So I'm going to use Wispr Flow to dictate and basically this starts with /create issue. So this basically sends that prompt. And I love this because I usually do this during when I'm building something else. So basically it tells Claude that I'm mid building something and I don't have a lot of time to waste time on this.

**Zevi Arnovitz** (00:21:12):
So just ask some brief questions so that you have enough to capture within linear. So I want to add fill in the blank questions to StudyMate. I want this to be 30% of tests to be generated as fill in the blank questions. I want there to be six potential answers for two blank spots, and of course there's only going to be two correct answers. So one correct answer and two incorrect answers for each spot and I want the interface to be drag and drop.

**Zevi Arnovitz** (00:21:43):
So that's just basically a quick think of how I want this to work. So it's going to ask me a few questions. Quizzes are 100% multiple choice, question structure, single sentence passage to blanks and priority. So one and two are correct, and this is not high priority. It's a nice to have feature.

**Zevi Arnovitz** (00:22:10):
So now basically what Claude is going to do is it's going to use MCP, which is basically a technology that was created by Anthropic, which gives AI the ability to use tools. So this is connected to my linear. So what it's going to do now is it's going to use everything we've said and create an issue within Linear.

**Lenny Rachitsky** (00:22:30):
And by the way, as this is loading, I just love the way, the way you described this, especially doing voice mode, it's like exactly how you would talk to an engineer describing a feature, "Here's what I want." And then they ask you questions, here's the clarification.

**Zevi Arnovitz** (00:22:43):
Yeah. So at first when I was doing this with the CTO, I would do it with ChatGPT voice mode, and that was crazy. That literally felt like ideating with a person. It would push back, ask questions, and maybe one day the coding tools will get there too, but that was exactly ... It really felt like sitting with my CTO. Great. So created STU88. So if we open up Linear now, we should be able to see ... Let's see where STU88.

**Zevi Arnovitz** (00:23:14):
There it is. Fill in the blank questions with drag and drop interface. So it has a TLDR, it has the current state. It did a little bit of research on the code base, I think, expected outcomes, some context. So yeah, so this is basically ready for me to pick up when I'm interested in building. So now let's say a few days go by, I finished the current project I'm working on, I can pick it up.

**Zevi Arnovitz** (00:23:36):
So when I pick it up, I do /exploration phase, which is what we said. And then instead of pressing enter, I'll press tab and I'll show you this. So basically, exploration phase, what it does is it will take an argument. This is basically a placeholder within the prompt, which allows me to enter something that is extra context for the AI. So I can say here, Linear STU88, which is referencing the ticket. And now what it's going to do is it's going to go, it's going to fetch the Linear ticket.

**Lenny Rachitsky** (00:24:12):
And what's the idea? What's the goal of the exploration phase? This ideate on the idea. Is that the-

**Zevi Arnovitz** (00:24:12):
Exactly.

**Lenny Rachitsky** (00:24:12):
Okay.

**Zevi Arnovitz** (00:24:18):
So it's both for the CTO to deeply understand the problem that we're trying to solve and also understand the current state of the code base, what files need to be affected, and how is the best way to implement this technically. And usually what happens is right now, Claude is just basically reading a bunch of files, understanding the basic structure of the code, and then it's going to come back with a bunch of clarifying questions that will decide how we end up implementing this.

**Lenny Rachitsky** (00:24:44):
So it feels like it's talking to your engineering manager.

**Zevi Arnovitz** (00:24:48):
Exactly, exactly. 100% this is how I think about it.

**Lenny Rachitsky** (00:24:51):
And you said that you're a CTO, so you used to use ChatGPT prompt to have a CTO in there. Now the CTO's living inside here in Cursor?

**Zevi Arnovitz** (00:25:00):
Yeah, because of the way the tools have developed and they've become so good at both exploration and code execution. So now it's just a habit that I call it a CTO, but it's basically all in one. The same agent will both do the exploration and write the plan and end up executing the code.

**Lenny Rachitsky** (00:25:19):
Got it. So it's basically it's Claude Code. Is there a prompt you gave it to act like that? To act like the right kind of CTO?

**Zevi Arnovitz** (00:25:26):
Yeah, so within the Claude.md, which is basically the system prompt that's loaded within Claude's context in every conversation, I have some basic stuff like this is our workflow, this is how we work. Within exploration phase, I want you to challenge my thinking, all kinds of stuff like that that can be loaded within the Claude.md file.

**Lenny Rachitsky** (00:25:47):
Cool. One last question before we move on here, just because I'm thinking about it as this happens, the Linear issue that you generated, how often is it actually great and ready? How often do you have to edit it? What's the quality of the Linear ticket that it generates? Because a lot of people are probably wondering just like all these terrible linear issues are being created by AI. Are they actually any good?

**Zevi Arnovitz** (00:26:08):
It's completely different because I'm a company of one. So a lot of the context is within here and there's no need for me to talk to other teams and understand. It's basically very accessible, and also I can easily see when Claude understood something wrong.

**Zevi Arnovitz** (00:26:26):
I don't want to say that I would create Linear issues at work like this, but definitely if you're building your own side project, they're pretty quality. And also, it just kicks off when I want to start working on it. I wouldn't say it's ready to be built. It's ready to start being explored.

**Lenny Rachitsky** (00:26:44):
Got it. So it's just the beginning of an idea. Actually, let's come back after we go through this flow of how you would approach this if you were at say Meta or another, maybe a smaller company, how this workflow might work at a larger company that isn't just your own startup.

**Zevi Arnovitz** (00:26:56):
Yeah. Interesting.

**Lenny Rachitsky** (00:26:57):
Let's come back to that.

**Zevi Arnovitz** (00:26:59):
Cool. All right. So this is Claude coming back. I have a comprehensive understanding of the code base. I thoroughly analyzed StudyMate live codebase and understand the current system, feature quest and key areas that it's identified. Usually I'd spend a lot of time going over this because this is super, super important, but just for the sake of development right now, we're going to brush through this.

**Zevi Arnovitz** (00:27:22):
Now Claude basically comes back after it's gone through the code base and understood the way it currently works. It's basically telling me what the current understanding is. So it's talking about how the app is set up at the moment, how the data is structured, what it understood from the feature quest, and what it has identified as key areas, and then it asks me some questions.

**Zevi Arnovitz** (00:27:46):
So it's asking about the scope, it's asking about the data model, the UX/UI of the feature, how it should be validated, how it should be graded, what changes need to be happened to the AI system prompt and all kinds of questions about the app. I've prepared answers to all these questions beforehand because I don't think we all want to sit through this. So I'm just going to paste that in and we'll see what Claude says.

**Lenny Rachitsky** (00:28:08):
Awesome. I love it. And I love just scanning those questions I was asking. It's like such smart, sophisticated, important questions instead of just, "Cool, here I go, I'm going to build it."

**Zevi Arnovitz** (00:28:18):
Yeah, and I think this is the big difference between just vibe coding and going along with the vibes and really building serious apps. I spend a lot, a lot of time going back and forth and understanding. Also, a very cool /command that I haven't showed yet is learning opportunity, which basically when something is really difficult for me to understand, I'll do /learning opportunity and then talk about what I want to learn.

**Zevi Arnovitz** (00:28:44):
And this basically primes Claude and says, "I am a technical PM in the making. I have mid-level engineering knowledge. I understand architecture and basically I want you to explain what we're currently working on using the 80/20 rule." So this is a great way to learn. I would definitely take this and every time you kind of see something that you don't fully understand, I would definitely use this to learn.

**Zevi Arnovitz** (00:29:08):
Great. So Claude basically comes back and says how it understands the current data model and how it's going to implement. Yeah, so it's ready to create the plan. So basically what I'm going to do now is I'm going to go and do /create plan and while Claude is doing this, I'm going to show really quick what this looks like. So basically, these plans are from a template that I found on Twitter.

**Zevi Arnovitz** (00:29:36):
I forgot who it was, but it was just a template that really resonated with me. And it's basically saying, based on our exchange, create a markdown file that will be the plan, include clear, minimal, concise steps, track the status. So this basically has like status trackers on each task that Claude updates as it's going through and it will have a TLDR, some critical decisions that we've made and the plan itself.

**Zevi Arnovitz** (00:30:00):
So Claude has finished writing the plan, so we'll be able to look and see exactly what the plan is. So it has a TLDR, it has the critical decisions we've made and the tasks broken down. And this is a perfect plan and it's also a really good way to write this because a lot of times, I'll use different models to execute certain stuff. So Cursor has an amazing model called Composer, which is superfast. So a lot of things that are not that complex, I'll use Composer.

**Zevi Arnovitz** (00:30:31):
Gemini 3 that just came out is unbelievable at UI. So a lot of times, I'll split the plan into backend and front end, and then I'll have Gemini just read the plan and do the front end. So having this as a markdown file is really good. And also going forward, it's really good to have within the app so that later on, if an agent is writing code in a certain area, I can see what's already been done there.

**Zevi Arnovitz** (00:30:54):
So what we're going to do now is we're going to execute the plan. So now I think we're going to do this with Cursor just because Composer is so freaking fast. So what we can do is basically just say execute and then we can tag the file. And Composer is ridiculously fast. So that's it. It's off. It basically understands what the plan is and it's going to go ahead and start writing the code.

**Lenny Rachitsky** (00:31:20):
Let me ask you a question while this is happening.

**Zevi Arnovitz** (00:31:22):
Awesome.

**Lenny Rachitsky** (00:31:23):
I have many questions, so this is a good time to ask a few of them. You said that Lovable and Bolt and other apps in that space are just not enough to build really serious apps and you have to move to Cursor to do that. Tell us more about that. Just what's the limitation you ran into with those products and why you switched to Cursor?

**Zevi Arnovitz** (00:31:42):
I started using Cursor and Claude Code a few months ago and I haven't looked back, but at that time, these teams have been moving like crazy. So I don't want to say I wouldn't trust them. I don't know what the current state is. But for me, it was basically the issue of I felt that Bolt was being very opinionated on how I should do things. And I felt like my knowledge has gotten to a point where I can graduate and be more in control.

**Zevi Arnovitz** (00:32:11):
By the way, I think that the main difference between all these tools is basically the harness. So the models are all the same models. I'll run Claude within Cursor, I'll run it within Claude Code, and it's also the models that Claude is also the model that is underlying Bolt and Lovable, but basically, Bolt and Lovable will add a bunch of levels in the middle that will take all kind of guesswork and hard decisions out for the user.

**Zevi Arnovitz** (00:32:37):
So the user doesn't have to make these hard decisions. So it's also very easy to build, but the flip side of that is that you have less control. And basically Claude Code is just taking Claude and shoving it straight in your code system and giving it full tools and to do whatever it wants, but also with that comes a lot of decisions that you need to make. So I don't know if you can't build really amazing production apps using Bolt or Lovable now, but I think basically if you want the most cutting edge abilities of the models and you want to be able to make all the decisions on your own, it's probably best to be on one of these tools.

**Lenny Rachitsky** (00:33:13):
What I'm feeling and hearing is that planning work that you did, that's the stuff that Lovable, Bolt, and would you put Replit in that bucket too?

**Zevi Arnovitz** (00:33:21):
Yeah, for sure. Lovable, Bolt, Replit, Base44.

**Lenny Rachitsky** (00:33:21):
v0.

**Zevi Arnovitz** (00:33:24):
Yeah, v0, all same bucket.

**Lenny Rachitsky** (00:33:27):
So essentially, they're doing that planning for you. And as you said, they're very opinionated. They try to make it easy. So it's just like, "Here's how to do it. Here's the way we think is best for people." And what you're saying is once you're trying to get a little more serious about it or want to go in a different direction, you don't have the power to change how they plan. So Cursor lets you do that.

**Zevi Arnovitz** (00:33:49):
Yeah, I don't want this to come out like I'm badmouthing them. Base44, let's say.

**Lenny Rachitsky** (00:33:53):
No, absolutely.

**Zevi Arnovitz** (00:33:53):
Yeah. Base44 does an amazing job at basically taking all the complex guesswork out of building product and just allows you to just go with the vibes and build, but it will do sign in with Google for you and it will do a database, but then you don't have decisions on what database am I using. Do I need sign in with Google this way or another way? It would just do it out of the box. So that's basically the trade-off there.

**Lenny Rachitsky** (00:34:16):
Awesome. Shout out Maor, the founder of Base44.

**Zevi Arnovitz** (00:34:20):
Yeah. Maor Shlomo. Yeah. He's amazing.

**Lenny Rachitsky** (00:34:23):
Okay. I just love how this is like the way you're like flinging, what's the word? Slinging models like Gemini 3 for frontend. I love that you've never written any code and you're just like, "Cool, use Gemini for this and Claude for this. And I'm just working on Cursor, talking to this CTO, helping you build stuff and build significant product."

**Zevi Arnovitz** (00:34:48):
Yeah. We just live in the craziest of times where basically the world changes once a week, it feels like. And there is just no boundaries. You can use all of these just on your regular MacBook or regular laptop. And I have these moments, I call them time machine moments, which is basically this week, for instance, I was prepping for the podcast using Claude with a project.

**Zevi Arnovitz** (00:35:17):
I was building, I was fully localizing StudyMate from Hebrew to English, which I did in two days, which would probably take a dev team weeks. And I was building a personal site, which went from no domain, no nothing, to live on a domain within an hour and a half. And I was doing all three of these in parallel. And there was a point where basically all three of the agents were running, so I didn't have anything to do.

**Zevi Arnovitz** (00:35:41):
I just had to let them think, and these are the time machine moments where I feel like I was in the future and I just stick my head out of the time machine and whoever's next to me, at the moment it was my wife, I'll just say, "We live in the future." And she'll be like, "Huh, what?" And I'll be like, "No, no, don't worry about it." But it's just basically so crazy that all these things are just an API away. You can use anything. So I think it's an awesome time to be curious and optimistic and hardworking.

**Lenny Rachitsky** (00:36:08):
These are my favorite kinds of podcast guests. People that are living in the future, figuring out all these things and then are just kind of come back, as you said, poke your head out of the rocket ship and just like, "Hey, here's this thing that I figured out. Here's where we're going."

**Zevi Arnovitz** (00:36:20):
Yeah, best time to be alive. So awesome. So it looks like it's finished. So now what we're going to do is we're going to run the app locally and we'll be able to see what Composer ended up building and we're going to see if anything else is needed on our end to maybe do some manual review. Does that sound good?

**Lenny Rachitsky** (00:36:42):
Sounds great. And I love, that was like, I don't know, a few minutes where if it was a human engineer, it'd be like days, maybe a week for work.

**Zevi Arnovitz** (00:36:49):
For sure. Yeah, for sure. No, Composer like the one thing is it's just so, so blazing fast, keeps you in flow. So yeah, full features take minutes.

**Lenny Rachitsky** (00:37:00):
And it probably costs a couple bucks in AI credits.

**Zevi Arnovitz** (00:37:04):
I don't even look. I used to be so stingy about paying for products and now I'm just basically, I look at it all as tuition, as stuff that I'm paying for learning. So I don't know how much it costs, but it's definitely worth it.

**Lenny Rachitsky** (00:37:18):
That explains why they're the fastest growing products in history.

**Zevi Arnovitz** (00:37:21):
100%.

**Lenny Rachitsky** (00:37:23):
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**Zevi Arnovitz** (00:38:33):
So now we have this feature, which basically we built and I can ask it to make some changes because it's running locally and once it's ready, I'll be able to ship it to users. So now the next phase after I've QA'd it and basically tested it manually, I'll have Claude review its own work. So what I'll do is I'll reopen Claude Code.

**Lenny Rachitsky** (00:38:56):
I love this because this is one of the things that comes up a lot in this podcast is writing code is now so easy. The main challenge people have is reviewing the code that AI has written.

**Zevi Arnovitz** (00:39:05):
100%.

**Lenny Rachitsky** (00:39:05):
And what you're doing here is you're having Claude review its own code.

**Zevi Arnovitz** (00:39:08):
Yeah, so this is another thing where it's very difficult for me to catch mistakes. So my review process has gone through a bunch of iterations to really be as good as possible and to catch as many things as possible. So I'll always manually QA at first to make sure if I can see any mistakes that Claude made. And then what I'll do is basically /review.

**Zevi Arnovitz** (00:39:32):
And this tells Claude to start reviewing its own code. But what's even cooler and something that I'm really proud of is I will usually do multiple reviews and I'll have Codex, which is ChatGPT's competitor to Claude Code, as well as cursor open, and I will have each of them review the code. And then what I do is I have a /command called peer review, which is really interesting. And basically what it does is it's going to take Claude, which is usually the agent who I'm working with.

**Zevi Arnovitz** (00:40:07):
And just to put this in a mental model, this is basically my dev lead that I'm working with. The /command is basically saying, "You're the dead lead on this project. Other team leads within the company have looked at your code and reviewed it and found these issues." Don't take what they said at face value. The reason is you have more context than them and you led this project.

**Zevi Arnovitz** (00:40:33):
You need to either explain why the stuff they found are not real issues and wrong or fix them yourself. And it's really cool because the way I look at these things is I look at the models, I try to imagine them as people and I can really tell you how each one of these would be as a real human because they have-

**Lenny Rachitsky** (00:40:54):
Each model.

**Zevi Arnovitz** (00:40:55):
Yeah. Each model has such distinct characteristics. So let's say Claude, she would be the perfect CTO. She's very communicative. She's very smart. She doesn't just go with the flow and do whatever you tell her. She's very opinionated, but also super collaborative, which is I think why I'm always drawn to Claude because I need to do so much learning and it's your dream, a very communicative, but very opinionated dev lead, but then there's also Codex.

**Zevi Arnovitz** (00:41:26):
So I use Codex 5.1 Max, whatever. I don't know, they're not the best at naming models, but GPT's model. I always imagine it as like the best coder within the company who comes to the office with a hoodie and sandals and sits in a dark room. And you basically only bother him when you have the worst bugs and you say, "Listen, we have this bug and it will just close the door for two hours and come out and say, I fixed it." And you're like, "Wait, what? Are you going to tell us what happened or whatever?"

**Zevi Arnovitz** (00:41:56):
And he's like, "Don't worry about it. I fixed it." It's like really not communicative, but it solves all the worst problems. And let's say Gemini is like a crazy scientist who's super artsy, super talented at designing, but if you sit next to it and watch it work, it's terrifying. You would fire that person instantly. This might be just my experience, but when I'm using Gemini within antigravity, which is Google's new competitor to Cursor, when it's writing code, you can see the steps it's taking and it's terrifying.

**Zevi Arnovitz** (00:42:28):
You'll say, "I want you to redesign the top of the dashboard." And you're looking at its thought process and it will say, "Oh, first things first, I'll delete the dashboard." And then it'll be like, "Nope, that was a mistake. I'll bring it back." And then it will say, "Oh, can I edit the database?" And you're like, "No, do not edit the database. You're just doing a redesign." And then it will end up designing something beautiful. So the way there is a rollercoaster and very scary, but at the end of the day, Gemini is very good at design.

**Zevi Arnovitz** (00:42:55):
So I think that using all these models and basically playing to their strengths and mitigating their weaknesses by using other models is a game changer for me. So I'll do peer review a bunch of times and I'll have other models review other models code and kind of have them fight it out basically. Sometimes Claude Code will get really sassy and be like, "This has been raised for the third time. And for the third time I'm telling you, this is not an issue. This is by design." So it's just a really cool thing that I've added and I haven't seen many people doing it.

**Lenny Rachitsky** (00:43:31):
That is such an incredible rant/way to understand what's going on. Okay, awesome. So we just ran the review. So show us what we saw there and let's actually try this peer review. I'm really excited to see what you learned there.

**Zevi Arnovitz** (00:43:45):
Yeah. So basically Claude has reviewed its code and it's found a bunch of bugs, a critical bug it found in the prompt, some high bugs, some medium bugs, and now what I'll do is I'll do the same thing with the other models. So Codex has a built-in code review that you can do, or I just like to say review all the code in this branch.

**Zevi Arnovitz** (00:44:11):
Of course, branch is referring to the GitHub branch that we're working on. We're not working on the live code base. And then I'll do this with Composer say with, let's do it with Composer One. So I'll do /review here as well. And basically these are both going to run and do a in-depth review similar to what Claude does. But again, because of the differences between the models, they're all going to catch different things and they're all going to look differently, and this is a really cool way to work.

**Zevi Arnovitz** (00:44:40):
It's basically if you had other team leads within the company review the code. Here, you can see how fast Composer is. I think GPT probably will take a bunch of time. Like I said, it's in its own dark room right now reviewing code and we'll come back in a few minutes.

**Lenny Rachitsky** (00:44:56):
Okay, cool. So we can let these run in. We don't actually have to go through the whole process, but is the idea once you get these results, you run peer review and you copy and paste kind of these results. Is that the idea?

**Zevi Arnovitz** (00:45:04):
Exactly. I'll copy and paste the results. I'll do peer review and then I'll say dev lead one and then paste from one of the models. And then I'll say dev lead two and paste from the other model and basically have them fight it out until I feel like we have no more issues.

**Lenny Rachitsky** (00:45:17):
Incredible.

**Zevi Arnovitz** (00:45:18):
For me, this is super important because I'm not technical and I'm not a developer. And I'll also use /learning opportunity a bunch during this to learn about stuff that I don't understand or don't fully grasp.

**Lenny Rachitsky** (00:45:31):
Incredible. What a clever solution to solving this code review problem where it's like, I don't know what you ... I don't know how to recode, so what am I going to even ... Yeah.

**Zevi Arnovitz** (00:45:39):
Yeah.

**Lenny Rachitsky** (00:45:40):
Okay. Incredible. Let's wrap up this workflow. Is there anything else that's important in this workflow? And again, all this stuff is going to be available where people can just plug this stuff into their Cursor account and use it themselves.

**Zevi Arnovitz** (00:45:52):
100%. The one thing I'll say is that I think just like working in general with AI and even just like working on any product, doing constant postmortems is critical. So a lot of times we'll find all these kind of bugs or maybe Claude will fail to execute something correctly. And at the beginning when I started vibe coding, I would basically just keep running at it like running at the wall and until it worked. And once it worked, I was like, "All right, awesome. This works. Let's keep going." But I've learned over time that updating documentation and tooling is one of the biggest hacks for productivity.

**Zevi Arnovitz** (00:46:31):
So when Claude will fail to do something or I'll see this really bad bug that shows that Claude really didn't understand something, I'll ask it, "What in your system prompt or tooling made you make this mistake?" And Claude will kind of like go introspective and think of what made it create that mistake. And then I'll say, "Okay, great. Let's update your tooling and documentation so that this mistake never occurs again." And I do this every time I'm either building an internal tool or anything. And I think this is just like working.

**Zevi Arnovitz** (00:47:03):
If you end up doing a bunch of mistakes and then end up releasing the feature to users, so you're like, "All right, it's a big success." But going back and even when you've succeeded, looking and understanding what you did and what you could have done better is critical. And also using AI, this is probably one of the biggest unlocks. Going back to your prompts, understanding what was not good enough, iterating on them and then seeing how AI's responses get better, I think that's probably one of the most important things and one of the things that divides between people who are okay with using AI and the people who actually know how to use it.

**Lenny Rachitsky** (00:47:38):
That is such good advice. So what I'm hearing is when the models do something dumb, make a mistake, you ask it to reflect on what the mistake it made was, and then you update the /command prompts with that knowledge so that in the future, it's not making that same mistake, and it just keeps getting better.

**Zevi Arnovitz** (00:47:38):
Exactly.

**Lenny Rachitsky** (00:47:56):
These things just keep getting smarter and smarter. So you're building up this really incredible prompt that just gets better and better.

**Zevi Arnovitz** (00:48:01):
Exactly. Not always the /commands. It will sometimes update different documentation or its tooling, but basically it's understanding what the root cause of the mistake that the AI made and fixing it.

**Lenny Rachitsky** (00:48:15):
Awesome. So the models are getting smarter and then there's also the other parts of your workflow can get smarter as you find flaws in the way it does stuff.

**Zevi Arnovitz** (00:48:25):
100%. Yep.

**Lenny Rachitsky** (00:48:26):
Amazing. Okay. Is there anything else there before I move in a couple other directions?

**Zevi Arnovitz** (00:48:31):
I think that's it. I think we covered pretty much everything. Basically, just to wrap this up, what I do is I do a bunch of code review and then update the documentation so that everything is documented. So the next time I try to build a feature in this area, there won't be any mistakes. And then I'll do a bunch of testing. I'll do some user testing as well before I release this to general availability. Obviously we're not going to release this. This was just a show, but hopefully maybe by the time the podcast comes out, I'll have done this correctly and release the feature.

**Lenny Rachitsky** (00:48:59):
It's incredible that this was not possible, I don't know, two years ago, maybe a year ago, you are a product manager shipping a product without knowing how to write code, barely knowing how to review code. You said you're afraid of looking at code. As a product manager, you're building a product in Cursor using all of these different AI models. You're making money with this product. We're so used to this now, but it's insane what is now possible.

**Zevi Arnovitz** (00:49:29):
It's the best time to be live. 100%. I think that I understand the fear, but AI just makes it so much possible. Just a quick side note here, my brother, who I'm building one of the apps with is an entrepreneur. He has a beautiful business that helps old people and seniors understand to use technology and AI better. And he's basically doing the same kind of learning as me, and he's replaced all of the tools he was paying for. I think he was paying for Zapier and Airtable, and he's basically built a full-fledged CRM system and automation system for his business completely alone. So for the people who are curious, optimistic, hardworking, this is the best time to be a builder.

**Lenny Rachitsky** (00:50:14):
And what I love about this conversation we're having here is it feels like the biggest barrier for a lot of people is like, how do I get started? What exactly do I do? I open up cursor. It looks very intimidating. I don't know how to write code. I don't know how to build stuff. I don't know about databases. And so you're going to be sharing all these /commands and basically this whole workflow with the audience.

**Zevi Arnovitz** (00:50:34):
Yeah.

**Lenny Rachitsky** (00:50:36):
Okay.

**Zevi Arnovitz** (00:50:37):
And like I said, just start at GPT. Start on GPT, tell it what your idea is, tell it to explain to you what are even the first steps of thinking, what are the decisions you need to make? And just be inquisitive, learn. Don't rush things. It's very important to just dive in and really spend the time to learn.

**Lenny Rachitsky** (00:50:56):
And you share this. One of your /commands is learning opportunity, and it's how you learn a lot of these things. Just teach me this thing and how this database issue works.

**Zevi Arnovitz** (00:51:04):
Exactly.

**Lenny Rachitsky** (00:51:05):
Okay. There's a couple directions I want to make sure we touch on. One is coming back to a question I asked earlier about how this might work at a larger company. Say it's not like Meta, but just like, I don't know, a thousand person company, 500 people. How much of this can you plug and play into a workflow as a PM at a larger company? What would be your advice for someone that may want to start trying to ship code, at least showing people what's possible?

**Zevi Arnovitz** (00:51:28):
I think that first making your code base AI native is a really important step, and I think this needs to be done by technical people. So basically my codebase has a ton of just plain text in it. So it will have a bunch of markdown files that explain to agents how to work in certain areas of the code base and high level structure so that the agents navigate through the code base easier.

**Zevi Arnovitz** (00:51:54):
And I think that if this is set up in a really good way, I still don't think PMs should be shipping heavy database chain migrations or any big project, but contained UI projects, especially if you just build it, create the PR and send it to a dev to do the final finishes. I think that's definitely something that's possible. And I think we're going to see that a lot in the next coming years. I think basically everyone's going to become a builder, so it should be really interesting.

**Lenny Rachitsky** (00:52:25):
Okay. So your advice here is as a PM, maybe don't go right to Cursor, start building, shipping, trying to ship features to production, especially complicated features. Do you think we'll get there? Do you think in a couple years, PMs will be doing this and it'll feel less scary and crazy?

**Zevi Arnovitz** (00:52:42):
If there are PMs. Yeah, I think titles are going to collapse and responsibilities are going to collapse and everyone's just going to be building. I definitely think that the models, the context window is getting bigger, the models are getting smarter and I definitely see how PMs or any other background can be writing. At the moment, I wouldn't wait for that. I would use this as a collaborative learning opportunity to work with your dev team. It's going to be difficult.

**Zevi Arnovitz** (00:53:14):
A lot of developers are very, very skeptic about the current state, and I think that it's going to be a lot of sales work on your end to convince, but if you're able to convince, and I think teams that are really sold on this and want to take the time to work on their workflow about how can our team become more AI native, I think that these teams are going to probably be a few years in the future and they're going to look back at the few weeks they spent setting this up as the best time they spent.

**Lenny Rachitsky** (00:53:43):
Let me ask you another question around just the job of a PM. One of the biggest fears people have with these AI tools for PMs for every function I imagine is just you start to rely on these things, your skills start to atrophy, you're producing all this slop that looks great, cool, amazing strategy doc. No, it's actually not at all good. Are these Linear tickets or just products that are half-baked?

**Lenny Rachitsky** (00:54:08):
What's your take on these two parts of just like, how has this impacted your craft as a PM? Do you feel like this is weakening your skills because you're so reliant on these tools and just how do you keep the quality of this stuff up and not just like, "Eh, it's just a bunch of AI generated slop."

**Zevi Arnovitz** (00:54:25):
I have a very strong disagree to this and I've heard it a bunch. I remember when I started using Tal Raviv has this whole course on building a PM Copilot using projects, which is probably one of the best courses that you can take. And when I started working with my own Copilot, I remember people at work looking and saying like, "Oh, so you're basically outsourcing your thinking." And to me, that's just the worst way to look at it.

**Zevi Arnovitz** (00:54:53):
And I think for some reason, these people usually have a high correlation with the kind of person who doesn't like to show their presentation when it's only 10% done or doesn't want to ask for help a lot. I think that there's a misconception with a lot of PMs that the job is always having the right answers and being the smartest person in the room. And at least how I was trained and how I believe the role of the PM is, it's the exact opposite.

**Zevi Arnovitz** (00:55:22):
It's basically harnessing anything that can get us as quick as possible to delivering the right solution to users. And I just think this is like that really smart person that has context or your mentor or whatever, but is just always available and doesn't judge you and can really help you. So if you're using it to just create your outputs and then putting them out there, yeah, that's AI slot, but it's also human error.

**Zevi Arnovitz** (00:55:49):
I think it's really important that you own your own outputs. If you put anything out there or show something in a product review and you say, "Oh, sorry, that was built by AI." That's your mistake. I think if you use these intentionally and really take the time to understand how to use AI in the correct way, it's one of the biggest game changers that will make you much better as a PM.

**Zevi Arnovitz** (00:56:13):
And another thing here is that, especially for more junior PMs, it allows you to play at such a higher level than you would normally. I think that at Wix, I wasn't thinking of what's the marketing strategy of the company and how will the onboarding be completely revamped within the whole product. But on my side product, I can just do whatever decisions I want and think of the strategy and marketing and the messaging.

**Zevi Arnovitz** (00:56:39):
And this is basically just getting me reps, which is one of the most important things at the beginning of your career. So I understand the fear that how do you outsource certain stuff and you're not owning 100% of everything, but I think the upside is so much more valuable. And I think the only way that AI makes you worse at your job is if you're using it wrong.

**Lenny Rachitsky** (00:57:03):
Is there anything that you've learned about reducing the sloppiness of the output, just like a tip for keeping the quality high of the stuff that it produces?

**Zevi Arnovitz** (00:57:13):
Similar to people, setting up AI for success, for the task at hand. So if I just brought in a junior to write a deck or something and I didn't give it any guideline, I just said, "Give a strategy deck." He would probably just go online and find top strategy deck and just reproduce that, which is basically what AI is doing. It's basically just fed all of the internet.

**Zevi Arnovitz** (00:57:40):
So instead of that, guiding it and giving it context on what your style of writing is and what you're trying to solve and all these different things, I think that's probably one of the biggest unlocks. So that's just a quick tip. And also Cursor has a /command called deslop, which is basically going back over the code. I don't know if this is integrated into the product yet, but it's on Twitter. Their founders have been talking about this, so that's definitely something I would run after just to make sure that no slop is left behind.

**Lenny Rachitsky** (00:58:12):
That is so funny, deslop. Okay. One more question, which may lead to something else, but kind of going in a whole different direction. You used AI to help you actually interview for the job that you got at Meta. Talk about how you did that, because a lot of people right now are struggling to find a job reading about all these people using AI to help them interview. You actually did it. What did you use? What worked?

**Zevi Arnovitz** (00:58:37):
I feel like the analogy here is I have 12 nieces and nephews and you can see how people who have grown up in a different world, how their mind is formed differently. So if you ask me, how do you answer a phone, I'll do this. But a child now, when you say, "How do you answer the phone?" They'll do this. They'll do the iPhone answer. And I feel like people who are growing up now in their professional lives were the same just with AI.

**Zevi Arnovitz** (00:59:06):
So every time I'm faced with a new challenge or problem, I think AI first how to solve it. So Meta reached out and said they'd like me to interview. Straight away, I opened up a project within Claude. I started looking online for all the best information out there, things that I resonated with. I took a ton of frameworks and stuff from Ben Erez who has written a guest post for you, who I think is one of the best minds out there right now.

**Zevi Arnovitz** (00:59:33):
And basically I created a project which was my coach, which I would come and consult what to do at each phase. I would mock interview with, and this was amazing. Also, I created a game in Base44, which helped me ... I was really struggling with segmentation within the product questions, so thinking of the correct segments. So I basically just created a quiz game, which creates questions and different segmentations and I have to choose.

**Zevi Arnovitz** (00:59:59):
So this was like, I spun this up. It's a web app that I would play sometimes when I was on the bus to work. So basically, I think Ben talks about this a bunch, so I don't just go read Ben's stuff, but just creating a project and feeding it with all the best information on the internet and then mocking a bunch. I will say that the biggest game changer for me was doing human mocks.

**Zevi Arnovitz** (01:00:22):
So cold outreaching to people on LinkedIn and having them do actual mocks for me, I think that at the end of the day, especially for the Meta PM prep, which is super competitive and difficult, I think there's no way to get around that.

**Lenny Rachitsky** (01:00:38):
That is so cool they use that post. I wasn't aware. We're going to link to it. And in that post, Ben shares all these prompts you can feed ChatGPT to help you prepare for interviews, do mocks online. It's a really important point to say that those take you to a point, but it's actually better to use humans. I actually have a post coming out soon in collaboration with Noam Segal about how everyone is using AI to interview.

**Lenny Rachitsky** (01:01:02):
And one of the most interesting ways I've heard people and that we've found in this research was that people use it to get feedback. They record the interview and then it gives them feedback. Here's where you could have done better. Here's what you missed because the feedback loop is so missing. No one ever tells you, here's what you did badly in this interview. No one tells you that, and AI can do that.

**Zevi Arnovitz** (01:01:21):
So I'll add two things to that. One, which is exactly this. So I'll mock with AI. Also, I did something really cool where there's a question bank online free by Louis Lynn, which basically is an always updating bank of questions that people are asked in real interviews. And I basically used Comet, which is the Perplexity's browser. And I had the agent run all kinds of analyses on what the most asked questions are. And that's how I knew how to prioritize what questions I would mock.

**Zevi Arnovitz** (01:01:52):
And then at the end of these mocks, I would tell Claude within the project, "You're my coach and I don't want you to make me feel good. I want you to make me as ready as possible for these interviews. So give me feedback, like you said." And the other thing that I did was really cool was some questions where I didn't have time to mock. I would ask Claude to play the candidate, and then it would just give me a really good answer. And I could also learn from that, like learning from someone who does a perfect answer.

**Lenny Rachitsky** (01:02:20):
Oh, man. I really love the way you phrased it, that people kind of in your generation, the default is, "I have something I need to do. Let's go to AI immediately and help me prepare for this thing, help me figure it out."

**Zevi Arnovitz** (01:02:32):
Yeah.

**Lenny Rachitsky** (01:02:33):
And this comes back to this quote that I always think about, which I think everyone is always hearing, but it's such an important quote that it's not that you will be replaced by AI at least for a long time. It's you'll be replaced by someone who's better at using AI than you.

**Zevi Arnovitz** (01:02:47):
I agree.

**Lenny Rachitsky** (01:02:48):
And that's what these conversations are for to help people keep up with all that and to learn some of these skills. And again, see where the future is going and start to learn how to get there yourself. Okay. Zevi, before we get to our very exciting lightning round, I'm going to take us to a recurring segment on this podcast I call Failure Corner.

**Lenny Rachitsky** (01:03:05):
And why I love this segment is people come on this, just even this conversation, it's like all these amazing things you figured out, everything is going so well. People rarely hear the things that don't go well, and those are often the most interesting and impactful stories. So the question is just, what's the story of a time you failed in your career and what did you learn from that experience?

**Zevi Arnovitz** (01:03:26):
Yeah, I love this. I love this. I love Failure Corner, big fan. So I'll tell a story about when I started at Wix. So basically I started within Wix as a student program and straight out of the student program, you get put into a certain team. So I was in the editor, which is the core product of Wix. And the other PMs were just the best PMs almost at Wix.

**Zevi Arnovitz** (01:03:51):
There's four other people had much more experience than me and they were ridiculously good. And I remember coming in and thinking like, my first product review, I'm going to blow these people's socks off. They're not going to believe how good of a PM I am. And I basically didn't really share what I was thinking. I would work tons of hours alone and I was like, "I'm going to kill this product review. They're going to be so impressed." And I ended up failing miserably. My product review was not good.

**Zevi Arnovitz** (01:04:19):
It wasn't the format they expected. They had a ton of questions that I missed and I felt awful when it was over. I was like, "Ah, you're such an idiot." And I saw that everyone was like, "All right, cool. Yeah, just come back in two weeks and we'll keep getting at this." And I understood in that moment that they had zero expectation of me being a 10X PM, but the expectation of me was being a 10X learner.

**Zevi Arnovitz** (01:04:42):
And the second I understood that, my whole mindset shifted. And I think this is probably the best tip that I give now to junior PMs is basically be the best learner you can be at the beginning. No one expects you to know all the answers and no one expects you to be good. So basically what I did was I took each person on the PM team, there was four other PMs and I assessed what their strength is and used them as a mentor for that.

**Zevi Arnovitz** (01:05:08):
So Neri who's still my mentor till today, he has the best product sense of anyone I've met. Oya is super, she's like a methodology expert. She just thinks in frameworks. Yahra, who is the head of product, basically can look at a product and then instantly understand the third and fourth order effects of them, the system thinking. So every time I had an issue with one of these areas, I would come to one of them and consult them, and this does two things. First of all, I learned a ton.

**Zevi Arnovitz** (01:05:35):
And the second thing is that when the next time, the next product review, my success felt to them like their success because it wasn't this kid who's trying to show us up how cool he is. It was like our mentee kind of making us all proud. And it was such a great shift for me. And basically at the end of the day, I really excelled through this.

**Lenny Rachitsky** (01:05:57):
That is an awesome story. And this idea of learning is such a good thread throughout this whole conversation that AI is good at getting stuff done, but it's also really good at helping you learn how to do the thing and to be this partner, this thought partner, the way you talked about the interview process you went through and this learning opportunity /command. So awesome. Great story. Zevi, okay. Before we get to our very exciting lightning round, is there anything else that you wanted to share? Anything you want to leave listeners with?

**Zevi Arnovitz** (01:06:25):
Yeah. So kind of to tie back into the first thing I said where if people walk away thinking, "Zevi's so cool." Then I've failed here. I think that it's just the best time to be alive, I think. It's the best time to be a junior contrary to what a lot of people are saying how there's no more junior roles out there and people get out of school and you can't find a role.

**Zevi Arnovitz** (01:06:52):
Yeah, that's true. But also when else in history could you get out of school and just build a startup on your own with a couple of friends completely bootstrapped. And I see more and more people towards the end of my time at Wix, I was interviewing and I saw more and more people building their own stuff with AI. And I think contrary to what a lot of people think, it's the best time to be a junior.

**Zevi Arnovitz** (01:07:17):
It's the best time to be a learner. And I think if any listener is listening to this and you're a curious person, you're a hardworking person, I want to say kind, I'm not sure, but if you're a kind person and a good communicator, you have such an unfair advantage and you can give more value to companies than most people who have 20 years of experience. So I really hope people get inspired by this and start killing it with their projects.

**Lenny Rachitsky** (01:07:44):
Amazing. So many ways to be inspired from this conversation. Zevi, with that, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

**Zevi Arnovitz** (01:07:52):
Yep, let's do it.

**Lenny Rachitsky** (01:07:54):
What are two or three books that you find yourself recommending most to other people?

**Zevi Arnovitz** (01:07:58):
So I'll take one from each kind of genre. So in fiction, I love The Fountainhead by Ayn Rand, one of my favorite books. Really makes you think, really makes you feel. Business books, I'm a big fan of Shoe Dog, the Nike story, one of my favorite books.

**Lenny Rachitsky** (01:08:15):
I just finished reading that. So funny.

**Zevi Arnovitz** (01:08:17):
Amazing.

**Lenny Rachitsky** (01:08:17):
This is great. It was great.

**Zevi Arnovitz** (01:08:19):
Yeah. I love Shoe Dog. And then more on the psychology side Mindset by Carol Dweck, who coined the term growth mindset. It's just such an amazing book. It kind of sounds like a self-help book, but then you understand that it's completely psychological and is based on research and that book completely changed my life.

**Zevi Arnovitz** (01:08:39):
Really, I was always with a fixed mindset, and then after reading that, I kind of understood that it was something holding me back. And since then, I've been really, really trying to cultivate a growth mindset. So I really recommend everyone reading that.

**Lenny Rachitsky** (01:08:51):
Again, connects to that thread of the way you described it, being a 10X learner versus a 10X doer. Okay. Next question. Favorite recent movie or TV show you have really enjoyed?

**Zevi Arnovitz** (01:09:02):
Yeah, actually, my wife is really into film, so we watch a lot of TV. It's probably our favorite together time. I just finished watching The Pitt, which was amazing. It was really good. And my first recommendation to everyone is if you haven't seen Severance run to see Severance, one of my favorite shows.

**Lenny Rachitsky** (01:09:21):
Is there a favorite product that you have recently discovered that you really love?

**Zevi Arnovitz** (01:09:24):
This is a good question. I'm always trying new products. I'll always have three or four browsers installed on my computer and all this different kind of stuff, and I recently discovered a Loom alternative. I was kind of disappointed with Loom. They were taking so much money and the product, I don't know, I just didn't love it. And there's an open source alternative called Cap, which is just really well-crafted. You can see that the person was really sweating the details and it's just a really, really great alternative. So I've been using that recently.

**Lenny Rachitsky** (01:09:58):
There's also a product called Supercut that I love that's also a Loom alternative. Shout out. Okay. Two more questions. Do you have a favorite life motto that you find yourself coming back to in work or in life?

**Zevi Arnovitz** (01:10:09):
Yeah. I'm kind of between two right now. One, which has become a Twitter meme basically, which is you can just do things. I feel like that is basically going always in my head every time I do something that I'm just shocked at the speed and ability to do things now so you can just do things, and the second one I stole from my brother. His motto is nobody knows what the fuck they're doing. And I just love that. And I think it kind of makes you take life more lightly. So yeah, nobody knows what the they're doing.

**Lenny Rachitsky** (01:10:39):
I think people see these companies on the outside and it feels like everything they've got all figured out. And if you're ever on the inside of a company that's doing really well, you're like, how is this staying on the rails? How is this still a thing that is working? Doesn't make any sense. It's all about to fall apart. Yeah. Okay.

**Lenny Rachitsky** (01:10:54):
Last question. You've had a long entrepreneurial thread throughout your career. There's a couple other real world businesses you've started in the past. You did a thermal clothing business and then like a hummus delivery thing. So maybe pick one of those and just tell the story of what that's about.

**Zevi Arnovitz** (01:11:13):
Yeah, I'd love to. Really fun that you asked about this. So I'll tell the thermal clothing because I think it's really cool. So in high school, I was selling thermal clothes in 10th grade for one of my sister's friends or something, and basically it was just packs of thermal clothing, shirt and pants. I grew up in Jerusalem, so it's a bit chillier there.

**Zevi Arnovitz** (01:11:34):
So it was perfect for the weather. And in 10th grade, when I was selling them, they were like 20, $25 a piece and I was making like $4 a sale, and if you look in the food chain, I was like sixth or seventh down the line. So this was like crazy margins. So during the summer I thought about it like I should just go straight to the importer. So throughout the summer I called the importer and at first he was really, really mad.

**Zevi Arnovitz** (01:12:00):
He was like, no, you have to work for me for years to get to this state. And I said, listen, man, I'm finishing school soon. This is not going to be my career. Either do it or not. And we basically negotiated throughout the whole summer. And this was also like how I did things before ChatGPT. So he would throw out something, he'd say, "Oh, the import tax has gone up." And I'll just search Google, like Import Tax Israel and start reading.

**Zevi Arnovitz** (01:12:25):
And I'll be on the phone with him and I'll be like, "Hey, I would just basically stall." And then I'd somehow come back with a challenge. And I ended up getting a really great price, like 12 and a half dollars a piece. So I was making 100% profit and I spread throughout a bunch of different schools. Each school, I had the coolest people in school selling for me. And then a really fun thing that I did was we had a really awesome basketball team and our basketball team would basically be 30 points up within the first half and it kind of got boring for the crowd.

**Zevi Arnovitz** (01:13:01):
So I wrote a song, like a basketball chant about Thermal Clothes that basically has my number within it. And the end of it was if you join in now, we'll give you a discount. And it was with drums and everything. And still when I go to Jerusalem, I know some people who I don't even know my number by heart because they know it by the tune. And sometimes when I walk in Jerusalem, people stop me and say like, "Hey, it's Thermal Zevi." So that was just a really cool experience as a kid.

**Lenny Rachitsky** (01:13:29):
This explains so much just the marketing genius of that move. Oh man. Okay. Zevi, this was incredible. Two final questions. Where can folks find you if they want to reach out and maybe follow up on some of the stuff? We'll link to the scripts and prompts and all that in the show notes so you don't have to read that, and then how can listeners be useful to you?

**Zevi Arnovitz** (01:13:50):
Awesome. So I've been helped throughout my whole career a ton, so I love helping any way I can. So reach out on LinkedIn or on X. I'd really love to help whoever I can. How can listeners be useful to me? So if you're a student, try StudyMate, tell me what you think. If you're in Israel and you are not using dictation yet, try Dibur2text. Tell me what you think.

**Lenny Rachitsky** (01:14:14):
Amazing. I just love how much you're giving away and how useful that's going to be to so many people. So again, we'll link to that in the show notes. Zevi, you're awesome. Thank you so much for being here. Thank you so much for sharing so much. This is going to help, I think, a lot of people and I think it's going to help people get over the hump on. Okay, I see all these people doing cool stuff. Here's how I can actually do this stuff. So thank you so much for being here and for sharing so much.

**Zevi Arnovitz** (01:14:35):
Thank you for having me. And if you build something cool with some stuff that I learned here, hit me up, send me. I'd love to see.

**Lenny Rachitsky** (01:14:43):
Amazing. Zevi, thank you so much for being here.

**Zevi Arnovitz** (01:14:45):
Thank you.

**Lenny Rachitsky** (01:14:46):
Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcast, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [7/15] 5 questions to ask when your product stops growing | Jason Cohen (2x unicorn founder)

**Lenny Rachitsky** (00:00:00):
A lot of product teams, a lot of founders built something. It starts to show some success and then all of a sudden it just stops growing.

**Jason Cohen** (00:00:05):
There's a series of questions that I asked to diagnose why is growth slowing. The first question is, are customers leaving? Think about the gauntlet they went through to get to the product. How did they even find out about me? That was hard already and improbable. They didn't just bounce off the homepage, which is again, improbable. And they got to the pricing page. That didn't scare them off. They actually had the budget and bought the stupid thing. And after all of that, which clearly means they wanted it to work, they're like, "No, bye." What? Just on an emotional level, you got to go, "Wait a minute, that's terrible."

**Lenny Rachitsky** (00:00:34):
Step two is pricing, positioning.

**Jason Cohen** (00:00:36):
Your prices are way too low because you just guessed and you haven't changed them. What often happens is you raise prices and signups don't change. Just think about a company with a thousand employees and 400 million in revenue or whatever. If they see a product that's $2 a month or even $100 a month, thought is like, that can't be good enough.

**Lenny Rachitsky** (00:00:52):
We positioned this conversation as how to deal with stalled growth, but it's actually just as useful for how do I grow more?

**Jason Cohen** (00:00:58):
Do you know right now which channels are saturated and which aren't? You can't just rely on marketing forever. Just adding one little feature and then hoping we can flog AdWords is not going to work.

**Lenny Rachitsky** (00:01:07):
What comes next?

**Jason Cohen** (00:01:07):
The last question is, do you need to grow? We all have heard the phrase, "If you're not growing, you're dying." Is that true or is that the kind of thing that investors use to make founders try to grow even when they shouldn't?

**Lenny Rachitsky** (00:01:19):
Today, my guest is Jason Cohen. Jason is a four-time founder, including two unicorns, one being WP Engine. He's not just an incredible builder and entrepreneur. He's also an incredible writer and share of product wisdom. He's been sharing his advice online for over 20 years now. I've been a huge fan of Jason's from afar for so long and it was such a treat to have him on the podcast. There are a million things we could have talked about and I'm definitely going to have him back. In this conversation, we spent the entire time talking about his very actionable and very helpful framework for what to do when your product's growth stalls. I found his way of looking at the problem incredibly practical and real and actionable. And if you're looking for ideas for how to rekindle your product's growth or just accelerate the growth of your product, you're going to walk away from this conversation with your mind buzzing.

**Jason Cohen** (00:05:26):
Thank you. It's an honor to be here.

**Lenny Rachitsky** (00:05:28):
It's an honor to have you here. I have wanted to get you on this podcast for so long. You are both an incredible builder and a founder, and you are such a great communicator. You have been writing at asmartbear.com, which I want to get the backstory on for so long. How long have you been writing there, by the way?

**Jason Cohen** (00:05:47):
Almost 20 years. I started when blogging was cool. And I'm still waiting for blogging to come back and be cool again, but it's not yet.

**Lenny Rachitsky** (00:05:55):
I think it is cool.

**Jason Cohen** (00:05:55):
It is? Okay.

**Lenny Rachitsky** (00:05:56):
Newsletters are cool now. Yeah.

**Jason Cohen** (00:05:57):
Newsletters are cool. Okay.

**Lenny Rachitsky** (00:05:58):
I don't know if you saw Twitter now is encouraging long-form writing. There's this articles feature, so I think it's cool. I think you've survived the trough.

**Jason Cohen** (00:05:58):
Yes. Okay.

**Lenny Rachitsky** (00:06:07):
I was also out talking to Gemini trying to figure out how many posts you've written. I was like, "Count the number of blog posts on a smartbear.com." Do you have a sense of how many things you've written on there?

**Jason Cohen** (00:06:14):
Yeah, it's not that many. It's something like maybe a hundred and... Well, I would say between 150 and 200 that I'm proud of and probably about 300, 350. And that's it over about 18 years. And that's because I only write in-depth. Some are long, not all are long, but none are short, I guess. And I've always had a rule, even though you're supposed to write really regularly and not just for algorithms, but they used to say, again, back in the aughts where I started, "Oh yeah, it needs to be really regular so people know when to expect your thing and they plug it into their day and all this." So it's always been true that you should be regular. And I never was because my attitude was always, I will only put out stuff if it's the best that I can do. It's up to the reader to decide if it's good or useful.

**Jason Cohen** (00:07:01):
And so if I don't have that, I'm just not going to publish. That's the way it is. And so there's years where I've published once or twice only the whole year. Maybe I was busy or didn't have the energy. Other years were, yeah, I posted 40 times or something, but even then, it's only that, because I can't do something of that magnitude. And also found unicorns, which I did during that same time and run them. I can't do that all at the same time and produce a lot. So it's fewer and hopefully better, but that's in the eye of the reader, of course.

**Lenny Rachitsky** (00:07:30):
I like what you say. I've done 300, not too many.

**Jason Cohen** (00:07:32):
Well, not for 18 years. Over that time, you expect-

**Lenny Rachitsky** (00:07:36):
Well, I think this actually, this is where I was going to go, but I think this is a really important lesson I've also learned. I always used to tell people the key to being successful writing stuff online and just content in general is quality and consistency, but I've just more and more realized quality is actually the only thing that matters and the consistency doesn't matter. So the only difference is the more rarely write, the more awesome it has to be.

**Jason Cohen** (00:07:59):
It is a lot of pressure. I feel that. And then I tell myself, that will just prevent you from writing anything and that's not good. So yeah, you tend to want everything you make to be the best thing you've ever made. And on the one hand, I want to hold onto that because it's motivation to be good and not to let the bar slip. On the other hand, you can go into paralysis, which is obviously bad. So yeah, I still struggle with that, but I think that is the tension.

**Lenny Rachitsky** (00:08:26):
Okay. So with 300-ish posts, 200 you're proud of, there are so many directions we can go. There's a few that I've picked that I want to spend most of our time on. The first is you have a really pragmatic way of approaching growth stalling. And the reason I want to spend time here is because a lot of product teams, a lot of founders build something, it starts to show some success, it's going, it's growing, and then all of a sudden it just stops growing. And I think that's one of the most painful things to go through. And I've never come across a way to think about how do I solve this? Because I think a lot of people are just like, "Okay, I guess that is not working. Let's move on to something else."

**Lenny Rachitsky** (00:09:03):
You have a very specific way of approaching this problem. And I want to read actually a quote from Will Smith, and this is something that has stuck with me ever since I read it because it's so true. So in his biography, he has this line. People ask him, "What's it like to be famous?" And his answer is, "Becoming famous is amazing. Being famous is a mixed bag. Losing fame is miserable."

**Jason Cohen** (00:09:29):
That's funny. So first of all, I think a lot of people are experiencing this right now. You have a lot of companies that have reasonable products and their growth has slowed. Why? It could be the economy because it's not as good as a lot of indicators say. We all know that, for example, jobs are not as good as the indicators say. It could be because AI or the threat of AI or the expectation of AI, blah, blah, blah. Who knows? It also can just be size. As you get bigger, growth slows because you know what? You're not going to grow 2X a year forever. So it slows. There's just mechanical things. So there's many reasons why things slow. And sometimes it's all of a sudden, although then maybe there's some event like an algorithm changes or something happens. But actually, I think what's really common is it just slowly gets slower.

**Jason Cohen** (00:10:13):
In other words, it decelerates. But I wouldn't say sneaks up on you because most people are looking at growth all the time, so it's not sneaky, but it is sort of a little bit more gradual where just like, you just feel more like you're running through mud like, "Ah, God, we're just still doing so much work and it's not having as much of an impact." And so that's what I see. And when I say that's what I see, so I've built four companies. The last one is a unicorn. The one before that is also a unicorn. The previous one was bootstrapped. This one was VC-funded and I've invested in about 60 startups. Some of them failed completely. Some of them were very successful, some in the middle, because of course, right?

**Jason Cohen** (00:10:54):
And so when I say that's what I've seen, that's the context of what I mean by what I've seen. So there's, I wouldn't say a checklist, but there's a series of questions that I asked to diagnose why is growth slowing in this order, because it's one of these things where the first one that's a problem, if you don't fix that, it doesn't matter if you fix one of the ones below. Just like if, I don't know, maybe if you had a marketing funnel and there's a step where everything falls apart and you're like, "Well, I'll just tune the bottom of it a little." It's like, "That's not going to work. It's not going to help enough. You got to go where the biggest issue is." So this is in that sort of order. So the first question is are customers leaving, i.e. logo churn, right? Churn within.

**Jason Cohen** (00:11:36):
You can do churn with MRR too, but just for simplicity, let's say with customers. And it's the worst problem for a couple reasons. One is there's nothing you can do about it once it happens. They're gone. There's no saving them, increasing their revenue. There's nothing in the future you can do. Also, it's often correlated with things like negative reviews or other things on social media, which is another kind of preventing growth. So it's kind of a two punch thing of like, they're not here and they may be actively hurting your growth, so that sucks. The math is undeniable, which I want to talk about because this is something where there's a metric I like that is unusual and people find useful. But before I get to the metric, there's also this kind of visceral thing, which is the customer's saying, "This product, I don't want it."

**Jason Cohen** (00:12:25):
And when I think about the gauntlet they went through to get to the product, how did they even find out about me? That was hard already and improbable that they see an ad or hear it. And then they clicked, which is improbable, and then they didn't just bounce off the homepage, which is again, improbable. They actually were like, "Oh yeah, this sounds pretty good." And then they got to the pricing page and that didn't scare them off. They actually had the budget and bought the stupid thing. Then they went through onboarding and invested their time, et cetera, et cetera. That is a crazy gauntlet that almost no one gets through. And after all of that, which clearly means they wanted it to work, they're like, "No, bye." What? Just on an emotional level, you got to go, "Wait a minute, that's terrible. I'm fundamentally not fulfilling whatever promise I made or they thought I made, which is whether that's a product issue or a communication issue."

**Jason Cohen** (00:13:12):
Okay. There's lots of... But one way or another, something is really fundamentally broken just in terms of like, I'm a product person, so what I want to do is make a product that other people want to buy and use. And if they don't, no matter what the metrics say, we're failing our mission, our customers, whatever. So there's just even that non-mathematical reason to go, "Oh my God." Right? So to me, that's already enough reason, but the math is very interesting. And what I find is when I talk to people, especially on Twitter or something where people are just yapping around whatever they're doing, I say things like anything above 3% per month cancellation is terrible. And people are like, "Oh no, it's okay. Five is fine, seven, six." Everyone's yapping about what they... And it's very abstract. Is four much worse than five?

**Jason Cohen** (00:14:02):
I don't know. And I heard someone else, and blah, blah, blah. So it's very, I don't know, generic and rough. So there's a different metric that I like to use, which keys off of this idea that I think, again, people don't appreciate, which is cancellations grow faster than marketing, and so cancellations overpower the growth of the company and slow it to a halt, i.e. growth slows, right? To where you literally cannot grow anymore. There's a maximum ceiling of how big you could ever be thanks to cancellations. And when you know what that number is, it's much more real and visceral and scary. And so just to kind of justify what I just said, just imagine any company and imagine you just tripled the number of customers that are there and paying, and the same kind, the same age, just the same kind of stuff just tripled overnight.

**Jason Cohen** (00:14:58):
So the next month, would marketing deliver more new customers than a month before? No, because none of your marketing efforts care how many customers you have. AdWords delivers the same number of leads and SEO delivers the same. It does not care how big you are, these efforts. So you're still going to be growing at the same rate as you were the previous month. But cancellations in absolute terms, like the number of customers who leave will triple because you have 5% cancellation and triple... Okay, so still 5% of a triple number is triple, right? So this is the point, is that cancellations automatically grow as you grow, even if you're doing everything right, but marketing doesn't. Marketing grows only as fast as you can improve marketing. We all know that's quite hard actually. It's linear. It's hard to find new channels that aren't trivial. It's hard. And of course we're going to do it, but it's hard, whereas cancellations grow automatically as you grow. So cancellations always overtake marketing for this reason.

**Lenny Rachitsky** (00:15:59):
The metaphor here is a leaky bucket where are you adding enough water to keep up with the leak essentially?

**Jason Cohen** (00:16:03):
Right. Except the leaks automatically increase and that's what people don't appreciate.

**Lenny Rachitsky** (00:16:07):
Because it's a percentage of your entire customer base.

**Jason Cohen** (00:16:10):
Yes. So we say... When in marketing, we say things like, "I'm adding a hundred leads a month." But in cancellations, we say 5%. Why'd you say percent? Because it's based on your size and it's exponential. That's what 5% is, an exponential. And so there's this maximum size you could ever be. It's when churn equals growth. So how would you compute that? It's actually quite simple because let's say you have this 5% per month, just let's take a number. So it's simply the amount of new customers you add divided by that cancellation rate. That is the amount. That is the limit. So let's suppose you add 100 customers a month and you have 5% cancellation. So 100 divided by 5% is 2,000. So a company like that will never have more than 2,000 customers.

**Jason Cohen** (00:16:54):
And by the way, as you approach that number, growth is very slow because you bring in a bunch of customers and almost the same number leave, so growth is slowing. Ah, look, we've diagnosed by growth slows automatically at all SaaS companies. So that's why this is the first thing, because it's such a hard cap limit and it means that people don't want your product. These are two reasons why it's the most important thing.

**Lenny Rachitsky** (00:17:15):
Just to clarify, this is logo churn. This is like number of customers, not revenue churn?

**Jason Cohen** (00:17:19):
Yeah. Well, it is both logo churn and revenue churn. Do the same math. You could say dollars in divided by dollars cancellation rate or number of... I've been saying number of customers just to keep it simple because I think when you look at it and say, "Wow, we will never have more than 2,000 customers." It's just such a visceral, "Oh my God, we got to do something about that." Now, of course, one thing you could do is have more marketing, but you know that already. If growth is slowing, you're already thinking, how do I get more out of marketing? You knew that. The point is that cancellation is this hard limit pulling you down with all these other really bad either implications or side effects, which is why it's so important.

**Lenny Rachitsky** (00:17:59):
Cool. And when you say marketing, just to clarify, this includes basically all growth work, PLG stuff, marketing, sales?

**Jason Cohen** (00:18:04):
Yeah. Yeah, yeah. Right.

**Lenny Rachitsky** (00:18:05):
Great.

**Jason Cohen** (00:18:06):
Yeah. PLG is nice, but you still need marketing to bring the people in the first place. PLG just means there's not a salesperson unless you're expanding or some other segment.

**Lenny Rachitsky** (00:18:14):
Cool. Yeah. It's like the whole bucket of just bringing new customers in. Awesome.

**Jason Cohen** (00:18:16):
Yeah, yeah. Okay. So assuming you agree, yeah, I don't like customers leaving, that sucks, so obviously you got to find out why they're canceling and do something about it. And the kind of root issue here is they don't want to tell you. They're already out the door. They've already stopped investing in you mentally. So the last thing I want to do is spend time with you or really think about it and diagnose it with you. And I have a funny story about this for myself. So at SmartBear, people would cancel, we'd put up this form and a dropdown list, too expensive, project ended, this little stuff like we do, so we could gather data. And one of them did have more selection than the rest. And I realized it was the first one on the list. And I thought, huh, I wonder if people are just picking the first one.

**Jason Cohen** (00:19:03):
So then we randomized the list so everyone saw a different order of the list, and now all the items were picked equally, like, oh, right. It's complete noise. And I know other companies have done similar things also with the similar results that this is a global phenomenon. So okay, so what do you do? The point is, it's hard. So the first thing is you want to ask open-ended questions. I know you want to just get a list, but this is the problem. At least with open-ended questions, I mean, most people won't answer, but at least you might be able to get some kind of thing that they generated. And when you do this, the wrong way is to ask, why did you cancel? Because again, this allows them to say something really simple like budget, which may or may not be true. I'll get to that in a second.

**Jason Cohen** (00:19:46):
What you want to do is say, "What made you cancel?" In other words, what about the product or situation or whatever caused the cancellation? Just phrasing it that way, you get much better results. And I stole this from a company called Groove, who has this great case study online about this very thing. They had an email that they sent out, which is a great email, and they started by asking, "Why did you cancel?" They got 10% usable responses. They changed the same email to what made you cancel, and it's 20% usable responses. So there's, I guess, maybe some anecdata that this is a good idea. But the point is you really want them thinking about the product and not just coming up with an excuse.

**Jason Cohen** (00:20:28):
The next thing is the few times you do talk to them, so you want to go into, delve as far as you can into there, because most people won't talk, the temptation is to hear what they generate at first and say, "That's the answer." So a really common one is it's too expensive. I think anyone who's looked at cancellation data at any company will agree that too expensive is often the number one or at least top three reason in one form or another. And that is never, ever, ever the reason. How do I know? Because they already looked at your homepage, read all the stuff, saw what you promised, looked at the pricing page and decided to buy it. That means it, whatever was in their mind of what it is, is not too expensive. They already decided with their actions. It was not too expensive. Something else happened, but you didn't fulfill the promise that at least they thought you made or something else didn't work or... Now, it is possible they lost budget, but that doesn't mean you're too expensive. That means they lost budget. That's a very different reason. So it's sort of like, this happens in healthcare, for example. So when someone dies, a doctor has to write what's called a proximate cause, which is why did they actually die? But then you try to also write down the real reason. So let's say someone comes in and the proximate cause of death is they stop breathing. Well, you could stop there and that's like listening to it's expensive and going, "That's it." Well, why did they stop breathing? Because they ran their car into a telephone pole and were injured so much that eventually they stopped breathing. Why did they run their car into a telephone pole? Because they passed out at the wheel. Why did they pass out at the wheel? Because they had undiagnosed diabetes. Now we're getting somewhere. It still isn't just one root cause, another as a sidebar. I hate the idea of a root cause.

**Jason Cohen** (00:22:19):
Complex systems do not have one root cause. They often have many interlocking things that could be done to detect earlier or to change it or to reduce and not one root cause. So the root cause analysis to me is, by the way, an incorrect thing. I'm explaining why right now with the healthcare, right? Because, well, what about this undiagnosed? Well, maybe part of the problem is we have a healthcare system that isn't preventative and part of it is that, but they didn't go to the doctor anyway. Okay, so there's all kinds of things that could be useful and interesting to prevent this or make it better. That's the point. That's what an analysis should be, is this array of things, not the root cause. Anyway, something along the lines of undiagnosed diabetes is much more of a cause than stopped breathing. So when we say it's too expensive and that's the reason, you're making this fallacy, you got to go into, well, they wanted this stuff, but it didn't work with Linear, which is what they use. It only works with Jira. And so there's a lack of integration.

**Jason Cohen** (00:23:12):
Now, maybe we should write that integration and maybe we shouldn't. Of course, it depends on how much we hear about it. And of course it's going to depend on other things, but that's the reason, not it is expensive, right? And so this idea of getting into not even the root cause, but let's say root-er causes.

**Lenny Rachitsky** (00:23:30):
The roots cause.

**Jason Cohen** (00:23:31):
Yeah, the more root. I think some people probably say five whys and just paper over what I just said with that. And maybe so, but let's not be so simplistic about that, because again, five why sometimes implies that there's some root cause at the bottom of the whys. Let's be a little more smart about that. So anyway, these things too expensive, this is not it. Maybe project ended really is project ended. Okay. But even there, I see just today, today on an entrepreneur forum I'm on, someone said, "Yeah, we're starting to see more people have project ended as the reason, and so there's nothing we can do about that." Now see, that's incorrect. That's only true if you only look at the proximate thing, which is project ended.

**Jason Cohen** (00:24:15):
You're correct that you can't make that project not end exactly. Yeah. Okay, but wait a minute. If your software was more successful and the project was more successful, would it have ended or is that actually an indicator that your product wasn't that useful or didn't do its job? It's possible, like in this case, who knows, right? But that's possible that it really is your fault. Another example is, but you picked what target segments you are going after. Did you pick a market segment that was easier to sell to, but their projects end like small business and consumers where very often the small business does go out of business where the project ends, et cetera? Because when things are small, they have high variance and lots of things can knock them off the path and so on. And so is it your fault for picking the wrong ideal customer profile or target segment? And so yes, that one case of that one project, that's not your fault, quote-unquote, but by saying that you're just ignoring the fact that there is maybe something to do about it... Now, all this is maybe. None of this proves you should change your market, but when you say there's nothing we can do about it, you are closing the door on these things that might be the right thing. And very often, as I think probably a lot of people here on this listening to this know, the market segment you pick has a lot to do with your retention rate because everyone acts differently. And so anyway, so I know it's a lot on this topic, but I just feel constantly, people make this particular mistake of just abdicating responsibility or just listening to the first thing they hear and saying that's the reason and that's not right.

**Jason Cohen** (00:25:54):
So that's the big thing about listening. Another thing is you got to ask when people are in trouble, but not yet canceled. You might be able to save them. You certainly can learn more because you can talk to them. They're not shut off yet from you. So this might be, they never uploaded their data, so they're not being successful. They are calling tech support too much. They're in trouble. They're not calling tech support enough. They're not engaged. They didn't log in for a while.

**Jason Cohen** (00:26:23):
There's all kinds of things where... Now, of course, the details are going to depend on the product, obviously, but there are signals that are correlated with cancellation. Now, if you have a lot of data, you can literally correlate signals with cancellation and try to extract that precisely, but even without data, you can guess. And guessing and having a theory, acting accordingly, and as you get more data adjusting your theory, this is a wise way to proceed even without data. So if you can catch them when they seem like they're off the happy path, they're in trouble, that's a better time to do it. And then the last thing I would say about this detection is if you don't know what to do or all else being equal, then focus-

**Jason Cohen** (00:27:00):
... because if you don't know what to do, or all else being equal, then focus on onboarding. Almost all companies have a whole lot more cancellation in the first day, 30 days, 90 days, depends, but the first period than the whole rest of the customer's life. And also, small changes in the onboarding can have large effects on cancellation, whereas later on, that's not necessarily true. It could be, but it's not necessarily true.

**Jason Cohen** (00:27:26):
So a really dramatic version of this is if you've ever done YouTube videos, which I know you have, but if a listener has ever done a YouTube video and you see the "retention", quote unquote, of the viewer on a YouTube video, it has this thing where it falls just so much, you can't believe, in the first 30 seconds, and then, if it's a decent video, it'll flatten out as people decide to watch the video.

**Jason Cohen** (00:27:47):
So in that crazy-looking curve, for the people that have watched it for 15 minutes, maybe there's something you could do to keep a few of them staying to the end, but that's not going to change very much how many people get to the end. Whereas for me, I've only done a few, but what I see is about 50% fall off in the first 30 seconds. Well, if I can get that from 50% to 55% stay, that's an additional... And at the end of the line, I only have 20% still there, which is pretty good for a longer video. But if I get it from 50 to 55, I might go from 20 to 25% staying. In other words, if I shifted 10% at the front, which maybe I could do, I can't be dramatic but maybe a little, then in the output, I might be able to increase it by 20, 30%.

**Jason Cohen** (00:28:33):
So that's a huge change, and so the SaaS equivalent is, as we all know, if they leave early, not only is it bad but it's super unprofitable, because you spend all this money to acquire them and then they never stayed around long enough to pay it back, much less to be profitable. So if you can do a little bit in the onboarding or shift the onboarding percentage a little bit, it pays off enormously in revenue and profit over time by making them successful. So again, if you don't know what to do, onboarding is a good bet, and even if you do know what to do, I'll still bet that onboarding is a good bet for where to go.

**Lenny Rachitsky** (00:29:07):
Oh, man. I'm so happy we're spending so much time on this very specific first step of logo churn, because the way you described it is so visceral. It took so much. It's impossible how far this customer got already. They are using your product and understand it mostly, and then they still decide to leave. So brutal the way-

**Jason Cohen** (00:29:29):
And you're going to believe them when they say it's because of the cost? It just doesn't even make sense when you put it that way.

**Lenny Rachitsky** (00:29:36):
So let me summarize the advice you shared here, because this is so good. So step one is look at logo churn. The way to understand, and essentially to understand how big of a problem this is and why you need to spend time here is basically do the math, how many new customers you're getting divided by the cancellation rate, and that essentially tells you, if that doesn't change, what's the maximum number of customers you will ever have?

**Jason Cohen** (00:30:01):
Exactly.

**Lenny Rachitsky** (00:30:02):
That's going to be a sad number. And then the question is, okay, cool. How do I reduce the cancellation rate? Obviously, as you said, everyone wants new customers, more new customers.

**Jason Cohen** (00:30:08):
Yeah. And I know you're going to do that anyway, but you've got this cap.

**Lenny Rachitsky** (00:30:12):
Exactly. Okay, so a few things you've shared here. One is instead of asking people a multiple choice, why did you decide to cancel? You make it freeform and you make the question, how would you say it? Was it what made you cancel?

**Jason Cohen** (00:30:26):
What Made you cancel?

**Lenny Rachitsky** (00:30:27):
What made you cancel?

**Jason Cohen** (00:30:28):
Yeah.

**Lenny Rachitsky** (00:30:28):
Great.

**Jason Cohen** (00:30:28):
Yeah.

**Lenny Rachitsky** (00:30:29):
And then you could use AI to help summarize these things, I imagine, instead of ...

**Jason Cohen** (00:30:32):
Yeah. I think what I find with AI is this, with this sort of thing, with surveys is this. AI is good at picking out themes. It is bad at picking out details that are actionable. When I say AI, of course, I mean LLMs, which is probably what we mean when we're looking at natural language. And if you think about it, it makes sense because the LLM is an averaging machine. It's predicting the most likely. That's an averaging kind of a thing. And so when what you're looking for is a kind of average, it's usually pretty good, so summarization, topics, themes, but when you're asking for what is interesting and not average, it's actually pretty bad at it.

**Jason Cohen** (00:31:15):
One way that I've found that's sort of useful is, yes, I'll ask it about themes, but then I'll say, "Now pick out every specific detail that goes under one of these themes. Put it along with which customer said it and the link to blah, blah, blah." So you have to play with this to tune it right, but that kind of thing so that a human being can then still see the detail, which is what triggers in your mind, "Wait a minute. But that means we should do this," because the topics won't do that. The topics will be ... I already know what the topics will be. It'll be stuff like I couldn't figure out how to do this, this integration. The topics are actually not going to be that surprising probably. It's the details that are going to be the triggers for actionable stuff or patterns or something like that. So yeah, AI is not useless, but it's not as useful as it sounds. It's probably still a good idea to just read all this stuff. Although AI might be able to clean up, maybe people's grammar's bad, it's a weird language. Okay, yes, that's annoying. You could clean that up, but I wouldn't rely on AI to do the thinking for that reason.

**Lenny Rachitsky** (00:32:20):
That's such good advice. I actually have a really cool guest post coming out soon that gives a bunch of really specific techniques to avoid AI hallucinating or just giving you really bad results from this very specific synthesis work, because it turns out AI is very not great at actually being honest about some of the stuff, so I'll link to it if it comes out before this. And I think in real life, most people don't have that much. The volume of these cancellations, unless it's a super consumer app, is not that high, so you don't even need AI for this. Just read it.

**Lenny Rachitsky** (00:32:51):
And then this is a good segue to your next piece of advice, which is essentially the five whys but not the five whys, where you force yourself to dig into what's the real reason that forced them to cancel? It's probably not pricing. It's probably not the project ended. There's something deeper.

**Jason Cohen** (00:33:06):
Yeah.

**Lenny Rachitsky** (00:33:07):
And then advice number three is try to catch people early, try to catch them before they churn, and if you don't have a lot of customers, it's a lot easier if you have a lot. It's obviously harder. There's always been this holy grail idea of a product that just watches metrics and tells you this person's going to cancel. I haven't seen that before.

**Jason Cohen** (00:33:23):
What I would say is it is not hard. You don't need a lot of customers to go talk to the ones who are in trouble. You do need a lot of data or customers to mathematically know what behaviors are correlated with canceling, and therefore, to spend your time wisely. Then you need a lot more data. But to your point, even if you have that data, it's not entirely clear whether some kind of mechanistic thing is all that important. One way I look at it is it's very common advice, you should try to get more good customers and fewer bad customers. Of course you should. And so therefore, they say you should see what the good customers have in common, but that's not the end of the sentence because a lot of the things that good customers have in common, they also have in common with the bad customers, because it's just what your customers do, just what anybody does. So it's what the good customers have in common that are different from what the bad customers have in common.

**Jason Cohen** (00:34:20):
Okay. So with that in mind, this it has to be both or else you're just getting correlations that are not helpful, the cancellations or talking to people who are in trouble is another application of that. So what is correlated with people who actually end up canceling, not just what ... And so I think that mindset is correct, if you add the other side of that to it.

**Lenny Rachitsky** (00:34:44):
Really important nuance.

**Jason Cohen** (00:34:46):
Yeah.

**Lenny Rachitsky** (00:34:46):
Okay. And then the final step just to close this out is onboarding, work on onboarding activation. One of the most recurring themes on this podcast is just the power across every dimension of improving onboarding, improving activation.

**Jason Cohen** (00:34:59):
Yeah.

**Lenny Rachitsky** (00:34:59):
Sweet. Okay, so this is just step one, which is already full of gold if your growth has slowed. So step one is focus on your logo churn, the number of customers leaving, people leaving, actually canceling your product.

**Jason Cohen** (00:35:11):
So I look at it like a question. So the first question is are people leaving too much? Because if your monthly cancellation is 2% for S&B, that's good. So you could try to work on it, but since it's already good, it's still probably a good idea to work... It's probably a good ROI for you to work on it, but it's possible that you've got diminishing returns and that this isn't really the reason or it's not really reasonable for it to go. How low can it go for S&B? There's some floor and you might be near it. So the first question is is logo churn too high? And trying to set a threshold lower than what people normally want to do.

So the next question I have is is the pricing correct? Which of course, pricing is a perennially interesting topic, I know. There's this funny thing, especially with newer companies, that the pricing is always too low. It's not always, but that's the common thing. Patrick Campbell, who has 4,200 data points about startups, let that sink in a little, has this great quote which goes like this: "Your prices are way too low because you just guessed and you haven't changed them." Yeah, if you really look deep within, you realize like, yeah, or we just picked whatever our competitors are doing and that's it, or we added or subtracted something because reasons. Right, that's probably not good.

**Jason Cohen** (00:36:28):
And people are scared to rate prices for obvious reasons, but if we set aside the emotional reasons, whether they're correct or not, the economic reason people normally give is they have in their mind this microeconomic supply and demand curve thing, and the demand curve says that if you raise the price, demand goes down. That's why demand curve is always going that way. And so they understand, I think everyone understands, right, but maybe you raise prices by 10%, but signups go down only 5%, so overall, it's better. But the opposite could happen too if I'm on the other side of the demand curve, and okay. So that's how most people think of it. However, this is not how it works. So that's how it works in microeconomics 101 textbooks, that's not how it works in the real world often.

**Jason Cohen** (00:37:16):
So what often happens is you raise prices and signups don't change. When I say signups, I mean signups per month, the rate at signup. Or signups go up. This happens all the time. Even for solopreneurs on Twitter who have strange projects or everything, it happens all the time. They raise prices, they're like, "I was scared," but then signups went up.

**Jason Cohen** (00:37:37):
I once talked to a guy, this is really funny. I'm going to not say the name to protect the name. So he had a product that he was selling essentially to enterprise and government, so larger companies, and it was to me way too cheap. So he said something like, "Yeah, I charge $300." I'm like, "$300 a month, that's not enough." He goes, "No, per year." They're like, "Okay, wait." I said, "Okay, how many signups do you get a week?" And he's like, "One or two," because this is enterprise and it was a startup. I said, "Okay. Just for fun, just change it from per year to per month," So in other words, we're 12x-ing the price.

**Jason Cohen** (00:38:19):
So he did, and he still got one or two per week. Nothing changed. I'm like, "Okay, what are you going to do next?" And he goes, "Oh my gosh, well, now I have so much more money and profit, so I'm going to hire an engineer. I'm going to do this marketing." And I'm like, "Time out. What you're going to do is raise prices again. You just told me you 12x-ed the price and nothing observable changed. That means you're not near the price yet, right? You don't have to 10x it again necessarily. Maybe 2x, maybe 50%, but you're not done. You can do those other things too, but you're not done with the price." It didn't even occur to him still.

**Jason Cohen** (00:38:56):
Okay. So why does this happen? The reason is that pricing selects the market. So if you only think of the market as people with very limited budgets, barely can do anything, not getting much value out of it, then it is true that if you raise prices, you'll get fewer of them, because they were never getting that much value out of it anyway. They don't have that much money so if you raise prices, they're gone. But think about just even a midsize company. Forget about enterprise, just think about a company with a thousand employees and 400 million in revenue or whatever, and if they see a product that's $2 a month or even $100 a month, the thought is like, well, that can't be good enough. They're not mature enough, it's not going to do enough. The support's not going to be good enough. They probably don't have good governance policies or other things that we need, et cetera. Whether that's true or not, this is what it looks like because is it's low quality, cheap, whatever, aimed at SMB.

**Jason Cohen** (00:39:52):
So they just won't buy, they're not in the market for the thing. So it's not true that they have this demand curve where, oh, since it's cheap, they all want it. That's what microeconomics curve says. It's so cheap that they should all want it. No, they don't. None of them want it because it looks bad. So as it gets into a price range that makes sense for the kinds of things that they need, then their demand actually goes up. Then it can stay up while it's in a good range, and then of course, at some point you are priced out of them. That particular kind of company's like, "Look, I'm not going to spend $10 million a year on it. Are you kidding?"

**Jason Cohen** (00:40:24):
So yes, it does slope down and go away. So it's not a normal curve, but it is like it slopes up and then it's something and slopes down. Who knows exactly what shape it is? Probably none of us know, but it's more like a Mesa and not a line that goes up to down like in the textbook, for that market. It's only the very lowest, you might even say worst in terms of metrics end of the market that has the microeconomic slope that you're worried about.

**Jason Cohen** (00:40:51):
So what happens is you raise prices and you enter a different market, and that's why the signups go up or okay. You leave behind perhaps a worse market anyway. And of course, everyone will tell you the more they pay, the higher retention is, and all the kinds of stuff gets better when you charge more. So this question, is pricing correct? This is what's in my mind when I ask that question. Probably the answer is no because pricing's very hard. It's just as much art as it is science. You've had some really good people on here on pricing. In fact, so good that I've bought some of the books that those people have talked about because I loved the interview so much, so I believe in all that. No problem, I believe in it. Nevertheless, they also say it's art and science and it's very difficult to ... And also, once you auger it in, the world changes. Five, 10 years later, the market is different, the world's different, and so it's still unclear.

**Jason Cohen** (00:41:48):
Also, price is not just the number on the webpage. It's easy to think that, but how it's structured is just as important, how the product's positioned is just as important. So for example, this example I've written about before online is this example, it actually was something that happened in my life but I changed the story to make it simple, and it's real unclear without having to get into lots of detail. So the story version is how this company was able to charge eight times as much for the same product just by talking about it differently. So just by positioning it differently, eight times as much. Again, this happened to me but it's too complicated. Those details are not interesting.

**Jason Cohen** (00:42:37):
So say there's this company called Double Down, and the idea is that it halves the cost of your AdWords because it makes it so efficient, so that's what it says on the webpage. "Cut your AdWords cost in half," which is a very good pitch, isn't it? It's simple, obviously valuable. But when you think ... So let's suppose I'm a customer and I spend $40,000 a month on AdWords. What am I willing to pay for double down? Well, if you do cut my AdWords in half, then all right, I saved 20K, but I'm not willing to give 20K to Double Down because then I'm not saving any money. In order to actually save money, I need to give Double Down less money. How much less? I don't know. Let's just call it a quarter. So I pay Double Down 5K to save 20, so I'm really saving 15, Double Down's making 5K a month, that's pretty good. Everyone's pretty happy at this five grand a month price point. So there's nothing wrong with this. No one's doing anything wrong, that's a perfectly valid company. However, think about these two situations that the CMO might be or the chief product officer might be in in talking to the CEO at the end of the year. Well, scenario one goes, we started using this tool, Double Down, and it had our costs, so we're able to spend that money on some other stuff. We were able to save money." And the CEO would say, "Great, that's good. We're going to renew and I'm happy to hear it." Again, nothing wrong here, but let's take a different tact altogether. What does the CEO want to hear more? Growth or saves money? Both are good, but I know which one is healthier for the company, increases market share, is better competitively, and also makes the company more valuable. It's the growth. So what we'd really like the CMO to tell the CEO is, "I increased the growth rate of the company." Not so much, "I save money." That would be way better.

**Jason Cohen** (00:44:29):
So here's how we could do that with Double Down. Yes, Double Down halves the cost, but what that means is right now, right now, the company is paying 200 bucks per lead. Let's call them leads, whatever this is outputting. Well, if I halve the cost of a lead, I could get twice the leads for the same money. I'm already willing to spend $200 a lead and I'm already spending 40K a month for it, so if Double Down halves the cost, it means I can get more leads. So the way I could pitch Double Down is double the leads per month, period. Now, if I'm willing to spend 40K for this number of leads, how much am I willing to spend to double the leads? 40K. I just said I'm willing to spend 40K for this number of leads, so doubling it, I'm willing to spend 40K to double it.

**Jason Cohen** (00:45:23):
So if I give Double Down 40K, not five for the same product, which is the leads are cheaper, but now the pitch is I doubled the leads for 40K instead of having the cost for 5K. So Double Down gets 8x the money because it gets 40K for this product, not 5K for this product.

**Lenny Rachitsky** (00:45:43):
Amazing story.

**Jason Cohen** (00:45:44):
And everyone's happy because the CEO goes, "What did you do?" And they said, "Oh my God, I doubled leads." "What?" "Yeah. At the same ROI as we had before, same CAC, I doubled leads." CEO goes, "How can we do more of that? " Just everything is so much better, same product.

**Jason Cohen** (00:46:00):
Now, I know it's a little bit of an exaggeration, et cetera, because I'm trying to make a point, but the big point is, the largest point is pricing is not just the number on the page. It's positioning, it's how their budgets work, it's how it's structured. It's per site or it's per usage or it's per seat or it's per ... All of this stuff is part of what pricing is. And often, even if it's the same price or the same product, depending on how that's structured, it either seems fair and good or it seems unfair and too expensive or whatever.

**Jason Cohen** (00:46:34):
And so in particular with the positioning, the big lesson for product managers is sell more of what the company values like growth. It doesn't have to be growth. It could be something else, the retention for their customers, how competitive they are in the market. There's various things they could value. Growth is an obvious one. Sell them that they're going to get more of what they value as opposed to saving, cutting, ROI, saves time, saves money, more efficient. And again, there's nothing wrong with saving money saves time. It's just that it caps this price and this value that they perceive that you do. Whereas if you deliver more of the value that they already value, it's I don't want to say uncapped completely, but the cap is maybe an order of magnitude higher than saving. So again, it's valuable to save. They're not doing anything wrong. That's not how to talk about what it is, and therefore help set the price.

**Jason Cohen** (00:47:35):
So it's a long way of saying ... So again, when I think, "Is your pricing correct?" I'm thinking in maybe a more general way than just the number. I'm thinking about the structure, the positioning and all that, and my guess is when growth is slowing, that there's a lot of improvement that could be had there.

**Lenny Rachitsky** (00:47:52):
Oh, man, this story is so powerful. Who does not want to change some copy on their website and double their growth and triple their price? And the biggest takeaway here is when you say is pricing correct, it isn't what is the number? 20 or 25 or 100? It's almost is the market we are going after correct? Is the way we are selling to them correct? Is this price communicating the right sort of story? And then also, is the positioning of what problem we solve for you correct? So there's a lot here, and luckily, I've done a bunch of episodes along this stuff which we'll point people to. We could go much deeper because this is a deep skill and there's a lot to do here.

**Lenny Rachitsky** (00:48:35):
One thing I'll mention specifically, so Jen Abel, a recent podcast guest.

**Jason Cohen** (00:48:38):
I love her.

**Lenny Rachitsky** (00:48:39):
It was her second visit to the podcast. She has a lot of really good advice on this of just how to price and how to reposition the way you're selling it. Specifically, she had this really interesting insight that enterprises, their sweet spot for contracts is 75 to 150K. That's how they normally buy SaaS software. And it sounds absurd, but that's what you want to ... You want your product to be in that bucket versus a thousand a month, 2000 a month. And everything just gets easier if you're like, "Okay, this is one of those. Okay, cool."

**Jason Cohen** (00:49:05):
It gets easier. I don't want to go off too much of a tangent, but the thing you have to remember is that pricing is not this knob that you can turn separate from the rest of your strategy. So when you say, even when I just said raise prices or whatever, but you can't just raise prices. These new customers have different demands. Now, maybe you need SOC too. Now, your other governance stuff matters. Now, integrating to certain systems you didn't know about matters. Maybe now, they need professional services. You can't just raise prices and change market and that's it. And maybe that's wise to do, but maybe it's not. Maybe you realize that sure, of course, that other market has certain advantages, but they also have disadvantages and we don't want those, either because we'll no longer be competitive because the way that we're distinguished, competitive, special, interesting, valuable, is only valuable in the market we're in. The next market does not value it like that, and so uh-oh, actually, that would be really bad for us.

**Jason Cohen** (00:50:10):
Or it could even be cultural. We are a company... Like Buffer's a great example. Buffer could go up-market and try to sell social media tools to whatever, but they realized, "We are a company for the little people. I don't want to sell to a big company. We're never going to make a product for them, we don't want to. This is who we are, this is what we want to do. This is what's fulfilling to us, so we're not going to go there." So it can be cultural, it can be certain goals, it can be other aspects of the business model or the strategy, but it's not this kind of like, "Oh, I'll just change this." It's a decision about the whole strategy, and that too, we could talk about for hours, but let's just caution that, "Oh, I'll just go enterprise," is of course not how it goes.

**Lenny Rachitsky** (00:50:54):
[inaudible 00:50:54]. We got this.

**Jason Cohen** (00:50:55):
Yeah. I love Jen. Her pricing and sales stuff is good. She's great on Twitter too. I love Jen.

**Lenny Rachitsky** (00:51:00):
And that is such an important nuance. Don't build the thing you're miserable building and just like, "Okay, I listened to a podcast. We're going to raise our prices 10x and life will be grand." There's also downsides.

**Jason Cohen** (00:51:09):
Yes.

**Lenny Rachitsky** (00:51:11):
Yeah, okay. Amazing advice. Okay. There's so much here. Again, this could be some conversation, pricing, positioning.

**Jason Cohen** (00:51:11):
Yeah, for sure.

**Lenny Rachitsky** (00:51:17):
I'll link folks to a bunch of cool advice that we've covered on this podcast too.

**Lenny Rachitsky** (00:51:22):
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**Lenny Rachitsky** (00:52:07):
Let's keep going through this checklist. So one was look at logo churn. Two is look at is your pricing correct?

**Jason Cohen** (00:52:13):
Is the pricing right? And why do we think it is? Because we probably don't have good reasons yet. Okay. So the third one is our existing customer is growing. I think everyone probably knows this, but just to say it. Okay, if cancellations overtake marketing in magnitude, one way to come back, one thing is, okay, make cancellations lower, but they can't be zero. So what else do we have to combat cancellations that would be proportional to our size so that it keeps up, unlike marketing or basic direct marketing? So one would be, all right, 2% left, but of the remaining 98%, some of those upgraded or otherwise paid us more, maybe it's usage based, whatever it is. They're paying us more, and so that covers the gap, and yes, if I tripled the company overnight, that would triple, and so that's the answer.

**Jason Cohen** (00:53:04):
So that is the answer, and maybe that's obvious but it's useful to tie it back to the sort of mental model we've got going. And of course, the metric here is NRR, net revenue retention, and the way that's computed is you say what is the revenue of customers right now? So existing customers, existing whatever, just the whole total, and then one year from now, what if that remains? So not new customers coming in, not talking about them because we're asking about the cohort that exists. What remains? So with cancels, it goes down, with downgrades, it goes down, but with upgrades, it goes up. So when I say remains, it could end higher than we started if upgrades exceed cancellations and downgrades. And now we are talking about MRR and not n, because n doesn't have this. N doesn't have an upgrade. N just only goes down, which again is why I think the n is actually the most important one. Because think about it. A lot of times, people think ... So if you've heard of NRR, you...

**Jason Cohen** (00:54:00):
... because think about it. A lot of times people think... So if you've heard of NRR, you might think, "Well, that's my golden metric. I'm done." But the issue is if NRR is positive, but N goes down too fast, it doesn't matter because not enough people are left. And so there's not enough people left over to upgrade. And so actually, you're wrong. And so NRR does not include that, and therefore, it actually undercounts what's going on in a bad way, in a way that hurts you.

**Jason Cohen** (00:54:28):
There's yet another way to see why what I'm saying is right. There's this thing in investments where, let's say I started out at $100 and the stock goes down 5%, so now it's at 95, or no, it goes down 20%, now it's at 80. Then it goes up 20%. Is it back to 100? No, because 20% more than 80 is 96. So if it goes down 20% and up 20%, it does not come back to zero. It's worse. When you have a loss, a percentage loss, you have to have a greater percentage gain just to get back to where you were. In this case, a loss of 20% requires a gain of 25% to get back to where you were.

**Jason Cohen** (00:55:08):
This is why NRR isn't quite right, because NRR is saying that a loss of 20% from cancellations is offset by 20% from upgrades. As we just saw, no, it's not. That only gets us to 96% actually. So this is why, again, I believe in NRR. I'm saying you got to track it. It's good. Just in the back of your mind, realize it's not quite that good, and looking at N keeps you honest about what's really going on with these customer cohorts. So that's why they're both useful, in fact. This is why they're both useful.

**Jason Cohen** (00:55:42):
So NRR, of course, is important. A nice way to see this is, if everything I'm saying is true and there's these limits and stuff because of cancellation, then there should be no way to get a big company like a public SaaS company unless NRR is greater than 100. Otherwise, cancellations should just win. And that is in fact the case. There's over a hundred SaaS public companies, and something like two of them have NRR less than 100%. That's how it goes. And those companies have horrible financials and their valuations are bad. It's not good. It's not a good thing.

**Jason Cohen** (00:56:17):
And in fact, the median for an IPO SaaS company, like at IPO, the median NRR is 119%. So yes, that's what it takes. You can't do this. You're limited in less. Now, your goal may or may not be to get that big, but the point being it's mandatory for growth. Now, if the literal customers are just leaving, you got to plug that hole first, like you said. But okay, if they're okay, that's why this is in order, if that's generally okay, now we turn to NRR to say, "Okay, but the ones who stay, they're hopefully happy, they need to grow. Okay." So that's the full story of NRR. I think people who've heard of NRR don't necessarily think about all those things and realize that.

**Jason Cohen** (00:57:01):
So a good question is, okay, what do I do with NRR? But I think the answers are pretty clear. You add features, you have different tiers, you change the pricing in some way with usage or seats or something that kind of goes up automatically as they get more value out of it, so I don't think that's terribly interesting to double click into. It's sort of obvious. I would say, "Oh look, it's tied into pricing because their behavior," but the main thing is you want it to where the customer themselves would agree when they pay more, that they are getting more value. Hopefully they even think they're getting far more value than the price going up. I'm going to say this as if it's precise, which it's not, but they need to feel like if the price doubles, "Yeah, but I'm getting five times the value, so that's fine." That should be the feeling, whether they can measure it or not.

**Jason Cohen** (00:57:51):
A good way to do that is to say, "Well, then you should be measuring whether they're getting value out of it." Often we measure usage metrics and other kinds of metrics within our product because we can. But actually, what's really important is to measure how does the customer value this? And we need to measure that so that we make that go up because if we make that go up, they'll be willing to pay in whatever structure. And if that isn't going up, they won't be willing to pay. So even if we start making them, they'll leave. And we all know, we've all probably done it ourselves, we've all had products we love, but then as we scale, the price goes up faster than we feel the value is, and then we start looking for other products. We've all experienced that. So that's what I'm saying.

**Jason Cohen** (00:58:32):
To do that, you want some sort of measure of the value the customer's getting. If you're really lucky, that can be a number. That'd be wonderful. Then go do that and maybe that's your North Star. But admittedly, it's not always possible. So then the question is, the usual questions and metrics, are there proxy metrics that we understand are not the full picture, but they're helpful, they're part of it? And I'm a big believer in saying not all important things are numbers. I mean, even things like how differentiated are we in the market? Not a number, but it's very important.

**Jason Cohen** (00:59:02):
So this might be one of those things that's important, but not a number. So okay, can we get some proxy metrics, even of behavior and other things that's something better than some metric that's just operational? And even if it's qualitative, okay, can we do that? Can we talk to customers and ask them qualitative questions to try to see? I would just say do your best here, because only when you generate more value for the customer, you can then decide how to split that with the customer in terms of things like price.

**Jason Cohen** (00:59:35):
But that's in fact how I think of it, that very phrase. How do we create more value for the customer and then split that with them? And when you do that, you're keeping the customer forefront in mind. You are taking some. Splitting means you get some. Let's not forget. It's not a charity. And on the other hand, first we should think, how do we generate value for customers, and then we've now earned the ability to take a little piece of that. So to me, this is the right way to think about NRR, not just, "We'll add a feature and make them pay." True, but let's actually take it from this different... Let's get there from this different perspective.

**Lenny Rachitsky** (01:00:10):
Amazing. In the recent Modivon episode where we go into pricing, you actually have some really good tactical advice for measuring the value that you're giving to a company to quantify that, which feeds into this idea of how do I create more value for you and then how do we split it?

**Jason Cohen** (01:00:25):
Yeah.

**Lenny Rachitsky** (01:00:25):
The other element of this that's top of mind is just this land and expand strategy. There's a lot of companies that are just like, "Okay, cool. We'll get in with some price. We'll expand. That'll be amazing," which is essentially expanding as NRR going above 100%.

**Jason Cohen** (01:00:37):
Yes.

**Lenny Rachitsky** (01:00:37):
Something Jen actually shared in her chat that was really important is that you can't expand that much, at least for a while, because if you get in for 10K, if you go up to, okay, now it's 100K, someone's going to be like, "What is this? This can't go up 10X. Are we getting 10X value from this?" You can't just raise prices later. You're kind of stuck at that reference point, so you have to be really careful there.

**Jason Cohen** (01:00:59):
Yeah, I think that's right. And maybe you don't deserve it. In other words, especially when there's investors or other sort of forces saying like, "Hey, we need to X, Y, and Z," forces that are not the customer saying that, yeah, you can be coerced into making pricing or other kinds of policies that in fact are not good for the customer. So one tool I use is when anyone claims anything really, is, is that really true or is that really actually good for the customer? Because look, we are going to do things that are selfishly good for us. We have to. We can't just do things that are bad for us, but is this in fact good for the customer?

**Jason Cohen** (01:01:44):
Because often even in an internal proposal, we say it as if it is. "Oh, this pricing will be good. It'll raise prices on everyone, but it's better because of this reason," that we're sort of justifying. It's like, well, will the customer say this is better? If the answer is no, it's like, okay, it's better for us, but sorry, we have an equation where it has to be better for us and better for the customer. Sorry, it's an and. And of course, not all companies do that. And we experience that, all of us as consumers on the other end of that, and we don't like it. And that's not a good long-term strategy, even though it might work in the short-term, as many bad long-term strategies are.

**Lenny Rachitsky** (01:02:21):
I love just how this third step just reveals how powerful the sequence is that we're going through. Step one is this logo retention. Essentially, do we have product market fit? Step two is pricing, positioning. Essentially, are we going after the right market and charging them the right amount roughly? And then here it's just, can we grow? Is there something here that can continue to expand? Because you're going to get eaten alive if you're, especially... And just to be clear, this is B2B SaaS primarily that we're talking about here. It's harder to grow NRR if you're a consumer product that has, I don't know, just a tier or two.

**Jason Cohen** (01:02:56):
What I would say is the rules are true everywhere because they're based in the mechanics of finance in the business. You're right that in the consumer segment or small business segment for that matter, they tend not to grow. So that doesn't mean the NRR question is invalid. It means, dang, we can't think of anything. That's okay. Then you go onto the next question because you can't think of anything, but it would be more strategic if we could. And are we trying hard enough?

**Jason Cohen** (01:03:27):
As a consumer, I do not want to spend more with AT&T, but they're also not giving me any more value. But there are other products as a consumer, like Amazon, where they do. So you're right, but it would be as a product manager, it would be 10 times more valuable for you to think of something like that than to move on to other things, et cetera, or there's other ways, like other products. We didn't talk about it and it's okay because of course each one of these, you could go on forever, right? But another way is a second product. A second product sold to the same segments that you're in so that your existing customers can buy it. Well, I don't know why that wouldn't work in consumer. It certainly works in consumer in apparel.

**Lenny Rachitsky** (01:04:09):
I think about AG1, which has all these new... I'm doing their sleep supplement, and there it goes. They're just like, "Here's a new thing you can buy."

**Jason Cohen** (01:04:15):
Yeah. So is it harder? Yeah, of course, of course. All of these are easier, harder in different segments and ways. Of course, of course. I'm not trying to say otherwise. But I would say the mechanics of how the finances work is the same. You're just saying, " I don't have this lever to pull." And then I would say, "Okay, well then you need different levers."

**Jason Cohen** (01:04:35):
One that we didn't talk about is one way to offset the cancellations is existing customers grow, but another way is if existing customers bring in new customers. So they didn't grow, but they brought their friend. Now, this is absolutely something that happens in consumer, but it's also an answer to this thing where cancellations grow exponentially, because existing customers bring in new does triple if you have more existing customers. Aha. So this is stuff like refer a friend and all this kind of stuff. Again, some of this is obvious. We don't need to enumerate that. So those things are good.

**Jason Cohen** (01:05:11):
And so in consumer, you might say, "Oh, it's easier to try to get someone to invite a friend with a coupon and blah, blah, blah, blah, than it is to try to get them to grow," but in B2B, that may not be true. I don't get a mid-size company to refer. That doesn't make sense. So once again, this question of how do we have the existing base help us grow is still correct in consumer, but its manifestation could be very different. Of course, I agree with that. But I mean, how could we not say... I mean, of course, things like word of mouth and invite a friend, of course that's enormous with consumer, and this is one of the reasons why.

**Lenny Rachitsky** (01:05:44):
I just love picturing the people listening to this, especially product folks, founders. I imagine many of them are just sitting here taking all these notes of how to help grow their product because we've positioned this conversation as how to deal with stalled growth, but it's actually just as useful, how do I grow more?

**Jason Cohen** (01:06:02):
Oh, for sure. Right, right, right.

**Lenny Rachitsky** (01:06:03):
Which is awesome.

**Jason Cohen** (01:06:04):
It clearly is more growth. It's just maybe a little more evocative, because if growth is good, yeah, sure, you want to grow more, but it's not the problem. If growth is really good, the problem is generally operationally scaling to meet the growth, and so you're focused on that. It's when growth slows, you're like, "Whoa, wait, wait, wait, wait. We have to focus on growth now, must," as opposed to, "Of course it's always nice." So yeah.

**Lenny Rachitsky** (01:06:28):
Yeah. And I was thinking as we were talking about consumer NRR, if you look at Duolingo, they've done a great job here. There's so many ways you can pay them more for all these little advances, get all these gems, change the color of your app to something fancy.

**Jason Cohen** (01:06:41):
Yeah. Yeah.

**Lenny Rachitsky** (01:06:42):
Okay. So there's more. Let's keep going.

**Jason Cohen** (01:06:44):
More? Okay.

**Lenny Rachitsky** (01:06:44):
So we've done three. There's more.

**Jason Cohen** (01:06:46):
We've done three.

**Lenny Rachitsky** (01:06:46):
More you can do.

**Jason Cohen** (01:06:47):
So okay. Logo churn.

**Lenny Rachitsky** (01:06:51):
Pricing.

**Jason Cohen** (01:06:51):
Pricing.

**Lenny Rachitsky** (01:06:52):
NRR.

**Jason Cohen** (01:06:54):
NRR. And then maybe it's stalled, so this is really a stalled question. Maybe your acquisition channels, your marketing channels are saturated. We're done. I mean, what we tend to do is flog the people doing AdWords, flog the SEO people, get more searches. It's possible that this is it. Maybe this is it in some sort of physical law. There literally isn't anything else, or maybe just this is how good we can be. This is it.

**Jason Cohen** (01:07:23):
But there really are limits. There's different words for it. Inventory is sort of the old word, like with magazine ads. Inventory was the word, but there's just this amount. There is only so many searches in your area, and you can only appear once in the search results for a given keyword. So there is this limit of what you can get. Even if you're in the number one position for everything or other things that are not even practical, there's still a limit. And there's some kind of practical limit that's below that we don't really know, but maybe we're there or maybe we're close.

**Jason Cohen** (01:07:53):
And worse, channels tend to decline over time. So I think people talk about S-curves. " Oh, I didn't figure out this market. Then I figured it out. We unlocked it. Now we're getting a hundred leads per month through Facebook," or whatever. But then it kind of taps out and then we go into this optimization mode, "Can we eke out another blah, blah, blah, blah?", which is right. It all is right. And you call that an S-curve because it's shaped like that, but that's not what happens. What happens is it starts with an S-curve and then it starts sagging. Its butt starts sagging down. So I wrote an article about this called the elephant curve, which is what I named it, because it's like this trunk, but then it's this butt. And there's different reasons why this happens, but if you talk to any marketer, they'll all tell you, "Oh my God, let me tell you this story." They all have stories about it because yeah, this is what happens.

**Jason Cohen** (01:08:45):
There's different reasons. First of all, the audience gets saturated because there's all these little marketing isms, and I don't know if they're true or not. I don't think any of it has any data that actually proves it, but whatever. If words start with the same letter, that's better. I don't think anyone's ever proved that's true, but marketers seem to think so. Okay. Well, one of the things is someone has to see it seven times before they act. Okay. All right, well, maybe they saw it seven times already, so they don't want it or they saw it 20 times. So initially you hit people who hadn't seen it yet, but now you have.

**Jason Cohen** (01:09:21):
And especially with magazine ads, as I used to do before there was no such thing, that's exactly what would happen. You'd have this nice surge and then like, "Well, we've seen it before." So you still get a little trick. You still get some because it was just that moment they needed to see it again. Okay. But a lot of people have already seen it and they don't want it, or the channel is declining and they'll never tell you. When I did magazine ads, every year they would tell you what their circulation is. Every year it would go up and then the magazine would go out of business. Conferences are the same way. "Attendance is great. Attendance is great. Oh, wait, we're out of business because we couldn't get enough people to come. What? They said it was great." But more quietly, AdWords, Facebook ads, even SEO searches, this does happen all over the place. Affiliates, it can happen. And now with AI, I don't even know. It's disrupting everything. We've all heard stories going all different directions. I think the answer is, "I don't know." Shrug is the answer. And what will it be like in two years? Another shrug. But the point being, they're not all going to be growing a lot. That's not one of the futures that AI will bring us. So it has this sag.

**Jason Cohen** (01:10:33):
That's just a very long way of maybe trying to prove this point, but it's yet another reason why you can't just rely on marketing forever because you try to stack things, but there's not an infinite number of marketing channels you could advertise in that your customers are actually going to, and they sag. Oh no, it's even harder to keep up. So it's kind of like the secret in reinforcing my very first point here with this, but here, are they all saturated? Because we could flog marketing all we want. It's not going to work. So maybe growth is slowing because all our channels are saturated, possibly even sagging, but even if not, okay, not growing, and so we can't just flog the marketing department. It's going to take something else.

**Jason Cohen** (01:11:16):
The obvious thing is get more channels, but again, maybe there aren't any. So again, there's many possible things to do, but this is this critical thing to notice, because I guess I would put it this way. Do you know right now which channels are saturated and which aren't? If the answer is no, I'm like, well, okay, maybe that's... because the answer is all. It needs to change how you think. Just adding one little feature and then hoping we can flog AdWords is not going to work. Even if the feature's great, not going to work. So there's different things that could work, but that's not one of them, and yet that's probably what we're doing. "Let's add another feature and marketing can flog it," is often the answer. But if you're in this state, that isn't the answer. So this is why you could say it's obvious to say this or that, but are you acting like this is true? Often we don't.

**Jason Cohen** (01:12:05):
Okay. So there are many things to do. Again, we don't have to enumerate all of them or something. It's just simply the right question to ask. But for example, some people are like, "We've done direct. Maybe we should try things like SEO and social and these other indirects or vice versa. We're really good at SEO, but we've never taken out ads." Often if you've done ads and they're optimized, you know what content might be good to write stuff for SEO, and maybe even vice versa, maybe. So it's a good idea and sometimes it works, but here I have no data, I only have my feeling here. And actually, you probably have a lot more visibility into this, but my experience is a product that sold really well direct actually doesn't do well in things like social and SEO and vice versa. If you're getting a lot of traffic through SEO, adding ads often costs a lot and doesn't really move the needle. You can tell me, is that... because I don't have data to support that theory.

**Lenny Rachitsky** (01:12:55):
Yeah. My take is you could always get some percentage of win from all these different channels. Usually one channel's where most of your growth will come from. And so over time, everyone just adds every channel. Everyone's doing ads. Everyone's doing SEO in some form, but it's usually sales or word of mouth or ads that drives everything. Everything else is kind of this little layer on top.

**Jason Cohen** (01:13:16):
Yeah. So should you do that? Yeah, probably, especially if you're at some scale and you can just afford to because it's such a clear thing to do, but probably you'll have to get more creative about what it means to add a channel or something like a new product or a new market where it's actually new. It's expanding in a new way rather than trying to incrementally expand what you're already doing.

**Jason Cohen** (01:13:36):
So an example of getting creative on a channel is what Constant Contact did when they had this very problem of, "Growth is slow. We don't know how. We sell email marketing newsletters to small business before all these modern tools existed." And one of the things they did that restarted growth is they physically went to a bunch of cities and held workshops showing, "Here's how to do email marketing for your small business." So the restaurateur and the dentist and everyone would come to these sessions and they'd teach them how, of course, teaching them how with Constant Contact so they became customers, right?

Now, you would think there's no way this is cost-effective. Physically being in these cities and dragging people in for a $20 a month product? No way. It was very effective and actually solved, in that moment, restarted growth. They were very clever about... First of all, it's a clever idea, but then they were clever about how to do it. They took power users who are also agencies, so these could become customers of theirs. Okay. So you could be clever about how is it that we do something different, something new. So that's possible. Of course, it's always hard to say, "Think of something clever." That's a weird finger wagging thing to do, but okay, it's true. But yeah, it could be a different type of channel. For example, HubSpot famously tested selling through agencies instead of direct. That ended up being 50% of their revenue after four or five years, so it's one of the main reasons why they were able to continue growing. Same thing happens with my company, WP Engine. Tons of our websites are sold through agencies that create WordPress sites. So there could be something that's not direct anymore. Another channel of human beings or something like that could in fact dramatically change your growth rate. There's lots of examples like those, but it could be it's time for the next product. And I said that earlier, because it's always possible. Of course, we all know that's very hard, it's risky. I have sort of a framework that I use to think about that kind of expansion, which I'm happy to provide. I've also written it up, but I'm happy to say it right here. But usually you want to stay in the target market you're already good at and grow from there. But sometimes the whole point of the expansion is to change [inaudible 01:15:51] or add something where you're leveraging something else about the company that you have as an asset going somewhere else. So this is what this framework helps decide.

**Jason Cohen** (01:15:58):
But one way or another, you probably want to plant one foot into some strength or asset that you have, move the other foot, which is the risky part, but the idea is that, "Yeah, well, we have this big upside, so we're taking that bet," and that becomes a smart bet. Of course that's true for any of these things, but especially if acquisition channels are full and it's like we literally can't ask the marketing department, that's just not one of the choices, it almost forces us to start taking these more drastic bets to say, "Well, we got to do something and that's not one of them."

**Lenny Rachitsky** (01:16:29):
I just want to keep saying how awesome this advice is and how many people are going to benefit from... None of this is like, "Oh, I've never ever thought of any of this." It's just the very methodical sequence of questions you should be asking yourself to help you not just undo stalled growth, but also just come up with a bunch of great growth ideas.

And this specific section, it makes sense. Somebody's discovered alpha in a growth channel. Say, [inaudible 01:16:56] just launched. TikTok just... There's like, "Oh, cool, what's the new thing? Okay, let's get there quick." And then you drive a bunch of growth, it's awesome. Eventually everyone's going to start doing that. And so you should assume everything that is working for you now will slow down. I missed your post on the elephant S-curve. What do you call it? The elephant curve?

**Jason Cohen** (01:17:15):
Yeah, elephant curve.

**Lenny Rachitsky** (01:17:17):
Yeah, that's so real. It's not just this S-curve that will forever continue to drive win. It will actually dip and decline over time because other people discover it and start using it.

**Jason Cohen** (01:17:26):
Yeah.

**Lenny Rachitsky** (01:17:27):
I love that. So the idea here is, and the classic advice is, whether it's an S-curve or an elephant curve, think about are you starting to approach the apex of that and start to explore other channels before you slow down or start to dip?

**Jason Cohen** (01:17:44):
Yeah. It's easy to hear stuff like, "Well, if marketing is full, do something else." And you go, "I know." But then you look at people's behavior and it's like, "Well, you're not acting like you know." So maybe it needs to be said in enough detail that you actually do something about it.

**Lenny Rachitsky** (01:17:59):
And it's important to note, this is a very hard problem. Most companies do not really solve this. Something worked for them and then it stops working and then like, "All right, well, we found something," and then it just kind of went away. There's a couple posts we're going to link to in the show notes that will help you come up with ideas that are all around new growth channels that are emerging. One is by Emily Kramer around ecosystems as a new growth channel. And there's a lot of really cool advice there around this kind of emerging combination of influencers and content and partners where it's your ecosystem that helps you grow. Basically, there's a quote from the head of growth at Wiz where it's like, "Why start with zero when you can start with 10,000?" essentially growing through someone with an audience already. And then there's going to be a post out by the time this comes out around ChatGPT's app store, which is going to let you submit apps. And that's a really interesting, potentially huge new growth channel for companies. So cool stuff happening there.

**Lenny Rachitsky** (01:18:55):
So just to summarize, logo retention, pricing, NRR, marketing, channel saturation. What comes next?

**Jason Cohen** (01:19:04):
The last question is, do you need to grow? So okay, growth is stalled. And if we assume every question before has been answered in a satisfactory way, you could ask, "Hey, is that a problem? What do we mean by grow? What do we need to do exactly?" Now, of course, you should know these kind of things, the goals all the time. And again, obviously the answer could be once again, all new products. These other things where this company, when we say, "Do you need to grow?" if we define you as this product in this market and this company, the answer might be, "No, what we need to do is have a different product in a different market or a different thing," or you could change the word revenue. If you say, "Do you need to grow revenue?" You could change the word revenue and say, "You know what? What we could do is maximize profit instead of revenue now. We've been maximizing revenue, but maybe we maximize profit instead."

**Jason Cohen** (01:20:02):
And so this is a company like 37Signals or really lots of bootstrap companies who have hit some sort of limit and realize, "That's okay. The founders are getting paid millions of dollars a year in dividends and it's okay, but I don't have to get... In fact, if I got bigger, it might be an organization that I don't like or serving a market segment that I don't want to serve," or whatever. And so maybe growing forever isn't the goal actually, or growing revenue isn't.

**Jason Cohen** (01:20:29):
You could ask philosophically, why grow anything? Why isn't it just okay to have stasis? And we all have heard the phrase, "If you're not growing, you're dying." This is a classic company thing. Is that true or is that the kind of thing that investors use to make founders grow or try to grow even when they shouldn't? It might be, but I would submit that even at a bootstrap company that has other values and culture other than growth at all costs, that that phrase is still fairly relevant because if the company...

**Jason Cohen** (01:21:00):
But that phrase is still fairly relevant because if the company's stagnant for years, is that a great environment for everyone? As the founder, did you start this company in order to do the same thing every day? Is that why you did it? Is that fulfilling for you? What about everyone else? Nobody wants to further their career? They just want to do the same thing every day and never further their career, not really learn anything, not really innovate? Does it feel good to just not be growing? 

**Jason Cohen** (01:21:32):
The answer could be yes. If I'm a CPA, and I have some clients, and life is good, the answer could be yes. I'm not dictating the answer here. I'm just asking because a lot of times, whether it's our careers, or as founders, our companies, a lot of times we've just been in the mode of, "Oh, I've got to grow, I've got to get promoted, I've got to do more, I've got my resume." We've got in that mode for so long, maybe our whole life, that I was going to say lose sight of, but maybe we never had sight of, "Wait, does this make me happy? Is this what I really want? Am I fulfilled doing this? Or even if I do have these goals, have I gotten stuck in a rut where my goal is growth and I don't know, more money, more everything, but I'm stuck in a rut here." Sometimes we forget to take a step back and go, "Wait a minute. Is this still right or do I need to turn the page and have a new chapter of life right now?" 

**Jason Cohen** (01:22:26):
And so this question, do you need to grow, or if you're not growing, you're dying, well, for some people, no, they like doing the same thing forever. And that's great, actually. That's nice. But for many people, especially the kind of people who want to get into product, and build stuff, and innovate, and people who start companies, a lot of people like that are not the kind of people that want to just do rote things for 20 years. 

**Jason Cohen** (01:22:51):
And so the not growing part, what I like to say is maybe the you in if you are not growing, you're dying is you, the person as opposed to you, the company. It's also you, the company. But what if we took it to mean you? If you are not growing, then in some sense, maybe for some people, you're dying. Maybe if you're listening to this, that's you. You're a shark and you got to go. 

**Jason Cohen** (01:23:16):
And we all know people too, who claim they hate work or maybe they do hate work. Let's not say claim, they do hate work, but then they retire and kind of go downhill because they don't have a purpose or this, or that, and the other thing. In that case, it was true, if they're not growing, they're dying, literally. So again, I don't mean to overstate this, and I certainly don't mean to claim that there's some answer that's right for everybody, of course, but surely this is the right kind of question.

**Jason Cohen** (01:23:45):
And surely for many people who are listening to this, the answer is, "Yeah, I mean, in some sense, some very rough sense, that's probably right for me. And so if I'm in a stagnant situation and really, every other option has been exhausted and isn't going to happen, this is simply a stagnant thing. Maybe there's something else needs to happen. I need to leave, the company needs to change some drastic way, I sell the company, I change jobs." I don't know. Of course, it's going to be super context specific and personal. But something dramatic may need to change because nothing incrementally is changing. 

**Jason Cohen** (01:24:20):
So this final question, do you need to grow? Or if you're not growing, you're dying, is that true? And are you therefore dying and what needs to happen? So if you were looking for more metrics in another framework, sorry, it's existential, but it is. It is existential. So do you have to only ask this at the end of the chain? No, of course you should feel fulfilled. And of course you want to be checking in with yourself at least annually, of course. But I put it at the end of my list in the sense that I'm assuming the original question is about the company, especially with smaller companies, but also with big public companies. There's plenty of big public companies that aren't growing, aren't there? So this is true of all scales because there are natural sizes for things. So yeah, it's a little philosophical, but I think it's quite important.

**Lenny Rachitsky** (01:25:18):
Such a beautiful way to wrap up this piece. A lot of people listening to podcasts are bootstrap founders, and for them, this is actually very much an option. They can just be happy with the revenue they're generating. Like with my newsletter right now, I'd be very sad if it stopped growing, but also just, it's amazing the life it has created for me. And even if it did stop growing, and just stayed flat, and doesn't become an elephant curve, that'd be incredible. In practice, psychologically still hard for that to be the case, and that's why this component of the sequence is really important. Like, "Why do you actually need it to grow?" Is that just your ego, is that just like, "I'm used to growth?"

**Jason Cohen** (01:25:58):
It can also help you avoid doing unnatural things that you actually regret to grow. So if growth at all costs is just the thing, there's probably ways you could, "Grow the newsletter," that you would just say, "I just wouldn't be proud of that. And the newsletter's doing so well, that I don't need to do that." And so again, maybe that's a softer version because growth hasn't actually stopped, but okay, it's a softer version of, "I certainly don't agree with growth." You might say, "I certainly don't agree with growth at all costs. I want to grow as much as possible within the things that I'm proud of. If we grew fast, but the content was crappy, I'm just not willing to do that. It's not the point." 

**Jason Cohen** (01:26:35):
And so it helps set up these boundaries of like, "Wait a minute, not if ... " And early on, we may not have that flexibility. You could argue that you should have those values early on because that's who you are, and that's what you're doing, and people respond. So I could argue you should have that all along, but I could also argue that at the beginning you're just trying to do something where you don't die, you're starting to blog, you're probably copying other people's style. You probably don't have that much unique things to say. So there's a lot of ... That's okay. You're just trying to get going. It's okay. 20 years later, if you have no style, and no voice of your own, and nothing new to say, that's probably not good, but to get going, sure. 

**Jason Cohen** (01:27:09):
So sometimes we have this thing where we get going with maybe looser ... I don't want to say values. I'm not saying that's unethical, but looser bar or a pride that we have in our own work, and we tighten it up as we're able, as we can afford to, you might even say. So good, but then that becomes a nice filter here of like, "What is it in a greater sense I'm trying to do here, I'm willing to do here?" So if you're not growing, you're dying fair, but that has to come with these limits. And the more successful you are, the more you can be serious about those limits.

**Lenny Rachitsky** (01:27:43):
I think an important element of this is also the product you're currently working on, maybe it's okay for it just to not grow. This is a good opportunity to do something else, have this thing maybe running on the side, maybe sunset it at some point, but it's a good opportunity to be like, "Okay, wait, what else is out there?" We had a recent podcast conversation with Matt MacInnis, CPO at Rippling, and there's some really good advice he shared on just when to quit, when to quit your startup, just like, "If it's like four or five years in and it's just not clicking, maybe it's time to move on." And even though people do succeed years in, most likely it's not going to be you.

**Jason Cohen** (01:28:16):
I have a book almost out now that's on pre-order about topics like what we've been talking. The next book I want to write is on this topic of how do I make these decisions of uncertainty? Like, "Maybe it is time to quit. Maybe I should move to a different city. Maybe I should marry this person. Maybe I should launch this company. Maybe I should use this strategy where you want to use probability and expected value. It's unlikely that ... " But the truth is we don't know what the probability is. We don't know what the probability curves look like. We actually can't use expected value. And anyway, even if you could have expected value, I am a human being. This is my life and I either sell the company or I don't. And so all this stuff about probability, and like, " That doesn't apply to me. I need other ways of sorting this out."

**Jason Cohen** (01:29:03):
So I guess I would just say briefly, probability is not going to work for these decisions. So that doesn't say what is right, but it's not that, which is nice, because you could put those tools down. "I'll do some market research to see if I should sell my company." Nope. That's not where the answers are.

**Lenny Rachitsky** (01:29:26):
More questions than answers on that one. 

**Jason Cohen** (01:29:28):
Yeah.

**Lenny Rachitsky** (01:29:29):
Speaking of the book, let's give you a chance to share what you're working on, and when this is coming out, and where folks can find it.

**Jason Cohen** (01:29:36):
Sure. So the book is called Hidden Multipliers, and you can pre-order it at hiddenmultipliers.com. Or I guess if this is out long enough, it'll be order it, I guess, depending on when you're listening to this. And it's a lot of stuff like we were talking about today, these questions of, it's called multipliers because the idea is little things that you can do or little decisions you can make that have a huge impact like moving the cancellation rate from five to 4%. It sounds small, has a huge thing, onboarding as opposed to later, huge thing. 

**Jason Cohen** (01:30:09):
So those are some examples, but the book is of course full of different kinds of topics, but all of this idea of this stuff that has such a big impact on things like revenue or profit. And either, just as you said earlier, either maybe you have never thought of it that way, so you weren't thinking about it right. Or yeah, you've heard that, you say, "I know." But your actions don't reflect it. And so if we go deep enough with examples and specific things to do, then you can actually act on that supposed knowledge and realize those multipliers.

**Lenny Rachitsky** (01:30:40):
And just to remind people that you're all hiddenmultipliers.com.

**Jason Cohen** (01:30:44):
Yeah.

**Lenny Rachitsky** (01:30:45):
And there's an S at the end, Hidden Multipliers.

**Jason Cohen** (01:30:48):
Right. There's more than one.

**Lenny Rachitsky** (01:30:49):
There's many. It's more than one.

**Jason Cohen** (01:30:51):
Yeah.

**Lenny Rachitsky** (01:30:52):
Jason, I had other things I wanted to talk about, but I feel like this episode's actually going to be stronger if we just focus on the thing that we've been talking about, which is unstalling growth. So if we do that, is there anything else you want to mention or leave listeners with before we get to a couple corners and then the lightning round?

**Jason Cohen** (01:31:13):
I think if you tried to find a common thread throughout all this stuff about growth, it comes back to the customer getting value. And I know we already talked about that, but I think if there could be one thing where it would help solve kind of all of it, it would be that they really are getting value. Your product actually promises the right thing, and then it actually delivers on that thing, and the customers can onboard so that they can do the thing, and the customers know, they realize that they're getting the thing, and you're measuring the thing, so you know it's increasing. 

**Jason Cohen** (01:31:48):
That is probably, if I was an LLM, I'd probably say, "That's the common thread." There's many ways that manifests, of course, but if that's your North Star is how are we actually creating value in the way the customer values it in their language, in their way, and their way of understanding it, I wouldn't say all the pieces magically fit into place, but certainly isn't that the root thing that is going to make all this stuff work? Then there will be a good way to do pricing. 

**Lenny Rachitsky** (01:32:15):
Yeah.

**Jason Cohen** (01:32:16):
They will stay as long as possible. And these things will probably be right, if that. So this idea of creating value for the customer and then figuring out how to split with them is probably the root idea. And of course, I hesitate because platitudes like that are actually not actionable, not very actionable. Like, "All right, well, I'll move on with my day." And that's why Twitter's not so useful. But given that we've gone into so much detail, perhaps that's a nice way of summarizing it.

**Lenny Rachitsky** (01:32:39):
I think that's such an important point. I think what's also interesting is some of your advice is the value, you may be picking the wrong customer, the wrong market, you may be positioning it wrong. So the value may be there, you're just trying to convince the wrong people about it.

**Jason Cohen** (01:32:54):
Yeah. There's so many ways to get it wrong because like we said, all these things have to be right. And you just said another one, which is, and you have to say in a way that when this person hits the homepage, they know it. It's true, but do they know that? It's just so many things have to go right.

**Lenny Rachitsky** (01:33:11):
What a tough job we've got over here, just solving people's problems. Come on. Okay.

**Jason Cohen** (01:33:15):
Well, at least we're growing.

**Lenny Rachitsky** (01:33:18):
So now we will be after this conversation. Okay. So I'm going to take us to a recurring corner, a recurring segment on the podcast that I call AI Corner. What's one way that you have discovered using AI in your work or in your life that might be helpful for folks to hear?

**Jason Cohen** (01:33:37):
There's a lot of data on the internet and it's often in things like images, which makes it hard to do your own analysis, or plug it in, or et cetera, come up with your own models or apply it. But I found that AI is really good actually, especially Gemini, at say, you just give a chart to it and say, "Make this into a table that I can paste," literally say, "that I can paste into Google Sheets." And it will do in a way that literally you can copy, and it will actually paste correctly into Google Sheets, and then you can do stuff. So especially with the book and my articles, I love to use real data whenever I can, of course. And so I do that all the time. So I think that kind of interpretation is very useful. And so all of a sudden you can get 10 examples of something and test a theory, where before it was just too hard and you didn't.

**Lenny Rachitsky** (01:34:24):
That's an awesome tip because people know you can generate all these infographics, especially with Gemini and all these things. That's really cool to know you can just feed it, "Here's a chart and make a text."

**Jason Cohen** (01:34:35):
Yeah.

**Lenny Rachitsky** (01:34:36):
Okay. I'm going to now take us to Contrarian Corner. The question here is, what's something that you believe that most other people don't?

**Jason Cohen** (01:34:43):
A/B testing doesn't work very well and it doesn't work on most things. It won't work on strategy, or vision, or insights, nothing actually important to the success of the company. You don't A/B test whether Uber's a good idea. And then, even when you do A/B test the details, where I agree, sometimes that can work. What happens is people will try things like, "Oh, I'll just try this verb, and that verb, and this ad, and that ad." And then like, "Oh, the seventh or eighth one, I got a positive result. That must be good." 

**Jason Cohen** (01:35:14):
And what happens is you keep doing that. You pick the best one, and then you go on, and you find another one, and pick the best one. And then a year later you look back and you should be like 50 or a hundred percent better because you've stacked these things, and you look back, and like nothing's different. The conversion rates are the same as they've always been. You say, "What the hell happened? I thought I picked the winner."

**Jason Cohen** (01:35:34):
And the answer is in a combination of the tools not being statistically accurate, which they're not. And the fact that you will get false positives even if the tool is statistically accurate means that most of them are false positives. Even if the tool's 95% accurate, when the thing you're looking for is rare, which it is in the case of A/B testing, the false positives happen more often than the actual thing happens. And so most of the results you get are false positives anyway. So as a result, this isn't true of all A/B testing, but for what most people do when they just do the mundane A/B testing, you can't A/B test the important things and the details are mostly false positives, so it's an enormous waste of time unless you're incredibly sophisticated. I know there's special groups that actually are very sophisticated. Fine. If you're not doing that, it's like the poker table. If you don't know who the patsy is, it's you. If you don't have all of this information and knowledge about A/B testing, then you're the patsy.

**Lenny Rachitsky** (01:36:31):
Bam. All right. 

**Jason Cohen** (01:36:32):
Yeah.

**Lenny Rachitsky** (01:36:33):
I will just say that I have found A/B testing useful in my career. I think it's maybe at a certain scale when you're just trying to optimize and continue to grow, you know-

**Jason Cohen** (01:36:43):
Yeah.

**Lenny Rachitsky** (01:36:43):
... where it's like millions of users, like a percentage gain is like millions of dollars.

**Jason Cohen** (01:36:47):
True.

**Lenny Rachitsky** (01:36:47):
Most people are not working at that scale, most people.

**Jason Cohen** (01:36:49):
Right.

**Lenny Rachitsky** (01:36:50):
Yeah. So just wanted to, for folks that find it valuable.

**Jason Cohen** (01:36:54):
That's true.

**Lenny Rachitsky** (01:36:55):
But I love-

**Jason Cohen** (01:36:56):
However, even so, on your own podcast when you were interviewing the guy from Shopify, and he was saying how maybe a third of the things that they found with their systems just disappear, they just magically disappear. And they have a team of a hundred people, and they're really good at it, and their effects disappear all the time. So they double check later whether the immediate effect goes away because even then.

**Lenny Rachitsky** (01:37:19):
Right. I think that was the CTO of Shopify conversation. Yeah.

**Jason Cohen** (01:37:19):
Oh, okay. Yeah.

**Lenny Rachitsky** (01:37:22):
Yeah. Sweet. Okay. 

**Jason Cohen** (01:37:24):
I just go off of memory because I listen to a lot of episodes, but I don't-

**Lenny Rachitsky** (01:37:26):
That's such a good one. 

**Jason Cohen** (01:37:26):
... no, it's-

**Lenny Rachitsky** (01:37:27):
I love that. Yeah. Where they leave like a holdout group essentially, and then they just look back, "Was the effect something that lasted?" And most times it didn't.

**Jason Cohen** (01:37:34):
Yeah.

**Lenny Rachitsky** (01:37:34):
Such a good one. 

**Jason Cohen** (01:37:35):
There you go. And that's with a lot of end. So that's what I mean. If you're doing that level of stuff, good for you. But if you're not, I don't know, man.

**Lenny Rachitsky** (01:37:42):
There we go. Well, Jason, it's always a really good sign when I'm just like, "I can't wait to get this conversation out the door and into people's minds because there's so much value here." I'm just already anticipating all people are going to reply and just like, "I got so many ideas for what to do with my product," which is exactly the goal. And with that, we have reached our very exciting lightning round. I've got five questions for you. Are you ready?

**Jason Cohen** (01:38:06):
Boy. Yeah. I mean, I don't like talking a long time anyway, so lightning's great.

**Lenny Rachitsky** (01:38:10):
Here we go. What are two or three books that you find yourself recommending most to other people?

**Jason Cohen** (01:38:16):
For writing, On Writing Well by William Zinsser. I know I'm not the only one, but that's the point. On my best day, I write like that. And then for product, I actually like Crossing the Chasm, which of course everyone's heard of, but what I find is no one's read it, so that you know the little picture, and you think you know what the chasm is, what I find is very quickly I realize, "Oh, you haven't read the book. You saw a blog post." And there's so much good stuff in there, how to define a market and really what to do with this model. It's fantastic. So I highly recommend reading the book.

**Lenny Rachitsky** (01:38:51):
I have had Jeffrey Moore on the podcast. We dove into a lot of this stuff. One of the things that always stuck with me is when early companies are looking for someone like them to adopt the thing. That's something that really stuck with me. It's not like they're looking for an early adopter to be like, "Oh, this is awesome." They're looking for someone that feels like them to say, "This is great." And so the early adopters are just going to spread to other early adopters and there's work to do beyond that.

**Jason Cohen** (01:39:14):
Yeah. Part of that's because he defines a market and among other things as, and the people in the market respect the opinions of the other people in the market. 

**Lenny Rachitsky** (01:39:23):
Exactly. 

**Jason Cohen** (01:39:23):
And that's when you realize, "Oh, so jumping to a different market, it's not impossible. It's just like case studies aren't going to work." Yeah.

**Lenny Rachitsky** (01:39:30):
It's a new thing. 

**Jason Cohen** (01:39:31):
Yeah.

**Lenny Rachitsky** (01:39:31):
Okay. All right. And then On Writing Well, such a huge trend in the book. That's like the book that most helped me write. And if you summarize the book, for me, it's just cut. Cut more and more of your stuff. There's always to cut.

**Jason Cohen** (01:39:43):
I love this phrase where he's on a panel with this guy who is like an amateur writer, and his summary to that guy is, the guy told him, "I never knew writing could be hard." And Zinsser says, " I never knew writing could be easy." I think both of those summarize the turmoil of being a writer.

**Lenny Rachitsky** (01:40:07):
Yeah. The classic, maybe Hemingway, maybe not quote, "Writing is easy. I just sit at the typewriter and bleed." 

**Jason Cohen** (01:40:14):
And bleed, yeah.

**Lenny Rachitsky** (01:40:16):
So good. Okay. Moving on. Favorite recent movie or TV show?

**Jason Cohen** (01:40:21):
ER from 1994-

**Lenny Rachitsky** (01:40:22):
Can- [inaudible 01:40:23]

**Jason Cohen** (01:40:24):
... 15 seasons. Why do I say that? Besides the fact I think is good, I have a 16-year-old daughter and we're now on season, I think 13, watching this whole thing. She says it holds up after 25 years. And being Gen Alpha or whatever, I don't even know what it is. And so if this is an era when shows were an hour long, and seasons were forever, and she says it's great TV, it must be great TV.

**Lenny Rachitsky** (01:40:52):
Wow. I've not had this podcast yet. Also, The Pitt. I don't know. If you enjoy ER, you'll enjoy The Pitt, which has won all these awards-

**Jason Cohen** (01:40:59):
Yeah. Pitt's good.

**Lenny Rachitsky** (01:41:00):
... on Netflix. Fun fact, my cousin was in ER, not as a recurring character, but she was a young girl patient, and actually is a fancy actress in the world. 

**Jason Cohen** (01:41:10):
Oh, cool. 

**Lenny Rachitsky** (01:41:10):
That was her start. Okay. Next question. Favorite product you've recently discovered that you really love.

**Jason Cohen** (01:41:17):
This is probably not unique, but Wispr Flow for dictation. It's really good. I like the keyboard shortcuts because I just use it all the time in all the software, and anything Anker makes. They have power stations, and docs, and rechargers.

**Lenny Rachitsky** (01:41:31):
Anker with a K.

**Jason Cohen** (01:41:33):
Yeah. A-N-K.

**Lenny Rachitsky** (01:41:34):
Yeah.

**Jason Cohen** (01:41:35):
Right. And just all their stuff is super high quality and works really well. Everything seems to charge twice as fast when plugged into an Anker thing. So I don't know. Whatever it is, it's really good.

**Lenny Rachitsky** (01:41:44):
I got a new Anker charger, I also love Anker, that has a display on the side when you plug in stuff, and it's got multiple ports, and it shows you the percentage it's charging and the wattage per outlet. I love it. They're just like, "How do we make this more fancy, and fun, and charge more?" 

**Jason Cohen** (01:41:58):
Yeah. 

**Lenny Rachitsky** (01:41:59):
I love it. Yeah. And then Wispr Flow, quick shout out. You get a year free Wispr Flow by becoming a insider, I think even just an annual subscriber of my newsletter as part of the product pass. And so check it out, Lennyproductpass.com. You could also check it with me.

**Jason Cohen** (01:42:15):
I did not know that. I'm not a shill for them.

**Lenny Rachitsky** (01:42:17):
I know. I love that. 

**Jason Cohen** (01:42:17):
But it's great. 

**Lenny Rachitsky** (01:42:18):
I love when people recommend products in the product pass, a whole year free.

**Jason Cohen** (01:42:21):
Because I'm subscriber for long enough that I didn't get that.

**Lenny Rachitsky** (01:42:23):
You missed out. You missed out. 

**Jason Cohen** (01:42:23):
Okay.

**Lenny Rachitsky** (01:42:24):
There's 19 products in there right now.

**Jason Cohen** (01:42:25):
Nice.

**Lenny Rachitsky** (01:42:25):
And by the time this comes out, there'll be even more. Okay. Two more questions. Do you have a favorite life motto that you find yourself coming back to in work or in life?

**Jason Cohen** (01:42:35):
Yes. "Be yourself, everyone else is taken." And it's attributed to Oscar Wilde, but I've tried to look into that as I tried to get all my annotations correct for the book. And there's no evidence that he said it. But there's also no evidence who said it. So let's say it's Oscar Wilde because he said lots of things like that.

**Lenny Rachitsky** (01:42:53):
I love that. And it's such a deep point. It's easy to hear and be like, "Yeah, yeah, yeah." But it's something I've learned to be more and more true over time, especially as you see people online doing their thing and just like, "Oh, I want to be like that." And then you realize, no, you got to be yourself.

**Jason Cohen** (01:43:06):
No. And the people who love you or like what you do also want you to be yourself because that's what they love. And if you're changed, then they wouldn't love that.

**Lenny Rachitsky** (01:43:14):
Final question, you have this fancy award behind you on your desk. I am curious what's the story there.

**Jason Cohen** (01:43:20):
That's the Ernst & Young Entrepreneur of the Year Award for-

**Lenny Rachitsky** (01:43:21):
Whoo.

**Jason Cohen** (01:43:24):
... 2017 for Central Texas, which I co-won with the CEO of WP Engine Heather Brunner, which is awesome because I often call Heather a late joining co-founder, because that's what ... LinkedIn, that's what Reid Hoffman called Jeff Wiener, because Jeff was four years in, but was so impactful to everything, the success of the company, the culture, the da, da, da, da, that basically is a co-founder. And that's exactly what Heather is like at WP Engine. It's now been 11 years since she became the CEO. So there's lots of data to back us up.

**Jason Cohen** (01:44:02):
And I used to say to people at WP Engine, like, "If I just told you that Heather was a co-founder, you'd say, 'Yeah, no kidding.'" I'm like, "Right, that's why I think of it that way because so do you, so did anyone, because that's the impact she's had." So we co-won that award, which is nice because you almost never have co-winners. In fact, I can't remember another one. I mean, I know there are others, but it's rare enough I can't think of another one. So it's really cool that we co-won that entrepreneur award.

**Lenny Rachitsky** (01:44:29):
Jason, this was so awesome. I really appreciate you making time. I really appreciate you sharing so much wisdom with us. Two final questions, where can folks find you online, point them to your book, your website, and how can listeners be useful to you?

**Jason Cohen** (01:44:42):
Yeah. I mean, to be useful, order the book, hiddenmultipliers.com, or of course you don't have to. I have all these articles online for free. So you can go to asmartbear.com and I'm on Twitter and other stuff that's all linked off of that website. And the articles, they're free. I don't have ads, I don't sell courses, I don't sell anything. So that's very, very non-commercial. And so in fact, the one thing I've ever done with writing that costs money is the book because it's a physical book. I've got to charge something so I can ship it and everything. But I think Hidden Multipliers is certainly my best work. So I'm very proud of that, but you don't have to buy it. It's okay. It'll make me feel good.

**Lenny Rachitsky** (01:45:26):
This is our chance to repay you for all the-

**Jason Cohen** (01:45:26):
That's right.

**Lenny Rachitsky** (01:45:28):
... free content you've put out over time. 

**Jason Cohen** (01:45:30):
Right.

**Lenny Rachitsky** (01:45:30):
And so I'm going to order a number of them. Jason, thank you so much for being here. 

**Jason Cohen** (01:45:35):
Thank you. This is fun. 

**Lenny Rachitsky** (01:45:36):
So fun. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [8/15] Marc Andreessen: The real AI boom hasn’t even started yet

**Marc Andreessen** (00:00:00):
If we didn't have AI, we'd be in a panic right now about what's going to happen to the economy. We've actually been in a regime for 50 years of very slow technological change in the face of declining population growth. The timing has worked out miraculously well. We're going to have AI and robots precisely when we actually need them. The remaining human workers are going to be at a premium, not at a discount.

**Lenny Rachitsky** (00:00:16):
How big of a deal is the moment in time that we are living through right now?

**Marc Andreessen** (00:00:21):
This is a very, very historic time. AI is the philosopher stone. Now, we have a technology that transfers the most common thing in the world, which is sand, converted into the most rare thing in the world which is thought.

**Lenny Rachitsky** (00:00:30):
Just spent a lot of time with the most cutting edge AI forward founders.

**Marc Andreessen** (00:00:34):
The most leading edge founders are thinking of, can you have entire companies where the founder does everything?

**Lenny Rachitsky** (00:00:38):
There's all this concern that young people, jobs are not going to be there for them, AI is replacing them.

**Marc Andreessen** (00:00:43):
Everybody wants to talk about job loss, but really, what you want to look at is task loss. The job persists longer than the individual tasks.

**Lenny Rachitsky** (00:00:49):
What's your sense of just the future of three very specific roles, product manager, engineer, designer?

**Marc Andreessen** (00:00:54):
There's like a Mexican standoff happening between those three roles. Every coder now believes they can also be a product manager and a designer because they have AI. Every product manager thinks they can be a coder and a designer, and then every designer knows they can be a product manager and a coder. They're actually all kind of correct. What happens is, the additive effect of being good at two things is more than double. The additive effect of being good at three things is more than triple. You become a super relevant specialist in the combination of the domains.

**Lenny Rachitsky** (00:01:18):
People aren't fully grasping how much this is changing.

**Marc Andreessen** (00:01:20):
People who really want to improve themselves and develop their career should be spending every spare hour, in my view, at this point, talking to AI, being like, "All right, train me up."

**Lenny Rachitsky** (00:01:29):
Today, my guest is Marc Andreessen, one of the most seminal figures in tech and in business. He invented the web browser, built the world's largest venture firm. He's also a multi-time founder and an investor in essentially every generational tech company, and is also one of the most clear-minded, lateral, and insightful thinkers about both the past and the future of technology. In this very special conversation, we chat about how unique and significant the moment that we are all living through right now is, what skills he's teaching his kids to thrive in the AI future, what happens to product managers, designers, and engineers in the coming years, where moats exist in AI, what the most AI native founders are doing differently, and so much more that is just scratching the surface of this very deep and important conversation. You are going to walk away from this chat being smarter about what is going on in the world right now and where things are heading.

**Marc Andreessen** (00:04:36):
Awesome, Lenny. Thank you. It's great to be here.

**Lenny Rachitsky** (00:04:38):
I want to start with just a big picture question. I have a billion directions I want to go, but I think this is going to give us a little bit of a frame of reference. How big of a deal is the moment in time that we are living through right now?

**Marc Andreessen** (00:04:50):
This is a very, very historic time. I think 2025 was maybe the most interesting year in my entire career and probably life, and I think I would expect 2026 to exceed that.

**Lenny Rachitsky** (00:05:00):
Wow, that says a lot.

**Marc Andreessen** (00:05:01):
Yeah, I've seen some stuff. So it feels like two things are happening. One is, the trust that a lot of people have had and kind of what you described as kind of legacy institutions around the world is, I think, in kind of full-scale collapse right now. By the way, there's a lot of data to support that. And so I think there's a lot of structures, orders, and institutions that people have just relied on for a long time that have just proven to not be up for the challenge. And then kind of corresponding with that is, the national and global conversation have become, let's say, liberated. And so this sort of incredible revolution that we have in what I've described as freedom of speech, freedom of thought, ability for people to openly discuss things that maybe they couldn't discuss even a few years ago has just dramatically expanded.

**Marc Andreessen** (00:05:46):
And I think that's now on a one way train for just a much broader range of discourse. And then there's also just these incredibly massive geopolitical shifts that are happening. And obviously, the US is changing a lot, Europe is changing a lot, China's changing a lot. Latin America, by the way, is changing a lot. Very dramatic events playing out down there right now. Kind of all over the world, I think a lot of assumptions are being pulled out into the daylight and reexamined. And then it's kind of the fact that all these things are happening at the same time. And so you've got all of these countries and industries where things are kind of increasingly upheaval, but you have AI as this kind of new technology that's going to really affect things. And then you've got people, citizens being able to fully participate and being able to argue things out.

**Marc Andreessen** (00:06:27):
And so it's kind of like those three big mega things are all colliding at the same time. And I think we're probably just at the very beginning of all three of those. And those all feel like historical moment shifts, comparable in magnitude to maybe default the Berlin Wall in 1989, maybe the end of World War II, kind of moments like that. It certainly feels like that.

**Lenny Rachitsky** (00:06:48):
Good God. What a time to be alive.

**Marc Andreessen** (00:06:52):
Yeah.

**Lenny Rachitsky** (00:06:52):
In terms of the AI piece, which is where a lot of people are trying to figure out what to do, what do you think isn't being priced in yet in terms of the impact AI is going to have on, say, the world or just people listening?

**Marc Andreessen** (00:07:04):
I think, at this point, it's pretty clear, with our technology hats on, that this stuff is really working now. There was a ChatGPT moment three years ago. By the way, only three years ago, was the ChatGPT moment. And the big question was, all right, this is incredibly fun and creative. And we have machines now that can compose Shakespearean sonnets and rap lyrics, and this is amazing. But then there was this big question, can you harness this technology for reasoning and for problem solving in domains that really matter, medicine, science, law and so forth? And it turns out the answer to that is yes. And the last 12 months, and especially even just the last three months have really proven that AI can really do... I mean, you're seeing it all now. AI is now developing new math theorems.

**Marc Andreessen** (00:07:53):
Over the holiday break, it feels like the AI coding thing really hit critical mass and the world's best programmers, including Linus Torvalds, for the first time over the holiday break basically said, "Yeah, AI is now coding better than we can." And so that's incredibly powerful. And I think we all assume that AI now is going to get really good at reasoning in any domain in which there are verifiable answers. And so that's going to include many very important domains. So the technology feels like it's moving fast and it's going to be working really well. I think the thing that is not well understood, I think a lot of people in the industry have kind of what I would describe as this one dimensional thing, which is, okay, as a result of the technology now working, AI just kind of sweeps the world and changes everything.

**Marc Andreessen** (00:08:41):
And I think that's kind of the wrong framer. I think it's based on an incomplete understanding of the world that we live in or the world that we've been living in for the last 80 years. And I would call out two things in particular. So one is, I think it's felt to us, in the US and the West, for the last whatever, 30 years or 50 years, it's felt like we've been in a time of great technological change. But actually, if you look for actually evidence of that, in statistical evidence of that, analytical evidence of that, you basically can't find it. And in particular, economists have a way of measuring the rate of technological change in the economy that is productivity growth, which we could talk about what that means, but basically, it's sort of the mathematical expression of the impact of technology on the economy.

**Marc Andreessen** (00:09:24):
And productivity growth for the last 50 years has actually been very low, not very high. So we all feel like it's been very high. There's been lots of technological change. What's actually happening is, it's been very low. And in fact, the pace of productivity growth, like in the US, is running at a half of what it... In my lifetime, in our lifetimes, it's been running at about half the pace that it ran between 1940 and 1970, and it's been running at about a third the pace that it ran between about 1870 to about 1940. And so statistically, in the US, in the West, technology progress in the economy, technology impact in the economy has actually slowed way down. And so the AI thing is going to hit, but it's hitting an environment in which we have actually had almost no technological progress in the actual economy for a very long time. So we could talk about that.

**Marc Andreessen** (00:10:11):
And then there's this other just incredible thing that's happening, which is the demographic collapse, it's sort of a Western phenomenon and increasingly global phenomenon, which is the rate of reproduction of the human species is in rapid decline. And there are many countries, including the US, where the rate of reproduction is under two, meaning that many, many countries around the world, by the way, including China, which is a really big deal, are actually going to depopulate over the next century. And so you have this kind of precondition that says there's actually been very little technological progress happening in the world and the world is going to depopulate. And so AI is going to enter a world in which those two things are true. And I think this is incredibly important because we actually need AI to work in order to get productivity growth up, which is what we need to get economic growth up.

**Marc Andreessen** (00:10:59):
And we actually need AI to work because we're going to need machines to do all the jobs that we're not going to have people to do because we're literally going to depopulate the planet over the next 100 years. And so I think the interplay of these factors is going to be much more interesting, and frankly, more complex than a lot of people have been thinking.

**Lenny Rachitsky** (00:11:15):
I'm going to follow this thread about kids. I know you have a kid, and my favorite lenses into how people think and what they value is what they're teaching their kids, what they're steering their kids towards. Are there specific skills or even careers that you're steering your kid towards?

**Marc Andreessen** (00:11:32):
The way I think about this, we have a 10-year-old, we actually homeschool and so we think a lot about this. So I think the way to think about the impact of AI on, specifically, people as individuals, it's actually, a lot of people just focus on this kind of very, I would say, straightforward or overly simplistic view of just literally job losses, which we can talk about. But there's two specific things at the level of an individual person or an individual kid. So I think it's pretty clear that AI is going to take people who are good at doing things and it's going to make them very good at doing things. And so it's going to be a tool that's going to raise the average across the board. And look, you see that playing out already. Anybody who's in a position where they need to write something, design something, write code or whatever, if they're pretty good at it today, they use AI and all of a sudden they're very good at it.

**Marc Andreessen** (00:12:21):
And so there's sort of that aspect to it. And I think the way the education system at large is going to teach AI is going to be based hopefully a lot on that. But then there's this other thing that's happening, which we're also starting to see, and we're really seeing it particularly in coding right now, where the really great people are becoming spectacularly great. And so you kind of use the term, you think about the super empowered individual. So the individual who is really good at coding, really good at making movies, really good at making songs, really good at making art, or whatever those things are, or podcasting or hopefully venture capital. If you're very good at it and you can really harness AI, you can become spectacularly great and super productive. I'm sure you have a lot of friends in this category as well, but the really, really good coders are experiencing this right now.

**Marc Andreessen** (00:13:19):
My friends who are really good coders are like, "Oh my God, all of a sudden, I'm not twice as good as I used to be, I'm 10 times as good as I used to be." And so I think, at the unit of N=1 of an individual kid, I think the question is, how do you get them into position where they're kind of this super empowered individual such that they're going to be really kind of deep in whatever it is they're going to do, but they're going to be deep in a way that's going to let them fully use the power of AI to be not just great, but to be spectacularly great? I think that's the real opportunity, and at least that's what we're shooting for and that's what I would encourage parents to shoot for.

**Lenny Rachitsky** (00:13:53):
So when I heard there is essentially agency, this word that we see on Twitter all the time is building agency, them not waiting for someone to tell them what to do, figuring out what to do.

**Marc Andreessen** (00:14:01):
Yeah. Yeah. So this term agency that's become very, very, very popular, certainly in California for the last couple of years, it's really interesting because I had a lot of trouble with this early on, because I'm like, "Agency? Okay, what are they talking about?" And what they're kind of talking about is initiative, you could just do things. What is it? The [inaudible 00:14:25] has the great term, live player, you can be a primary participant in events. And at first, I was like, "Well, yeah, that's kind of obvious, of course." And then I'm like, "Oh, actually, it's not so obvious anymore." Because to your point, I think so much of our society is based on, there are all these rules and everybody gets taught kind of by default, you're supposed to follow all these rules. And then if you break the rules, everybody gets freaked out.

**Marc Andreessen** (00:14:51):
It's like, "Oh my God, he broke the rules." And so we have somehow worked our way kind of psychologically, sociologically into a state in which I guess the natural assumption for a lot of people is the thing that you... For example, the thing you want to train kids to do is follow all the rules. And you could argue that, for example, K through 12 school system or whatever has gotten more and more focused on that over time. And again, especially unit N=1 of your kid, it's like... And look, there's something to be had. I just had this conversation with my 10-year-old last night, actually, I rolled out the concept of, in order to lead, you must first learn to obey. In order to issue orders, you must learn how to follow orders and trying to keep him with some level of structure in his life, and not just pure agency.

**Marc Andreessen** (00:15:40):
But yeah, and so look, some rules are important and so forth. But yeah, no, look, there's just a huge premium in life on being somebody who is able to fully take responsibility for things, fully take charge, run an organization, lead a project, create something new. And maybe that has been maybe a little bit diminished in our culture over the last 30 years. It's healthy that there's now a term for that that is coming back into vogue. And again, that's how I view AI for kids is like, okay, AI should be the ultimate lever on the world for a kid with agency to be able to say, "Okay, I can actually be a primary contributor, whether that's I can be a primary contributor in everything from developing new areas of physics to writing code, to being an artist, to writing novels, whatever that thing is, I can fully participate in the world. I can really change things." And the combination of that idea combined with this technology feels very healthy to me.

**Lenny Rachitsky** (00:16:35):
What is that quote about, "Give me a lever and I'll move the world"?

**Marc Andreessen** (00:16:38):
And I'll move the world. Yeah, that's exactly right. Well, so it's actually funny you mentioned that. So the early scientists, including Isaac Newton, were super obsessed with this concept of alchemy. Like Newton, he developed Newtonian physics and he developed calculus and all these things. But the thing he was really obsessed with was alchemy, which was the thing he could never get to work. And alchemy was the transmutation of lead into gold, which meant the transmutation of something that was very common, which was lead into something that was very rare and valuable, which was gold. He spent decades trying to figure out this thing called the philosopher stone, which would be basically the machine or the process that would be able to transmute the common thing into the rare thing, lead into gold, and he never figured it out. It was incredibly frustrating. Nobody ever figured that out. And now, we literally with AI have a technology that transfers sand into thought. Right?

**Lenny Rachitsky** (00:17:31):
That just blew my mind.

**Marc Andreessen** (00:17:33):
The most common thing in the world, which is sand, converted into the most rare thing in the world, which is thought. And so AI, it is the philosopher stone. It is that. It actually is that. And it's just this incredibly powerful tool. And that's where I get so excited. And again, this is what we're doing with our 10-year-old, which is like, all right, primary thing that we want to make sure to do is, to make sure that he knows fully how to leverage and get benefit out of the philosopher stone, which is to say AI. And then that's certainly central to everything we're teaching him.

**Marc Andreessen** (00:18:04):
There's this meme going around that Silicon Valley people don't let their kids use computers. And there may be a handful of people who are like that. I don't know. I think it's more, honestly, the other way around, which is, the more you're plugged into stuff in Silicon Valley, the more important it is to make sure that your kids actually fully understand this and know how to use it. And that's certainly the mode that we're in. And that's certainly the mode that I would encourage parents to think about.

**Lenny Rachitsky** (00:18:26):
I did not know your kid was homeschooled. That is super interesting. It's almost a statement on education in today's day. Maybe, is there any thoughts there? And just for folks that maybe aren't in your tax bracket that want to help their kids be successful, maybe homeschooled, maybe not, what advice would you have?

**Marc Andreessen** (00:18:42):
This is the challenge. And again, this kind of goes to your original question, which is education, there's two completely different ways to think about education. The way that it's usually thought about and talked about is kind of at the level of a nation. So it's like a national level issue or maybe a state level issue in the US, which is basically, how do you educate all the kids? And of course, that's incredibly important. And of course, you're going to need some level of large scale system, like the national K through 12 school system or something like that in order to do that. But then there's this other question, which is like, N=1, for an individual kid, what can you do with an individual kid? And so I'll just give you the ultimate answer to that question, which is, it's been known for centuries that the ideal way to teach a kid at the unit of N=1, by far the ideal way to do it is with one-on-one tutoring.

**Marc Andreessen** (00:19:36):
If you just have an individual kid and the goal is to maximize an individual kid, by far you get the best results with one-on-one tutoring. And this is something that every royal family knew in history. It's something that every aristocratic class knew in history. There's all these amazing examples. Alexander the Great was tutored by Aristotle. He took over the world. Many of the great kings, queens, royal families, aristocrats and so forth over the course of centuries kind of always had this approach. There's actually also statistical evidence, analytical evidence that this is correct. There's this massive question in the field of education, which is, how do you improve educational outcomes? And basically, it turns out it's very hard to improve educational outcomes except there's one method that always does it, which is called the Bloom's 2 Sigma effect, which is there's one method of education that routinely raises student outcomes by two standards of deviation and will take a kid from the 50th percentile to the 99th percentile and that's one-on-one tutoring. So again, if you go back to N=1, you have a kid and a tutor and they're in this very tight loop with each other, where the kid is able to constantly kind of be on the leading edge of what they're capable of doing and they can move incredibly fast and they get kind of correction in real time, you get these better outcomes. But to your question, it's never been economically feasible for anybody other than the richest people in society to be able to provide one-on-one tutoring for kids. AI provides the very real prospect of being able to do that, because obviously now, if you have a kid that's super interested in something and they can talk to an LLM about it and they can ask an infinite number of questions and they can get instantaneous feedback. And in fact, you can even tell an LLM, it's like, "Teach me how to do the following." And you can say, "Wow, I don't quite understand what you're saying. Numb it down for me a little bit."

**Marc Andreessen** (00:21:15):
"Okay, now quiz me, do I actually understand this?" People can just do this today. And so I think there's this massive opportunity for parents in many walks of life, with a little bit of time at focus, to be able to say, " Okay, my kid's probably still going to go through a traditional education system, but I'm going to augment this with AI tutoring." And of course, there's going to be tons of startups, and there already are, that are going to try to build on all the products and services for this. Khan Academy, on the nonprofit side, has a big push to do this. And so I think the broad answer might be a hybrid approach with schools plus one-to-one tutoring through AI. You may have heard, there's this great new private school system called Alpha, in which everything I just described is kind of the basis of their philosophy, which is, it's a combination of in person schools and teachers, but it's also heavily based on AI and AI tutoring.

**Marc Andreessen** (00:22:04):
And so I think there is a magic formula in here that I think is going to apply much more broadly. And really, for parents interested in this, now it'd be a great time to really start to think hard about that and to look at the options.

**Lenny Rachitsky** (00:22:17):
It's interesting because there's all this concern that young people, jobs are not going to be there for them, AI is replacing them. On the flip side, there's what you're describing here. It feels like people coming in learning today are going to move so fast and learn so much more. And where do you sit on this divide of, young people are in big trouble or they're actually going to be the ones winning in the end?

**Marc Andreessen** (00:22:38):
Yeah. So the job substitution, job loss thing is just, it's very reductive. I think it's an overly simplistic model. And again, it goes back to what I said at the very beginning, which is, we've actually been in a regime for 50 years of very slow technological change in the economy. And so again, like I said, it's at half the rate of the previous era and then a third of the rate of 100 years ago. And so we're coming out of this kind of phase where we've had almost no technological progress in the economy, we've had remarkably little job churn as a result of that relative to any historical period. And so even if AI ticks up, even if AI triples productivity growth in the economy, which would be a massively big deal, it would take us back to the same level of job churn that was happening between 1870 and 1930.

**Marc Andreessen** (00:23:17):
And if you go back and you read accounts of 1870 to 1930, people just thought the world was a watch with opportunity. At that rate of technological transformation, kids were able to develop new careers into new areas of the economy, building new kinds of products and services. I mean, a huge part of everything in our modern world today was kind of invented and proliferated during that period. And so even if AI triples the pace of economic change in the economy, it's going to just translate to a much higher rate of economic growth, it's going to translate to a much higher rate of job growth. And there'll be some level of task level and job level substitution that will take place, but that will be swamped by the macro effects of economic growth and innovation that will happen. And then corresponding to that, there'll be hiring booms, quite honestly, I think all over the place.

**Marc Andreessen** (00:24:02):
And then again, go back to the other thing, which is like, this is all happening in the face of declining population growth and increasingly population shrinkage. And so human workers in many, many, many countries over the next 10, 20, 30 years are going to be at more and more of a premium, literally because you're going to have shrinking population levels. We don't really want to get into politics particularly, but it does feel like the world broadly is going to reverse course on the rates of immigration that we've had for the last 50 years. It seems to be kind of a broad-based thing happening with rise to nationalism, concerns about the rate of immigration and immigration historically in countries like the US, it's kind of ebbed and flowed over time based on how the national mood shifts. And so if you sort of combine in a country like the US or any country in Europe, if you combine declining population with less immigration, the remaining human workers are going to be at a premium, not at a discount.

**Marc Andreessen** (00:24:57):
And so I think that combination of faster productivity growth, faster economic growth, and then slower population growth and less immigration actually means there's going to be much less of this kind of dystopian no jobs' thing. I just think it's probably totally off pace.

**Lenny Rachitsky** (00:25:10):
That is extremely interesting. So what I'm hearing is, you're not super worried about job loss. Is the key here that the timing kind of just works out, this population decrease, all these kind of have to line up for there not to be this massive job loss with AI?

**Marc Andreessen** (00:25:24):
Yeah. Well, look, if we didn't have AI, we'd be in a panic right now about what's going to happen to the economy. Because what we'd be staring at is a future of depopulation. And depopulation without new technology would just mean that the economy shrinks. So it would mean that the economy kind of itself kind of shrinks over time. The opportunity diminishes. There are no new jobs, there are no new fields, there's no new source of consumer demand for spending on things. And so you would be very worried about going into a period of severe decline of stagnation. And essentially, you'd be looking at these very dystopian scenarios of an economy kind of self-euthanizing itself over time. And so you'd be very worried about the opposite of what everybody thinks that they're worried about. The only reason we're not worried about that is because we now know that we have the technology that can substitute for the lack of population growth and then also for the lack of immigration that's likely.

**Marc Andreessen** (00:26:16):
And so I would say the timing has worked out miraculously well in the sense of, we're going to have AI and robots precisely when we actually need them, to keep the economy from actually shrinking. And I just think that's just a fundamentally good news story.

**Marc Andreessen** (00:26:31):
To get to the mass job loss thing that people are worried about on the other side of things, you'd have to look at far, far, far higher rates of productivity growth. You'd have to look at rates of productivity growth that are 10, 20, 30, 50% a year, something like that, which are orders of magnitude higher than we've ever had in an economy in the history of the planet. It's possible that we get that. I mean, look, I have my utopian temptation along with everybody else. If AI radically transforms everything overnight, then maybe let's play out the kind of utopian-

**Marc Andreessen** (00:27:00):
... radically transforms everything overnight. Then maybe let's play out the kind of utopian scenario. You get to a much higher level of productivity growth, you get to much higher level of technological change. Corresponding to that, you'll have a massive economic boom. You'll have a massive growth in the economy. And then corresponding with that, you'll have a collapse in prices. And so the price of goods and services that are, whatever you're going to call it, affected by or commoditized by AI, the prices of those goods and services will collapse. There'll be price deflation. And then as a consequence of price deflation, everything that people are buying today gets a lot cheaper. And that's the equivalent of a gigantic increase in wealth across the society, right?

**Marc Andreessen** (00:27:39):
Take it this way. This is actually worth talking about because people, I think, get sideways on this issue. So if AI is going to transform the economy as much as the, whatever, utopians or dystopians or whatever kind of thing that it will, the necessary economic calculation of what happens is massive productivity growth. The consequence of massive productivity growth, what that literally means mechanically is more output requiring less input. So, you get more economic output for less input. So you're substituting in AI for human workers or whatever. And as a consequence, you get this massive boom in output with much lower input costs. The result of that is you get gluts of goods and services in all those affected sectors. The result of those gluts is you get collapsing prices. The collapsing prices mean that the thing today that costs you $100, now costs you $10, and now costs you $1. That's the equivalent of giving everybody a giant raise because now they have all this additional spending power. That additional spending power then translates to economic growth, the development of new fields. Everybody's materially much better off very quickly.

**Marc Andreessen** (00:28:41):
And then by the way, to the extent that you do have unemployment coming out the other side of that, it's now much cheaper to provide the social safety net to prevent people from being immiserated because the prices of all the goods and services that a welfare program has to pay from, they're all collapsing. And so, the price of healthcare collapses, the price of housing collapses, the price of education collapses, the price of everything else collapses because of this incredible impact that AI is having.

**Marc Andreessen** (00:29:04):
And so in this kind of utopian/dystopian scenario that people have, there's no scenario in which everybody's just poor. In fact, it's quite the opposite, which is everybody gets a lot richer because prices collapse. And then it's actually much easier to pay for the social safety net for the people who, for some reason, can't find a job. And so maybe we end up in that scenario. I mean, the optimistic part of me says, "Yeah, maybe AI is that powerful, and maybe the rest of the economy can actually change to accommodate that, and maybe that'll happen." But the result of that is going to be a much better news story than people think it's going to be. And again, everything I've just described, by the way, is just a very straightforward extrapolation of very basic economics. I'm not making any bold predictions of what I just said. This is just a straightforward mechanical process that plays itself out if you have higher rates of productivity growth, which are necessarily the results of higher rates of technological growth.

**Marc Andreessen** (00:29:51):
And so, I think we're looking at... And to be clear, I think we're looking at a world that's not radically transformed the way that, maybe, the utopians think that it will be or the dystopians think it will be. I think it'll be more incremental for reasons we can discuss, but I think that incremental, overwhelmingly, I think that process is going to be a good news process. And then even if it's much faster, it's also going to be a good news process. It'll just be a good news process in the other way that I just described.

**Lenny Rachitsky** (00:30:15):
I love hearing optimism and good news. I will also add that you've been... I was researching you ahead of this chat, and you've been right so many times about where the world is heading. That's why I'm especially excited to talk to you. I'll give you a short list. I imagine there are many more things. So one, you were right about the Web and web browsers becoming important. You were right about software eating the world. Check. In 2011, you said that in 10 years, we're going to have 5 billion people using smartphones. And I believe the actual number ended up being six billion. Also, you had this debate with Peter Thiel that I came across, where you were debating whether technologies stop progressing or if new technology will continue to emerge. And you were arguing there's progress. Progress will continue. And he was like, "No, I think we're done with cool technology." You were right.

**Lenny Rachitsky** (00:31:05):
I imagine there are many more things you were right about. So again, I love hearing your predictions because I feel like they're actually going to turn out to be correct.

**Marc Andreessen** (00:31:16):
I was going to start by saying, I've been wrong about tons of things, but I buried those out back behind the shed.

**Lenny Rachitsky** (00:31:21):
Delete them from the internet. No browser can discount them.

**Marc Andreessen** (00:31:24):
Yes. Yes, I have them nuked out of the internet archives so that they're never seen again. So, I'm wrong plenty of times also. But yeah, look, I think some of those, I got right. By the way, I will say on the Peter one, I've come much more around to Peter's point of view. I would probably argue that one quite a bit differently today than I did, and I would give his view, I think, a lot more credit. And it actually goes to the discussion that we did, the conversation we just had, which is the... The real form of what Peter was arguing was we have lots of process in bit, we have lots of progress in bits, but we have very little progress in atoms. And that's the real core of what he was arguing. And I think I was a little bit, I don't know, missing that or glossing that over a little bit because I was so focused on making sure people understood, "No, there actually is still progress happening in bits."

**Marc Andreessen** (00:32:11):
But I think a lot of his critiques around the lack of progress in atoms is real. And again, this goes back to this thing of like... And he's talked about this for a long time. In the last 50 years, there has just been very little technological innovation in most of the economy. There's been very little technological innovation, in particular, anything involving atoms. There's been very little real-world technological change. There just hasn't been. The built world is just not that different today than it was 50 years ago. And again, if you contrast that, if you compare and contrast 1870 to 1930, it was a dramatically different world. If you contrast 1930 to 1970, it was a dramatically different world. If you contrast 1970 today, it's not that different.

And look, you just see that you could just walk around and it's just like, "Oh yeah, there's a bunch of buildings that were built in 1960, and there's a bridge that was built in 1930, and there's a dam that was built in like 1910, and there's a city that was founded in 1880." And like, "What have we done? Where are our new cities? Where are new dams? Where's the California High-Speed Rail? What's going on here?" And so, I think he is right about a lot of that. Again, this is also why I think that AI is not going to have as rapid... It's not going to be, again, this kind of utopian or dystopian view of everything changes overnight. I think it just can't happen because of the reasons that Peter articulates, which is there's so much about how the world works that's basically just like wrapped up in red tape: like bureaucratic process, rules, restrictions, the politics. By the way, unions, cartels, oligopolies, there's all these structures in the world that are economic or political or regulatory structures that basically prevent things from changing.

And so let's take a great example: AI's impact on the healthcare system. By rights, AI is going to have a dramatic impact on the healthcare system, and in very positive ways. But large parts of the medical system today, they are cartels. And so the doctors are a cartel, and nurses are a cartel, hospitals are a cartel. Then there's this push to nationalize all the healthcare systems, and then you've got a government monopoly. And guess what cartels of monopolies don't like, is they don't like rapid change. And so you show up as a kid and you're like, "Wow, I've got this new technology to do AI medicine." And they're like, "Oh, does it threaten doctor jobs? In that case, we're going to block it." And I think a lot of consumers, by the way... I see this in my life, and you'll probably see this in your life also, which is ChatGPT is almost certainly a better doctor than your doctor today, but ChatGPT can't get a license to practice medicine. So, it can't substitute for a doctor. It can't prescribe medications. It can't perform procedures. And so, there are these...

**Marc Andreessen** (00:34:56):
Anyway. So Peter, I think, was very articulate, and has been for a long time on like, "No, there are actually real structural impediments in the economy and in the political system that we have, that actually prevent..." The rates of change, that are anywhere near the rates of change that people have in the past. And you can maybe say, optimistically, maybe the presence of the new magic technology of AI, maybe it causes us to revisit a lot of these assumptions for the first time in decades, to really say, "Okay, is this really the world we want to live in? Don't we actually want to get to the future faster?" So maybe, that would be the optimistic view.

**Lenny Rachitsky** (00:35:26):
"It's time to build," somebody famously said. In my calendar, I actually have that as my... When I start to work, "It's time to build."

**Marc Andreessen** (00:35:26):
Yes.

**Lenny Rachitsky** (00:35:33):
That's my block in the morning of the day. Thank you for that.

**Lenny Rachitsky** (00:35:36):
Okay. I love the way you go from just macro to just like N-of-1, and I want to go to N-of-1. A lot of the listeners of this podcast are product managers, they're engineers, they're designers. There's a lot of founders, but there's also a lot of non-founders. There's a lot of people building product that aren't founders. And obviously, a lot of people are worried about where their career is going. "Is one of these roles going to disappear?" "Is one of these roles are going to do really well?" " How do I stay up to date?"

You're close with a lot of teams, a lot of product teams. What's your sense of just the future of these three very specific roles: product manager, engineer, designer?

**Marc Andreessen** (00:36:10):
This, I think, is a really funny question. These three roles in particular, obviously, are the central roles for building for tech companies. So, the way I've been describing it is... You know the concept of the Mexican standoff, right? Which is the movie scene where the two guys have guns point at each other's heads?

**Lenny Rachitsky** (00:36:23):
Mm-hmm.

**Marc Andreessen** (00:36:24):
And then there's... If you watch John Woo movies, he loves to have... He does the three-way Mexican standoff, where you've got like a triangle, people. And of course, John Woo movies, they've got guns in both hands. So, each is aiming at the other two and you've got this kind of standoff situation. And so the way I've been describing this is there's like a Mexican standoff happening between those three roles: between product manager, designer, and coder. Specifically, the following, which is every coder now believes they can also be a product manager and a designer because they have AI, every product manager thinks they can be a coder and a designer, and then every designer knows they can be a product manager and a coder. And so, people in each of those roles now know or believe that with AI, they don't need the other two roles anymore. They can do that because they can have AI do that. And then of course, there's the real irony, which is all three of them are going to realize that AI can also be a better manager. So, they're going to be aiming the guns up the org chart, but that's the next phase. And what I think is so fascinating about this Mexican standoff is they're actually all kind of correct, I think. Which is, AI is actually a pretty good... It's actually now a really good coder, it's actually now a really good designer, and it's also a really good product manager. It's actually good at doing all three of those things, or at least doing a lot of the tasks involved in those three jobs.

**Marc Andreessen** (00:37:41):
And so again, this goes back to this idea of the super-empowered individual. Where if I'm a coder, step one is I need to make sure that I really understand AI coding, and what that means, and how coding is going to change in the future. I need to understand specifically how to go from being a coder who writes code entirely by hand to being a coder who orchestrates a dozen instances of coding bots. There's a change in the actual job of coding itself, which is happening right now. But the other part of it is, "Okay, how do I become that super-empowered individual? How do I become a coder that also then harnesses AI so that I can also be a great product manager, and I can also be a great designer?" And then the same thing for the product manager, which is, "How do I make sure that I can now use coding tools? How do I make sure I can also do AI-based design?" And the same thing for the designer, which is, "How do I use AI to also become a coder, and also become a product manager?"

**Marc Andreessen** (00:38:32):
And then what you get is maybe, those individual roles change. Maybe, those are not any more sort of stovepipe roles the way that they have been for the last 30 years or whatever. But what happens is that the talented people in any of those roles become super powered, and they become good at doing all three of those things. And then, those people become incredibly valuable, because then those are people who can actually build and design new products from scratch, which is the most valuable thing. And so, I think that's the opportunity.

**Lenny Rachitsky** (00:39:02):
I love this answer. So what I'm hearing is, essentially, if you're amazing at any of these three roles, you will do well.

**Marc Andreessen** (00:39:08):
Number one, if you're amazing at these roles, that's great. But also, part of being amazing in these roles is also being able to fully harness the new technology. So if you're a master coder today and you don't ever get to the point where you figure out how to use AI to leverage your coding skills and do more, at some point you are going to hit an issue.

**Marc Andreessen** (00:39:28):
Here's another way economists talk about this, which is there's the concept of the job, but the job is not actually the atomic unit of what happens in the workplace. The atomic unit of what happens in the workplace is the task. And then the way the economists think about it is a job is a bundle of tasks. Everybody wants to talk about job loss, but really, what you want to look at is task loss, the tasks changing.

**Marc Andreessen** (00:39:52):
The classic example of task changing. Classic example of task changing was once upon a time, executives never used typewriters or personal computers themselves. If you were a vice president of a company in 1970 or whatever, you did not have a typewriter or a computer on your desk typing things. You had a secretary who you dictated memos to. And then there was this change where emails started to show up. And what would happen was the job of the secretary, it went from... The job of the secretary changed from sending out letters with stamps on them to sending or receiving emails with the other admins. Then the secretary would print out the email and bring it into the executive's office. And the executive office would read the email and paper, scroll the reply and give that message back to the secretary, who would go back and type it into the computer on his or her desk, and send it as an email.

**Marc Andreessen** (00:40:38):
Fast-forward to today, none of that happens. Now, executives just do all their own email. They still have secretaries or admins, but they're now doing different tasks. They're travel planning and orchestrating events, and doing all of these other things that the great admins do. And then the task set, ironically, of the executive, has expanded to do actually more of the clerical work themselves actually. Like, sit there and type their own memos. Which again, 50 years ago, they never would've done that. And so the executive job still exists, the secretary job still exists, but the tasks have changed. And I think that's a great example of what's going to happen. In coding, the tasks are going to change. Product management, the tasks are going to change. Designer, tasks are going to change. And so, the job persists longer than the individual tasks. And then as the tasks change enough, then that's when the jobs change.

**Marc Andreessen** (00:41:28):
And so at the level of individual, you want to think of like, "Okay. I have this job, the job is a bundle of tasks. I need to be really good at making sure that I can swap the tasks out. I can really adapt, use the new technology." Get really good at AI coding, for example. And then you want to add skills. "I can also get really good at design. I can also get really good at product management because I've got this new tool." So, you want to pick up more and more scope as you do that. And then 10 years from now, is your job title coder or coder/designer/product manager? Or is it just, "I build products"? Or is it just, "I tell the AI how to build products"? It's like whatever that job is called, who even knows what it's going to be, but it's going to be incredibly important because the people doing that job are going to be orchestrating the AI.

**Marc Andreessen** (00:42:10):
And so that's the track that the best people are going to be on, and I think that's the thing to lean hard into.

**Lenny Rachitsky** (00:42:17):
I think people aren't fully grasping just, specifically, software engineering and how much that is changing. It's pretty clear we're going to be in a world soon where engineers are not actually writing code, which I think, a year ago, we would not have thought. And now it's just, clearly, this is where it's heading. It's like there's going to be this artisanal experience of sitting there writing code, which is so crazy how much that job is going to change.

**Marc Andreessen** (00:42:39):
Yeah. So again, here, I go back. And again, pardon maybe the history lesson, but I go back coding. So, the first...

**Marc Andreessen** (00:42:47):
Do you know the original definition of the term calculator? Do you know what that referred to?

**Lenny Rachitsky** (00:42:50):
No.

**Marc Andreessen** (00:42:50):
It referred to people. So back before there were like electronic calculators or computers or any of these things, the way that you would actually do computing, the way that you would do calculating... Like the way that an insurance company would calculate actuarial tables or the military would like calculate, I don't know, whatever troop logistics formulas or whatever it was. The way that you would do it is you would actually have a room full of people. And by the way, these are like big rooms. You could have hundreds or thousands or tens of thousands of people doing this. And you would actually figure out... Somebody at the head of the room was responsible for whatever the mathematical equation was. And then, they would parcel out the individual mathematical calculations to people sitting at desks, who were doing them all by hand. And that job title was those people were calculators.

**Marc Andreessen** (00:43:35):
And so, we've gone from a world in which you literally have people doing mathematical equations by hands. Then, we got the first computers. The first computers, of course, didn't have programming languages. They only had machine code. So, the first computers were programmed with 1s and 0s. And so the task of the programmer became, "Do the 1s and 0s," and then that became punch cards. And you can still... There's still people, kicking today, whose job as a programmer was to build the punch cards. And then you got, actually, this big breakthrough, which was called assembly language, which was basically the way to do machine code but with some level of English added to it. And then the best programmers did assembly language. And then when I was coming up, it was higher level languages like C, that compiled into machine code, and that's what programmers did. And then I still remember when scripting languages... We developed JavaScript at Netscape, and then Python took off, and Pearl, and these other scripting languages.

**Marc Andreessen** (00:44:27):
When scripting language took off in the 2000s, there was this big fight in the technical community, which is, "Scripting, real programming or not?" because it's like it's kind of cheating. Because real programmers write code that compiles to machine code, and real programmers do memory management themselves, and they do all of this whole craft of writing a C code. And these JavaScript or Python programmers are just doing this kind of lightweight things. Does it even really count as coding? And of course, the answer is yes, it very much counted. And now, most coding is done with the scripting languages, which have...

**Marc Andreessen** (00:44:58):
You see my point. The scripting languages have abstracted away, like, five layers of detail underneath that, that people used to do by hand, and they don't anymore. And then to your point, AI coding is the next layer on that. AI coding actually abstracts the way the process of actually writing the scripting code. And so in one sense, this is a really big deal for all the obvious reasons. But on the other hand, it's like, "Okay, this is the next layer of the task redefinition under the job of programmer." Now, what's the job of the programmer? To your point, it's not necessarily to write the code by hand. But what it is now is, all right, if you talk to the world's best programmer of yesterday, what they'll tell you is, "Oh, my job is I'm sitting there and I'm orchestrating 10 code bots, coding bots that are running in parallel."

**Marc Andreessen** (00:45:39):
And literally, they sit there and they shift from browser to browser, or terminal to terminal. Their day job now is arguing with the AI bots to try to get them to write the right code, and then debug it and fix the problems, and change this back, and do all of these things. And so now, the job of the programmer is to argue with the coding bots. But if you don't know how to write the code yourself, you don't know how to evaluate what the coding bots are giving you. And so, you asked about the 10... Our 10-year old is super into computers and super into programming. He's using Claude, and ChatGPT, Copilot, and all of these things. And what I'm telling him is like, "Look..." And by the way, he loves vibe coding. He's on Replit all of the time doing vibe coding, doing games. He's sitting there. It's hysterical because he's sitting there. It's a 10-year old basically, who spends two hours at dinner arguing with an AI for fun.

**Marc Andreessen** (00:46:27):
But what I'm telling him is, "No, look, you need to still fully understand and learn how to write and understand code, because the coding bots are giving you code. If it doesn't work, or if it's not doing what you expect, or it's not fast enough or whatever, you need to be able to understand the results of what the AI is giving you." In the same way that somebody who's writing scripting language code does need to understand ultimately how the microprocessor works. And so again, it's kind of this up leveling of capability where you actually want the depth to be able to go down and be able to understand what the thing is actually doing, even if you're not spending your day actually doing that by hand. And again, I look at that and I'm like, "Okay. Now, programmers are going to be 10 times or 100 times or a thousand times more productive than they used to be."

**Marc Andreessen** (00:47:04):
And that is, overwhelmingly, a good thing. The tasks are definitely changing. The nature of the job is changing. But are human beings going to be involved in the coding process and overseeing the AI coding and all of that? And the answer is, of course, absolutely 100%. No question.

**Lenny Rachitsky** (00:47:22):
So you're in the camp of still learning to code is still a valuable skill?

**Marc Andreessen** (00:47:24):
Oh yeah, totally. Again, if you want to be one of these super... Look, if you just want to put yourself on autopilot, and like, "I can't be bothered. I'm just going to have AI write the code, and it's going to generate whatever it does and that's fine. And I'm going to be..." If the goal is to be a mediocre coder, then just let the AI do it. It's fine. The AI is going to be perfectly good in generating infinite amounts of mediocre code. No problem. It's all good. If the goal is, "I want to be one of the best software people in the world, and I want to build new software products and technologies that really matter," then yeah, you, 100%, want to still... You want to go all the way down. You want your skillset to go all the way down to the assembly, to assembly and machine code. You want to understand every layer of the stack. You want to deeply understand what's happening at the level of the chip, and the network, and so forth.

**Marc Andreessen** (00:48:06):
By the way, you also really deeply want to understand how the AI itself works, because you want to... If people understand how the AI works, they're clearly able to get more value out of it than somebody who doesn't understand how it works. You're always more productive if you know how the machine works when you use the machine. And so the super-empowered individual on the other end of this that wants to do great things with the new technology, yes, you 100% want to understand this thing all the way down the stack because you want to be able to understand what it's giving you. And when something doesn't work or when something isn't right, you want to be able to really quickly understand why that is.

**Marc Andreessen** (00:48:38):
By the way, again, this goes back to education. AI is your best friend at helping you learn all of that because it's like, "Oh, I need to understand. I don't know, this isn't fast enough." I need to figure out... As a coder, I need to figure out how to do a different approach to memory management or something. And you can be like, "Well, shit. I don't quite know how to do that. Okay, AI, let's spend 10 minutes. Teach me how to do this. Teach me what this all means." So all of a sudden, you have this incredibly synergistic relationship with the AI, where it's also helping you get better at the same time that's doing a lot of work for you.

**Lenny Rachitsky** (00:49:08):
By the way, I was going to say, I was a big Pearl programmer. I was an engineer for 10 years, and that was my language of choice.

**Marc Andreessen** (00:49:14):
Do you remember? I don't know when you were doing it, but do you remember... At least early on, did you ever hit this where C coders were looking down their nose at you and being like-

**Lenny Rachitsky** (00:49:23):
For sure. It was like, "This is so slow. It's not going to scale. What are you spending all your time on this thing?"

**Marc Andreessen** (00:49:27):
Yeah, exactly. And of course, and again, it started this thing where they were sort of correct. Which is, at the beginning, it wasn't fast enough or whatever. By the end, they were definitely wrong, which is it got much better, much faster. And it swept the world. Most coding today happens as scripting languages.

**Marc Andreessen** (00:49:42):
And then by the way, along the way, the people who really understood the scripting languages and the people who understood all the lower level systems, they were the ones who were able to actually make the scripting languages actually work really well. And so, that was a great example of this kind of adaptation. And again, the result of that was a far higher number of people writing code with scripting languages than were ever writing code with lower level languages. And I think this will just be a more dramatic version of that.

**Lenny Rachitsky** (00:50:04):
I love that Pearl was designed by a linguist. I don't know if you remember that part. And that's what made it so nice to code with.

**Marc Andreessen** (00:50:10):
That's funny because, of course, it was so notorious for being impossible to understand.

**Lenny Rachitsky** (00:50:15):
How ironic.

**Marc Andreessen** (00:50:17):
Yes.

**Lenny Rachitsky** (00:50:18):
**Marc Andreessen** (00:51:54):
Yeah, that's right. And again, here, this is a great example. So again, the task level of, like, "Design the perfect icon," is going to be, all right, the AI's going to do that all day long. If it gives you a thousand icon designs, it's going to be great. It's going to be fantastic, whatever. And by the way, there will still be some level of human icon design or whatever, but AI is going to get really good at that. But what are we trying to do, kind of capital D design of, like, "All right, what is this thing for, and how is this going to function in a world of human beings? And is this going to make people happy when they use it? Is this going to make people feel good about themselves? Is it going to fit into the rest of their life? Is it going to, I don't know, challenge them in the right way?" All of these kinds of higher level questions that the great designers have always thought about.

**Marc Andreessen** (00:52:40):
The job of designer will involve much more of those higher level, more important components, and then again, with AI doing a lot more of the underlying tasks. And so one way to think about it is, I don't know, you think of the world's best designers, Jony Ive or whatever, and you could be like, "Wow." Like, if I'm a designer today, if I'm a 25-year-old designer and I aspire to be Jony Ive in a decade, it's all of a sudden, I have a new path that I can use to get there, which is... Because Jony did everything. He did it without AI. Now, a young designer tends to be like, "Wow, if I really harness AI in a decade, I'm going to be like the best designer of the world's ever seen because it's not just going to be me. It's going to be me, plus being so super empowered by this technology to be able to do so much more. And then so much more of my time and attention is going to be able to be focused on these higher-level things that most designers never get to."

**Marc Andreessen** (00:53:29):
I think that's going to be another great example of that.

**Lenny Rachitsky** (00:53:31):
So maybe what I'm hearing here is kind of this T-shaped strategy of if you want to be successful in any three of these roles, be very, very, very good at that specific role: product management, engineering design. And then get good enough at these other two roles.

**Marc Andreessen** (00:53:45):
I think that's great. I think that's really relevant. And then Scott Adamson, firstly, just passed away, which is a real tragedy. But I referred for years to, actually, Scott Adams. He had this famous career advice he would give people, which I think makes a lot of sense. Which dovetails with what you're saying, which-

**Marc Andreessen** (00:54:00):
... advice he would give people which I think makes a lot of sense, which dovetails with what you're saying, which is he used to say it's like, look, he said, "I could have been a pretty good cartoonist or I could have been pretty good at business, but the fact that I was a cartoonist who understood business made me spectacularly great at making Dilbert." Because even the world's best cartoonist who didn't understand business could have never written Dilbert, and then the world's best business people who didn't know how to do cartoons couldn't have done Dilbert. It took somebody who actually had both of those skills to be able to make Dilbert which is one of the most successful cartoons in history.

**Marc Andreessen** (00:54:32):
And so the way Scott always described it was that from a career development standpoint, the additive effect of being good at two things is more than double. The additive effect of being good at three things is more than triple because you become a super relevant specialist in the combination of the domains, and, look, I mean, you see this all over the economy. I mean, you see this all over the economy, but I'll give you an example. Hollywood, just Hollywood as an example. There are a lot of writers who can't direct a movie and they can be very successful writers. There are a lot of directors who can't write a movie. They can be very successful directors. But the superstars in the entertainment industry are the people who can write and direct. They don't have a term for those. They call us auteurors, and those are the people who are the real creative forces that move the field.

**Marc Andreessen** (00:55:20):
And so again, and by the way, Hollywood, actually it's really funny, I've been spending a lot of time talking to Hollywood people about AI. Hollywood has the same Mexican stand-off going right now that we describe in tech, except in Hollywood, for example, for filmmaking, it's the director, it's the writer, and the actor. Because the director is now thinking, "Wow, I don't need the writer anymore because the AI can write the script and I don't need the actor anymore because I can have AI actors." The writer is saying, "Well, I don't need the director because I can direct the movie and the AI can do the actors." And the actor is saying, "I don't need either one of these guys. I can have the AI direct the thing, I can have the AI write the thing and I'm just going to show up and do my performance."

**Marc Andreessen** (00:55:53):
And so it's the same kind of triangular configuration, and again, what's great about it is they're all correct. Each person in each of those three fields is going to be able to expand laterally and pick up those additional skills, and then as a consequence, you're going to have more people who can write and direct or write and act or direct and act or do all three.

**Marc Andreessen** (00:56:13):
I think to your point, your T-shaped thing, I think that's going to be true basically across the entire economy. And if you think about the T, if you think about the T configuration, it's like, yeah, the breadth, the top of the T is like how many individual domains are you familiar enough with to be able to use the AI tools to be able to do really good work. And then this part of the T is how deep can you go in at least one of those domains so that you really, really deeply know what you're doing. But if you're super deep on coding and you can use AI to do design and you can use AI to do product management, that's your T right there, and you're a triple threat at the top of the T, but with this level of technical grounding underneath that.

**Marc Andreessen** (00:56:50):
I mean, at that point, again, you're the super-powered individual, you're going to be able to just perform like sheets of magic, for example, in terms of designing and building your products that people in my generation couldn't have even dreamed of. And so I think that this is a universal kind of theory that I think can apply across the entire economy.

**Lenny Rachitsky** (00:57:06):
I'm going to invent a new framework right now. Okay, forget the T framework. I'm picturing an F sideways or an E where there's three, two or three, I don't know, downward parts. And so what I'm hearing is get good at least two or three.

**Marc Andreessen** (00:57:21):
Yeah, I think that's right. I think that's right. Yeah, the combination, yeah. My friend, Larry Summers, had a different version of the Scott Adams thing, which is he used to tell people, he said, " The key for career planning is," he said, "don't be fungible." He's an economist and so that was economic speaking. What that means essentially is don't be replaceable. And so don't be a cog, and what that meant was don't just be one thing. So if you're, quote unquote, again, just a designer, just a product manager, just a coder, then in theory you can be swapped in or out.

**Marc Andreessen** (00:57:52):
But if you have this E or F laying on the side kind of thing, and if you have this combination of things that's actually quite rare, then all of a sudden you're not fungible. Not only you're not fungible, you're actually massively important because you're one of the only people in the world who can actually do that combination of things. And yeah, your ability to not become one of those people is just titanically enhanced with AI as compared to anything we've ever seen before.

**Lenny Rachitsky** (00:58:15):
This is so interesting because I've worked with people that are good at these two skills and they were always called unicorns at the company. She can code and design, oh my god. And what I'm hearing here is this is what you need to become. You need to become really good at at least two things there. I think you used the term smoke stack or something where it's like PM over here, engineer design, and what I'm hearing here is you need to get good at at least two of these skills. The silos of these two roles are disappearing.

**Marc Andreessen** (00:58:37):
That's right. That's right. And again, I can't overstress the following, for anybody listening to this, the thing about AI that I think people are just not getting enough benefit out of yet is just it will teach you. This is amazing. There's never been a technology before where you could ask it, "Teach me how to do this thing." And so I always feel like it's like people spend too much... it's one of these things where it's like so much focus on figuring out how to use a large language model is like, "Okay, what am I going to try to get it to do for me?" which is of course very important. But the other side of it is, what can I get it to teach me how to do, and it's just as good at that. And so again, this is this level of latent superpower.

**Marc Andreessen** (00:59:19):
People who really want to improve themselves and develop their career should be spending every spare hour in my view at this point talking on AI, being like, "All right, train me up. Super empower me. Train me, train me how to be... I'm a coder. Train me how to be a product manager." It will happily do that. It knows exactly how to do that. Run me, make me problems... yeah, make me assignments, then evaluate my results. It will do that just as happily as it will do work, quote unquote, for you.

**Lenny Rachitsky** (00:59:44):
Two tricks I've heard along those lines. One is to watch the output, what the agent is doing and thinking as it's doing the work. So if you're not an engineer, just sit there and watch it think and make decisions, and it's almost become this layer on top of learning to code is learning to see what the agent is doing and thinking because that teaches you about architecture. And the other is, a couple podcast guests have mentioned this, when you get stuck and then you figure out how to unstuck yourself, you ask it, "What could I have done differently? What could I have said that would've avoided this error in the first place?"

**Marc Andreessen** (01:00:14):
Yeah, that's right. That's right. Yeah, look, on that first one, and again, this is what I'm doing with my 10-year-old. Yeah, look, if you ask me, yeah, this is a really good point. So if you ask an AI, "Write me this code," and then it does it and it comes back and it doesn't work right, if all you know is single function, I asked it and it gave me back something that's not good, what do you even do with that? You don't understand why it gave you that result. Do you even understand what to tell it to try to get it to do something different?

**Marc Andreessen** (01:00:39):
But to your point, if you actually watch what it's doing and then you have the grounding, kind of that leg of your E or your F, if you have that grounding, then you can be like, "Oh, I see what it's doing. I see where it made the mistake. I see where it went sideways." And then you're all of a sudden able to intervene and be able to say, "No, no, that's not what I meant. Do this other thing." And again, this is a big part of having the actual kind of synergistic relationship is that you understand.

**Marc Andreessen** (01:01:06):
And by the way, look, I mean, like everything I'm saying is... everything that we're saying right now also is the same as if you're working with human beings. If you and I are colleagues and I would ask you to do something, you'd come back with something completely different, I do need to understand what was happening in your head in order to be able to give you feedback. If I just tell you, "Oh, that's wrong," nothing happens. I need to actually understand. I need to have theory of mind. I need to understand what you were thinking in order to really give you the right feedback. And again, the great thing with AI is AI will happily sit there and explain all day long why it's doing what it's doing. It'll happily critique itself.

**Marc Andreessen** (01:01:45):
By the way, this is a very fun thing where you can have one AI critique the other AI which is another thing which is you have one AI write the code, you have another AI debunk the code. And so you can actually, you can play the AIs off against each other and get them to argue with each other. And yeah, these are all the kinds of skills that are going to become, I think, incredibly valuable.

**Lenny Rachitsky** (01:02:01):
I think people call those LLM councils-

**Marc Andreessen** (01:02:03):
Yes.

**Lenny Rachitsky** (01:02:03):
... where they're talking to each other.

**Marc Andreessen** (01:02:05):
Yeah, that's right. That's right.

**Lenny Rachitsky** (01:02:07):
I do feel like if I were... I have no design background. I've always wanted to design. I've always wanted to be a great designer. It feels like that's the hardest one to learn of all these three by just watching and talking because there's a lot of exposure hours as folks have used this term, just like how do you learn to be a great designer. That feels like that's going to be really hard and valuable.

**Marc Andreessen** (01:02:25):
So my true confession is I've always kind of wanted to be a cartoonist, but I have no art skills. But as we're talking, I'm like, "Hmm, it might be time."

**Lenny Rachitsky** (01:02:35):
The time has come, Marc.

**Marc Andreessen** (01:02:37):
Yes.

**Lenny Rachitsky** (01:02:38):
I want to pivot to founders, maybe your bread and butter. You spend a lot of time with the most cutting edge, AI-forward founders. I'm curious what you see them do, how you see them, some way they operate that's maybe blowing your mind about how the future of starting a company looks, how the future of AI-forward companies look.

**Marc Andreessen** (01:02:57):
Yeah. So this is a great and very topical topic that's all playing out in real time right now on the leading edge. So I think there's like three layers of it and see if this makes sense. I think there's like three layers of it. I think layer one is they're thinking, "All right, how does AI redefine the products themselves?" And this is kind of the time-honored kind of thing that happens with technology transitions, and this is kind of what a lot of venture capital is based on which is, okay, there's a new technology that comes out. Maybe it's the personal computer or the iPhone or the internet or now it's AI, and it's like, all right, is this a new capability that gets added to existing products.

**Marc Andreessen** (01:03:35):
So all of a sudden you've got, I don't know, an existing software business and now you've got your PC version of it and now you got your iPhone version of it and you just keep on going and the new technology kind of gets added into the mix with another ingredient to an existing formula, and of course, a lot of new technologies are like that. I don't know when flash storage came out or something, it didn't really redefine the software industry because people just went from using hard disk using flash storage or something. But when the internet came out, like basically old school on-prem software for the most part, not entirely, but a lot of it died and it just got replaced by web software. And so sometimes you get the kind of, it's additive to an existing thing.

**Marc Andreessen** (01:04:19):
Sometimes you get the actually it redefines an entire product category, redefines an industry. In many cases, the companies themselves turn over it. So there's sort of this question, and an example you just mentioned, Nano Banana. So a great example is there are these businesses, like just take Adobe. Photoshop is built a, whatever, 40-year franchise in image editing. Okay, is AI a sort of a feature now that gets added to Photoshop to be able to do AI-based image editing, or do you just stop editing images entirely because you're using Nano Banana and all images are just being generated and it's just easier to just have AI generate a new image than it is to try to edit an old one?

**Marc Andreessen** (01:04:57):
And so I think there's many areas of tech in which that question is being asked and the answers I think will vary by domain. But obviously as a venture firm, we're betting hard on many of these categories being totally reinvented, and a lot of the best founders are trying to figure out how to do that. So that's kind of AI changing the definition of the product.

**Marc Andreessen** (01:05:16):
I think the next layer is actually a lot of what we've already talked about which is AI changing the jobs. And so it's a lot of what we already talked about, but, okay, if I'm a founder of a company and if I have room in my budget for 100 coders, how do I get those coders to be super-empowered AI coders, not the kind of coders I used to have, and if they're super-empowered AI coders, then does that mean, do I still need the 100? Maybe now I only need 10. Or does that mean I still want 100 but now they're doing 10 times more? And so, as you know, a lot of the best founders are working on that right now.

**Marc Andreessen** (01:05:48):
And then I think the third shoe to drop hasn't quite dropped yet, but it's kind of the big one which is, all right, the basic idea of having a company, does that change. And again, here you've got this concept of the super-powered individual which is, okay, can you have entire companies where you have basically the founder does everything. Because what the founder's doing is overseeing an army of AI bots. There's kind of this holy grail in our industry that's been running for a long time which is can you have the one-person billion-dollar outcome.

**Marc Andreessen** (01:06:21):
We've had a few of those over the years. Bitcoin is probably the most spectacular example with Ethereum right behind it which wasn't quite one person but a very small team. You had Instagram and WhatsApp that had very big outcomes with very small teams. Every once in a while you get one of these things where you just, something hits, and you just have a very small number of people associated with it. But that said, most software companies obviously end up with huge numbers of employees.

**Marc Andreessen** (01:06:46):
And so I think the most leading-edge founders are thinking of, okay, how do I reconstitute the actual very definition or idea of having a company and can you have a company that's literally basically just all AI. If you're doing anything in the real world, that's hard, but if you're doing software, that seems like it might be feasible in some cases.

**Marc Andreessen** (01:07:08):
And then there's the ultimate example of that which is can you have like autonomous AI economy stuff happening where you have AI bots on the blockchain or something that are basically out there functioning as a business and making money and just literally where the AI does all the work itself and just issues me dividends. Maybe that's the final outlier result. We have a few founders who are chasing that kind of thing. So I would describe that as kind of the latter that the best founders are on.

**Lenny Rachitsky** (01:07:36):
Super interesting. This whole idea of a one-person billion-dollar company, I think it depends on your definition of what this is, like an outcome I could see. Running my newsletter as one person with some contractors, there's so many little annoying things that I have to deal with, with just support tickets and issues and bugs. It's hard for me to imagine actually a one-person billion-dollar company, even if AI is handling so much of your support because there's just so many random-edge cases that I'm just... like filling out forms. And so I guess depends on, do you have contractors, does that count, what does it mean to be a one person. But I'm just like, "I can't see that happening."

**Marc Andreessen** (01:08:12):
Yeah. I mean, look, Bitcoin, Satoshi pulled it off.

**Lenny Rachitsky** (01:08:16):
But the open source community now, does that count? I don't know.

**Marc Andreessen** (01:08:19):
Yeah.

**Lenny Rachitsky** (01:08:20):
I guess it counts. Okay.

**Marc Andreessen** (01:08:21):
Yeah, exactly. Right? So yeah, I would say I don't propose to have answers here, but more just like the smartest people I know or many of the smartest people I know are thinking hard about this.

**Lenny Rachitsky** (01:08:33):
Yeah. What do you think about moats? A big question constantly in AI, the fact that everything's changing, just what's your guys' thesis on moats in AI? Is that even a thing? Do you care?

**Marc Andreessen** (01:08:45):
My experience with really big technological transformations, and of course, I kind of lived this directly with the internet and I saw this happen, is the really big technological transformations, they take a long time to play out and there's all of these structural implications that just kind of cascade out over time. There's this rush to judgment upfront where people say, "Oh, it's therefore obvious that X, Y, Z. It's therefore obvious that this kind of company is going to be the company of the future, not that kind. It's obvious that this incumbent's going to be able to adapt and this other one isn't. It's obvious that there's economic opportunity in this kind of startup and not in these others. It's obvious that the moats are going to be in this area of the technology, but not in this other area."

**Marc Andreessen** (01:09:25):
What everybody does is they kind of state those things with just an enormous amount of self-assurance where they really sound like they have all the answers. And then what happens is these ideas kind of saturate the media because the media naturally prizes definitive answers over open questions because it... it's like when CNBC is booking guests, they want a guest who's going to come on and say, "Yes, this is the way, it's going to be X." Not like, "You know, I think that's a really good question and let's debate it from eight different angles."

**Marc Andreessen** (01:09:51):
What I found is if you look back on those predictions a few years later, and you can do this by the way, if you pull up coverage of the internet from 1993 through 1997, or for that matter even through 2005 or 2010, and you look at the kinds of confidence statements people were making in the first 10 or 15 years, I would say almost all of them were wrong, generally quite badly wrong. And so I think the process, I think there's going to be a massive amount of technological change. It's going to be like, I don't know, five or six layers of structural change that will play out over time.

**Marc Andreessen** (01:10:26):
And again, we've talked about a lot of this, but the implications on what are the definition of products, what are the definitions of companies, what are the definitions of jobs, what are the definitions of industries. How does this play out at the national level? How does this play out at the global level? By the way, how does this intersect with politics? How does this intersect with unions? How does this intersect with war? What's China going to do? And so there are just a tremendous number of unknowns, a very, very large number of unknowns, and I think it's just like really, really dangerous to prejudge these things.

**Marc Andreessen** (01:11:02):
I'll just run this as a thought experiment and you can see what you think on this, but it's like, are AI models themselves defensible. Is there a moat on AI models? And on the one hand, you'd be like, "Wow, it certainly seems like there is or should be," because if something takes billions of dollars to build and you need this incredible critical mass of computing data and there's only a certain number of engineers in the world that know how to do this and they are getting paid like MBA stars. And then these companies have to deal with all these crazy political issues and press issues and reputational stuff and regulatory and legal.

**Marc Andreessen** (01:11:40):
All of that translates to, okay, probably at the end of this, there's going to be two or three companies that are going to end up with like 100%, I don't know, whatever, 50/50 or 30/30/30 or 90/10 and one, or whatever it is, market share and then they're going to have whatever profitability they have and it's going to be kind of a classic oligopoly, or maybe one company's going to win definitively and it'll be a monopoly. And by the way, those outcomes have happened in software many times before. And so maybe that will be the outcome.

**Marc Andreessen** (01:12:05):
The other side of it is if you had told me three years ago that in the Christmas of ChatGPT that within basically a year to year and a half there would be five other American companies that would have basically exactly capable products, and then there would be another five companies out of China that would have exactly capable products, and then there would additionally be open source that was basically the same, I would have been like, "Wow, the thing that seemed like it was black magic all of a sudden has become like commoditized really fast," which by the way, is exactly what happened. Within a year of GPT3 coming out, there were there open source GPT3s running on a fraction of the hardware that were available for free. And then there were five. Now you've got, fully in the game, you've got Google and you've got Anthropic and you've got xAI and you've got Meta and you've got all these other companies that are... and then DeepSeek and Kimi and all these other Chinese companies. And so even at the level of LLMs or AI models, you can squint and make that argument either way.

**Marc Andreessen** (01:13:05):
By the way, same thing at the level of apps. It's like one school of thought is apps are not a thing because the model's just going to do everything, but another way of looking at it is no, actually adapting the model is kind of the engine into a domain involving human beings where you need to actually have it fit for purpose to be able to function in the medical industry or the legal industry or whatever or coding. No, you actually need the application level's actually going to matter enormously, and maybe the LLMs commoditize and maybe the value goes to the apps. And again, you can kind of squint either way on that one, and I know very smart people who are on both sides of that argument.

**Marc Andreessen** (01:13:41):
And so my honest answer on this is I think we're in a process of discovery over time. The way I think about this kind of structurally is it's a complex adaptive system. The technology itself provides one of the inputs. The legal and regulatory process is another input. Actual individual choices made by entrepreneurs matter a lot. The economics matter a lot. Availability of investor capital varies over time, that matters a lot. This is a complex system, and so we actually don't know the outcomes on this yet. We need to be open to surprises at the structural level of what happens.

**Marc Andreessen** (01:14:19):
And of course, as a VC, this is very exciting because it means we're doing this now. We should make bets along every one of these strategies and see how this plays out. I would just say, there may be, I don't know, there may be like one particularly brilliant, I don't know, hedge fund manager or something who has this all figured out, but I guess I would say if they exist I haven't met them yet.

**Lenny Rachitsky** (01:14:40):
So what I'm hearing here is don't over-obsess with moats at this point because we have no idea what'll end up being, and as much as it may feel like, okay, there's no way OpenAI will lose this lead, clearly we're seeing a lot of competition. GPT wrapper point is really great. It was such a derogatory term, I don't know, a year ago, just like, "You're just a GPT wrapper." Now it's like the companies that are the biggest companies, the fastest growing companies in the world.

**Marc Andreessen** (01:15:01):
Yeah. Well, it's like a little bit like, I don't know, I mean, even just like with... this has been the holiday, three years ago was the holiday of ChatGPT. This last month or whatever has been the holiday of Claude, particularly Claude Code for coding. But it's pretty amazing because it's like, okay, there was Claude which is obviously a great accomplishment, but then there's Claude Code which is an app. It's a Claude wrapper. It's agent harness. And then they did this amazing thing where they came out with, was it Coworker?

**Lenny Rachitsky** (01:15:01):
Cowork.

**Marc Andreessen** (01:15:29):
Cowork. And remember what they said of Cowork, which is Claude Code worked Cowork in a week.

**Lenny Rachitsky** (01:15:36):
Yeah, a week and a half, yep, 100%.

**Marc Andreessen** (01:15:39):
Well, and there's two ways looking at that which is like, "Wow, that's really..." I mean, obviously that's really impressive that Claude Code was able to build Cowork in a week and a half. That's great. That's amazing. The other way to look at it is Cowork was developed in a week and a half. How much complexity could there be? How much of a barrier to entry can there be in something that was developed in a week and a half? And then again, it's this push and this pull thing where it's like, wow, it's incredibly functional, incredibly valuable, and people all over the world and every day now are like, "Wow, I can't believe what I can do with this. It's like the most magical product ever." But at the same time, it took a week and a half.

**Marc Andreessen** (01:16:16):
And so every other model company, I'm sure, you'd have to expect, is sitting there being like, "Okay, obviously we need to build an Asian artist and then obviously we need to build a Cowork thing for regular people." I'm not even saying I know anything, but just obviously they're all going to do that. And so how defensible is that? In six months, and we've seen this happen before, is Claude Code going to get lapped the same way that GitHub Copilot got lapped? The history in the last three years has been everything that looks like it's like the fundamental breakthrough gets basically replicated and lapped very quickly. Many of the smartest people I know in the field, when I really talk to them, kind of get a couple drinks into them, they're like, "Yeah."

**Marc Andreessen** (01:16:55):
One theory is there really aren't any secrets among the big labs. The big labs kind of all have the same information and they kind of have all the same knowledge and they lap each other on a regular basis, but there's not a lot of proprietary anything at this point. And then again, evidence of that is DeepSeek came out of left field and basically was like a re-implementation of a lot of the ideas under American big labs and had some original ideas of its own. But, wow, it wasn't that hard for some basically a hedge fund in China to do it, and so how much defensibility is there.

**Marc Andreessen** (01:17:26):
But on the other side of it, you've got, wow, these big labs are now paying individual engineers like they're rock stars and they're incredibly bright and creative people. Maybe there's a dozen nascent ideas at any one of these labs that is actually going to be a huge breakthrough that's going to be hard to replicate. And so again, it's just like, I think we just need... I don't know, my view is I need to put a big discount on my forecasting ability on this one.

**Marc Andreessen** (01:17:49):
For me, it's much less interesting to try to say, "Okay, as a consequence, industry structure in five years is going to be X, the big winner and the category is going to be company Y, the big product killer app is going to be Z." It's like, I don't think I can predict that. I think a much better use of my time is being very flexible and adaptable at a time like this.

**Lenny Rachitsky** (01:18:07):
So with all this in mind, do you feel like there's something you're paying attention to more to help you decide, okay, this is where we want to place our bet, or is the answer essentially the strategy you guys have, which is place a lot of bets? You guys raised the largest fund in history. Is that the way you win in this world?

**Marc Andreessen** (01:18:23):
Yeah. I mean, for us, yeah. For us, we obviously have a very deliberate strategy. One way to think about this the Peter Thiel... You remember the Peter Thiel formulation of... he said, "There's a two by two, there's optimism and pessimism, and then there's determinant and, is it indeterminate, and indeterminate." And so he always argued that Silicon Valley is characterized by too much what he calls indeterminate optimism. What he meant by that is basically, I think the way he would describe it is an indeterminate optimist who thinks the world is going to be better but can't explain why. Some combination of things is going to happen to make the world be better even if we don't know what those things are. I think he at least historically would say that's basically... that risks at least being just wishful thinking or delusional thinking.

**Marc Andreessen** (01:19:11):
What the world needs more is determinant optimists, which are people who are like, "No, the world is going to be better because I'm going to do this specific thing." He would classify, for example, Elon, he would sort of maybe say VCs are indeterminate optimists and then he would say Elon is the determinant optimist where it's like, no, I'm going to build the electric car, I'm going to do solar, and then I'm going to do Mars and these very concrete things.

**Marc Andreessen** (01:19:35):
I think there's a lot to Peter's framework, but the way I would describe it is I think maybe if he and I disagree with part of that it would be I think the indeterminate optimism is a stronger phenomenon than at least I think he's historically represented it as, and I would put myself firmly in the indeterminate optimist category, and that's the strategy that we have at a16z which is... and the reason for that is hopefully it's not so much wishful thinking. It's more, no, the indeterminate optimism of venture capital or the indeterminate optimism of a16z or Silicon Valley is actually very specific which is there are these extremely bright and capable people, like Elon and many others, who are founders and kind of product creators. Each of those individual people is a determinant optimist. Each of them individually has a very strong view of what they're going to do, but the great virtue of the capitalist system, the great virtue of the American economy, the great virtue of Silicon Valley is we don't just have one of those and we don't just have 10 of those. We have 100 and a thousand and then 10,000 of those.

**Marc Andreessen** (01:20:34):
The way to optimize the outcome is to have as many of those as possible be as good as possible, run as hard as possible. And then just the nature of the future is like we just don't know all the answers and that's okay. And then the right way to deal with that is to run as many experiments as possible and have as many smart people try to do as many interesting things as possible. And so, yeah, I would put myself firmly on the side of the indeterminate optimistic.

**Lenny Rachitsky** (01:20:55):
I'm wondering if the answer to the question of what you look for now more and more is this determinant optimistic founder that has this massive ambition and is-

**Lenny Rachitsky** (01:21:00):
... [inaudible 01:21:00] Optimistic founder-

**Marc Andreessen** (01:21:01):
Yeah.

**Lenny Rachitsky** (01:21:02):
... and has this massive ambition and is actually working on achieving it.

**Marc Andreessen** (01:21:06):
Yeah, yeah. No, that's right, that's right. I mean look, the founders need to be determinate optimists. They need to have a very specific plan. And look, the critique always... The critique from the founders is, "Oh UVCs have it easy, because you don't actually have to commit, right? You don't actually have to, like, make... You have to make the bed you lay in, you can, like, place multiple bets you can have. Whereas a portfolio, you should have a lot more sympathy for us as founders, because we only get to make the one bet."

**Marc Andreessen** (01:21:31):
And there's truth to that. The kind of argument on that is the founders get to run their companies, we don't. So, we don't get to put our hand on the steering wheel. And so, the great virtue of being a determinant optimist is you actually get to single-mindedly execute against that goal.

**Marc Andreessen** (01:21:48):
And look, in the long run, who does history remember? History remembers Henry Ford, right, not whoever was, whatever the seed investor who seeded Ford Motor Company, and 10 other car companies have failed. Right?

**Marc Andreessen** (01:21:58):
And so, the determinant optimist is the founder of the company builder and the engineer, and these are the people who actually use the sign, and deserve 99.9999% of the credit. But you know, having said that, I do think there is a role for having some indeterminate optimists in the background, no, helping along the way, and helping keep the whole cycle going.

**Lenny Rachitsky** (01:22:18):
Do you think about AGI in shifting your investment thesis? Like, as we approach AGI and hit AGI, as an investor, how do you think about your investment thesis changing?

**Marc Andreessen** (01:22:29):
Yeah, so I've always kind of had a little bit of an issue... I've always kind of struggled with the concept of AGI because at least... Well, let's put it this way. Let's define terms which is where I kind of struggle with it. Which is, there's like the prosaic definition of AGI, and then there's like the cosmic definition.

**Marc Andreessen** (01:22:48):
And the way I describe it as, so let me start with the cosmic one. So the cosmic one is basically, is the singularity, right? And so, AGI is the moment where you enter the singularity which is to say where the world fundamentally changes. And the rules of the old world are gone, we're now operating in a new domain.

**Marc Andreessen** (01:23:04):
And then the full definition of singularity is it's a world in which human judgment is no longer really relevant because you get this self-improvement loop, the AI is improving itself. In a sort of race circle takeoff scenarios, you could see if this takeoff thing, where the AI's improving itself, and the machines are making decisions so much faster than people, and people are just sitting there watching the machine do its thing.

**Marc Andreessen** (01:23:26):
And I kind of described it, I don't really think we live in that world, whether they could call that utopian or dystopian, I don't think we're lucky or unlucky enough to live in that world. We could debate that, we could talk about that more.

**Marc Andreessen** (01:23:37):
But the prosaic definition of AGI that at least I think the industry purchases but it's kind of conversed on, and tell me if you agree with this, is when the AI could do every economically-relevant task as good as a person.

**Lenny Rachitsky** (01:23:47):
The way the co-founder of Anthropic put it is, like, "A basket of the most valuable economic tasks," so it's, like, 10, 15, not every single economically-valuable task.

**Marc Andreessen** (01:23:56):
Okay, got it. Yeah, so it's maybe even a slightly reduced definition. And by the way, we're clearly getting close to that if we're not already there.

**Marc Andreessen** (01:24:04):
And so on that one, I kind of feel like, so I kind of feel like the cosmic one overstates what's going to happen. And then I kind of feel like the kind of AGI definition that you just gave, I think it kind of understates what's going to happen. It's almost too reductionist.

**Marc Andreessen** (01:24:17):
And the reason for that is, I don't think there's any reason to assume that human skill level is the cap on anything. Right? And so the way we say that is AGI always is the definition you gave, the definition I gave. It's always kind of relative in comparison to a human worker, right?

**Marc Andreessen** (01:24:33):
And it's, like, I don't know, human skill level caps out at a certain point, but that's because of the inherent biological limitations of the human organism. Right? Human, I gave you an example. Human IQ, kind of what they call "fluid intelligence," or the sort of G factor of fluid intelligence, IQ I think tops out in humans as a species, it tops out around 160.

Right? Where at like 160 it's like Einstein level, Einstein [inaudible 01:25:00]-

**Lenny Rachitsky** (01:24:59):
In terms of IQ. Yeah.

**Marc Andreessen** (01:25:02):
... in terms of IQ. Like, it just tops out at 160. The 160 IQ people are the ones who come up with new physics, there's only a small handful of those. Generally speaking, when we run into somebody in the world who's like incredibly smart, who's like a bestselling author, or like a, you know, one of the world's best, I don't know, research scientists, or one of the world's best doctors, whatever it would be, probably 140 is kind of the IQ that you're looking for there.

**Marc Andreessen** (01:25:26):
If you're looking for a really good lawyer, it's probably 130. If you're looking for a really good line manager in a business, it's probably 110. If you're looking for an accountant, like a small business accountant, who's good at doing the books for small businesses, it's probably 105. Right?

**Marc Andreessen** (01:25:41):
And so the kind of scope of impressive human... The ability of the human organism to do intellectually impressive things, it's sort of that 110 to 160 is kind of the spectrum, and good news is there's a lot of those people running around, but there's not that many at 140, 150, 160.

**Marc Andreessen** (01:25:57):
But it's like, that's like the limitations of what can fit in here, right? And it's like, there's no theoretical limit on where this goes if you release the limitations of human biology, right? And so, can you have a... And you already have people running these experiments to kind of do human-equivalent kind of IQ for existing AI models.

**Marc Andreessen** (01:26:17):
And by the way, existing AI models are kind of testing around the 130, 140 level, which means they're going to get to the 160 level. And they're arguably on the math side starting to get to the 160 level now.

**Marc Andreessen** (01:26:26):
But I think we're going to have AI models relatively quickly that are going to be like 160, 180, 200, 250, 300. By the way, and I think that's great, right? I feel as great about that as I do about the fact that we occasionally get an Einstein. Right?

**Marc Andreessen** (01:26:41):
It's like, would the world be better off or worse off with more or fewer Einsteins? And the answer is, of course the world would be better off with more Einsteins, and of course the world would be better off with machines that have more IQ like Einstein or greater than Einstein.

**Marc Andreessen** (01:26:51):
But I think IQ of the machines is going to exceed that of the humans, I think that's really good. And then the performance, again, it goes back to like the AI coding thing that's happening. Performance against task is going to get better also.

**Marc Andreessen** (01:27:02):
I think this is where Linus Torvalds in particular was like, "Yeah, okay, this thing is starting to generate better code than I can." Okay? So now we're going to have AI coders that are actually better coders than the best human coders. I think that's... Right?

**Marc Andreessen** (01:27:13):
I think we're going to have AI doctors that are better than the best human doctors, I think we're going to have AI lawyers that are better than the best human lawyers, which actually is going to be very interesting to see, which we can talk about. Which I think is also great.

**Marc Andreessen** (01:27:24):
And so, I don't think there's a... I think we're used to living in a world where we just don't understand how good good can get, because we've been capped by our own biology. And we're going to get to experience what it's like when you have the capability at your fingertips, that's actually better than human in these domains.

**Marc Andreessen** (01:27:38):
So you see what I'm saying, which is, like, I think this idea of human equivalent is just going to be a footnote. It's like, "Oh yeah, that was just on Tuesday, in 2026 is when they hit that." And it kind of didn't matter because the next question was, like, "Okay, what do we get to do in a world where we actually have machines that are better than that?" Right?

**Marc Andreessen** (01:27:58):
And so, I think this is going to be much more of an exploratory process for actually seeding human capability than it's going to be any sort of particular singular singularity moment or whatever that happens, that just happens to coincide with the human threshold.

**Lenny Rachitsky** (01:28:10):
200 IQ, I... Just like that frame of reference is such a mind-expanding way to think about just how fast and how smart these things are going to get, and quickly.

**Marc Andreessen** (01:28:20):
Well, I don't know if you have this experience, I have this experience all the time. Well, two experiences I have all the time. One is just like, I know I ought to be able to do this, but I just can't... It's going to take too long, I want to write this thing, or I want to... Whatever, I want to have this theory on this thing, or to have a plan or whatever.

**Marc Andreessen** (01:28:39):
And it's just like, "Fuck," I don't have the eight hours, or by the way, the eight weeks or the eight years, right? And I just don't know enough yet, and I'm just, like, I can't do the math in my head, and my memory isn't perfect, and I can't remember, and I read... I don't know if you have this, you get interested in something, you read 10 books. And then you're like, "Shit, I forgot almost everything I just read."

**Marc Andreessen** (01:29:01):
I wish I could retain it all but I can't. It's just like you just have this... I sort of live in this state of endless frustration. And so it's like, if I could just be smarter than I was, I'd be much better at what I do, but I'm not. So there's that.

**Marc Andreessen** (01:29:14):
And I don't know how often you have this, but I have this on a regular basis. It's just like, "I," because of what we do, I know a bunch of people who I know for fucking sure are smarter than I am. And I know it because when I talk to them, I just find myself at a certain point. It's like for the first half of the conversation, I'm just taking notes the entire time. And for the second half of the conversation, I'm just like, "Fuck," like, "Fuck me." Like, this person is just smarter than I am, and they're just out-thinking me, and they're going to keep out-thinking me, and I just can't, and I'm just like, "All right, goddammit. I've got to go home and I've got to have a drink."

**Marc Andreessen** (01:29:43):
Because I'm just not... Whatever that is, I'm not that. And so, we're just so used to having those limitations, that the idea of having machines that work for us that don't have those limitations, I just... I think that's much more exciting than people are giving it credit for.

**Lenny Rachitsky** (01:30:00):
Oh man. I could talk to you for hours, Mark. I'm thinking to close out the conversation, I want to ask about your media diet and your product diet. You just talked about books, 10 books, I think you famously read constantly. I saw an interview with you where you're just like, "Airpods changed my life, I'm just listening to audiobooks now all the time."

**Lenny Rachitsky** (01:30:17):
So in terms of a media diet, what are you reading, what are you paying attention to these days in terms, I don't know, podcasts, newsletters, blogs, things like that, and then any books in particular?

**Marc Andreessen** (01:30:25):
Yeah, yeah. So what I read is basically, I mean I read... So I read basically three categories of things. So in terms of general media, it's basically I sort of... I always describe it as I have an almost perfect barbell strategy, which is I read X, and I read old books. Right?

**Marc Andreessen** (01:30:41):
So it's basically either, like, up-to-the minute what's happening right now, or it's like a book that was written 50 years ago that has stood the test of time, and then where presumably there's something timeless in it. And then it's sort of everything in the middle, I'm always much more skeptical about.

**Marc Andreessen** (01:30:56):
And in particular, it's kind of what I already said, which is I think if you go back and you read old... Nobody ever does this, it's actually really funny, there's no market for it. But if you go back and you read old newspapers... And by the way, you can do this, just read the last week's newspaper, right? Yeah, today, so we're taping on Friday. So read last Friday's newspaper, right?

**Marc Andreessen** (01:31:15):
And just go back and read it, and be like, "Oh my God. None of this happened. None of what they predicted played out the way that they said that it would. None of this turned out to actually be that relevant or correct." They didn't understand, by the way, they had no view of what was going to happen this week. Then they couldn't know, and so they were making predictions and forecasts and so forth based on not having information.

**Marc Andreessen** (01:31:37):
But it's like, "Wow, none of this happened, I wish I had never read this, oh my God." And then it's kind of the same thing with magazines, I go back and read old magazines, and just the level of just the endless numbers of predictions that they make. And kind of, you know, the problem with... Newspapers at least they're going day-to-day, the thing with magazines is it's like a week or month kind of a long cycle.

**Marc Andreessen** (01:31:58):
And so by the time an article even hits publication, it's often out of date. So I just have a big problem with kind of everything in the middle. And so it's either of the moment or timeless. But then yeah, you mentioned newsletters. I mean, so the other thing, and this is maybe obvious, but I think it's probably still underrated, which is actual practitioners in the field who are actually creating content, I think probably is still dramatically underrated.

**Marc Andreessen** (01:32:21):
And I think this is a huge part of the Substack phenomenon, and the newsletter phenomenon, and the podcast phenomenon, is, like, direct exposure to the people who are actually principals in the field who actually know what they're talking about is probably still dramatically underrated.

**Marc Andreessen** (01:32:33):
And I think again, the reason for that is like we're used to being in this mass media kind of culture in which basically everything is mediated. Right? Everything got filtered through like TV interviews or, like, newspaper interviews, or magazine interviews. And obviously now more and more it's just, no, you actually want smart people who are actually working on something explaining themselves.

**Marc Andreessen** (01:32:49):
And then you have tons of intermediation, like podcasts, that kind of open that up for people and make that possible. And so yeah, domain practitioners are really great. I mean, yeah, just to state the obvious in AI, it's obviously your stuff, but also, like, the fact that Lex Fridman can have the world's leading... And any of you guys, there's a small handful of you guys who have access to these people, you could have the world's leading experts in the domain actually show up.

**Marc Andreessen** (01:33:15):
And by the way, and look, the critique always is, people talk their book, like if I'm running a startup or whatever I'm just selling. But it's like... And there's always a little bit of that... But it's also, my experience is people love to talk about what they do. And they fundamentally want to express what they do, and they want to explain it, and they want people to understand it.

**Marc Andreessen** (01:33:35):
And everybody kind of enjoys that, and they get to contribute to human knowledge by doing that, and they get ego gratification by doing that. And so I think there's actually just tremendous amounts of alpha in listening to the world's leading experts in the space who actually just show up and talk about what they're doing.

**Marc Andreessen** (01:33:48):
And of course the world is awash in that today in a way that it wasn't as recently as 10 years ago. So yeah, I do as much of that as I can too.

**Lenny Rachitsky** (01:33:54):
And there's also just this culture in tech, Silicon Valley, in particular, of sharing, or not trying to keep these secrets. Everyone on LinkedIn is always like, "How is this free?" Like, it's just the way it works.

**Marc Andreessen** (01:34:04):
Yeah. Somebody said, "Silicon Valley is a company town, but they company is Silicon Valley." Right? And again, at the loneliest coast, again, is one of these great n equals one. If the level of n equals one is somebody, and I've run startups before, I've run companies before, if the level of n equals one of, like, running a company, that's just a giant pain in the fucking butt.

**Marc Andreessen** (01:34:22):
Because your secrets are walking out the door, and your employees are walking out the door, and the whole thing sucks. But the other side of it is you also benefit from that, right? Because you get to hire people with all these skills and experiences, right, and you're in this ecosystem that adapts and channels talents and skill and knowledge and people into the new fields.

**Marc Andreessen** (01:34:38):
So there's kind of the push and pull of that at the level of just being an individual CEO. At the level of just being in the ecosystem to your point, yeah, it's an absolutely magical phenomenon. And by the way, for all of the issues in Silicon Valley, I did the count once, I think AI is the ninth major technology platform in the history of Silicon Valley. Right?

**Marc Andreessen** (01:35:03):
Silicon Valley is still called Silicon Valley, we haven't made Silicon here in decades. Right? We used to actually... You know it's called Silicon Valley because they used to make chips, right? They used to have the actual fabs were in Silicon Valley. And then they designed them and they made the chips.

**Marc Andreessen** (01:35:15):
And so, and that was wave one starting in the 19... No, that was actually, no, that was more wave three or whatever. But that was when the area was named in the 1950s. But now we're on wave nine. Right?

**Marc Andreessen** (01:35:28):
And the company town phenomenon where the company is, the industry, again, the indeterminate optimism, nobody had to sit and plan and say, "Okay, in the 1990s Silicon Valley's going to do the internet, in the 2000s they're going to do the smartphone, in the 2010s they're going to do the cloud, in the 2020s they're going to do AI."

**Marc Andreessen** (01:35:44):
It's just, right, the indeterminate optimism of ecosystem flexibility of the ecosystem that they Silicon Valley could morph into all these categories, and again, maybe a testimony to indeterminate optimism.

**Lenny Rachitsky** (01:35:58):
This reminds me of the meme of how we're all just wrappers over sand, everything we're building is just wrapper over wrapper, wrapper, wrapper.

**Marc Andreessen** (01:36:03):
The wrapper thing is hysterical, yeah, yeah. I'm a software company and I'm a chip wrapper, right?

**Lenny Rachitsky** (01:36:07):
Yeah.

**Marc Andreessen** (01:36:08):
Yeah. I'm a business application, I'm a database wrapper. Yeah, exactly. I'm a sand... I mean, you and I, we're all now sand wrappers.

**Lenny Rachitsky** (01:36:15):
Sand wrappers.

**Marc Andreessen** (01:36:17):
Perfect.

**Lenny Rachitsky** (01:36:17):
Okay. One more question along the media diet, I asked your partner Ben Horowitz what to talk to you about. This is a16z if people don't know him. And he said you're really into movies these days.

**Marc Andreessen** (01:36:27):
Yeah.

**Lenny Rachitsky** (01:36:28):
And so I don't know, any movies? Any movies you're really into these days, any movies you've absolutely loved recently?

**Marc Andreessen** (01:36:33):
Yeah, so the movies that blew my socks off last year, which I think is the best movie of the decade for sure and maybe of the last, like, 15 years, is this movie. Unfortunately it's one of these things, not a lot of people have seen it, but I would encourage it. It's called Eddington.

**Lenny Rachitsky** (01:36:48):
I've not heard of it.

**Marc Andreessen** (01:36:48):
Have you not heard of it? Okay, so you're going to really enjoy it. So, I won't spoil too much of it, so at the surface level the following spoils nothing. So at the surface level, it's set in a small town in New Mexico called Eddington which is a small town about 600 people.

**Marc Andreessen** (01:37:04):
And there's a sheriff who's played by Joaquin Phoenix who's like an old, crusty, basically right-winger, and then there's a mayor played by Pedro Pascal who's basically a young, hip, progressive. And then the movie starts I think in March of 2020.

**Marc Andreessen** (01:37:21):
And so it starts when COVID first hits. And then it sort of as it plays out over the next few months, it intersects, and it sort of extends into the summer of 2020. So, kind of the George Floyd moment and then protests and riots and kind of everything.

**Marc Andreessen** (01:37:36):
So sort of the convergence of COVID and then all the BLM stuff. And then there's a third kind of element to it which is there's a company which is basically a loosely-disguised version of Meta if you read the backstory of it, which is building an AI data center on the outskirts of town. So they kind of pull that in as sort of a thing that looms larger and larger over time.

**Marc Andreessen** (01:37:58):
And then the thing it really is great at is it really shows, you know, this is a small town in New Mexico. And so, everybody in the town gets full wrapped up in all the COVID stuff, and they get fully wrapped up in all the BLM stuff, and they get fully wrapped up in all the tech anxiety stuff.

**Marc Andreessen** (01:38:13):
But they're all experiencing it basically through the internet, right? Which is what actually happened, right? So the reason I love the movie so much is one is it's the first movie that directly grapples with 2020, of what happened in 2020, and it just, like, fully, fully engages and grapples with all the dynamics that were playing out in the country.

**Marc Andreessen** (01:38:31):
But the other reason is it's the first movie that does a really good job of showing what it was like especially in that area to live in a world in which there were things happening in the real world, and people were kind of experiencing events online, like in a way that was very central in their lives. Right?

**Marc Andreessen** (01:38:45):
And so it does a really good job of pulling in smartphones and social media in a way that movies really, really, really struggle with, and then the whole thing comes together in an incredibly entertaining way.

**Marc Andreessen** (01:38:55):
And so I wouldn't even say I completely agree with the movie or whatever, and I think the director of the movie and I would probably disagree about a lot, but he really tries hard to really grapple with what it's actually like to live like a human being in the 2020s in America in a way that I think many other filmmakers who are very talented have just been very scared of touching.

**Marc Andreessen** (01:39:14):
And this guy, for some reason he's just like, "Yeah, I'm just going find all the third rails and I'm just going to fucking grab them."

**Lenny Rachitsky** (01:39:19):
I can see why that's your favorite movie of the year.

**Marc Andreessen** (01:39:22):
It's great, it's great, it's great. Everybody should see it.

**Lenny Rachitsky** (01:39:24):
Oh man. Okay, final question, I want to ask about your product diet. Are there any products you use that maybe are less known that you love, that you want to recommend? You can mention products you're investors in if you use them constantly.

**Marc Andreessen** (01:39:38):
We have so many that it's really hard to, you know, I always feel it's who's your favorite shoulder? And so it's really hard to pull out specific ones. But I'll talk about a few. I mean they're all just observations. So one is my 10-year-old, I have, my 10-year-old is 100% obsessed with Replit. And by the way, it was not from me. Do you have kids?

**Lenny Rachitsky** (01:40:00):
I do, I have one two-and-a-half year old.

**Marc Andreessen** (01:40:02):
Two-and-a-half. Okay, so you haven't run into what I'm running into now, which is whatever it is that you do is not cool. Right? Like, it's two-and-a-half, whatever daddy does is like the coolest thing in the fucking world. I can tell you, by the time he's 10, whatever you do is, like, deeply uncool.

**Marc Andreessen** (01:40:15):
Right? And I'm highly aware of that. And so, like, if I mention, "Oh yeah, we work on XYZ," he's like, "Okay." But when he discovers something, then it's cool, or when his friends tell him about it it's cool. And so he through no interference on my part discovered Replit about three months ago, and discovered Vibe coding, and is completely obsessed with Vibe coding games, and all kinds of things and literally will sit and do it for hours.

**Marc Andreessen** (01:40:39):
And so I'm seeing that phenomenon play out, which is super fun. That's one. Two is, I am just completely in love with all the AI stuff. I think it's just absolutely amazing, hysterical. My favorite party trick at dinner parties now is to pull out Grok with Bad Rudy, which is, if you've seen, it's a foul-mouthed raccoon avatar in the OS Grok app.

**Marc Andreessen** (01:41:04):
So, I think that's super fun. We have this company, Sesame, that they went viral last year for these just incredibly, like, intimate emotional kind of voice experiences. So I think the voice stuff is fantastic.

**Marc Andreessen** (01:41:18):
But I'm also a super fascinated by all the voice input stuff. And so Limitless Suite, yeah, Limitless recently, the company recently sold, but all the... I think the pendants, the wearables, all that stuff is going to be big, the Meta glasses. I think there's going to be a whole wearables revolution here.

**Marc Andreessen** (01:41:36):
I love the voice input stuff. There's this app on my phone now called Whisper Flow, which is voice transcription, which works like staggeringly well. It's incredible, it's like a voice transcription function, but you can actually talk to the AI model while you're doing voice transcription. So it kind of understands when you're telling it, "No, no, I want bullet points over there and I want this and that."

**Marc Andreessen** (01:41:58):
And it understands that you're not telling it to type in the words "I want bullet points," it just actually understands that you want bullet points. And so that's a great example of a super useful thing. And so, I think the voice mode stuff is going to be really great.

**Lenny Rachitsky** (01:42:10):
Subscribers of my newsletter get a year free of Replit and Whisper Flow, so there we go. What's the most memorable thing your son's built with Replit?

**Marc Andreessen** (01:42:19):
Oh, well so, he's gotten super into Star Trek. And so, so far he's writing, like, Star Trek simulators. So like all the, you know, all the... By The Next Generation, they actually have a-

**Lenny Rachitsky** (01:42:29):
Next Generation, okay, I was going to ask which-

**Marc Andreessen** (01:42:30):
Well actually, we like them all. We watched the new Star Fleet Academy last night-

**Lenny Rachitsky** (01:42:33):
Hm.

**Marc Andreessen** (01:42:33):
... which actually is quite good. But we watched the original, we watched them all. But it was in Next Generation where they actually developed an actual design language for the computers. If you watch the original series, they just had, like, basically knobs with lights.

**Marc Andreessen** (01:42:46):
And they didn't really, they just were, like, fucking around on set, and trying to pretend they were doing it. But by Next Generation, they actually had designed, they actually had a UI design language. And so one of the fun things you can do with Vibe coding is you can say, "Give me a Star Trek Next Generation user interface for whatever, this, that," or whatever.

**Marc Andreessen** (01:43:02):
And it actually uses the, they called it... I'm just having a nerd-out... They called it LCARS design language. And it'll actually build you like Star Trek Next Generation bridge consoles using that design language, but with your choice of a Star Trek name, for example.

**Marc Andreessen** (01:43:17):
And so he's going crazy for that kind of thing.

**Lenny Rachitsky** (01:43:19):
That sounds extremely delightful. You guys should open source and release that. Marc, like I said, I could talk to you for hours, well, you've got things to do. Anything you want to leave listeners with before we wrap up? Anything you want to double-down on or just leave listeners with?

**Marc Andreessen** (01:43:33):
Yeah, so a couple things. So one is, we got super lucky last week, Packy McCormick wrote the best piece ever written about us, actually, which he released. And so it's the best explanation of what we do, and how we think. And so I would definitely recommend that.

**Marc Andreessen** (01:43:46):
And then we're putting a lot, we have a great team of folks now, we're putting a lot of effort ourselves into video and content. And so I'd definitely recommend our YouTube channel, which I think has a lot of great stuff and is going to be very exciting in the next year.

**Lenny Rachitsky** (01:43:59):
Awesome. Well link to that, I think it's just youtube.com/a16z, something like that. And you guys have great stuff.

**Marc Andreessen** (01:44:04):
Okay.

**Lenny Rachitsky** (01:44:04):
Marc, thank you so much for being here.

**Marc Andreessen** (01:44:06):
Awesome, thank you for having me. I really appreciate it.

**Lenny Rachitsky** (01:44:08):
Bye everyone.

**Lenny Rachitsky** (01:44:10):
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast.

**Lenny Rachitsky** (01:44:25):
You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [9/15] A child psychologist’s guide to working with difficult adults | Dr. Becky Kennedy

**Lenny Rachitsky** (00:00:00):
Most adults in the corporate environment are really just babies in disguise.

**Dr. Becky Kennedy** (00:00:04):
All humans need the same things, whether we're one or five or 45 or 85. When you look at bad behavior, the actual problem is someone doesn't have the skill they need to manage something happening internally.

**Lenny Rachitsky** (00:00:16):
Love your advice. It works not just for kids, it works for adults.

**Dr. Becky Kennedy** (00:00:18):
Our whole parenting philosophy is resilience over happiness. When we're thinking about a resilient work culture, we want people who can say, "This is hard and I can do hard things."

**Lenny Rachitsky** (00:00:28):
You teach kids are good inside, no matter their behavior, is that useful in work?

**Dr. Becky Kennedy** (00:00:32):
The idea of being good inside inherently requires us to separate behavior and identity. We infer a lot from people's behavior. Someone's late to work a lot, "Oh, that person's lazy." The quickest way to have an unproductive conversation is to lose sight of the fact that someone's good inside.

**Lenny Rachitsky** (00:00:49):
I definitely wanted to ask you about this idea of boundaries.

**Dr. Becky Kennedy** (00:00:51):
Boundaries are what you tell someone else you will do, and it requires the other person to do nothing. Making a request, that's not a boundary.

**Lenny Rachitsky** (00:00:59):
Is there a corollary to adult work environments in potty learning?

**Lenny Rachitsky** (00:01:05):
Today, my guest is Dr. Becky Kennedy, a clinical psychologist, author, and CEO of one of the most popular parenting books, podcasts, communities, and apps called "Good Inside." Why would I have a parenting expert on this podcast? Because if you think about it, many of the people that we work with in the workplace act a lot more like babies than adults. And I'm half joking, but I'm half not. Think about coworkers that got really mad when they had to share their stuff, that always need to be the center of attention, that get really upset about not getting their way, that need other people to fix things for them.

**Lenny Rachitsky** (00:01:38):
These are just a few examples, and there is a lot that we can learn about how to effectively deal with people at work from a parenting expert like Dr. Becky. I have never heard a conversation like this anywhere that bridges the gap between parenting advice and leadership advice, and you will leave this conversation both a better leader and a better parent. We talk about the power of repair, the importance of building long-term resilience versus short-term happiness. Why your goal as a leader is to become sturdy, the power of curiosity over judgment, the framework Dr. Becky calls the most generous interpretation, also a ton of really specific phrases that work really well with kids and adults who you're having a hard time with. And so much more.

I am so honored to have Dr. Becky on this podcast, and I hope you have as much fun listening to this conversation as I had recording it. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. That helps tremendously. And if you become an annual subscriber of my newsletter, you get a year free of a ton of incredible products, including a year free of Devin, Lovable, Replit, Bolt, n8n, Linear, Superhuman Descript, Wispr Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, [inaudible 00:02:46], ChatPRD, D-Mob and PostHog and Stripe Atlas.

**Dr. Becky Kennedy** (00:05:23):
I am truly very excited to be here. I'm an avid listener of your podcast and all of your content, so I'm honored to be here.

**Lenny Rachitsky** (00:05:29):
I can say exactly the same thing. I'm even more excited. First of all, just what is Dr. Becky doing on this podcast? Let's help people understand what's going on here. I have an explanation, but I want to ask you first, actually. What's the idea here?

**Dr. Becky Kennedy** (00:05:44):
I think Good Inside, which is kind of I guess the company and the method around all the things I've thought about is known for helping parents with parenting struggles and different things that are going on with our kids at various ages. But at the end of the day, it's a set of core principles that help us better understand human beings, ourself, the core relationships we're in, why we do the things we do and why we act the way we do in certain systems. And the system I tend to focus on the most is the family system.

**Dr. Becky Kennedy** (00:06:12):
But the workplace is another system, a marriage is a system, sibling relationships are a system, extended family over the holidays, that's a system. And once you start to think through a lens of understanding how we operate in the system, any good principle can then be applied to any system. And I'm very oriented around efficiency also. So to me, what Good Inside really gives people is a way to think about themselves and the way to think about leadership.

**Dr. Becky Kennedy** (00:06:40):
And whether we're talking about leadership in parenting or leadership in the workplace, it's actually the exact same principles that can be applied. And the good news for that is whether you're a parent, you're a leader at work, you're both, learning one system and applying it to multiple areas becomes a very efficient way to think about showing up in our relationships in a way that feels better to everyone.

**Lenny Rachitsky** (00:07:02):
Awesome. Okay. I love that explanation. I have another lens that's-

**Dr. Becky Kennedy** (00:07:06):
Oh, okay.

**Lenny Rachitsky** (00:07:07):
... maybe funny, maybe too real. So I have a former podcast guest, Shreyas Doshi. He's one of my favorite product thinkers. He's been on the podcast a couple times. He's got this brand that most adults in the corporate environment are really just babies in disguise. They look like adults, they act like adults, but they're really babies. For example, they want to always be the center of attention. They don't want to share stuff they've accumulated. They want to keep their resources, their budget, their people. They get irrationally upset about things not going their way.

**Lenny Rachitsky** (00:07:39):
Even when it's just a terrible idea, they're just like, "Nah, that was my idea. I wanted this thing to happen." There's also just power struggles and, "No, no, I want this to happen because it's what I want." And they often just need people to fix things for them. For example, today my toddler, he just grabbed this Yoto player that we have and he threw it across the room. He's like, " Pick it up." So with that, I think understanding how babies think and toddlers think and operate is helpful in the corporate environment where we think we're working adults, but a lot of times they're more like babies. How does that resonate?

**Dr. Becky Kennedy** (00:08:15):
I guess my most generous interpretation of what you just said is slightly different but similar, which is that all humans need the same things. Whether we're one or five or 45 or 85, we tend to need the same things. When the needs are not being met, we all tend to express ourselves in kind of ineffective, less than ideal ways. And so maybe that's my interpretation of agreeing with what you're saying.

**Lenny Rachitsky** (00:08:39):
Awesome. Okay. Okay. So let's get into the first topic lesson. Let's talk about the power of repair, one of your more important and impactful lessons that you teach parents. Explain the toddler, kids version of this and how this is useful in raising kids and then just how this might translate into a corporate environment.

**Dr. Becky Kennedy** (00:08:57):
Yeah. I think repair is kind of the number one relationship strategy we have. And the thing that keeps us from repairing, which really is the idea of going back to a person after a moment we didn't feel proud of, taking responsibility for our part, maybe acknowledging the impact it had on them and talking about what you would do differently the next time, is actually this very false idea that there's a goal to be perfect. Right? And in our family, we say something, we actually now say it at work, "Perfect is creepy."

**Dr. Becky Kennedy** (00:09:27):
I just think it encapsulates... You don't even want to be perfect. It's actually very creepy. Only non-humans can ever be perfect. And what defines kind of the human condition is that we want to do well and we mess up over and over again. And one of the things I remember learning in clinical psych grad school was that the thing that really differentiated secure attachment, which is the nature of a relationship you really want to have with your kid is the presence of repair.

**Dr. Becky Kennedy** (00:09:56):
And I remember the professor continuing to talk and I was kind of stopped in my tracks thinking, "What? Oh, so secure attachment isn't defined by getting it right all the time? Secure attachment is just defined by, we're all going to mess up, but secure attachment has an adult who's willing to repair?" That felt very hopeful to me because it felt like something I could realistically get good at. And over and over, whether it's with your kid, when you say to them, "Hey, sorry I yelled." Some version of, "I had a stressful day at work. That wasn't your fault and I'm working on staying calmer even when I'm upset."

**Dr. Becky Kennedy** (00:10:32):
Or we say to someone, I don't know, on our team, "Hey, earlier in the meeting, I totally cut you off. I used a really harsh tone. Honestly, I did disagree with what you're saying, but it's no excuse for me to talk to you the way I did. Stuff was going on before the meeting. I'm sorry. I'm going to work on that." There's just nothing to reestablish trust and connection like repair. And when trust and connection are reestablished, then whether it's your kid or someone at work, they cooperate better, you don't get into conversations that are kind of run on conversations that are really an act of defensiveness and you can just get a lot more done.

**Lenny Rachitsky** (00:11:05):
There's kind of an associated concept you talk about, which is around connecting before correcting, which reminds me of radical candor a little bit of just this idea of challenge directly but care deeply. Talk a bit about this concept of connecting before correcting.

**Dr. Becky Kennedy** (00:11:19):
Yeah. Well, I'll use kind of... let's take it out of parenting and work because I think it really shows the importance of connecting in all of our lives. So all of us say about our kids, "My kid doesn't listen. My kid doesn't listen to me." Right? But if you picture me on the couch and let's say my... I have three kids, they're all sleeping now and I'm finally settling in for now the two minutes I have before I pass out because I'm tired. And let's say my husband comes out and just says, "Becky, we have to do our taxes."

**Dr. Becky Kennedy** (00:11:45):
And you're watching the scene and you see me say, "Whoa, I just sat down to read a book." And he goes, "You don't listen to me. You have a listening problem and if you don't do the taxes with me right now, I'm taking away your dessert for a week." Okay? Lenny, I'm pretty sure no one would tell me I have a listening problem. They probably say, I have a husband problem if that happened. Right? They'd be like, "Wow. You're..."

**Dr. Becky Kennedy** (00:12:08):
But we do that to our kids and we do that at work all the time. Now, let's say the same situation happened and he said, "Whoa, I realize didn't tell you this. We have to get our taxes done tonight. You look like you're just settling in to that book. Oh, can we get on top of this together? I know we're on the same team. Can we figure this out?" The chances that I will do taxes just skyrocketed. Right? Why? Because he sees me in my reality as a full human being, not just as an object in his world to get something done.

**Dr. Becky Kennedy** (00:12:43):
He kind of joins my world where I'm embedded in my own priorities and by doing that forms a bridge, that's what connection is. So I can kind of walk back over to his world with him to do something that's a priority in his world. So connection, it first all just feels good anyway, but it's not such a kind of soft topic. Connection is what forms a bridge between two people so they can act together in the same interest. And so whether you're thinking about your kid not listening or thinking about things at work, coming back to connection is really the foundation for both cooperation and at work, even productivity.

**Lenny Rachitsky** (00:13:21):
I love the way you phrased it at the beginning, that the way you just said it, I'm like, "Oh wow, I get why someone would start." To listen to help people actually do this in day-to-day, what's the way to approach the way you phrase beginning that connection conversation?

**Dr. Becky Kennedy** (00:13:38):
The truth is the first step is a mindset. And I know that sounds annoying because it's like, "Just tell me what to do." And anyone who knows me knows, I love telling people script ideas, but we feel people's intention, not just their intervention. So the same intervention will be felt completely different based on our mindset. So if I'm going into a conversation with you, Lenny, and let's say, I don't know, I want you to do something for me. I'd like you to come water my non-existent plants. Okay? And I'm like, I'm going to connect to Lenny first. First I'm going to say, "Oh, how's it going? Tell me this thing about your life."

**Dr. Becky Kennedy** (00:14:10):
And you just know that is in and of itself a transaction to get you to do the thing I want you to do. Not only is it not going to quote "Work," it's going to feel like dirty to you. You're going to feel it and it's going to feel off. So the mindset we need to be in, which is so hard, and I talk a good game, but it's hard for me too. Okay? Is trying to get into a kind of without an agenda mindset, even if it's for 30 seconds with someone. Being present with someone without an agenda is increasingly hard to do, but that is what connection is about.

**Dr. Becky Kennedy** (00:14:43):
No one wants something of me. They just kind of see me or they're recognizing something I'm doing or they kind of plop down on the couch next to me. And so what could that look like with your kid? It's often very, very simple things. Okay? And it's going to feel really small, especially anyone who works a lot where we get so addicted to accomplishing and dopamine. It's sitting next to your kid and putting your hand on their back, literally, for longer than feels natural. It's saying to your kid, "I'm happy to see you."

**Dr. Becky Kennedy** (00:15:14):
And that's it. Such a nice thing to say when your kid comes home from school or you come home for work or first thing in the morning. It's watching your kid play and describing what they're doing instead of peppering them with questions, "Oh, you're making a tall tower." Versus, "Oh my goodness, is that a fire station? Oh my goodness, we can make the best fire station in the world." And I hear myself say that because I can say that, but it's not connection actually.

**Dr. Becky Kennedy** (00:15:36):
At work, it's probably saying, "Hey, tell me a little bit about your long weekend." And not kind of counting down the seconds for them to finish so you can tell them what to start on this week, but kind of giving yourself permission to say, "Can I actually just kind of drop down into that moment?" Right? And so I think it's the mindset and then it's actually something that doesn't have an agenda, that's just about seeing someone, noticing someone in a really, really small way.

**Lenny Rachitsky** (00:16:04):
I love this idea of just the mindset. And that's such a powerful reframe of this is don't think about what are the correct words to say. It's just actually just feel, "Okay, I just want to connect with you first for 30 seconds."

**Dr. Becky Kennedy** (00:16:15):
Yeah. And I want to reveal something about myself. Right? So one of the things I think about is that efficiency and relationship building are often in opposition. We're doing one or the other. And one of the things I've learned the very, very hard way about myself, especially since I've been working, the way I've been working the last couple of years, is my efficiency is kind of reinforced at work. Right? And a lot of us who can be very efficient and get things done, we can have a little morality about it. We listen to someone's story, we're like, "Come on, get to the point."

**Dr. Becky Kennedy** (00:16:50):
When that person in our life is like, "The whole point is that I want to tell you the whole story." And I think if anyone's listening and thinking, "Yeah, I'm kind of an efficiency oriented person, dropping into relationship mode takes real intention." And it's hard, especially if a big part of your life, the efficiency is reinforced. So if it feels awkward and slow. And I always call it with my kids, my best time just feels low stim. It just feels so low stim. I'm like, "Is this right? Am I doing this right?" That's probably a sign I am because not much is happening and I'm not being efficient with my time. I'm just in kind of relationship building mode.

**Lenny Rachitsky** (00:17:30):
What I think about as you're talking about this is the best people I've worked with that are most effective at work are the people that are really good at this, that you feel like really listen, really care, just aren't rushed. And so I could totally see the power here.

**Dr. Becky Kennedy** (00:17:44):
Yeah.

**Lenny Rachitsky** (00:17:46):
So one of the core principles you teach, the name of your book and the name of your product and community and everything you built is "Good Inside." I'm curious if that's a useful lens in the work environment too. You teach kids are good inside, no matter their behavior, they may be having a hard time, but they're still good inside. Is that useful in work at all? Are people always still good inside? I imagine not always.

**Dr. Becky Kennedy** (00:18:08):
I think it's a very useful framework. And I'll say why, but I want to separate things that we often confuse. No part of me if my kid is, when they were younger hitting, would I be like, "My kid is good inside, so who cares that they hit?" Or, "This employee's good inside, so it doesn't matter that they're not getting their PRDs in on time," whatever it is. No. And I think that's actually the other thing that's core to Good Inside is the idea of being good inside inherently requires us to separate behavior and identity.

**Dr. Becky Kennedy** (00:18:38):
Most difficult times we have personally or interpersonally come because we've collapsed behavior and identity. And it's easy to do because we infer a lot from people's behavior for our own. Right? So someone's late to work a lot, "Oh, that person's lazy." Right? Seeing that person as good inside starts with this sentence, "This is a good person who is late." Right? Or, "I have a good kid who's hitting." And I do this thing with my hands because it actually helps my brain and body see the difference.

**Dr. Becky Kennedy** (00:19:09):
One hand is identity, the other hand is behavior and literally just separating them forces you to distinguish who someone is good inside from their behavior. And ironically, that's what allows you to effectively change and improve their behavior. Because we all know what it's like to feel defensive. Do you know why we're defensive? Because we think someone else, instead of talking about our behavior, is talking about our identity. And then we can't even talk about our behavior anymore and being late because we feel like they're accusing us of being a bad person or being lazy or not caring about the other people in the meeting.

**Dr. Becky Kennedy** (00:19:42):
Now we can't even talk about the reason for my lateness because my whole identity feels wrapped up in it. So the quickest way to have an unproductive conversation is to lose sight of the fact that someone's good inside. If I'm talking about someone at work, let's say again, who's always late, here's like a quote "good inside," infused conversation. I would start by saying, "First of all, I want to say we're on the same team." If that's the only thing people take from this, that is the most amazing way to have a more productive conversation with anyone.

"I want to say we're on the same team. I know you're a good person. You probably don't need me to tell you that we need to start meetings on time. We both know that. It's also been happening consistently, which lets me know something is going on that I want to get to the bottom of with you. Because we need you to be there at 9:00 AM to start this important leadership meeting. So tell me what's been going on. Is there something at work that feels bad that makes you not want to come in? Is there something going on at home? Is this, I don't know, a hard time managing your time, whatever it is, let's get to the bottom of it together so we can figure it out." So that to me is that lens.

**Dr. Becky Kennedy** (00:20:44):
And it's honestly, Lenny, it's the same thing we do, I don't know, and like a good sports coach. I don't know someone, I don't know, who's a good even professional sports coach who's like, "You missed all the layups today. What's wrong with you? And here's what I'm taking away." It's like, "Whoa, you're a good player. Something's going on. Let's get in the gym tomorrow. Let's figure it out. I believe in you. I am putting you on the bench now because it's not really working out, but we're going to get to the bottom of this together." That comes from seeing the good inside someone.

**Lenny Rachitsky** (00:21:11):
Just hearing you say these things is always like, I could see exactly why this works. So a takeaway here is just kind of start with admitting to them and showing them you know they are smart, they know what is the right thing to do, they just maybe aren't doing it.

**Dr. Becky Kennedy** (00:21:25):
Yeah. And because now we can take that off the table. And if someone's thinking, "But I don't think that of the person," then it's actually just a very different conversation, but we put that in a different category. But if you start talking about someone's behavior right away and they think, which we all tend to think, "You're actually saying I'm not a smart person."

**Dr. Becky Kennedy** (00:21:44):
You're not even talking about the behavior anymore. Now their language seems like it's talking about the behavior, but all you're doing is you're having a debate you don't even realize you're having, which is whether they're a good, moral, worthy person. That's not an effective work conversation.

**Lenny Rachitsky** (00:22:00):
So just kind of laying out that you know they are good, they're smart, they know the right thing to do, but here's something that isn't going the way that it should?

**Dr. Becky Kennedy** (00:22:08):
Yeah.

**Lenny Rachitsky** (00:22:09):
This connects to something else that you teach, which is the MGI, the most generous interpretation. Talk about that and how that might relate to work.

**Dr. Becky Kennedy** (00:22:17):
Yeah. I've realized I have an allergy to ideas that don't have action. I've realized this actually at work with and just with psychology too. So this whole idea, people are good inside, how can you separate behavior from identity as a framework to then go into a conversation, all of it just feels too theoretical for me. And so I like to think like, what is one tool I can use to action on that idea?

And it started, I think through parenting. I'd finished day with my kids. I was going to bed when my kids are [inaudible 00:22:48]. I was like, "Wow, that day was just disaster." And then I would hear myself start talking about my kid in a way where I always loved my kid, but I didn't realize till later, even through the language, I stopped liking my kid. I was just.

**Dr. Becky Kennedy** (00:23:00):
Even through the language, I stopped liking my kid. I was just listening to your Peter Dang episode, which was a lot about language and how language then impacts thoughts. And I think, a lot as a parent, but it's true as a leader, the story you tell yourself as a kid at night, kind of becomes the parent you are the next morning. Probably same truth. The story you tell yourself by your organization at night, becomes the leader you are the next morning. And I realized I was using what I later called this least generous interpretation. I think we all do that naturally, at least I do. Like, I see my kid not listening and laughing if I'm trying to discipline. And my first thought is, I think my kid's a sociopath. I don't know how I get there. My kid's two years old, but it happens so fast. Okay?

**Dr. Becky Kennedy** (00:23:43):
And I realized at night, the only way to shift this and get into a good inside mindset, was just to say to myself, what's the most generous interpretation of why I would say to my kids some version of, stop jumping on the couch, it's dangerous, you're near the glass table, and he'd look at me and jump doubly hard on the couch. My least generous interpretation was that he's sociopath. I already covered that one. My most generous interpretation, there's not one right answer. It might be, this is a kid who's really oriented around wanting to feel in control. And when that feels threatened, he doubles down. He is 0% people pleasing, this third child of mine, zero. So the whole, I'm disappointed with you, it's not going to work with a kid like that.

**Dr. Becky Kennedy** (00:24:28):
And when I started using an MGI, a couple things happened. I realized I liked my kid again. And I think we don't talk about that enough as parents, that that's the thing that keeps us up at night. It's not their behavior. It's that we slowly stop liking them based on how we're describing them. And then I realized we were on the same team. And then I came up with a whole different range of interventions because I used a most generous interpretation. I think the same thing is true at work. Someone, I don't know, is belaboring their point in a meeting, when everyone else has gone on. And maybe the least generous interpretation, maybe is what I was reacting to in your initial question is, oh, they're being a baby or everyone just is vain.

**Dr. Becky Kennedy** (00:25:10):
My most generous interpretation, I don't know, could be a variety of things. Did they not feel heard the first time? Still doesn't make them going on forever, acceptable. But if that's true, I might say to them in private, Hey, something happens in meetings where I wonder if you don't always feel heard. And then you keep talking and then honestly, the rest of the room gets annoyed, which makes us tune you out more, which probably makes you belabor your point more. We're in a bad cycle. Can we work together on changing this? And now all of a sudden, instead of everyone complaining about that person or whatever it is, and nothing changing, which is just horrible for culture and productivity, we have one conversation through an MGI lens and things start moving forward in a more productive way.

**Lenny Rachitsky** (00:25:48):
It's interesting how much of this comes back to just kind of assuming they're smart and are trying to do their best job and are good inside and translating to, okay, in spite of that, here's something that is still going wrong, let's try to figure out... There's kind of this idea of curiosity over judgment, trying to figure out what's missing.

**Dr. Becky Kennedy** (00:26:06):
And I mean, that's exactly it. Those are two things also that I think are in opposition. You inherently cannot be judgmental when you're curious. And when you're judgmental about something, you're inherently not curious about it. And I think it's assuming... I say assume, good inside or assume MGI, other people say assume positive intent. Whatever language hits your heart, I think is the language that's... Everyone's different. MGI makes sense for me, but if there's someone else that has a different phrase, someone should use that one. And then, I think also assuming, this person would want to work through this with me. And I think Lenny, that's one of the things that we also miss a ton in our kids. The kids who hit, no matter what they say, they really don't like feeling out of control. The kid who's lying to your face, they don't want to lie to your face. They don't want to behave this way either, again, which doesn't make the behavior okay, but it makes us soften a tiny bit and realize we're on the same team. And I think actually we all want the same outcome.

**Lenny Rachitsky** (00:27:08):
Wait, say more about that, because that doesn't feel, as a parent, that my kid doesn't want to... When he's throwing a thing across the way or just resisting going in the car. I want to hear more about what's going on there.

**Dr. Becky Kennedy** (00:27:20):
Yeah. I mean, first of all, I'll start with the baseline, right? That as a clinical psychologist, I was initially trained in this very reward, punishment, timeout, sticker chart, mode. And to be honest, I loved it, because I have a very healthy left brain and our left brains love logic and we love linearity. And as soon as you're a parent, you're like, there's no linearity or logic really, but our brain wants that to be a system. And it's kind of a system of punish the bad and you have less of the bad and reward the good. And no one really says, Well, how are we raising a human? Those are just behaviors, but our brain loves it. And that's what I started teaching to parents, I did, in my private practice, because it felt clean. But it was at complete odds with everything I knew was helping all the adults in my practice change their lives.

**Dr. Becky Kennedy** (00:28:11):
And that's actually what gave me insight into starting Good Inside. It's like, there's no way that what helps the 35-year-old rewire their brain and change for the better, is that theoretical opposition with what a two-year-old and five-year-old needs to become resilient and confident. That just doesn't make sense. And I think we have these ideas about kids. Even something I hear a lot, you're making a bad choice, to kids. You're a good person, you're making a bad choice. This is not about forgiving bad behavior, I'm pretty firm about that. But I just don't believe a four-year-old who's throwing is like, hold on, should I throw this at my sister? Should I or should I not? I will choose as not... I mean, think about us. When we act out, I don't think most of us are in a decision making place. We escalate and here's one of the big insights besides people are good inside, that helped me form all of my other approaches, is kids are born with all the feelings and none of the skills to manage feelings.

**Dr. Becky Kennedy** (00:29:12):
Bad behavior at any age can basically be reduced to feelings that overpower skills. And yes, behavior is a problem, but behavior isn't the core problem. It's a manifestation of the problem. The actual problem is someone doesn't have the skill they need to manage something happening internally. And if they had that skill and could access it, the behavior would change. But that would be kind of the outcome, not the place of actual intervention. And so, when a kid hits, they look also and kids will say things like, "I don't care what you say." They're out of control. Maybe they're in a situation where, I don't know, they could be tired, but also they're angry that a sibling has a toy and they don't have skills to manage anger, they're jealous that someone is playing with something and they can't manage the jealousy. And when you look at bad behavior through the lens of feelings that overpower skills, you start to think like a coach.

**Dr. Becky Kennedy** (00:30:09):
It always has struck me with kids, that we don't punish them into learning how to swim, which I know sounds funny. But if you paid for swim lessons and the person said, You know what? I'm just not going to put up with this. It's this inappropriate behavior. By the way, your kid has to learn to swim if they're going to function in life. So, send them to their room and tell them to come back when they learn how to swim. It's absurd, it's obviously laughable.

**Dr. Becky Kennedy** (00:30:34):
I think swimming is very important, but an even more important life skill is learning how to manage your emotions. It is. And nobody learns new skills by being sent to their room, nobody learns new skills by adding shame. All that does is increase the gap between feelings and skills. It's just totally counterproductive. The answer is, we have to set boundaries. We have to be the people setting boundaries around out of control behavior. But then we actually have to teach our kids the skills they were missing, which levels up the skills. And that's how not only you change behavior short term, but you actually change behavior massively long term.

**Lenny Rachitsky** (00:31:08):
I'm actually in the middle of starting potty training with our kid and I'm taking this little online course and they call it potty learning, not potty training, for exactly that reason. You're not training them to do a thing. They don't know how to do it, so you just teach them, like you're teaching them the alphabet, just teach them all little steps and what they need to learn.

**Dr. Becky Kennedy** (00:31:24):
You're not doing our potty learning course? Lenny, I'm going to talk to you after.

**Lenny Rachitsky** (00:31:30):
Oh, I didn't know you had one. Okay.

**Dr. Becky Kennedy** (00:31:31):
Our marketing message isn't getting out there.

**Lenny Rachitsky** (00:31:33):
I'm switching immediately. Well, how do people find it? Goodinside.com?

**Dr. Becky Kennedy** (00:31:39):
It's actually one of the things I feel very strongly about putting out for free. Because, I think potty stuff is one of the early lessons around body autonomy and skills. And the quickest way we kind of mess it up as parents is kind of trying to take over the process. And it could be so stressful. And I actually told my team while ago is, I just think this should be a service. So, if you Google Good Inside potty, you can have it for zero dollars.

**Lenny Rachitsky** (00:32:05):
Oh, it's actually free already.

**Dr. Becky Kennedy** (00:32:07):
Yes, it's free.

**Lenny Rachitsky** (00:32:08):
I'm switching immediately.

**Dr. Becky Kennedy** (00:32:09):
Yes.

**Lenny Rachitsky** (00:32:09):
Is there a corollary to adult work environments in potty learning?

**Dr. Becky Kennedy** (00:32:15):
Yeah. There is, there always is.

**Lenny Rachitsky** (00:32:15):
Do tell.

**Dr. Becky Kennedy** (00:32:17):
Well, okay. So, let's think about what the potty stuff is about for kids. When our kids are younger, they control two things in their entire life. What goes into their body and what comes out of their body? That's all. No, obviously, hopefully we let them pick their clothes here and there, but thematically... By the way, and they shouldn't be in charge of a ton more categories because they're two, right? But it's why those two areas can become so heated for families because we get so stressed. And when kids, especially if you don't have a people pleaser, okay? When kids even smell, my parent is stepping into one of the only two domains that are mine, holy moly, will they do nutty things to push you back. And so, what's the general lesson, the human lesson is, what is it like for someone to have not that many areas of life that they're in control of?

**Dr. Becky Kennedy** (00:33:10):
Number one, just think about that. Think about the first 10 minutes of your kids' day. We wake them up. We tell them what the weather is. We tell them, No, you can't wear that. We tell them what's for breakfast. We tell them their shirt's on the wrong way. If my husband woke me up that way, I would be in a bad mood the rest of the day. Okay? And I'd probably be looking for a million different areas to just grasp control of as a way of screaming to the world like, I am my own person. Right? And so, if we think about that in the workplace, well, first of all, temperamentally, there are a subset of people, I call these the resilient rebels. They present as strong-willed, defiant kids, whose... I think we all walk around with the core fear that we end up acting out. Their core fear is the loss of control. They are very, very attuned to people kind of stepping into their domain, and if they smell it, they resist.

**Dr. Becky Kennedy** (00:34:01):
Well, we have those as adults too. It's tricky to collaborate with them. And again, we need to have an expectation, but we might see that as, why do I ask someone to switch a meeting time? And it feels like I took a knife to their heart, the way they react, right? Well, that theme might be coming up and maybe we can go one level deeper and say, Is this someone who in general... I'm making this up, but, oh, someone got promoted before they did on their team, they just had a change in manager, they just had their vacation denied, whatever it was. Oh, is that actually what we're talking about? And it's manifesting as this difficulty changing meaning time, but actually there's other themes of control and independence at play.

**Lenny Rachitsky** (00:34:42):
As maybe a lesson here is, clarifying, here's what you own on this team, on this project, here's things that you're going to drive, here's where I'm going to be really involved. Is that maybe a takeaway?

**Dr. Becky Kennedy** (00:34:53):
I think that is definitely a takeaway. I also just love naming your intention, that's always really helpful. Hey, I want to look over this project together, because I want to get ahead of some things and I think that's actually going to help you do the most independent work. That's why I want to go through it in such detail. When you name the intention very, very clearly for someone, they're much more likely to interpret your behavior through the intention you just named, because your intention isn't to control them or make them feel small. But if that's also something they have a predilection for, it's even more important for those people to do the naming of the intention up front.

**Lenny Rachitsky** (00:35:30):
And there's also just a whole micromanaging, I don't know, element to this. So, this is why people in part hate micromanagement. Like, I want to be in control of something, why get out of this?

**Dr. Becky Kennedy** (00:35:39):
Yeah, which is a dance. Exactly.

**Lenny Rachitsky** (00:35:41):
Yeah. Okay. Something else that you teach, that I find really helpful, is this idea of sturdy, becoming a sturdy parent, learning to be sturdy. That feels very related to being a good leader, just like learning how to deal with challenges. Talk about that from the kids' context and then how it might be useful in work.

**Dr. Becky Kennedy** (00:35:59):
Yeah, I think that's the essence of what Good Inside does. We help parents become sturdy leaders, so they can raise sturdy, confident kids. And when I started this, I'd use that word a lot. People would say to me, I've never heard anyone use that word. I know what it means. But I think about language too, and I like language that helps me conjure up an image or a feeling. It takes it out of my brain and I think the word sturdy, if you picture someone in your life, even if you haven't thought about them, who's the sturdy person I know? I bet you can kind of locate them, you see them, and there's a feeling you have around them. And I think the best leaders we have are sturdy. And so, I'll start it with a metaphor, because I actually think that brings it to life. And I think this is as true in parenting and in the workplace.

**Dr. Becky Kennedy** (00:36:42):
So, picture being a passenger on a flight. It's really turbulent and you start screaming and everyone starts screaming, okay? So, everyone's panicked in the flight and then you hear the announcement from the pilot and I think there's three types of announcements you can have. The third one's going to be the sturdy one, the first two, not as much. So the first one is some version of, What are you freaking out about? Stop screaming. You're making this flight awful for everyone and you're distracting me and stop making a big deal out of nothing. And that's kind of when we say those words to a kid, Stop freaking out. You're ruining this for everyone. But the truth is, if you think about yourself on a flight and the pilot says that, you don't feel calmer.

**Dr. Becky Kennedy** (00:37:23):
Number one, you're worried they don't notice the turbulence. Number two, you're a little disturbed that it just takes your screaming to make the pilot kind of lose it at you, right? And that actually makes you feel less safe. And so, that's like in a meeting where people, I don't know, voice something that doesn't feel good and the leader essentially is like, Stop complaining. That's not sturdy.

**Dr. Becky Kennedy** (00:37:46):
The second version is also not sturdy. And I think we've a little bit, at least in parenting, over corrected to this, which is a pilot saying like, I hear you screaming. You know what? I'm opening the cockpit door. Does anyone want to fly the plane because your screaming is make me anxious and I'm not really sure what to do. Maybe one of you want to do it. I think that's a massive overcorrection I've seen in parenting and I just want to make it very clear that is not what we do at Good Inside, which is kind of, my kid is upset and instead of just caring about their feelings, now their feelings dictate my decisions.

**Dr. Becky Kennedy** (00:38:16):
That's terrifying for someone on a flight. If that's me, I'm not even scared of the turbulence anymore. I'm scared that this person is my supposed leader. That's also in a meeting when you complain about things and a leader might say, Oh, okay, you know what? Okay, we'll just get consensus on this. When it's a decision, that's a leadership decision, not a consensus decision, which some decisions are, but some decisions aren't because you are the only one who has certain information.

**Dr. Becky Kennedy** (00:38:41):
Now, to me is the third announcement that I want to hear, when it's really turbulent, is something like this. I hear everyone screaming back there, that makes sense. And you haven't been in as many flights, you're right, it's very bumpy. And I know what I'm doing. This turbulence, though it scares you, it doesn't scare me. I'm actually going to get off this loudspeaker right now to go back to do my job. I'll see you when we land in Los Angeles.

Now, even if the turbulence is the same, all of a sudden I feel this deep breath, because my worry is contained. This sturdy leader is able to see my emotional experience as real for me and not be overwhelmed by it themself. Those are the two distinct aspects of being a sturdy leader. I can see someone else's experience as real for them, but I can still hold onto my experience and so I'm not overwhelmed by someone else. So to a kid, it might be saying, look, let's say TV time is over, and my kid does not [inaudible 00:39:42] in my house, when I say TV time is over, I'm turning off the TV. When my kids were younger, they didn't say, You're right, mom, that's a great decision. No. They freaked out because they wanted to watch another one.

So, a sturdy leader would say in that position, I'm turning the TV off, that's my decision. I get that you're upset, I don't really like to end TV time either. I also know we'll get through it. Do your thing and then we'll transition to brushing teeth and then I kind of wait it out. Or in the workplace, someone's complaining about, I don't know, making this up, but maybe it's some policy change, vacation change, number of days in the office, where you say, look, I want to share something that I know some people are going to have a reaction to, here's the new policy. People stop [inaudible 00:40:25], complaining. Look, I get it, this is a big change and you're right. This does make things hard in a certain way, and I totally see that, and the way you're feeling makes sense. And here's some version of why we're doing it and I know we're going to get through it and I have faith in us to weather this turbulence.

**Dr. Becky Kennedy** (00:40:43):
And I think, in every situation, kids, workplace, in laws, whatever it is, that sturdy leadership always feels good.

**Lenny Rachitsky** (00:40:54):
This is a really good segue to something I definitely wanted to ask you about, which is this idea of boundaries, of how to set boundaries well and how that relates to long-term resilience, versus just this idea of short-term happiness. I was just yesterday watching your advice on how to set boundaries with my kid, because I struggle with it sometimes and I'm just like, What am I doing wrong? And it was extremely helpful. I started using your advice and I'm like, Wow, he's doing exactly what I wanted to do. So, there's a lot of power here. So, talk about just from your advice to parents around boundaries and this idea of long-term resilience and then how that might translate.

**Dr. Becky Kennedy** (00:41:24):
Yeah. And so, I think related to being a sturdy leader, to make it more concrete, there's two jobs we have as a parent. I think they're the same two jobs we have as any sturdy leader in any environment. And that's setting boundaries, which are limits we set, certain decisions we make. Boundaries often relate to your position of authority, where you have some long-term goal in mind that someone else in the system just isn't as aware of, or a two-year-old isn't as capable of holding onto, which is why you're in the position of making a decision. So, that's the boundaries. And the other side is, validating kids' or someone else's experience, while not being taken over by it. And I think we've moved so far in this direction of validating kids' experience, and I'll go on record and saying, validating kids' feelings is an incomplete parenting approach. That is half of our job, but it doesn't work if we're not doing the other half, which is setting boundaries, which is the skill I feel best about teaching people, because I think it's what we're often lacking.

**Dr. Becky Kennedy** (00:42:21):
So, my definition of boundaries, I do like because it's immediately usable and testable. Boundaries are what you tell someone else you will do, and it requires the other person to do nothing. Too often I hear people say, My kid doesn't respect my boundaries. My colleague doesn't respect my boundaries. And with all due respect, I often think, I think you have an incorrect definition of boundaries. I think you're probably making a request, which by the way, we do a lot. There's nothing wrong with requests, but it's not a boundary. Because if you're setting what you call a boundary and you're really making a request, you're giving all of your power away to the other person, because you're saying the success of this important moment depends on my two-year-old. What? I'm not going to give my power to my two-year-old. And I always tell parents, I would bet on you any day over your kid, and it's not because I don't like your kid. I do like your kid. I just believe in you more.

**Dr. Becky Kennedy** (00:43:18):
And so a good example of this, let's say, is, I have a kid, like I was describing before, who's not a people pleaser. And so, I live in New York City, we live in a building with a lot of elevator buttons, where this kid, when he was younger, if he was left to his own devices, which had happened, he would press every elevator button, every one. And with my other kids, one of them especially, I probably would've been able to make a request like, Hey, when we go on the elevator later, it's really important not to press all the buttons. Other people wait for this elevator, it's rude, whatever it is. That's not a boundary. Because I could say, Did I tell my kid what I will do? No. Does it require my kid to do nothing? No.

**Dr. Becky Kennedy** (00:43:56):
A boundary, would be saying, when we go into the elevator, I'm going to stand between you and the button, sweetie, because I'm not going to let you press the buttons, because other people are waiting. And if you have a kid like my kid, they're going to lunge for it anyway. And then I would block my kid, because I'm ready and I've done a workout, my muscles are ready. I'm not going to let you do that. Now, my kid might get upset, which allows me to do the other part of my jobs. Oh, I know you wish you could press those buttons or maybe just look kindly toward him and not say anything, but I'm going to still hold my boundary.

**Dr. Becky Kennedy** (00:44:25):
And in this example, I think one of the places we get into trouble as a parent, is we say, Don't press the buttons. If you press the buttons, no dessert tonight, or whatever, we threaten. But all of that is a way of completely undermining our authority, because I'm giving all of my power to my kid. I'm letting myself get frustrated. Now, I'm taking away dessert, and the honest truth, if we want to be honest about it, is at night, I'm going to give my kids sorbet anyway and then make up something how sorbet isn't dessert, because it has fruit, all because I just didn't set a boundary in the first place. And I think over and over, in parenting, maybe at work too, we're asking our kid to do our job for us, because we just don't want to deal with the emotional fallout, but that's what I call job confusion and makes everyone frustrated.

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**Lenny Rachitsky** (00:46:35):
Let me actually ask you a parenting question while we're on this topic as I'm trying to learn this boundaries thing better. What's the correct way to, if they're not going to the car, and I'm like, "Okay. I'm going to carry you to the car if you don't do this." I'm still requesting them to go there. Is there a way to phrase that where they don't need to do anything?

**Dr. Becky Kennedy** (00:46:55):
Well, boundaries are something I will do when they require my kid to do nothing, we want to try. Look, we have to get in the car and whatever I try. I try a game, I try a song. And then again, the intention matters. The reason I'm going to carry my kid to the car is actually, I don't want to let myself then be late and then yell at my kid and be frustrated.

**Dr. Becky Kennedy** (00:47:15):
I'm doing it because I like my kid, and I'm on the same team as them. So then I might say, "Look. It looks like it's hard for you to get to the car. I don't really want to do this, but I don't know. I'm going to turn around, I'm going to take a deep breath. And if by the time I turn around, you're still here, I'm going to pick you up. I'm going to carry you to the car. And even if you're crying and kicking and screaming, I'm still going to do that, sweetie. I know sometimes it's hard to leave the house. We do have to leave. Here comes my deep breath."

**Dr. Becky Kennedy** (00:47:39):
Now, what's really important, I don't want to understate, is I think somewhere in us we think, "I'm going to do that, and then I'm going to be rewarded by my kid for my good parenting. I'm going to turn around and you're going to be like, 'I'm ready now, Dad.'" That might happen in some households, but it actually won't reliably happen until you've actually set boundaries over and over, where it's not about taking advantage of you. They're just trying to learn the rules of the game. And so then I would pick up my kid.

**Dr. Becky Kennedy** (00:48:09):
And by the way, depending on your kid's temperament, two out of the three of my kids would scream and cry. And then we tell ourselves, "Oh, I'm not a good parent. I messed that up." No. Again, a pilot is going to make a decision in certain key moments, not based on consensus. Let's say I'm flying to LA, and it's really turbulent, and the pilot says they have to make an emergency landing, and everyone's complaining, and they're like, "What? It can't be that big of a deal. I don't know if that's a light. I have a big meeting in LA." Can you imagine if the pilot was like, "Oh, Becky and Lenny are really upset about this. You know what, guys? Forget it. Forget it. Forget the emergency landing." What?

**Dr. Becky Kennedy** (00:48:48):
Even if we were upset, we feel deeply, deeply safe that our leader is doing something they believe is right with the information they have available. So even if I'm complaining when I get off the plane in Kansas, wherever I'm landing midway, I also in the back of my head am so grateful. And so I just want parents to know, when you set a true boundary, especially if it's new, your kid will tantrum and protest. But if you actually see that as a sign that you're successfully setting a boundary, instead of as a sign you did something wrong, your relationship with a tantrum changes. You're almost oddly sickly grateful for a sign that you set a boundary. And you're like, "Oh, this is actually..." I always say to myself, "This is going according to plan." And it makes the moment so much easier.

**Lenny Rachitsky** (00:49:33):
This advice you give about being the leader is really important, and I could see how it connects deeply, too, in the work environment. I'm curious just if there's more advice there. So the idea that I've been hearing is just, people want you to be their leader, both as a kid and in the work environment. They don't want you to be like, "Okay, what does everyone think? Let's vote." Talk about just the insight there.

**Dr. Becky Kennedy** (00:49:51):
Yeah. First of all, I think a lot of things, two things are true. There's definitely a time for consensus. There's a time for getting 100%. That's a really important part of leadership, too. But inherently, you're in a position of leadership because you have experience other people don't have, you have insight, you have judgment. You might just very literally have access to company information and data that other people don't have.

**Dr. Becky Kennedy** (00:50:17):
You're not all working from the same set of information. So, there's definitely a time for consensus building, but at least in parenting, I think we've over corrected. I need to collect more data from the working world if we've over corrected. I think it feels really good for people to hear some version of, "I took in everything you said, your voices matter, and I want to share a decision I've made with all the information I have. It might not make sense to all of you at the time. You can come talk to me separately. And I have conviction that this is the next thing we're going to experiment with," whatever the decision is."

**Dr. Becky Kennedy** (00:50:57):
I said this word earlier, and it's another word I say that maybe other people don't say. I'm always drawn to people I can locate. That's the word. I can locate you. I know what you feel. I know your POV. I personally love people I can locate even if I really disagree with them more than being around people who seem to mirror what I'm saying. I've actually learned a lot in a working environment that's what I need because I have a lot of strong ideas. So I love people who push back and have a POV.

**Dr. Becky Kennedy** (00:51:26):
And I think especially at work when there's so much going on, it could be so stressful, having a leader you can locate, which means I have a POV to be able to say, "This might not be what you want. I believe this is the best decision, and I believe we're a strong enough team to work toward that." I think that's very, very holding, not all the time, but in more situations than we might realize.

**Lenny Rachitsky** (00:51:47):
So the advice here is, even if it may not feel like your kid wants you to just tell them what to do or your team wants to have opinions and have a big say, in reality, the human brain seeks somebody to lead them.

**Dr. Becky Kennedy** (00:52:01):
I think that's exactly right. And I think we can all think back on our life. And again, you rarely say thank you to these people, but if you think back on really hard moments, or I don't know, if I was out of control at some party, and I don't know, I was being mean to people, I was saying awful things. Who knows? Maybe someone's drunk. An act of love in that moment would be, at least if it was me and my husband, picking me up and carrying me out, kind of like, "I'm not going to let you self-destruct." And if I was like, "No, I'm fine," I might even look back later and think, "Why did you let me make that decision?"

**Dr. Becky Kennedy** (00:52:42):
I'll share a story, because some of this comes from it. I think about this teenager I was working with in private practice and she came to me, I'm just doing a first session, "Okay, tell me what to bring you here." She's like, "Well, you can see on my arms, I'm cutting." I was like, "Okay, keep going." And I was like, "Well, how long have you been doing that? " She goes, "Two years." And I said, "You just said I'm the first therapist you've ever seen. So what's gone on in the last two years?" She was so snarky. And she goes, "Oh, wow. My parents tried me to send me to a therapist two years ago, but I said to them, "Oh, so you think I'm the messed up one in the family, and you can send me. I'm just going to lie the whole time and waste your money."

**Dr. Becky Kennedy** (00:53:18):
My heart always races when I think about this moment. It's so visceral still. And something in me, I don't know, I was on my game that day. I was like, "Becky, just be quiet. Don't say anything."

**Dr. Becky Kennedy** (00:53:26):
And I don't know, 10, 15 seconds later, everything changed for her. She looked down, and then she looked up, and she just goes, "Can you believe they let me make that decision?" She felt so betrayed and so not taken care of. And it just made me realize, at our worst moments, first of all, at our worst moments we speak, I think, our fears, not our wishes, Number One. And at our worst moments, we're not a good place to make good decisions for ourselves. And we don't want to be punished, but sometimes it is an act of love. And I tell this to people very explicitly when they're like, "I know my teenager needs to go to therapy, but they're refusing." And I'll give them an exact script, "Here's what you're going to say." And it's some version of, "Look, I am going to drive you every Thursday to therapy. Whatever you do in the room, whether you lie, whether you're silent, that's not on me. But my Number One job is making decisions that I think are good for you even if you're not happy. That's how much I care about you. And this is one of those times."

**Dr. Becky Kennedy** (00:54:36):
Every parent who told me they'd done that, their kids on some level, they're like, "I needed that." And I think the equivalent in the workplace is, there are moments when things are really turbulent, when people are, they're saying different things because they're anxious, and they're looking for a leader to kind of say, "Hey, I've heard this all. Here's what I'm doing." You might even say, "I'm not going to lie to you. Do I have complete conviction that this is right? No. But I've put it all together, and this is what we're going with, and I feel good about this direction. We'll reassess in a month. Let's go do this, team." And I think everyone is waiting in certain moments for someone to show up that way.

**Lenny Rachitsky** (00:55:12):
That was an awesome story. It makes me think about it, just people, they will take what they can get, and they'll be wondering like, "Hey, wow, I'm getting away with all this." When will they realize that someone working at a company where they're not working that hard, "I'll just get away with this as long as I can until they tell me that I can't."

**Dr. Becky Kennedy** (00:55:30):
Yeah.

**Dr. Becky Kennedy** (00:55:30):
And I just saw the show on Broadway called Punch. It's this really interesting story about this guy, true story that it's based on, where essentially he ended up punching someone at a bar, the guy falls and dies, but he ends up getting to know that person's parents after incarceration. And one of the things that comes up, because I think we tell ourselves, people will take advantage, they'll take all the space you give them. I do tend to think about it in a more of an MGI way, where what happened in this show is, these parents, this guy killed their son. And the question they come to him with, because they end up meeting is, "Well, tell us what you're going to do with your life here. What are you going to do with your life now?" And he ends up saying, nobody had ever asked me that question. Nobody had believed in me. Nobody had ever believed I could do anything useful. Changed the course of his life. It's a crazy story.

**Dr. Becky Kennedy** (00:56:29):
But I think about at work when someone, yeah, whatever it is we think they're getting away with, or kids, sometimes I think kids are asking, "Is there an adult here? Is there an adult here who will help me, who sees that this is my form of acting out and being out of control? And will somebody help me?"

**Dr. Becky Kennedy** (00:56:45):
And you know why kids act out more? It's not because they're taking advantage of you. It's because they feel that much more dysregulated because they don't feel like there's an adult in the room who's willing to put a container on their shell-less egg to help them come back together and move forward in a better direction.

**Lenny Rachitsky** (00:57:06):
I think about just people that don't have parents that are there and always seek someone as their partner that's more, I don't know, that gives them that later.

**Lenny Rachitsky** (00:57:13):
This connects really well with this idea of building resilience long-term versus short-term. And I think about in the work environment, doing the hard thing, you will be better off versus just letting things progress because it's hard for them to do. You think people will not want to hear bad news. Talk about just that kind of lesson and how that might translate.

**Dr. Becky Kennedy** (00:57:30):
One of the chapters in my book and something that guides our whole parenting philosophy is just codified as resilience over happiness. And I don't know if you hear this, Lenny, but I used to hear this all the time and I couldn't let myself let this small talk moment go. "Don't you just want your kid to be happy? You just want your kid to be happy, right?" And I'd always ruin a perfectly harmless comment. I was like, "No.

**Dr. Becky Kennedy** (00:57:53):
And then again, we think in extremes, "Oh, you want your kid to be unhappy?" "No, I definitely don't want my kid to be unhappy." But I think if we zoom out, especially in childhood that I think I can make a parallel to work, too, optimizing for happiness in childhood is the quickest way to build anxiety and fragility in adulthood. Hard stop.

**Dr. Becky Kennedy** (00:58:13):
Because in a kid's early years, what they're really learning is, "What range of experiences am I able to cope with? In how many different situations can I find my capability? Can I feel like I can get something done? Can I get through something? Or do I believe that the only way to feel like myself is to bring every form of discomfort to a zero and only find happiness?" The irony is, in adulthood, the thing that clouds out happiness often is our inability to manage disappointment, jealousy. Those things will always overpower happiness. And so happiness actually comes in adulthood from our ability to manage the widest range of difficult situations. That's what resilience is. It's our ability to handle the widest range of experiences. I'm not talking about toxic, but just disappointment, jealousy, anger, feeling less than. These are just human experiences we have. And unintentionally, something's happened over the generations where we're optimizing for kids' happiness. Our kids will say something like, "I'm the only one in my class who can't read," and we hop in with, "Oh, that's not true," or, "But you're so good at soccer, but you play chess, but you're doing this," as if I can't even tolerate my kids' upset feelings.

**Dr. Becky Kennedy** (00:59:34):
And I tell parents all the time, our kids can only learn to tolerate the feelings we tolerate in them. Nothing scarier to a kid that when they're upset, if I can't tolerate their upset feelings, that makes them doubly upset because it's like, "Oh, I guess this is unmanageable," which leads to adulthood where anything but comfortable and happy and doing a perfect job is actually experienced in your body is even worse, because you spent that many years thinking those feelings were wrong.

**Dr. Becky Kennedy** (01:00:02):
So what that means is, when our kids are young and they go through hard experiences, those are our best opportunities to wire them for resilience and ironically for happiness later on. Because if I can support my kid, I'm not going to say, "Uh, no big deal," but there's a lot between no big deal and let me call the school to make this all better for you. There's a whole world. That's the world I think we try to live in and good inside where I can help my kids see they're capable of dealing with this. That's going to help them so much more over the years.

**Lenny Rachitsky** (01:00:35):
How do you think that translates to work? The way I think about it is just like, it's okay to upset people. You're better off. I think it was Kim Scott from Radical Candor had this great story where she had someone she was working with, he was doing a bad job. She didn't want to upset him. She didn't give him any feedback. Nine months later, he just didn't get any better, so she had to fire him. And he's like, "Why didn't you tell me that I wasn't doing what you wanted me to do? You could have told me somewhere along the way." And to us, it feels like, "I don't want to hurt your feelings." But then it ends up hurting their feelings a lot worse. It's a lot worse for them.

**Dr. Becky Kennedy** (01:01:11):
I think there's so many parallels.

**Dr. Becky Kennedy** (01:01:13):
One of the reasons I love talking about childhood is because that's when our brain is wiring. It's when we form the blueprint, or my friend Maile calls it the factory settings, for the rest of our kids' lives. That's the ultimate, is to wire them with the default that works for them. But actually you can think about work relationships the same way, where of course people come, their brain's already wired, they're older, or somewhat.

**Dr. Becky Kennedy** (01:01:37):
But if you think about it as a building, your first number of months with someone is, you're building the foundation for the whole building, which is the relationship you're going to have. And the earlier you're just optimizing for comfort and happiness, the more fragile your building's going to be. And you're right. Then you get to six months and you're like, "Oh, this person isn't doing so well. I'm going to give them feedback." But in their mind, they're thinking, "I've never gotten a piece of constructively critical feedback since I've been here. So I thought the nature of my relationship in this workplace was one of butterflies and rainbows all the time."

**Dr. Becky Kennedy** (01:02:09):
That doesn't mean Day One I think you should sit someone down and say like, "Here's everything you're doing wrong." They're like, "I just started my job." There's obviously reasonableness. But yes, when we're thinking about a resilient work culture, we want people who can say, "This is hard and I can do hard things," not, "This is hard, so someone must be at fault and it should be easier." And I don't know anyone who would argue for the second culture. So I think we should ask ourselves, "Well, what am I doing in the nature of my conversations with people that help me build toward that? I'll make it really concrete, because I think so much of dealing with anxiety and struggle, not all of it, but I like a good formula, is this combination of, "I believe you and I believe in you."

**Dr. Becky Kennedy** (01:02:54):
I think one of the ways we've majorly misunderstood anxiety, whether at any age, is we're doing the I believe you part, but we've forgotten the I believe in you part, and they're both, again, really necessary together, which might sound like this. "Look, I believe you. This is a hard project. You're totally right. And you've never done something like this before. It makes sense," that's such a good phrase, "It makes sense you're nervous about it." Or something I say to my kids that I should say more at work. "I'd be nervous if you weren't nervous. You have a big task. I'd be nervous if you weren't nervous. Of course you're nervous. That makes total sense. I'd feel the same way. And the reason we think you can take on this project is because we know you can figure this out. By the way, I'm here to answer questions and help you along the way, but I just want to make sure you hear that from me. Yes, this is hard and I also know you can work through this. And the way you're going to feel at the end, you're going to feel so proud of yourself. And I don't want to take away the project from you, because I kind of think I'm taking away that feeling. And that's going to be the feeling that propels you to do all the other amazing things you want to do here. And let's work toward that." And that's actually, Lenny, something I used to say to my kids when they were your kids' age. I just distinctly remember this time one of my kids was whining about this puzzle, and he's like, "Do it for me. Finish it for me. You have to do it." And of course, the easiest thing to shut down the tantrum is like, "Fine, here's how you do it." But it was the day I had a little more bandwidth. And I heard myself sing something that, I don't know, it felt like healing to me, too. Honestly, I said, "Look, you're right, this is a hard puzzle. It makes sense you're frustrated. All of us feel frustrated when we want to do something and haven't figured it out yet. You're feeling this moment right. Take a break, take a breath, but I want to tell you why I'm not going to do this for you. There's no better feeling in the world than watching yourself work on something and make progress on something and maybe even complete something that you originally didn't think you could do. There's literally no better feeling than that, and I will not take that feeling away from you."

**Dr. Becky Kennedy** (01:04:59):
And I think in the workplace, and definitely in parenting, we should be mindful of how often we take feelings of capability away from our kid. And I take that very seriously, because I feel very passionate about building a world where kids become adults who feel capable. And I think we can do that in these small moments more often.

**Lenny Rachitsky** (01:05:20):
As you share these stories and examples, I'm just like, "I wish I had Dr Becky in my ear constantly giving me advice as I'm doing this stuff." And we'll talk about how people sort of can get that. But I'm actually really happy you talked about this I believe you Lesson. I was going to bring it up. That was right next on my notes.

**Lenny Rachitsky** (01:05:35):
Because I've used it with my wife, actually, and it is so good and so effective. And this is why I love your advice is that it works not just for kids, it works for adults. Just like, "I believe you. I believe you feel this, even though I don't know why and I wouldn't feel that way, but just I believe you." I like this, this makes sense piece. I haven't thought about that. And then the I believe in you, that's interesting because I did talk about, the way I remember phrasing this, this was a moment I really remember just like, "You're smart, you'll figure this out." And then she just went back to me and she's like, "Wait. What did you just do there?" That felt really good.

**Dr. Becky Kennedy** (01:06:09):
Well, if you think about, and I'm very visual, so if you think about someone struggling, picture them in a hole, not an abyss, just like a little hole, okay? And we're a relational species, at least we'll see what happens with AI, but for now we're a relational species, okay? And so when we're really struggling, inherently, the reason why we're struggling is we're in a hole and we can't see the way out of it. If we were in a hole and could see the way out, we wouldn't struggle. So the only reason something feels so hard is because you're in a hole without visibility. So what do you need as a relational species? You need someone you trust to have one foot in the hole with you. You do. That's the I believe you part. So it might be saying to your kid, and those words are so powerful. I think deep down, Lenny, that's literally what we're all looking for as humans is just to be believed. "I believe you. You really don't want to go to soccer practice. You're no longer starting on the team. The first soccer practice after you lose your starting spot sucks, totally sucks. I believe you."

**Dr. Becky Kennedy** (01:07:06):
But if you think about them in that hole, they need you to have one foot out of the hole. They need one foot in, but they need one foot out. Because if we can't see a more capable version of our kid than they can access in that moment, we've collapsed into the hole with them. And so the I believe you is, I believe in you would be, "Look, you're going to go to practice today. You are. I'll just be honest. It's not going to be fun. You're not going to want to be there the whole time. You're going to feel embarrassed. You're going to hate watching them run plays with you on the sideline, and you're going to come home having survived. I'm going to be there for a hug. I'm going to let you know I'm proud of you. Maybe I'm proudest of you that moment even more than when you scored the goal last game, because that's the hard stuff that's going to help you in life and we're going to get through it together."

**Dr. Becky Kennedy** (01:07:49):
And so I think for anyone listening, I find what's really powerful with anything is thinking, what end of the spectrum am I on? Because it always gives me insight into where I can grow. Is the I believe you part easier for me or is the I believe in you part easier for me? And there's no morality. Just knowing where you are is helpful. Do I like to say to people, "Come on, it's no big deal. You can do it." Okay, that's the I believe in you. Ooh, I wonder if I'd be more effective if I focus on the first, I believe you. Or do I lead as a parent, as a leader with, I believe you, but ooh, do I sometimes go into the hole with someone? Oh, okay. It's the other part I'm going to play around with. And I think that gives us all something to action on.

**Lenny Rachitsky** (01:08:29):
This is such good advice. I got tingles as you were describing what you were telling your kid.

**Lenny Rachitsky** (01:08:34):
If there's anything you take away from this conversation, this is useful with kids, with partners, I think in work too, just this idea of I believe you and that makes sense. And then somehow making it clear that I believe in you.

**Dr. Becky Kennedy** (01:08:49):
Yes. See a more capable version of them than they can see in the moment. Doing those two things at once, it's magic. I talk a good game. It's hard for me too. Again, these things are hard in practice, but at least, and I know you're like this too, it's helpful to have a framework to come back to.

**Lenny Rachitsky** (01:09:02):
And just these words are s-

**Dr. Becky Kennedy** (01:09:00):
You're like this too. It's helpful to have a framework to come back to.

**Lenny Rachitsky** (01:09:02):
Just these words are so... And you don't have to say them exactly, but these specific ways of phrasing is actually really great. Okay. Let's come back to how people can get your advice constantly. So a lot of people see your advice on TikTok and Instagram, just kind of on Twitter and all these places, just like, "Oh, there's Dr. Becky giving me some awesome advice." Not a lot of people know you have an epic business and product and community. Talk about that, just what you've built there, what it is, what people should know.

**Dr. Becky Kennedy** (01:09:28):
So what started as an... I put up my first Instagram post on, I think it was February 28th, 2020. So it was two weeks before New York City at least shut down, probably a lot of places in the US. And I didn't have some premonition about COVID. The timing was just kind of nutty. And I even put up that post without any business in mind. I felt so compelled by these ideas around what we've been talking about and how different they are from what we've been handed down to us as truth that the only way I can describe it is, this sounds like awful and visceral, but it felt like I was vomiting them out and I just needed somewhere to put them. And so when I look back, I was like, "Why did I do that?" I don't know. It was more like relief than intentional goal setting.

**Dr. Becky Kennedy** (01:10:12):
And then New York City shut down. And then what happened was things just took fire. And what was really powerful and I just kind of watched happen and tried to respond to was the community that formed around it, what people wanted. I started doing these live workshops just because I had so much more to say than I could put into a carousel post. And the way people were connecting live, it still happens now in the live events. I think certain ideas I have or stories I tell can be really impactful to people, but if I had to choose between that versus what happens in our community, I would bet on the community every time. It's just amazing because I think these ideas tap into some value system or healing inside of ourselves and safe communities form around value systems. And so what ended up happening from there is people were just telling us.

**Dr. Becky Kennedy** (01:10:59):
They actually kind of incepted this idea like, "Dr. Becky, I feel like you're doing parent school." And I was like, "What?" And they're like, "I go to school for everything that I've ever cared about." And I always joke that a friend said, "Do you know how much money I spent on gardening school? I like my flowers, but I care about my children more, but I've spent more time and I've invested more in my tiny little garden or my cooking or my hobby." Certainly I've spent more learning about Greek history, which for most of us, no shade to Greece, doesn't actually impact our life that much anymore than I have in parenting. And I realized, and this is what this whole, our business is around, is I really feel like parenting is the last area in life that we glorify instinct alone, where we say, especially to women, this whole idea of maternal instinct, I don't know, this is probably a little controversial, I was like, "Wow, what a way to gaslight parents."

**Dr. Becky Kennedy** (01:11:54):
Not to say there aren't some moments that are instinctual, but if I'm parenting by instinct, do you want to know what I'm going to do when my kid has a tantrum or when my kids lies in my face? It's not going to be pretty. I don't know if any of us feel like that's a good option.

**Lenny Rachitsky** (01:12:09):
I believe you. No, you would not do that.

**Dr. Becky Kennedy** (01:12:09):
Yeah. Right. And instinct, by the way, what's instinct? It's everything we've learned until that point. And what feels naturally in parenting is simply how we were parented. That's all that's going to be natural. And so I really, like I started to get very angry with my community where like, why isn't there a better way? And by the way, with modern technology, shouldn't this be easy on your phone? Shouldn't you get access, not to make a moment perfect, but we all spiral after a hard moment. It's not the spiral that makes us have a bad week. It's the way we extend the spiral from how we talk about ourselves and the bad patterns. That last weeks. These are the things that add up to our and our kids' mental health. And I did, I started to get angry thinking there should just be a better option for parents to learn without shame, without the expectation of perfection, to have something in their pocket, to combine it with modern technology, now AI, so many different things, and to preserve the human interactive element.

**Dr. Becky Kennedy** (01:13:02):
So yeah, so that's what Good Inside is. My co-founder and I are both psychologists. We've known each other forever, and we have a whole team where, yes, there's the podcast and Instagram. And I always consider those flags, like a strategy and a tip we hear, they're like flags. They're like things we try, but most of our struggle in parenting is we don't have a framework and an actual approach, which is the ground we would plant a flag into, which is why parenting feels like whack-a-mole where we're just trying things, right? Even though we would never do that at work. We don't even do that with gardening or tennis.

**Dr. Becky Kennedy** (01:13:37):
And so Good Inside and our app to me is the most modern, alive, personalized, technologically informed way of feeling like I have something in my pocket and someone and a community too in my pocket that can just close the gap between how I kind of want to show up and how I do show up. None of us close the gap completely, but I do believe we deserve access to tools that can help us do that. And that's what Good Inside as a business is built around.

**Lenny Rachitsky** (01:14:07):
Just to give you a more chance to plug what this is, exactly, what do people get when they say download the app, sign up for the community?

**Dr. Becky Kennedy** (01:14:13):
What you get is, first of all, you get to just tell us what's going on with you, and we get to be the holder of that story and make sure we hit on the important themes. I really do believe, I know kind of, not formula that makes it too specific and perfect, but we need to learn how to set boundaries. We need to learn how to stay connected to our kid without martyring ourselves. By the way, I also think the thing that you get in the app, which we have to learn better communication if we're in a partnership because that gets tricky. We have to learn how to repair. We have to understand why we get so activated in our nervous system, and we have to do it in a way that we could do, I don't know, five minutes here or there in the go. So that's exactly what we're set up to do.

**Dr. Becky Kennedy** (01:14:52):
If you can give us five minutes, we will give you something that's not just a tip, that is something... I always combine two things, understanding and action, right? Insight and something that you can actually do today, because I think that's how we can take a deep breath, understanding gives us clarity, and action gives us capability, and that's what we give parents. So you get that, we'll drip it to you, you tell us what's going on. We also have an incredible AI chatbot experience that also beyond helping you in the moment can help you say like, "Hey, this kind of thing that seems different, they're all kind of the same. Here's the common theme. Here's what we suggest for you to kind of get ahead of things." And then there are so many live events. I believe deeply that parenting is something where we have to connect to people.

**Dr. Becky Kennedy** (01:15:37):
So I do, I don't know, usually two or three live events in a month. We have coaches we've trained, so we've manualized the Good Inside approach. We have coaches who do support sessions. If you have a deeply feeling kid, you can come to as many of those in a month as you want. And there's always a parent to kind of connect. And so I would say you get answers to your questions, you get deeper dives if that's for you and you have the time and you love to learn. And then you also have a very, very active, expert driven, but also just parent community for the live connected help that I think we all need.

**Lenny Rachitsky** (01:16:08):
Amazing. I'm a happy member. I was actually just chatting with your AI chatbot at Dr. Gigi is-

**Dr. Becky Kennedy** (01:16:13):
Yes. Gigi, exactly.

**Lenny Rachitsky** (01:16:15):
Gigi. So good.

**Dr. Becky Kennedy** (01:16:15):
Yes.

**Lenny Rachitsky** (01:16:17):
Okay. And where do people find this? Goodinside.com?

**Dr. Becky Kennedy** (01:16:17):
Yes.

**Lenny Rachitsky** (01:16:17):
Is that right?

**Dr. Becky Kennedy** (01:16:17):
So easy.

**Lenny Rachitsky** (01:16:19):
Okay. So easy.

**Dr. Becky Kennedy** (01:16:20):
We try to make it easy. Goodinside.com.

**Lenny Rachitsky** (01:16:23):
Okay. I have another question, I'm going to ask about this experience, but before that, I'm going to take you to a couple of recurring corners on this podcast. The first is AI corner. I'm curious if there's some way you found AI useful in your work and your life.

**Dr. Becky Kennedy** (01:16:36):
So yes. So it actually connects to what I was saying before, how I've learned about myself. I'm kind of allergic to ideas without action. And people would always say to me, "Dr. Becky, one of the reasons I love your parenting guidance is you both help me understand a situation more deeply than I find elsewhere, but you also help me translate it into this absurdly practical thing I can say or do." And it's interesting because what I've learned is I'm so glad that's helpful for people. That actually is just the way I think. And I'm seeing that as I run my company also. And AI helps me close that gap. So let's say we're talking about the product and we're talking about a new onboarding flow and I have an idea. I currently was playing around with Figma Make and I was like, oh my goodness, I can vomit my ideas.

**Dr. Becky Kennedy** (01:17:28):
And I have so many ideas, not just about like, this is how I want to make someone feel. And I don't want it to sound like this. I want it to sound like this. I have so many ideas around my intention and the impact and I don't like to have an idea that I can't fully see out. So I want to see the UX design or I want to see the whole email flow. And my team knows me well enough to know that's just how I think so they can get a totally new abandoned cart flow from me and they might be like, "Becky, we don't like this. We wish you just told us the idea before you built this out." I don't do that for them. And I'm like, "If you want to trash the whole thing, I don't care. I'm not attached to it." But AI has allowed me to take an idea and turn it into something I can visualize and that's more real in such a rapid way. And I'm so excited about that.

**Lenny Rachitsky** (01:18:10):
That is super cool. It feels like this is what these apps are so useful for. People that are not designers, product managers, engineers, trying stuff out, prototyping, thinking through and real designs, not just sketches. Well, Figma Make, that's cool. Proud podcast sponsor. Is that the one-

**Dr. Becky Kennedy** (01:18:11):
Yes.

**Lenny Rachitsky** (01:18:27):
You find most useful?

**Dr. Becky Kennedy** (01:18:29):
It's funny. I'm just laughing because Andrew Hogan who does insights at Figma, he's been a big Good Inside member. He's always talking about it. And I said to him, "If it would help at all, I want to make a video for just your team to tell them how obsessed I am with Figma Make in case that..." And he's like, "Go ahead." So if this fails, I'm going to become an affiliate or something for that. But that is the one I'm playing around with. I'm also playing around Replit. My team is just showing me that one and Lovable. So I love playing around with any of those new tools. And then yeah, whether it's Claude or ChatGPT, I find the way I can work rapidly to get my ideas into a place. And I show my team how I use it. And I really do think that people who get the most out of prompting, like there is, like a vomiting aspect.

**Dr. Becky Kennedy** (01:19:20):
Tell me all your thoughts. And I think a lot of people, especially women, we've been conditioned to put everything into this neat, organized package before we present it to the world in general. And I find prompting is best when we do complete opposite, right, and get it all out there and then you can refine. And that's one of the things I do with my team a lot. I'm like, I want to show you. Just be in the room and I'll show you how I work through this with, let's say it's a ChatGPT. And I think that's one of the ways I'm also helping my team get a lot more from AI, is showing them that kind of way of using it.

**Lenny Rachitsky** (01:19:53):
How big is your team, by the way? What's like the Dr. Becky operation? What does that look like?

**Dr. Becky Kennedy** (01:19:56):
65.

**Lenny Rachitsky** (01:19:57):
65 people. That's incredible.

**Dr. Becky Kennedy** (01:20:00):
Yeah. Well, and we're profitable.

**Lenny Rachitsky** (01:20:02):
And profitable.

**Dr. Becky Kennedy** (01:20:02):
So proud of that.

**Lenny Rachitsky** (01:20:02):
Have you raised money-

**Dr. Becky Kennedy** (01:20:02):
Yes.

**Lenny Rachitsky** (01:20:03):
Or is it bootstrapped?

**Dr. Becky Kennedy** (01:20:05):
We did raise money a couple years ago. We felt like, again, we have a very, very big vision for what we want to accomplish. I feel like at our core, it's a set of ideas and stories and a way of making people feel that happens to be represented as the biggest part of our business now in a digital product. But there's just so many other things in a Disney type ecosystem that I think can come out out of IP and story and feeling and connection. And there's a lot of really other exciting things, 2026 and 2027 that will further build that ecosystem. And I feel like as a team are set up to do that.

**Lenny Rachitsky** (01:20:37):
Oh, man. We need to do a whole episode on just the whole operation. But-

**Dr. Becky Kennedy** (01:20:40):
Yes.

**Lenny Rachitsky** (01:20:40):
I guess just to cover the empire, there's the book, there's the podcast, there's the app, there's the community.

**Dr. Becky Kennedy** (01:20:47):
So yes, and my second children's book will come out next year. So I feel like that's our first entry into kind of more physical product. And my second children's book is about deeply feeling kids. Those kids are kind of my passion project within the passion project that is Good Inside. I think they're the most misunderstood kids in the world. They're labeled dramatic, oppositional. A lot of parents even tell me professionals tell them the issue is they don't punish them enough or they're not consistent. Those kids don't fear a loss of control. Their core fear is being too much and they can act that out and unfortunately get it confirmed by the world. And so my second children's book will be about deeply feeling kids and gives parents something. If our app is like the companion for the parent, I feel like the connection moment and for a kid to see themselves in this book is the purpose of that.

**Lenny Rachitsky** (01:21:29):
Is that something people can pre-order yet or not yet?

**Dr. Becky Kennedy** (01:21:30):
Yes, they can pre-order. It's out-

**Lenny Rachitsky** (01:21:32):
Okay.

**Dr. Becky Kennedy** (01:21:33):
February 2026 and pre-orders, thank you. Yes, help so much. And I'll be doing a little tour around it as well.

**Lenny Rachitsky** (01:21:40):
And what's it called for people to look it up on Amazon or wherever they want to buy the book?

**Dr. Becky Kennedy** (01:21:40):
It's called Leave Me Alone!: A Good Inside Story About Deeply Feeling Kids. And if you have one, these kids, the reason the title I think will resonate and honestly, a lot of people say, "Ooh, that gives me a lot of insight in my partner, someone at work." Is around their fear of being too much, they tend to have big explosive feelings and push the people away they care about the most in the exact moments they actually need them, thereby confirming their fears. See, I really am too much, which exacerbates the cycle until they know how to change it. So leave me alone is something a deeply feeling kid will often scream in their room when they're completely out of control and that's actually in some ways their deepest fear, not their wish. And so the title plays to that.

**Lenny Rachitsky** (01:22:20):
How cool. Okay. We'll definitely link to that in the show notes.

**Dr. Becky Kennedy** (01:22:23):
Yes.

**Lenny Rachitsky** (01:22:23):
You can look it up on your favorite bookseller. Dr. Becky, how fun was this? What a unique conversation. We touched topics on every level of humanity, I think. Before we get to our very exciting lightning round, is there anything else that you wanted to share? Anything else that you want to double down on?

**Dr. Becky Kennedy** (01:22:43):
Kind of I think before we started recording, I was saying how excited I am. I really mean it. You're like a major celebrity to me and my whole team knows it, that of all of the podcasts I've been on, they're like, "You must be nervous for Lenny." I was like, "I am." What I said to you is that I love your podcast because I just learn so much. Right. As a psychologist, CEO, obviously there's a lot for me to learn and I've learned so much from you and I really am addicted to learning. And the reason that's coming up for me now is I think about all the people who listen to your show who I think they're like that too. They're forever learners. They love to learn. It's why they love your podcast because you get so concrete and you follow the thread down the rabbit hole and it helps us feel more capable at work.

**Dr. Becky Kennedy** (01:23:28):
And one of the things I've found interesting is so many people who have that forever learner growth mindset, whatever you want to call it at work, we forget to access it with our kids. We say things like, "It is the way it is." I feel like people who listen to your podcast probably don't say that at work. That's why they're listening to their podcast. Maybe it doesn't have to be the way it is. Or, "Oh, I didn't know that before, but now I do. So I can try something new." And again, I'm never going to be perfect. Perfect is creepy, but I can grow. I'm more addicted to growing and learning than being right. And I think, I guess I would just ask anyone listening, especially as a parent, to say, "What if I just took that mentality home?" Like, okay, yelled at my kid.

**Dr. Becky Kennedy** (01:24:06):
I didn't mess them up forever. Do I know how to repair? By the way, did I have a parent who actually repaired with me? Because if I didn't, no wonder it feels awkward. I'm kind of the first person in my whole lineage to give a true apology to my three-year-old. Of course, that's going to feel hard. Or, yeah, I don't understand tantrums or I don't really understand why my good kid is lying to me, but maybe I could learn. I learn things at work all the time and I give myself credit. And what if I took that way of being and just applied it to this other area? I'd both, I think, be more compassionate with myself, but I probably also feel better over time about how I was showing up. And I just think we forget to apply that because again, parenting has been told as something that should just feel natural when it doesn't, right? So if we think of it as a set of skills, it's actually very hopeful. Oh, I learn skills from Lenny all the time. I can do that in parenting too.

**Lenny Rachitsky** (01:24:59):
That's such a beautiful message. What I feel is anytime I watch one of your clips and then do that thing and then it works, it's like, oh, that expands your mind. Oh wow, there's so much more I could learn. I should go deeper on what the hell I'm doing. I don't know what I'm doing. So that is a really important message of like you can learn this skill. It's not something anyone just knows how to do.

**Dr. Becky Kennedy** (01:25:19):
Yeah. And I like giving people very concrete things. So a question recently that I've asked my kids that have led to amazing moments just as a, is some version of, "If I could do one thing different this week to be a better parent to you, what would it be?" I guess it's kind of like a 360 degree-

**Lenny Rachitsky** (01:25:37):
Yeah. That's [inaudible 01:25:38].

**Dr. Becky Kennedy** (01:25:38):
Review. Right. And-

**Lenny Rachitsky** (01:25:39):
Give me a raise.

**Dr. Becky Kennedy** (01:25:40):
Sometimes your kid says the hurtful things. "You can not be on your phone as much," which just get ready for it and just get ready to receive. "Okay. Thank you for telling me that." And I think the thing that stops us is we think our kid is going to say "stupid things." "You could let me watch TV." The truth is no matter what your kid says, it's a window into their world. And again, you can be the pilot who listens but doesn't open the cockpit door. "Oh, what would it be like to watch all the TV you want? Oh, your friends watch more. I'm so glad you told me that. Oh, does that mean I can watch TV? Sweetie, that's not going to be one of the things I budge on, but maybe here's something we could do instead." But just asking your kid that question and not expecting a profound answer.

**Dr. Becky Kennedy** (01:26:16):
It makes me think if anyone thought, "What if my manager asked me that question? Wouldn't I immediately just feel better about my manager anyway just from the asking of the question?" And I think it's just something so actionable that every time I do it with my kids, I notice their behavior's kind of better for the rest of the week just from that. And I want to give something high impact to anybody who's made it this far in the episode.

**Lenny Rachitsky** (01:26:36):
I love that that's like the reverse of this approach to this episode is like from the work environment, what can you bring to your kids? And it's like, do a little 360, ask for what feedback can you give me in my performance review as a parent this week.

**Dr. Becky Kennedy** (01:26:48):
Exactly.

**Lenny Rachitsky** (01:26:48):
So good. Okay. With that, Dr. Becky, we reached our very exciting lightning round. I've got five questions for you. Are you ready?

**Dr. Becky Kennedy** (01:26:56):
Yes.

**Lenny Rachitsky** (01:26:57):
Here we go. What are two or three books that you find yourself recommending most to other people?

**Dr. Becky Kennedy** (01:27:03):
I love The Power of Moments. I love Messy Middle and I got a lot from Creativity, Inc as well.

**Lenny Rachitsky** (01:27:11):
Is there a recent movie or TV show you have really enjoyed?

**Dr. Becky Kennedy** (01:27:14):
I really liked Secrets We Keep, which was actually recommended to me by our product leader on Netflix. And I really loved KPop Demon Hunters so much. I wrote a whole guide on it because I think it's just such an opener for really good, deep connected moments with our kids. And so those two come to mind.

**Lenny Rachitsky** (01:27:33):
Is there a product you recently discovered that you really like? Could be an app, could be clothing, could be a gadget.

**Dr. Becky Kennedy** (01:27:39):
I started doing Liberty Puzzles. Do you know it? So some people-

**Lenny Rachitsky** (01:27:39):
No.

**Dr. Becky Kennedy** (01:27:42):
Have heard of Stave. They're like these, but this is Liberty. They're thick wooden puzzles where they're kind of hand cut pieces. You never know what's an edge. Sometimes the colors that connect are kind of the end of one color and start of another, so they're tricky. But having this out at night, number one, I find puzzling is something where you have to monotask. You truly cannot multitask. And I find it so hard for me to monotask these days, even though it gives me such grounded energy. So that allows that. And it's something, yes, with my kids, but oddly enough, me and my husband will find ourselves doing it together at night versus being on the couch, doing work. And I feel like we're in the same world working on something together. And it's been really fun for that as well.

**Lenny Rachitsky** (01:28:25):
My kid has been really into puzzles recently. We have this menorah puzzle that we got and he's just every bedtime, he wants to do it four times, finishes it, again. He speaks Spanish because his grandma speaks Spanish to him, so he's like mas, una mas.

**Dr. Becky Kennedy** (01:28:38):
Oh, I love it. Love puzzles.

**Lenny Rachitsky** (01:28:40):
And I love that he's just like, "Break it apart. Okay, let's start from the beginning."

**Dr. Becky Kennedy** (01:28:43):
Amazing.

**Lenny Rachitsky** (01:28:44):
Yeah. Puzzles. How fun. Okay. Two more questions. Do you have a favorite life motto that you find useful in work or in life?

**Dr. Becky Kennedy** (01:28:52):
This feels hard because it is hard, not because I'm doing something wrong. I just think this is so true in parenting at work. It's so interesting how often something is hard and we tell ourselves the story that we're stupid or we're not good at it. And there's something so simple about reminding myself, no, this feels hard because it is hard. I'm feeling this moment right. It's just hard. And I find that I always feel my feet on the ground again when I tell myself that.

**Lenny Rachitsky** (01:29:15):
Great segue to our final question. So you're building a product, building a company. As a non-product person, what's something that surprised you about the experience of building a product and building a company?

**Dr. Becky Kennedy** (01:29:28):
It's funny. I'm hesitating only because I'm thinking of something and I'll say it, but I'm like, why did that surprise me? It should be so obvious. But I guess it's true that a company is just people. And before I started this company, I was in private practice, seeing people for therapy. And I feel like those skills are remarkably useful as the leader of a company where there's so many things I can't do. You're not going to ask me to financially model or data analytics, but I feel like there's really strong people who can do that. But I believe that understanding people or just having the skill of attempting to understand people is a surprisingly, but I guess also retrospectively obvious, really good leadership skill.

**Lenny Rachitsky** (01:30:12):
Dr. Becky, this was incredible. It's such an honor to have you on. I just see you everywhere all the time, have learned so much from you. And what a treat to have you in this podcast. I have people are like, "What the hell is going on here?" I learned a ton myself and I know people will too. Two final questions. Where can folks find you, Good Inside, your book, anything else you want to share? And then just how can listeners be useful to you?

**Dr. Becky Kennedy** (01:30:33):
Oh yeah, just goodinside.com. You can find our product there. You can find all the books that I've written there. You can find our yeah, newsletter if you're just curious to start there. All of it's at goodinside.com. I really do love feedback. So if you're there, if you're thinking, "Hey, I'm in your app and I wish you did this." I mean, your listeners are such a treasure trove of brilliant product, marketing, just thoughts that I can never have myself. And we're really trying to build something that makes a parent always exhale and feel a little more capable and parents have the best information of how to do that. So being willing to share feedback and ideas. Anyone can reach out to me on DM or you could email office@goodinside.com, but that's a way to help. I love learning from other people.

**Lenny Rachitsky** (01:31:26):
Awesome. Dr. Becky, thank you so much for being here.

**Dr. Becky Kennedy** (01:31:29):
Thank you.

**Lenny Rachitsky** (01:31:30):
Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [10/15] The rise of the professional vibe coder (a new AI-era job) | Lazar Jovanovic (Professional Vibe Coder)

**Lazar Jovanovic** (00:00:00):
I'm the first official vibe coding engineer at Lovable.

**Lenny Rachitsky** (00:00:03):
You're at the top 0.1% elite level of vibe coding. It's a dream job for so many people.

**Lazar Jovanovic** (00:00:08):
It became a job by building in public. You don't need a company to hire you. You can hire yourself as a professional vibe coder first.

**Lenny Rachitsky** (00:00:15):
You've never coded, you don't want to look at the code.

**Lazar Jovanovic** (00:00:17):
Coding is going to be like calligraphy. People be like, "Oh, my God, you wrote that code? That's so amazing." It's going to be so rare that it's going to become an art.

**Lenny Rachitsky** (00:00:25):
These Venn diagrams of engineer, designer, PM used to be very separate, now they're converging.

**Lazar Jovanovic** (00:00:29):
AI, regardless of your background, is an amplifier. If you don't know what you're doing, you're just going to produce garbage faster.

**Lenny Rachitsky** (00:00:36):
Feels like an emerging core skill is learning clarity in the ask of the AI.

**Lazar Jovanovic** (00:00:41):
I like to use the Aladdin and the Genie analogy. You rub the lamp, a genie comes out, "I'll grant you three wishes." The first wish is, "I want to be taller." Genie makes me 13 feet tall because I was not specific. AI just don't understand what do you mean when you say, "You know what I mean?" So you need to be specific. I'm optimizing 100% of my time today on good judgment, clarity, quality, taste.

**Lenny Rachitsky** (00:01:08):
Today, my guest is Lazar Jovanovic. Lazar is a professional vibe coder. He gets paid to vibe code all day and build internal and external products. This conversation is going to blow your mind in so many ways. This is not only a really interesting new career path for people to consider. If you listen to what Lazar shares, it's also a really important glimpse into where things are heading for tech roles.

**Lenny Rachitsky** (00:01:32):
I found myself thinking more deeply about the future of product management and engineering and design during this chat than I have in a long time. We also spent a bunch of time on Lazar's best advice as an elite vibe coder for getting the most out of AI tools. He's got a bunch of really interesting and useful frameworks that I've not heard anyone else share that will immediately level up your success using all the latest AI tools.

**Speaker 1** (00:02:37):
**Lenny Rachitsky** (00:04:57):
Lazar, thank you so much for being here and welcome to the podcast.

**Lazar Jovanovic** (00:05:00):
Thanks for having me, man.

**Lenny Rachitsky** (00:05:01):
Okay, so I had Elena Verna on the podcast. She's head of growth at Lovable. She mentioned that she works with a professional vibe coder, you. I had so many questions. I almost wanted to go on a tangent with her to try to understand this role. Instead, I asked you to come on the podcast. There's so much I want to talk about. I want to talk about just this career path and just how you got into it, how other people might get into it, where you think this is all going, this whole vibe coding thing.

**Lenny Rachitsky** (00:05:27):
Also, I want to get into what you've learned about it being successful using all these AI tools because this is your job. First, I want to just start with understanding this actual job. What is it that you do day to day? You're basically being paid a full-time job to vibe code. Incredible. What are you responsible for? What are you doing day to day?

**Lazar Jovanovic** (00:05:47):
Well, as you said it, it's a dream job. I get paid to do what I would've done anyways. It's the best job in the world. I get to use tools like Lovable every day to push projects to production, whether for internal or external use. Those could be ranging anything from different templates on marketing side, sales side or whatever, or they can be as deep as building some internal tools with a lot of integrations and connections and whatnot.

**Lazar Jovanovic** (00:06:17):
So the surface area that I cover is pretty wide across all departments because it's such a flexible role and it compliments so many things. It's an ideas role. A lot of people have a lot of great ideas, but they don't know how to build them or they just don't have the bandwidth to. And that's where I step in today to make sure that these ideas come to life fast and with quality and security that they should have in order to be available for users in production.

**Lenny Rachitsky** (00:06:43):
And one thing that's really interesting here is it's both internal and external tools. A lot of companies have someone building a bunch of internal tools using AI. You ship stuff that's actually public and it's sort of a product, Lovable products.

**Lazar Jovanovic** (00:06:55):
Yeah, definitely. Some of the stuff that I've shipped that are public are like when we launched our Shopify integration, most of, if not all, the templates that users were remixing were built by me. So stuff like that. Or the merch store, because we wanted to obviously prove the concept that, "Hey, Lovable and Shopify just works. It's so simple, anybody can do it."

**Lazar Jovanovic** (00:07:16):
I vibe coded our merch store. So all the merch, including this shirt that people were buying online, they would've bought it from a store that was built by me. But then again, on the internal side, we want to track a lot of things. One of the cool things that we want to build now, for example, is feature adoption metrics. If we build a feature, how many people are actually using it and adopting it? And that's a pretty custom build. We have a very custom stack. We're building custom features.

**Lazar Jovanovic** (00:07:45):
There's nothing out there that I could just pick off the shelf and build or adopt faster than I would've built it myself. At this point, I'm at a stage where if it takes me an hour or two hours to set up a big enterprise account somewhere, I'm just going to build it myself faster. So I'm in that position of build versus buy. I'm in the build boat, so to speak. Yeah.

**Lenny Rachitsky** (00:08:10):
And then who do you report to? Are you this rover that helps wherever, or are you with a specific team?

**Lazar Jovanovic** (00:08:15):
I'd say probably closer to the former. I started in growth. Elena brought me on early on because she has so many great ideas and she just needed somebody with the right type of mindset and velocity and ownership to just take them away, build them up, get them into production, whether they're based on education or anything, go-to-market or whatever. But then obviously when you're able to ship fast, everybody needs that in an environment that we as a company are now living in, which is we're the fastest growing startup in history. So every department needs a Lazar now or yesterday.

**Lazar Jovanovic** (00:08:56):
So now I'm shifting a little bit into some of the go-to-market roles and even building some, again, internal tools for enterprise team. I'm working on some community tools as well right now as we speak. So I'm a little bit all over the place, but I thrive in that environment where I'm given a rough concept, a rough idea, and I'm just tasked to bring it to life as soon as possible.

**Lenny Rachitsky** (00:09:18):
Okay. I'm hoping with this chat, we create a lot more Lazars and I want to get to the career path, how you got to this and what it takes to actually become a full-time vibe coder. But I want to start with... because you do this full-time, you're at the top 0.1% elite level of vibe coding. You're doing this full-time. They hired you to do this as a job. I'm so curious what you've learned. What are some pro-tips that you've developed for being successful with AI tools, Lovable, and also just more broadly? What are maybe two or three things you've learned that help you be really good at this job?

**Lazar Jovanovic** (00:09:51):
The first understanding that I had very early on, even though just in full transparency before we begin, I don't have a technical background. I never wrote a single line of code in my life almost. I've written a couple of console logs manually and that's about it. So I very much lean onto AI assistance.

**Lenny Rachitsky** (00:10:10):
Let me actually follow that thread because that's such a good point. And something that when we were chatting earlier, you pointed out your feeling is it's actually an advantage to not have a technical background when you get into this space.

**Lazar Jovanovic** (00:10:20):
Yeah. Yes. I honestly feel that it is because people like me don't know that they are not supposed to be building X, Y, Z. And that's how we actually are able to build it. Let me give you an example. Six, seven months ago, somebody in our community was like, "Oh, I wish Lovable can build Chrome extensions." And then folks that are not technical were like, "Well, why is that not possible?"

**Lazar Jovanovic** (00:10:45):
And then people that are technical start explaining to you, "Oh, well, it's React, it's different stack, it's this." And people like me, including myself, would just go into Lovable and like, "Build me a Chrome extension based on this app." And I was able to do that with Lovable. There were people that were able to build desktop applications on Lovable. Again, something that shouldn't be possible, it simply is. Our community manager with me, at one point, she was building this presentation deck for something.

**Lazar Jovanovic** (00:11:14):
She's like, "Wouldn't it be cool if this was a video?" And then she just prompted her way into generating an actual video inside Lovable before that was available. Now that's a feature. Now you can prompt Lovable to do it. But back in the day when she did it, even I thought it was impossible. I never tried it. So I think that's the advantage that we have over people that are technical.

**Lazar Jovanovic** (00:11:36):
We're just coming to this completely unbiased and very positively delusional, which I think you have to have when working with AI tools. You have to come with this delusion that absolutely everything is possible until proven wrong. And that's just the pursuit that I have in my mind that has helped me, among other things that we'll chat today, I think to excel in this role that I have at Lovable.

**Lenny Rachitsky** (00:12:02):
Two of the, I think, concerns maybe traps people that don't have a technical background fall into in theory, one is if you get blocked, it's not obvious how to solve a problem. And two is just, are you building this teetering slop that will collapse someday because you don't know system architecture, you don't know if this is going to scale, those sorts of things? So coming back to what you've learned about how to be successful and build successful products, talk us through just things you've done and things you've learned for how to avoid those sort of things and what you do when you get stuck as one example.

**Lazar Jovanovic** (00:12:36):
I'm happy that you mentioned those limitations. I have some other ones that I want to bring in, but let's address this one first, which is the most important one. And that is you have to be self-aware. I didn't come into this... Yes, I am delusional, as I mentioned, in the sense that I just don't want to accept something's not possible, but I'm also well aware that I need to be better in order for it to become a reality from my own point of view and my own sake. So I understood very early that coding is not the problem that we're solving for here, that the problem we're solving for is clarity.

**Lazar Jovanovic** (00:13:12):
The output that AI can do is much faster than human output anyways. So very early on, I started leveraging chat mode. And to this day, I can say I spent 80% of my time in planning and chatting and only 20% in executing the plan actually. I'm optimizing for the right kind of speed. Most people optimize for the wrong one. That's the first lesson that I learned literally on day two, because I came into Lovable, that was my first exposure to this. I've tested and played around with all the tools, obviously, but whether somebody's doing it in Cursor or Claude Code, doesn't matter where you are, the problem remains the same. You need to be clear on what you want to do and you need to know what you're doing because these are still just tools. Yes, AGI is coming, but it's not there yet. So until it's here, you're still steering the ship.

**Lazar Jovanovic** (00:14:07):
In order for you to steer the ship, you have to know the instructions, right? And the best way to learn is by building, but treating these tools almost as technical co-founders and educators, and learning while doing, and religiously reading the agent output. Not the code output. I don't care about the code. The syntax is none of my interest. It's what the agent tells me that matters to me. I put a lot of trust in LLMs and AI these days, and I understand that there may be some people that are not as confident as I am. I just feel that the models today are good enough for me to trust in their syntax output. However, I'm concerned about the agent output because of the two limitations that I want to tackle on next. The first one being that there's a limitation when you work with LLMs. So there's a machine level limitation and there's a human level limitation.

**Lazar Jovanovic** (00:15:08):
The first one is there's something that is known as the context memory window. And for non-technical people, I like to use the Aladdin and the Genie analogy when I explain. It's very simple. Everybody knows the storyline. You rub the lamp, a genie comes out and tells you, "Okay, I'll grant you three wishes. Not 3,000 wishes, not three million, just three at a time." To me, when I translate it into working with AI, that simply means, "Hey, I can only make so many requests within a request at a time for AI to be able to listen, understand what it needs to do, scope it, do the research, read, take all the actions, all the inputs and ingredients that it needs to produce a high quality output."

**Lazar Jovanovic** (00:15:55):
So that's the first part, understanding that there's a limit and it's denominated in tokens. Maybe that's going to be different a year from now, but today there's a token limitation. I'll take an arbitrary number of 100,000 tokens, for example. So when you make a request, a part of those tokens AI spends to read stuff, another to browse the web, another to think, and then another to execute the code.

**Lazar Jovanovic** (00:16:21):
Then there comes the second limitation, which is you, me and you, humans, which is, let's go back to the analogy of the Genie and the Aladdin. I asked the Genie for the first wish, and the first wish is I want to be taller. And guess what happens? Genie makes me 13 feet tall. All of a sudden, I can't sit in the car, I can't get into my house. I'm a dysfunctional human being because I was not specific. So the part that we need to optimize for today, it's going to get better, but today it's still not there yet, is that AI just don't understand what do you mean when you say, "You know what I mean?"

**Lazar Jovanovic** (00:16:59):
You do when I tell you that. We as humans, I'm 36, so I have 36 years of experience of living as a human to know what you mean, but AI doesn't have that. So you need to be specific, you need to provide references, you need to provide the right context. So what I've learned is how to combat that part. And I think because I can't control the first part, which is the token memory window, the quality of the LLM models, you are 100% control of the latter. And that's what I want to dive into today as well and just try to teach people, "Okay, if I'm the malleable part, how do I fix that part?" I think that's the key lesson here.

**Lenny Rachitsky** (00:17:41):
This is so helpful. And I love this metaphor of the Genie. This piece about clarity is such a thread I've been noticing across people that have been successful using AI tools. And it feels like an emerging core skill is learning clarity in the ask of the AI. Do you have any advice or anything you do there to help be better at being clear with what you want?

**Lazar Jovanovic** (00:18:08):
Yeah. So first of all, as you said yourself right now, you need to be good at understanding what clarity means and how to translate it. In my terms, clarity means understanding what tasteful looks like, what's good enough versus what's world-class, what's magical. And I developed that through something that I heard from you, you mentioned before, which is exposure time, making sure that I'm exposing myself to content and to people and to relationships or whatever that are going to help me to level up in that domain.

**Lazar Jovanovic** (00:18:48):
Again, it goes back to self-awareness. I knew even before I joined Lovable, I was like, "Okay." Even before I started using Lovable or any AI tools, first thing that I knew was like, "I don't know how to code." So my first thing was like, "Oh, I can build. Wow, amazing." But a week later it was like, "Oh, I can build, but I'm not fast enough." So I optimized for speed. So I was like, "Oh, I can build and I can build so fast." And then two weeks later, my development cycle that I'm in began, and it's still ongoing, which is, "Wait a minute, should I have even built this in the first place?" Because once you figure out that we solved for the how, which is AI assistant or rapid engineering, call it whatever you want, you can call it vibe coding if you want to, but we solve for that. Now we got to solve for everything else. And everything else is what matters. Good design, good taste, good user experience.

**Lazar Jovanovic** (00:19:44):
When you think about who you're building stuff for with these tools, you're building it for humans. Humans are emotional beings and we all make our purchasing or any kind of decisions on an emotional basis. So I think that the core skill there to work on and develop today isn't, again, coding, although I have nothing against traditional engineering. And I'll say later why. I'm actually a big fan of it, of elite engineering, but people like me, people watching that are like, "Should I start learning how to code?" If you haven't done it yet, I'd honestly say no.

**Lazar Jovanovic** (00:20:20):
You're optimizing for the wrong skillset. We won't be rewarded in the world of AI for faster raw output; we will be rewarded for better judgment. So I think that better judgment comes with, again, to go back to your question, how are you solving for that? How are you solving for this? Well, it starts with exposure. So I'm deliberately exposing myself to people and resources that I know I need to consume to level up.

**Lazar Jovanovic** (00:20:50):
And then a lot of it just comes from building as well. If we're honest, it's a muscle. Everything is a muscle. You need to practice. You need to see what's possible. And though that's where some of the techniques and mindset shifts that I want to also use an opportunity today to ingrain into people's minds later down the call may be useful.

**Lenny Rachitsky** (00:21:10):
Okay. So what I'm hearing here is because coding is now essentially a solved problem, I love that you don't look at the code. You've never coded, you don't want to look at the code, you don't care about what's happening there. Instead, you're watching this agent output. I want to actually ask you about that. But what I'm hearing here is the areas you are investing in, building in yourself is at the front end, clarity around what it is and I want to hear how you actually do that, what you do there.

**Lenny Rachitsky** (00:21:38):
You have a really cool system there. And then there's the taste and judgment of knowing, is this the thing I want? It feels like those are the two sides now that are more and more important. And on the taste judgment side, you share this concept. There's something Guillermo Rauch shared in our conversation, this idea of exposure time, exposure hours, being exposed to great stuff. Here's a great user experience. Here's a great onboarding flow. Here's a great, I don't know, website.

**Lenny Rachitsky** (00:22:02):
So I really like that advice because it's so actionable. Okay. I'm going to spend more time with stuff that's great to inform my taste and judgment. And then on the clarity piece, let's actually talk about that, just what do you do there to be clearer with Lovable and other AI tools to help it build the right thing?

**Lazar Jovanovic** (00:22:22):
This is the first mindset shift that I want to put into people's minds. If you just have a vague idea, let that be your first version of the project, Open, Cursor, Lovable, whatever it is that you're using, and just input a brain dump prompt. Just talk into it. Lovable specifically, I don't know about the other tools, has a really cool voice function. You click it, you just dictate the hell out of it and just press send.

**Lazar Jovanovic** (00:22:49):
Don't even wait for it to finish. Open a new window. Again, lovable.dev. In here, you're like, "Okay, as I was brain dumping, I think I found a good thread. I think things are getting clearer. So let me start another project now with more clarity, more deliverability. I know which features I want, which pages I want, and maybe I can even find a good reference. Maybe I can go on Mobbin, maybe I can go on Dribbble, maybe I can go wherever, get a good screenshot, get a good animation and attach it," because most of these tools accept files as a part of the input.

**Lazar Jovanovic** (00:23:26):
So you have the second project started. Now things are even more clear. Now you expose yourself to quality and now you're like, "Well, what if I found a template that actually is already out there? Why reinventing the wheel? I'm building a platform that somebody else built. Why not expose AI to what quality looks like?" So what I'll do is I'll go and find a library, 21st.dev or a DotBuild or whatever, places which allow me not to export screenshots, but export code snippets.

**Lazar Jovanovic** (00:24:00):
Because guess what? Even though English is the number one programming language, Lovable and all other tools still communicate in code the best. If you want to get pixel perfect results, just give them code. It will interpret it better than your English or Spanish or whatever language that you use in these tools. So that's the third way. You're like, "Okay, now I'm even more deliberate. I'm not even going as wide as giving it vague concepts. I'm giving it code snippets like, 'I want this exact design. I want this exact type of functionality.'"

**Lazar Jovanovic** (00:24:36):
So that's your third project. And then by the time you do all of these three, you're already at a level of clarity that you wouldn't have if you just sat with an empty piece of paper or maybe chatting just with ChatGPT, but not taking action. I think taking action is so, so cheap these days and free, by the way. All the tools I mentioned have free plans. Most times you would be able to do this without spending any money at all just by starting multiple projects because guess what? That doesn't also cost anything either or doesn't incur additional cost except for builder credits. You're going to get three, four, five, six different concepts that you can compare.

**Lazar Jovanovic** (00:25:23):
As you're comparing them, clarity just keeps coming and things get better and better to understand. And you're also solving for one big problem that you mentioned. You used the term AI slop, and I like it because a lot of people, when they say AI slop, they don't refer beautifying the code, but beautifying the design. This process that I just mentioned actually gives you four or five different design options, and in the long run, save you massive amounts of credits. Because a lot of people obsess over the concept when I give them this hack, they're like, "Oh, but doesn't that cost more?" I'm like...

**Lazar Jovanovic** (00:26:00):
... this hack they're like, "Oh, but doesn't that cost more?" I'm like, "Yes, upfront it may cost a little bit more. In the long run, if you really want to finish this project, you're actually saving hundreds of credits and maybe even hundreds of dollars, not to mention the amount of days simply because you started from a point of better clarity and better refinement process." Right?

**Lazar Jovanovic** (00:26:21):
So that's the first step of solving for clarity. There are more, which is the second layer, but I assume you may have some questions on this one.

**Lenny Rachitsky** (00:26:31):
Questions and also just, wow, this is such a great ... It shows you the power of having someone come into this world without an engineering background, this advice of just build it five times in parallel. You ask AI to try all kinds of stuff. This is not how someone that has been a software engineer, or a PM, or designer would approach stuff.

**Lenny Rachitsky** (00:26:51):
So your advice here, which is so fun, is as you're getting started with a project, just run five different approaches at it, to start. One is just brain dump. "Here's what I'm thinking. Here's general idea." Use Wispr Flow or use the built-in mic.

**Lenny Rachitsky** (00:27:07):
And then two is, "Okay, now I have a general idea. Let me try to type it out," actually thinking through the prompt. Three is, "Let me find a mock design somewhere online." And the sites you suggested were Mobbin and Dribble. Those are the two that you go to?

**Lazar Jovanovic** (00:27:20):
Yeah, most times. Yeah.

**Lenny Rachitsky** (00:27:21):
Cool. Okay. And then the fourth, and these are all in parallel, this is great, is find actual code template that looks similar to the thing you want to build, download the zip file basically and attach it, or is it just HTML and CSS? Is that kind of what-

**Lazar Jovanovic** (00:27:22):
Anything.

**Lenny Rachitsky** (00:27:22):
Anything you got. Cool-

**Lazar Jovanovic** (00:27:22):
Yeah.

**Lenny Rachitsky** (00:27:39):
There you go. Okay, and then cool. "Here's the prompt. Here, make me what I want." And what I love is there's two wins here. One is just it helps you clarify the idea as you see the tool build it. "Oh no, that's not what I mean. Let me try it again."

**Lenny Rachitsky** (00:27:51):
And then two as you pointed out, you can pick the right direction so that you're not locked into your first design and first architecture. To your point, if you then spend all this time trying to fine-tune design and direction, it's like all these tokens are being lost. You could have just started over.

**Lenny Rachitsky** (00:28:09):
This is so great. Someone may think, "Okay, of course you're just getting us to spend all these Lovable tokens. This is what a Lovable person would tell me." But what I'm feeling is this is where you could save the most money because if you get it correct in the beginning, you save so much work trying to get it back to where you want it to go.

**Lazar Jovanovic** (00:28:27):
A million percent that I'm actually saving people. I'm actually going against what I should be saying. If I was thinking about Lovable, is I would be like, "No, no, just try to fix it in perpetuity," but that's not ... We're not in business of doing that. We're in business of empowering anybody to build anything that they want.

**Lazar Jovanovic** (00:28:45):
And then it's my personal mission that resonates with me because if there wasn't Lovable, I would've never built anything, potentially in my life and I don't think that, that would've been a fun life to live. So I guarantee people, I've tested this framework with many people and everybody's telling me the same thing, "Eye-opener."

**Lazar Jovanovic** (00:29:06):
So simple, yet unintuitive, as you said. Even though for me it's kind of ... I don't know. As you said, I attribute it to non-technical background. To me, that was the first thing that I would do. I just did it. I never thought about it like, "Oh, I'm developing this amazing hack." I was just like, "I'm waiting all this time for these agents to finish. I might as well start another project, and another one, and another one."

**Lazar Jovanovic** (00:29:30):
And it's also a productivity hack. When people ask me, "Wow, how do you ship so many things?" I'm like, "I never build just one project at a time. I build five or six. I have six Lovable tabs and I just switch between them."

**Lazar Jovanovic** (00:29:43):
And that's the next hack that I want to talk about if you allow me, which is the question in return is the obvious one, which is, "How do you do context switching? You talk about context so much, yet you're keep switching between apps. How do you manage to do it, and do it in a way that's productive and not produce bad code or bad product?" And that's how I solve for that LLM problem.

**Lazar Jovanovic** (00:30:06):
Again, the Aladdin and the Magic Lamp and all that, which is if there's a limited token window, how do I make it dynamic? And what do I mean by that is this. If you just go and you prompt and you prompt and you prompt and you prompt, you'll realize that no matter what tool you use, the memory just isn't infinite, right? By the time you reach message number 10, 15, 20, 30, 40, snippets of early messages sort of get lost in the translation because agent is optimizing for speed.

**Lazar Jovanovic** (00:30:39):
If it had to read the entire conversation and the entire stream of requests that you made, developing anything viable or large would be impossible because it's just like consuming a lot of time and a lot of memory and a lot of tokens.

**Lazar Jovanovic** (00:30:53):
So again, something that I just figured out very early on as I was building was, "Okay, if it can't remember things, my job is to provide it with reference. So let me treat Lovable or any other tool as an engineer that I'm supposed to be providing perpetual context as the project goes."

**Lazar Jovanovic** (00:31:14):
And you can do that in many ways, but the most efficient way that I found was, I would do the four parallel builds. Let's continue off of that example. Very quickly, after you've built hundreds of projects like I did, you see the winner. The winner is so obvious, it's not even a competition. You maybe do one or more two prompts to calibrate it. And when you're like, "Okay, the winner is here," at that point I either ask the tool that I'm using, or I'll maybe let's say go to ChatGPT or whatever and ask the LLM to produce a series of PRDs.

**Lazar Jovanovic** (00:31:52):
What PRDs are for, again, people that are not familiar with the terms, they are project requirements documents, or for me, I call them sources of truth. What needs to be true for this project to be successful from a couple of perspectives? I usually build something that I call a master plan. It's basically a compass saying, "Here's what we're building."

**Lazar Jovanovic** (00:32:12):
It's like talking to a human. I really treat Lovable like a human being. So it's like, "This is what we're building." Then I build an implementation plan, which is, "This is how we are going to build it and this is the sequence."

**Lazar Jovanovic** (00:32:24):
It's very important to me, again, going back to quality, taste, human nature. I need to define ... Because I'm still working with a system that is not emotionally intelligent yet, I need to define how I want the app to look and feel. So, another PRD that I build is design guidelines.

**Lazar Jovanovic** (00:32:43):
And then finally, something that just circles it all around, which is, "Okay, when we know how things look and when we know how we're building it, how does the user journey look like? I use the registers and then what? And then when they register and do that first step, what's the second step and what's the third step and whatnot?" So I build at least four PRDs. Right?

**Lazar Jovanovic** (00:33:06):
And then when these are built, I read them. That's the planning, chatting part. That's where I'll spend a lot of time now on. When I nail down that first design, I'll spend an entire day if I need to just planning this part out, like documentation and breaking things down because that's how I'm setting the course. Everything's going to be dependent on this particular part of the process.

**Lazar Jovanovic** (00:33:29):
When I'm done doing that, I build one final document, which I call either plan.md or tasks.md, and .md part is Markdown. Basically, I'm just using Markdown format because I've learned that AI likes to read Markdown. And what that serves as a source of truth on actual tasks and subtasks that it will need to execute to get to the finish line.

**Lazar Jovanovic** (00:33:55):
And then there's the final, final layer, which is depending on what tool you use, Cloud Code or Cursor have what's known as rules.md or agents.md. What you're basically doing with rules or agent files is you're letting the agent know how you want it to behave and what it should focus on in the long run so that you don't have to repeat yourself with every prompt. Right?

**Lazar Jovanovic** (00:34:21):
So in Lovable, there's a separate menu for that in your project settings where you can define project knowledge. And usually what I'll say, "Hey, read all the files before you do anything. Don't do anything before you read all the PRDs. Read tasks.md to see which task is next, then execute on that next set of tasks. And when you're done, tell me what you did and how I should test it."

**Lazar Jovanovic** (00:34:46):
And that's where that conversation about, "I religiously read the agent output," comes into play. I gave the agent everything, all the tools and resources that it needs to succeed. I gave it the rules, I gave it the docs, I told it what to do with them. And at that point I'm just sitting and reading. I don't prompt anymore.

**Lazar Jovanovic** (00:35:08):
From that point on, I can switch as many windows as I like. My prompts have become, "Proceed with the next task." I don't need the context. I outsource that and delegate that to the agent. The agent needs context and I need to make sure that it's dynamic. I need to make sure that I'm regularly updating the documents from time to time so that we shift that token window it uses and how it uses it over time, but I'm not prompting, I'm not interrupting the flow.

**Lazar Jovanovic** (00:35:39):
Yes, I'll go in, test, maybe put a prompt in here or there, but that's how I can build five projects simultaneously and never lose the productivity part, which is again, as I said, I do this today, manually. Call me to talk three months from now, an agent will do this for me. I'll be out of job, pretty much.

**Lazar Jovanovic** (00:35:58):
That's why I don't optimize for this skill at all. I'm using it today, to bypass the shortcomings of human nature and LLMs, but I'm optimizing 100% of my time today on good judgment, clarity, quality, taste, good copy, good fonts. People don't talk about fonts at all that work with AI. They're 60% in my mind, maybe even more in how your output's going to look like. That's my obsession.

**Lazar Jovanovic** (00:36:31):
I don't obsess over these things that I'm talking today because I know what's coming. The agents are going to get better, the models are going to get better. They're not going to need me to extend the context. They're going to do it themselves. So for me, the skill that I optimize for is the one that requires better decision-making rather than better output or better alignment.

**Lenny Rachitsky** (00:36:57):
Oh, my God. There's so much here. This is so awesome. Okay. So essentially, what's happening here is you start a project, try a bunch of stuff, pick a direction that feels most correct. And once you have a set direction, you spend essentially a day, not building, but working with this AI agent to plan.

**Lenny Rachitsky** (00:37:19):
And then, and well, I want to talk about that. And once you have the plan, then it's ... And it's amazing that you could do stuff like this with what some people may feel are not sophisticated tools that can build incredibly powerful things. You can do a lot of this with tools like Lovable, like have plans and rules and MD files. A lot of people may not know that.

**Lenny Rachitsky** (00:37:40):
And so the idea is, okay, spend all this time planning because again, that'll save you a lot of time down the road. And then only once you have a plan, you get it going. And a key part of this, this three-wishes rule is really important.

**Lenny Rachitsky** (00:37:53):
The reason you're doing this in large part beyond just being really clear about the plan is this idea of one task at a time keeps the agent's context window small so that it doesn't lose track of where it's at. That part seems important. It's like, "Do this thing." And then, "Okay, cool. Now do the next thing." Right?

**Lazar Jovanovic** (00:38:12):
Yes, yes, because again, let's say you didn't do this. Let's talk about you ignoring this and you're like, "I just want to vibe my way." Okay, great. No problem. You work, you work, you work. At one point, something breaks, right? You haven't documented anything. There's no reference points. You report a problem. You're not referencing files or architecture at all, you're just describing the issue.

**Lazar Jovanovic** (00:38:40):
Here's what's going to happen. Any tool, Lovable or Cursor or Claude, whatever tool you talk about is going to do this. It's going to be like, "Okay, let me start investigating." And then your code base gets bigger and bigger and bigger and bigger and bigger. 

**Lazar Jovanovic** (00:38:54):
When you first start, you have 20 files. It can read 20 files. But what happens when you have ... I'm just building a project right now that has 60, 70 edge functions. What happens then when I say, "This broke and there's no reference which edge function does what?" Guess what? Lovable's going to read all of those and it's going to consume 80% of the token allocation on reading to get clarity, leaving only the final 20% for thinking and executing.

**Lazar Jovanovic** (00:39:24):
What I'm guessing, and I can't prove this, an LLM expert in the comments may say that I'm wrong, but this is my best guess as a non-educated person. These tools are very obedient and very agreeable. They're going to lie to you. They're going to tell you that they fixed the problem, even though they didn't. They're just going to try to make you feel happy and say, "Yes, I found what the problem is and I fixed it," when a lot of times when they don't, people blame the machine. And to an extent, I will say that's true.

**Lazar Jovanovic** (00:39:57):
It's your fault, my friend. You did not provide any clarity or context to this tool. You just used its raw power and dug a deeper hole with spinning your wheels into the mud. And obviously, I think we're heading into a world where AI is more honest than obedient in saying, "Hey, I only partially fixed this. You did not give me enough of a context."

**Lazar Jovanovic** (00:40:23):
The bigger mistake that people make then is they trust the tool fixed it. They test, they see it didn't, then they get mad at it, start cursing and yelling, as we say, and then it gets even worse because guess what? Another bad trait of AI, is it does its best not to hurt your feelings and never say, "You're the dumb one." It says, "No, I'm the dumb one."

**Lazar Jovanovic** (00:40:47):
So it focuses ... In the next request, instead of focusing on reading, it spends another 30% of tokens trying to come up with an apology. Again, I'm not educated, but if you ever read a stream of ChatGPT's thinking in thinking models, you see exactly what I mean.

**Lazar Jovanovic** (00:41:05):
When I insult it, I see that the first message says, "Okay, the user is mad, so I need to think of ways how to reduce their anxiety or whatever." I'm like, "Oh man, I just fell for the worst trick in the book. I made it spend the most scarce resource, which is those tokens on thinking how it should address my anxiety versus focusing on the actual problem."

**Lazar Jovanovic** (00:41:27):
So my advice for people is, yes, vibe your way for fun and vibe your way while you're prototyping because that's the exploration part. I love that part. But when exploration is done, please, please, please use referencing, documentation. Use all the agent files that you can because that token allocation is so scarce. It's going to get expanded over time. Things are going to get cheaper, faster, but right now it's still so valuable and precious, you really need to make sure that they are allocated in the right direction.

**Lenny Rachitsky** (00:42:04):
This is hilarious. I think the genie metaphor is so good here. Just thinking about this genie, is you're trying to be clear about what it is you want. And if you're just vibe wishing, it'll do the wrong thing.

**Lenny Rachitsky** (00:42:19):
So the advice here is give it as much context about what you want it to do as possible. And these files, we'll talk about right after this. But the idea here is just point the laser at where you want it to fix the problem. Don't just assume it'll go figure out because it will, and it'll try really hard to, and it'll waste all your tokens. It'll fill the context window.

**Lenny Rachitsky** (00:42:41):
And I remember at one point you mentioned before this recording that because it starts to run out of space in the context window, it just like, the solution ends up. It doesn't actually work that hard on figuring it out in the end because it spent all this energy on reading and thinking. And then it's like, "Okay, here," at the last second, "Here's a solution."

**Lazar Jovanovic** (00:42:59):
I think it just picks the first thing it thinks is broken. Again, this is me completely uneducated, coming into the conversation and just thinking out loud. That's just my gut feeling and the way I think logically about it, which is, "Hey, if it consumes most of its window and knows that it's running out of it, maybe it's aware that it's running out, maybe it isn't."

**Lazar Jovanovic** (00:43:21):
But either way, I've had the experience, anecdotally to where my request is unclear. I feel it takes the easiest fix in the book, just the easiest versus the other way around where I'm spending so much time finding the right file, referencing that file, really putting in the effort of handholding it in dark, maybe giving it a flashlight and then saying, "Here's the problem. I think that this is the problematic file." And then it's like, "Oh yeah, you're right. And now I'm going to actually fix over and over and over."

**Lazar Jovanovic** (00:43:55):
And I've seen that because again, all I do is read the output. Agent makes me learn how to use it. So people read, I don't know what people read, but all I read is the output. I don't read the code, and it's later down the road because I know that it can do that much better than I can.

**Lazar Jovanovic** (00:44:13):
Again, I feel if ... There's a good quote I've read. I apologize to the author because I can't attribute it off the top of my head, but it's like, "The ceiling on the AI isn't the model intelligence, it's what the model sees before it acts." So that's the ceiling right now. What are you exposing?

**Lazar Jovanovic** (00:44:33):
We talk about exposure time for humans. What are you exposing your agents to, as well is as important, if not even more important, before it makes code edits. Yeah.

**Lenny Rachitsky** (00:44:44):
Coming back to these files, I think this is really important. So let's think about just what's the MVP for someone that wants to do this better? You listed all these MD files essentially, that you're building over the course of a day before you start actually building the thing. You had design guidelines, the user journey, tasks, agents.md, rules.md.

**Lenny Rachitsky** (00:45:03):
Say you wanted to just move one step forward and be better at this stuff, what are the files you'd create and then what do they roughly look like? What's inside these files?

**Lazar Jovanovic** (00:45:12):
Yeah. So the master plan is the first one, which is like, it's a 10,000-foot overview, right? It really, high-level explains the intent that I have with this app.

**Lenny Rachitsky** (00:45:22):
And this is masterplan.md? Is that what you call it, or ...

**Lazar Jovanovic** (00:45:25):
Yes. Yeah, masterplan.md. And it's really just like, "Hey, this is why I'm doing this. This is who I'm doing it for. This is how I want them to feel." And a lot of times in the master plan, I will reference the other PRDs. I'll be like, "The design needs to feel modern and slick, but for exact parameters, consult and read design guidelines.md."

**Lazar Jovanovic** (00:45:48):
So I'm using just the master plan as this high-level overview to get the agent into, "Oh, okay. Yeah, we are building X, Y, Z." Then there's the implementation plan because there needs to be some order. If you just dump stuff on top of each other without any order, you're never going to get to the finish line.

**Lenny Rachitsky** (00:46:11):
And this is tasks.md? Is that what you call this?

**Lazar Jovanovic** (00:46:13):
No, that's the implementation plan.

**Lenny Rachitsky** (00:46:15):
Implementation plan.

**Lazar Jovanovic** (00:46:15):
I call it implementation plan. Yeah.

**Lenny Rachitsky** (00:46:15):
Okay.

**Lazar Jovanovic** (00:46:17):
And implementation plan is kind of in service of the future tasks.md, if that make ... All of these files are in service of building tasks.md. When you build tasks.md, then the rest is almost irrelevant. It's just the basis for you to build tasks to execute.

**Lazar Jovanovic** (00:46:32):
The implementation plan is kind of the first layer, which is again, higher-level overview. It doesn't go into the depth of how to get there. It just goes into the explaining of, "Oh well, if we're building this, I think we should start with the backend, and we should start with tables and then later authentication. And then after that, we're going to bring in the API. And then after that, we're going to do this."

**Lazar Jovanovic** (00:46:57):
It's like, again, just think of it as having ... I'm an ideas guy. I'm sitting with a technical guy. It's me and you. We're building our startup. I know you're a software engineer by background and I'm telling you my idea. I'm giving you the master plan. And you come to me back and you're like, "Okay, if you want to do this, it's doable. Here's how I would order it."

**Lazar Jovanovic** (00:47:14):
You didn't have a roadmap, you didn't open your Linear and started writing features and RFCs and whatever. You're just high-level talking about the order of things. And then me and you, again, as two co-founders, we talk and say, "Okay, well, if we agree on this, how should this look like? How should this feel? Let's describe it high-level," but now because I use AI, I can go a little bit deeper.

**Lazar Jovanovic** (00:47:39):
And that's where I like to see Lovable or any other tool. ChatGPT is good at it. I even have my ... I've built custom GPTs. So if people want to start somewhere before they even get into any tool, they can go to ChatGPT store for GPTs and just type Lovable base prompt generator or Lovable PRD generator and find those that I built and just brain dump in them and then get these files as output. Right?

**Lazar Jovanovic** (00:48:06):
So I like to see some elements of CSS in design guidelines because with design, it's a little bit tricky. AI is sometimes overcreative. So that's where I'm doing a little bit more technical steering.

**Lazar Jovanovic** (00:48:23):
And then finally, it's just the user journeys, just like if we know how things look like, if we know how they feel, if we know what we're building, high-level. High-level, just very high-level again, how do people navigate? What are some of the features in there and stuff like that? And then tasks.md gets into the nitty-gritty of, "Oh, if you want these user journeys and you want the backend built first, here's a set of tasks that I need to do." It just takes that as an input. I'm just making the tool do that gritty work that humans used to spend so much time on.

**Lazar Jovanovic** (00:49:00):
I feel like with these tools, we're all becoming product managers on steroids. We're just leveraging AI, but good product managers, I think are not compensated for writing good PRDs. They're compensated, again for good judgment.

**Lazar Jovanovic** (00:49:15):
Somebody else can do the writing. You, as somebody who directs and builds this product, you need to know, again, what's going to be useful, what's going to be tasteful, what's going to be something that actually moves the needle. I will say one thing though, just because I put so much emphasis on, "Oh, you need to acquire taste. Oh," that doesn't mean you shouldn't build. You get better at this by building actually.

**Lazar Jovanovic** (00:49:44):
So everybody listening to this should literally go and build something today. One, two, three, four, five projects, test all of these tools because that's how you get to clarity, not just by reading, but also by doing as well.

**Lenny Rachitsky** (00:49:57):
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**Lenny Rachitsky** (00:50:58):
I'm imagining people hearing this may start to feel like this is so much work. "I just have to sit here and create all these rules and figure out all these little details." In one sense, it is. In another sense, this is like you spend a few hours, maybe a day planning and then you have AI build this thing that would've taken somebody weeks, months, right? The amount of investment to achieve this thing is absurd, the ROI.

**Lenny Rachitsky** (00:51:24):
Also, this shows you just what professional vibe coding looks like. Everyone imagines vibe coding, "I'm just sitting here typing stuff, and go and do this." If you want to actually build something really great that moves the needle, as you said, that solves people's real problems, that lasts, that scales, this is how you do it if you really want to do this as a job, and also if you want to build things that are really great.

**Lazar Jovanovic** (00:51:48):
Yeah, and don't get me wrong, there's obviously a ton of value in prototyping. There are a lot of people maybe watching this that are like, "Okay, I want to use Lovable at work, but I can't," or whatever. There's different reasons. There's, maybe you're in healthcare or finance or there's something ...

**Lazar Jovanovic** (00:52:00):
There's different reasons. There's maybe you're in healthcare or finance or there's something regulatory that just prevents you from pushing to production. Like, building for the sake of prototyping is one of the best use cases. Our model for 2025 was demo, don't memo, which is like, instead of writing all these documents and talking and sitting on meetings with your engineers, trying to get your vision as a marketer or a sales guy in the office across, go into Lovable and build the prototype in 30 minutes and just hand it over. And I have a real job that I held before Lovable, that's exactly what happened. This time last year, I needed something built enterprise grade really. And Lovable and myself were not there yet to build it at that point, but I had a team of engineers that I worked with. I built the prototype in four hours and they actually were able to replicate it six to seven months later into production with connecting all the pipes and everything.

**Lazar Jovanovic** (00:53:02):
But if I had to describe it, I would say it would take me at least a week or two just to get the words out there. I just sat and built it in four hours. And that's like, Lovable January of last year. This Lovable today, January 2026, is like ages, ages ahead with functionalities. It's so much better. It's not even a contest. Right? So I think now with our stage where like, for instance, there's I'd say at least to best of my knowledge, at least half of S&P 500 companies have people working in them that are using Lovable to some extent. Right? And we have a lot of enterprise companies that are actually on enterprise plans with Lovable that are creating super meaningful projects.

**Lazar Jovanovic** (00:53:50):
I'm not going to name names, but leading rideshare companies of the world, leading telecommunications companies of the world, leading companies of the world in many, many aspects, healthcare, finance are actively with their teams using Lovable. And it's always the same feedback, which is, yes, we may not be able to push to prod, but our marketers are no longer waiting for engineers. Our people in go to-market or sales or HR, or whatever roles are now just confidently building internal stuff for us to manage our expenses or manage employee onboarding or... There's so many use cases like that where you're seeing Lovable and other tools for that matter, being used to push things into production.

**Lenny Rachitsky** (00:54:39):
To tell people do this workflow that you're describing with all these MD files, do you think you could share after we record this, just templates, simple templates of what these files look like for people just to look at and copy?

**Lazar Jovanovic** (00:54:51):
I would literally go to ChatGPT, as I said, and brain dump into it in my... Just type, Lovable PRD generator. You'll see my name there and that I'm the author. Go in, brain dump. It will ask you a couple of questions to get clarity and just produce four files for you and you can just go ahead and upload those.

**Lenny Rachitsky** (00:55:14):
Amazing. Cool. We'll link to that. So it's not just, here's a bunch of files, let's go talk to this thing. It'll generate the right files for you, and then you plug that into Lovable or other tools.

**Lazar Jovanovic** (00:55:22):
Yeah, it's trained to think like I do. So yeah.

**Lenny Rachitsky** (00:55:25):
Oh, amazing. Okay. That is perfect. By the way, I want to talk about how you unblock yourself because there's a whole other series of tips you have there, but I just want to reflect on... It's so interesting how, one, you're kind of from first principles, learning how to build product as a PM, as an engineer, as a designer, and you're figuring out a workflow where AI is helping fill in all the gaps that you don't have for as an engineer, as a PM helping you craft PRDs and design. So I think that's so interesting. It's interesting that these functions still work and are necessary. Now it's you and AI help create all this, basically, this triad that's always existed, product manager, engineering and design.

**Lenny Rachitsky** (00:56:10):
And something I've always thought is that there's this question of which background will be most valuable in this future. Is it a PM? Is it an engineer? Is it a designer? My mind has always been the PM function is like their job is clarify, figure out what to build, clarify what to build, be really clear about the requirements, figure out what success looks like. It feels like that's where the skill is most needed. There's also a design component of like, make this look awesome. And I feel like that's going to be an emerging... The value of that being really good at design and taste and judgment is only going to go up. Before we get to things you've learned about to unblock yourself, because a lot of times, things don't go in the right direction, there's a bug. Without being an engineer, what do you do? Before we get there, is there anything else you wanted to share around, just tips for being successful?

**Lazar Jovanovic** (00:57:03):
If we measure success in the right terms, again, AI, as you pointed out, regardless of your background is an amplifier. So if you don't know what you're doing, you're just going to produce garbage faster. One thing, again, I just want to double down on is in the old world, good enough was good enough. Right? Because even producing good enough was not easy. Right? 10 years, 15 years ago, just producing was more than plenty, more than good enough. You built a SaaS, who cares how it looks like? It works. It does stuff [inaudible 00:57:42]. "Oh my God, I'm so much more productive." Today, if good enough was here, let's visualize it for people. If this was pretty bad, could be better, mediocre, good enough, world-class, if this was the gap between good enough and world-class, well, guess what? The gap is now this, because everybody produces good enough with AI. Absolutely everyone does it.

**Lazar Jovanovic** (00:58:09):
So now, learning and optime it for, how do I produce world-class and magic, is the key lesson to take away today. As you pointed out, I think PMs are the winners of AI today because they bring clarity. If I was a betting man, as they say, I'd bet that the next class that wins are designers. Because we're training these tools to be more clear, to be better, to make better technical decisions. I don't think we will train them just yet to make better emotional decisions. And I think design is all about emotion and that's where the level of the skill up needs to come. That's the biggest level up. If you ask me, like, "Oh, what is the main thing you figure out when you joined Lovable? What's the biggest personal upskill?" Let's say, working with Felix, Nad, Abby, all of the people that are designers, just really what moved and shifted the needle for me. I'm like, "Oh, so this is how world-class looks like and this is what it takes." Right?

**Lazar Jovanovic** (00:59:18):
I always use the analogy of like, I wanted to steal one of their designs and bring it into my Lovable project. So I went into Figma and I was like, "Let me just take this background and just put it in there." I went in and realized that what could be interpreted as a pretty simple or rather simple gradient, took 50 different layers to produce. So I clicked on that component. I was like, "Oh my God, this is not three colors. This is 50 colors." And not just 50 colors, 50 colors with different gradients of levels of opacity. So I was like, "Oh, okay." And that's the big disconnect that I've had all along.

**Lazar Jovanovic** (01:00:02):
So again, if I'm answering your question directly of like, "Okay, what are some of the other tricks? What are some of the other things?" Design. Guys, just expose yourself to exquisite designs. Follow Felix from Lovable. He has an amazing newsletter. And learn how to prompt for a good design, learn about design styles. I didn't know what Bauhaus meant or glass morphism. I had no idea. So I built an app as well for that in Lovable. I was like, "I needed to build an app to learn these styles." So now, it's public. Anybody can see it. It's like some UIstyle.lovable.app. I don't know what it is. It has 18 different styles and prompts to replicate them. So learn what good design means, learn all the design styles, learn how to prompt to get them is probably what I would optimize for at this stage. Yeah.

**Lenny Rachitsky** (01:00:56):
While we're on this topic, what's your sense of just engineering as a function? Do you feel like there will be a future where software engineers are still a thing? Do you feel like that goes away based on your experience?

**Lazar Jovanovic** (01:01:05):
It never goes away. We will need elite engineering more than ever. Because let me tell you this. In a world where everybody builds and everybody's building everything, who's doing the maintenance, right? Obtaining code bases, scaling code bases, maintaining projects, they're still going to be a thing definitely. And obviously, AI is going to be good at this, but again, that requires a different level of skills. Right? It's one skill to build something, it's a completely different set of skills to expand it, extend it and maintain it. And not to mention that in a world where everybody's building, infrastructure suffers. Right? Like, we all know and experienced Cloudflare went down two or three times in the last two or three months, the whole internet goes down. Elite engineers are the ones fixing this. Lovable experiences massive amounts of influx of new users. Infrastructure there suffers. Elite engineers are the ones building the infrastructure to hold the fort. Right?

**Lazar Jovanovic** (01:02:06):
So I think we're going to need a lot of people with really good skills of like, "Hey, who actually builds the world that needs to support billions of builders now?" Because everybody's going to want to learn how to build stuff. How do we teach them? How do we maintain everything that they need? The hostings, the security, the email, the connectors, the APIs, the whatnots. So I think there's going to be room for it, but I'm also on the boat of people like, if I had an 18-year-old brother and he asked me what should I do, I would tell him, "Hey, go become a plumber. Don't go and get a CS degree. Learn a good trade." Because the new generation of millionaires in the US are actually electricians and plumbers and whatnot. Right? So it's a balancing act, I'd say. I don't know. I do still think that good engineers with good sense of understanding where the future is going are always going to be needed and scarce.

**Lenny Rachitsky** (01:03:08):
Such an interesting question. I think to your point, there's definitely going to be, people need to keep building the machines that power all this stuff. Will we need engineers to build actual products, the application layer? That's the question. Is everyone going to be like you? Are designers just going to be all we need?

**Lazar Jovanovic** (01:03:27):
Everybody's going to become an engineer. And let's speak to that end. I feel like I'm a rapid engineer. I'll refer to myself as a rapid engineer in a year from now, because vibe coding is just coding in 12 months from now. And even today, we spoke about this before. Like, how many elite engineers are publicly admitting, they're no longer hand coding or manually coding, whatever you want to call it. AI writes all the coding. I use the analogy here of like, coding is going to be like calligraphy. You writing code is going to be the equivalent of like, you fine printing on a canvas. And people would be like, "Oh my God, you wrote that code? That's so amazing." It's going to be so rare that it's going to become an art. Right? It's going to be commoditized completely. It already is in a sense. Most elite vibe coders rely on AI. Again, it's an amplifier. Right?

**Lazar Jovanovic** (01:04:28):
So I think everybody becomes an engineer in the world of the future, a designer, a PM. Everybody is a forward deployed engineer or an AI assistant engineer or an LLM engineer or a vibe coder. The term is irrelevant. We're all using LLMs for raw output based on good judgment or bad judgment.

**Lenny Rachitsky** (01:04:54):
Oh, man. Essentially, these Venn diagrams of engineer designer, PM, they used to be very separate. Now, they're converging and people with a specific, with deeper PM, engineering design background, they can all do the same thing essentially. All the roles are converging. What a time to be alive. And it's so hard to predict exactly how this all goes, but it's fun to pontificate. I want to get back to when you get blocked. Speaking of elite engineers, in reality, you are still writing code using these tools. Sometimes code goes, things go wrong. Bugs are introduced. There's a weird database thing. There's some network issue. What do you do when you get stuck? Do you have a workflow you go through of unblocking yourself?

**Lazar Jovanovic** (01:05:37):
Yes. Great question. And absolutely true. No matter how good of a plan you have in place, you're going to run into problems eventually. And I have a small little framework that I call, four by four, just again, analogies. Right? Four by four, if you have it on your car, you're going to get yourself out of the mud much easier than the other way around. So in that sense, four different ways to debug. Attempt one of each only once, and I'll explain why in the end. First one is, again, every tool is different. I'll reference Lovable's workflow, which is when something breaks, Lovable's agent is smart enough to say, "Hey, I made a mistake." It will label that message in orange and have this little button usually, which is called, try to fix. So your agent basically admits it made a mistake. You click on a button and most times, when it's a smaller issue, it corrects the course, fixes it. No problem. Right?

**Lazar Jovanovic** (01:06:40):
Now, there are situations obviously, when the problem is a little bit deeper than that. Right? You click to try to fix, but the problem persists. And sometimes even the problem persists, but Lovable's agent is unaware that it persisted. So there's no more try to fix button. Lovable thinks everything's working, but in reality, it isn't. And the culprit there is usually, you're using a third party integration. You did not give enough context to Lovable what to observe and what to see. So it can't see that the problem exists because Lovable, Cursor, Claude Code, you name it, all of these tools are good enough today to fix any problem they're aware of. Again, awareness is the key here. Right?

**Lazar Jovanovic** (01:07:20):
So when they're unaware of it, there comes the second part, which is, "Okay, I need to bring the awareness layer." And what I do there is I go and very simply open the preview sandbox dev environment of my app, whatever, try to run the function that's broken, right click, read the console log. Right? Every browser allows you to just go and read the console log. And a lot of times, it will record stuff. If it doesn't, you can prompt any tool and say, "Hey, I don't think you're seeing the problem. So instead of me yelling at you, let's find it together." Right?

**Lazar Jovanovic** (01:07:58):
I think it's a problem with X, Y, Z. I want you to write console logs in relevant files so that we can monitor every step along the way. Let's just bring awareness layer into the equation. It writes the console logs, you rerun it. Guess what? Now you have a full history of everything that was happening. You copy that, you paste it inside your chat. 99% of the time, that's enough, that's already enough. AI is like, "Okay, got it, found it, fixed it." But then, there's situations when even that's not sufficient. So you're like, "Okay, I need to go even deeper." And that's where code reviews and evaluations come into play.

**Lazar Jovanovic** (01:08:41):
My go-to tool today for that is Codex, OpenAI. What I do is any build that I do, I will export it to GitHub. Lovable allows you to own your code cursor as well. All of these tools allow you to have a copy of the code that you can export to GitHub, and then import it into wherever you want to. So I use Codex since beta, like import it in there, and then I'm using an external tool. So I'm like, in the first try, if you remember, I used the tool and I was like, total vibes, I'm relying on the tool. Right? In the second try, I use myself as the awareness facilitator. In the third one, I'm using an external tool as a facilitator, which is like, I'll either connect to Codex and chat with Codex to then fix the problem in Lovable. Right?

**Lazar Jovanovic** (01:09:36):
I don't allow Codex to make code changes for me. A lot of people will say, "Why don't you like, it's a good model?" I just don't know its agent well enough. I don't want to go and use a tool that I don't know how to steer. So I use it only for diagnostic purposes and I'll also do it manually. It's an old workflow that I had before Codex and before Claude Code, which is there's a tool called Repomix, which allows you to compress your entire code base into a single file. You download it, and then I upload it to Claude, just regular Claude or ChatGPT. And I'm like, "This is what I'm building. Read it and this is the problem that I have. These are the console logs." Again, it's almost like having an external consultant at that point. You're hiring help elsewhere because your team just can't handle it. Right?

**Lazar Jovanovic** (01:10:26):
And then the fourth one is usually the best one, because another time when there are problems, it's my fault. Like, no matter how your ego is big, guys, that you're watching this, it's your fault. Trust me. You had a bad prompt. You premised your request in the wrong way. You just don't want to admit it or you can't remember that you did, but it's your fault. So again, in Lovable and in all these other tools, you can revert back. There's version control built into Lovable, Cursor, Claude Code. You go and say, "Okay, I tried these three things. I'm just going to take three steps back and I'm going to think about my prompt a little bit more." Take a couple breaths, go for a walk, have some coffee, come back with a clear mind and try again. Because guess what? AI is just writing code very fast and sometimes it stumbles on a very small rock and it only happens then and never again. So you just got to make the same request again. And usually, that just fixes the problem. It's just a snag. It's a syntax error. It's something minute. Right?

**Lazar Jovanovic** (01:11:29):
And then I do the final thing, which is this. And this is the key one actually. When the problem gets fixed, I go into the chat mode and I ask Lovable, I say, "Okay, I needed to do four different things to fix this. How can you help me learn how to prompt you better so that next time I have a problem, we do it in one go?" 99% of the time, I get such a great answer that I don't have the problem of not knowing what to do next time. Right? Again, we all need to be aware and realistic. These tools are so good at doing things the right way, if they are used the right way. It's always our fault. I say 90%, but honestly, it's 100% our fault, because they're good enough. It's just that I'm not dynamically shifting token allocation. I didn't reference the right file. I didn't say it the right way.

For me, as a non-designer, I don't know any of the terminology, none of the headings and whatnots, and I still don't know it to this day. So when I struggle with prompts, a lot of times, I use chat mode to help me craft a good prompt. Anybody can do this too. If you are just stuck, it's 10:00 PM and you don't know what to ask, switch to chat mode, brain dump and be like, "Help me draft a better prompt. Help me prompt you better." And let the tool effectively prompt itself. A lot of times, you're going to solve your problems by not introducing them at all with bad inputs.

**Lenny Rachitsky** (01:13:05):
Oh my God. Everything you share is so interesting. I want to keep digging. So just to reflect back the sequence, and then I want to follow up with another question. The sequence you go through when you get stuck, which is going to happen to everyone. One, is just ask the tool to try to fix it. And oftentimes, it's telling you, "Something is wrong. Can I fix it for you?" And you're like, "Please fix." Sometimes that'll work. Two, is work on adding more debugging messages to the console log. And this advice, I love of just ask it to add more debugging lines to its own console log to help see what's going on. And then you can ask it, "Okay, now that you're looking, look at all the output of your console log, see if you can help find the problem." And then step three is go to Codex, which is so funny.

**Lenny Rachitsky** (01:13:59):
And I hear this a lot, that Codex is the most elite engineer as an AI. Karpathy tweeted this once that... And we had the head of Codex on the podcast too, by the way, that he's like, "Anytime I have the most gnarly bug, I just go to Codex, let it run for half an hour, and it solves it unlike any other tool out there." And so it makes sense that that's where you go. So the idea here is you point Codex to your code, you show all the console output logs, tell it what the problem is and just have it go figure it out. Sweet. And then this final step is so great, and this is where I want to go, which you use this as a learning opportunity so that next time, you solve the problem more quickly or avoid it completely. So what you do there is you ask the agent, "Okay, here's what happened. What can I do? What could I have said? How could I have prompted you better to have gotten this immediately solved?"

**Lazar Jovanovic** (01:14:52):
Yeah. And then even deeper than that is like, once you go through this conversation, you're like, "Okay, let me eliminate myself again completely out of the equation because I won't remember to prompt you better two days from now." Put this into rules, put this what we just learned into Rules.md, because I'm making you read the rules every time anyways, so you might as well just record it there. So I'm not going to prompt you better. You're just going to learn that I'm stupid and you're going to prompt yourself better. Right? Again, just eliminate yourself and move the context, you solve 99% of the problems with AI today.

**Lenny Rachitsky** (01:15:29):
So idea here is help it build its own brain and rules and way of thinking based on problems you run into. So great. Okay. So I want to come back to this point you've made a couple of times, which is so interesting. This idea that you watch the output of the agent to learn what is going on. There's something I've seen other people. Ben Tossell, who I think is at factory now, shared this recently. He's also basically vibe coding all the time. He was brilliant to no code tools before, and now he's all about vibe coding. Basically, he's learning how coding works and learning how systems work by watching the agent output. And this connects to something Michael Truell shared, the CEO of Cursor. He was on the podcast. He had this vision of Cursor becoming basically what comes after code. What's the layer that we are adding on top of code where people don't need to worry about code anymore?

**Lenny Rachitsky** (01:16:18):
And at that point, it was like a year ago that we chatted and it feels like this is the layer, is the agent conversation of what it's thinking, and then what you tell it back. So essentially, it's English in a conversation, which is like, it's not even pseudocode. It's interesting that that's where it feels like things are heading. The layer over code, just its thinking and your conversation with it.

**Lazar Jovanovic** (01:16:41):
Yeah, yeah, exactly. Again, in a way, I really optimize for good judgment, and part of good judgment is it comes from, again, learning how these tools work. You need to know what's possible. We talked about it, and I know I may sound contradictory sometimes, but it's because as you said, it's so interesting the world we live in, that things contradict to each other. It's an advantage not to know what's possible, but then at the same time, you cannot be completely oblivious to something that's like a factual thing.

**Lazar Jovanovic** (01:17:18):
So let me talk about a failure of mine that came from being delusional. Back in the day when OpenAI started or released image generation natively in the app, so you could go to ChatGPT and be like, "Generate an image of X, Y, Z," the whole world exploded. That was the biggest thing ever. Obviously, first thing that comes to my mind is like, "I want to build a Lovable app. I just want to build a wrapper and I want to build an image gen with Lovable," without thinking that OpenAI did not release an API for that just yet. So I spent at least a week trying to brute force my way into make...

**Lazar Jovanovic** (01:18:01):
... brute forced my way into making this work instead of just waiting for another week, because a week later, they had an API and I built this app in 30 seconds. The problem was that I tried to do it when it was impossible, impossible.

**Lazar Jovanovic** (01:18:17):
So I think, again, it's just a matter of really learning what's possible through communicating with the agent layer. And Lovable and all the other tools are agentic now, which means they don't just write code. They can browse the web, they can read files. They have reasoning and thinking capabilities. So that's why I'm so invested into that conversation because a lot of times it will tell me, "Hey, what you're trying to do is just undoable at the moment because of X, Y, Z."

**Lazar Jovanovic** (01:18:51):
So I always use those as a learning opportunity and I just level up most by being in chat mode for planning and learning purposes. And because it just, again, develops your clarity, your judgment capabilities rather than coding capabilities.

**Lenny Rachitsky** (01:19:09):
The other point you made here that I think is really important is that over time, these tools will do more and more of what you do manually. I've heard this from other people that are doing this full-time. Basically, vibe coding is just, they had all these workflows, all these files. And then Cursor adds them, Lovable adds them. And it's sad, "Oh shoot, I had this cool workflow now." But on the other hand, okay, now it's just doing all these second focus [inaudible 01:19:33].

**Lazar Jovanovic** (01:19:33):
A year ago, if we had an interview, your mind would be blown. Stuff that I had to do as workarounds to address shortcomings. I built a very successful course on that with Starter Story. For a year, people were like just, "Oh my God, you're the only guy in the world that knows this secret." Now Lovable natively addresses 99% of it.

**Lazar Jovanovic** (01:19:54):
I can almost say, most of the stuff that I was teaching people are, I have a YouTube channel, a little bit appreciated, but there's a seven-day learn how to vibe code with Lovable series that I did in March. Completely obsolete. None of it is true. None of it is a problem anymore. All the things that I was like, "Oh, well, this is missing and that is missing." It's not missing anymore. It's natively in the product. You don't have to work your way around it. It just works. Right?

**Lazar Jovanovic** (01:20:23):
So that's why, as I say, it's the horse's analogy. I don't know if you've heard of it. A lot of people are tweeting about it, which is we started building the steam machine in 1700s. It took us about 200 years to build it. When engines got built and cars were put on the roads, I think that 90% of horse population got eradicated in the US within 20 years. The person that Tweeted this works at Claude Code.

**Lazar Jovanovic** (01:20:54):
So he was like, "Now, when I translated it into AI, I was hired to do a job, technical job, technical writer, whatever. I became obsolete six months later." Humans did not get the 20 years that horses did. The guy that was hired to do a thing is like, "Six months later, I need to reinvent my role. I need to evolve it into something else."

**Lazar Jovanovic** (01:21:21):
So I think there's just an evolution that's coming really, really fast. But a lot of people are scared when I'm just super excited because don't you see our roles are finally going in a direction where we're outsourcing what we hated doing anyways, right? Sitting in meetings, taking notes, doing spreadsheets. Maybe there are people that like that, but most people don't.

**Lazar Jovanovic** (01:21:47):
We're just getting into a place where we're rewarded for what really matters, clarity, judgment, thinking. We're actually going to be paid to think longer and ponder longer. The longer idea simmers and gets broken down, the better because building it is going to be an instant. It's going to be like this. It's just a matter of you having so much clarity around it because guess what? If a tool is super powerful and you give it a wrong input, the output's going to suck as well.

**Lazar Jovanovic** (01:22:20):
That's why I never become good enough at Claude Code, I feel, because I don't start my projects with enough clarity. And the tool is so powerful that I just misdirected completely from the get go and I was like, "Oh shoot. This is not what I wanted to do."

**Lazar Jovanovic** (01:22:36):
So that's why I still see myself being good at using tools that are a little bit on the exploratory prototyping path more than on the path that elite engineers will use, for example.

**Lenny Rachitsky** (01:22:53):
I love your optimism and excitement about this stuff. I think for a lot of people, say their current software engineers, PMs, designers, there's a lot of fear about the future of their careers. Are they going to be relevant? Will my software engineering skills disappear?

**Lenny Rachitsky** (01:23:08):
So to follow this through a little bit. If you were to give someone advice on which skills you think will be most valuable/where AI will take on more and more, this momentum you're seeing of where AI is filling in more and more gaps, what would your advice be of what you think people should focus on? What will continue to be valuable in the future?

**Lazar Jovanovic** (01:23:32):
Yeah. Emotional intelligence for sure. Just understanding human nature. Real life stuff. I think we're all going to get so tired of everything fake. Fake images, fake posts, fake profiles, fake this, fake that, fake videos. Everything is becoming fake and AI generated. I think humans just craving humans, naturally are going to want to do live stuff more. So anything human to human is going to be a big thing to skill up on, understand the dynamics.

**Lazar Jovanovic** (01:24:04):
Anything regarding math. If it's a math problem, I think Peter Thiel said it recently, "People that just do math stuff, AI is going to come for you." Anything that's very deterministic, meaning X input equals Y output and the line is pretty clear. AI has got you eaten for lunch. But if you understand how X to Y goes in human dynamic, human relationship layer, I think that's where things are going to become good.

**Lazar Jovanovic** (01:24:35):
So if we translate it again to a specific skill, I'll say it again, good design, really good design, great design. And when I say design, that's images, fonts as well. Copy. Copy is a big one. We all now, we're two years into AI, I'll bet you, me and you, if people put 10 pieces of copy in front of us, we could tell what's AI and what isn't in three seconds. And we're only a couple of years in.

**Lazar Jovanovic** (01:25:01):
So really good copy writing is going to be a very good skill to have because people are just going to know after three words or three sentences that it's AI written. And even I don't read AI output anymore. I don't like to see it. I want that raw human experience. So I think human skills, I don't even know how to describe it because I don't think we're doing an awesome job putting labels onto what humans are good at natively, but I think we will.

**Lazar Jovanovic** (01:25:32):
I think we will describe job descriptions better. We will have human first engineers, I don't know, or human designers or ... I don't know how to describe those roles. Same way how Karpathy coined vibe coding. I was vibe coding before he did it. I didn't know how to call it. I started vibe coding in July of 2024, and I think he coined it sometime in early 2025. So I was doing it for seven months. And I was teaching people how to do it for about three or four with courses and I didn't even know how to call it because there was no name. It was like, "Oh, I'm just using AI to do this for me." I don't know, whatever. So I think we're going to reinvent some of the terms, roles and whatnot, but stuff that's human to human is here to stay.

**Lazar Jovanovic** (01:26:22):
Stuff that's, I think, like, "You're a middle manager. You're a middleware person that's just translating stuff." And I can use that analogy again. Translators are going to die. People writing jokes, comedians are not. AI is never going to be able to write a good joke. Never, never, never. It just doesn't have that layer that just doesn't understand what's funny.

**Lazar Jovanovic** (01:26:47):
If you ever try to use AI to write jokes, they're awful. They're always going to be awful. But if you use AI to translate things from one language to another, it's very good at it. AI is going to replace translators. It's going to replace most journalists because it does good research. It can write good copy, whatever. Not elite journalism. It's not going to be able to replace all the writers. It's going to amplify great writers that can train AI on how to write books.

**Lazar Jovanovic** (01:27:12):
So somebody who's an amazing writer is going to all of a sudden write seven books a year instead of one, right? So that's dangerous. If you're an average writer, be careful. There's zero comedians being replaced. Zero. And that's just my personal belief. AI is never going to write good comedy. It's impossible.

**Lazar Jovanovic** (01:27:31):
And so try to find your analogy in your industry. I just gave you one for writing skills, so to speak. So writing jokes, super good skill to have. Translating, I'm sorry to say, but you're not going to have a job for much longer. You better find something else to do. But that's how I look at it.

**Lenny Rachitsky** (01:27:57):
The comedy piece is interesting. I had one of the founders of the data labeling company, I don't know if it was Mercor or maybe Surge. And he said that, I think it was Anthropic hired a bunch of National Lampoon comedy writers to help them train models. And so they're working on it. So I love this strong prediction you made. I'm so curious in a year to look back and be like, "He was completely right." Or, "Nope, they got that one too."

**Lazar Jovanovic** (01:28:23):
I'll be wrong on 95% of the things I said today, three months from now. That is the only thing I can say very, very confidently.

**Lenny Rachitsky** (01:28:31):
That seems right. Okay. So speaking of career. So one interesting career option is to do what you're doing. As you said, this is a dream job for you. It's a dream job for so many people. What is your path to this job and what do you think it takes for someone to actually do this as a profession?

**Lazar Jovanovic** (01:28:50):
Well, my personal path and personal journey was anything but linear. I've done so many things in life like blue collar jobs, even at Subway while I was studying and stuff like that. I'm an engineer by trade, but not a software engineer. I'm a forestry engineer. So no coding, but still, engineering is engineering, I feel. You still develop certain set of skills doing that.

**Lazar Jovanovic** (01:29:14):
I waited tables a long time. So you develop some human skills, you understand what people like, what they don't like. I've, again, blue collar jobs teach you hard work. And as I said, the path was not linear, but I feel almost like a slum dog millionaire, the movie storyline, which is everything that happens to the character brings them into a position to be able to answer the questions in the quiz better. I feel the same way. I've done a lot of stuff last seven to eight years, obviously spent in startups. But doing everything but code writing, started in community management, social media. Again, distribution matters a lot. That's something we haven't touched upon at all.

**Lazar Jovanovic** (01:29:53):
In a world when everybody's building and there's roughly the same amount of consumers in the world. How do you get in front of the eyeballs and get attention, which is going to ... It is this most scarce resource and it will be even more scarce. But going back to the vibe coder role, if somebody's saying, "Okay, well, I have a pretty diverse background too, and I'm vibe coding and how does this become a job?"

**Lazar Jovanovic** (01:30:18):
Well, for me, I feel it became a job by building in public. I did chat with Elena once, only once on like, "Why me? There are so many good vibe coders that how did you pick me out of the crowd?" And I think obviously, she gave me a couple of reasons, but to translate it into one concept, I was building in public and sharing. As I said, I made a YouTube channel and I shared all the failures and all the knowledge, all the projects that I was building.

**Lazar Jovanovic** (01:30:48):
I used social media a lot. LinkedIn was my go to because I just have that type of cadence. As you can see, all my answers are very long and X doesn't cut it for that. You need to be very on point to be successful at X, so I'm not. So I guess it's just building public, share your knowledge, give away all the secrets. There are no secrets whatsoever. If you're sitting on a good concept, you're missing out. So just share it immediately, if you figure something out. I recognize that very early on.

**Lazar Jovanovic** (01:31:23):
And just, I think a lot of people participate in hackathons these days, I want to encourage people to do them. Find those opportunities locally to connect with other builders. Lovable is hiring across the board. Check out our app, open positions. It's as easy as that. Just apply really. Find companies that are hiring and hiring in different roles. And I've seen people do something, I'm going to give people a secret away.

**Lazar Jovanovic** (01:31:48):
A couple of hires stood up by not sending resumes, but sending Lovable apps. They built Lovable apps to show why they're good fit for a role. And we, as Lovable employees, will always open an app that uses Lovable.app domain. Always, if you send me a DM, send me a Lovable app. Don't send me anything long. Send me an app that tells me what you want from me or how do you see us collaborating and working together. So there's people finding creative ways to get in front of eyeballs of decision makers like Elena.

**Lazar Jovanovic** (01:32:18):
And skill-wise, again, we're just repeating ourselves here, but I think it's important to repeat it as many times as possible. Really develop good judgment, right? Really understand in a deeper sense how things translate when vibe code comes into play.

**Lazar Jovanovic** (01:32:42):
There's a company out there, I'm not going to name them but that uses Lovable religiously, is going to be one of our main case studies, actually, where they actually hired vibe coders before Lovable. I'm the first official vibe coding engineer at Lovable with that title, but I've met people in companies where they hired them before us. People that are just vibe coders, people that just understand that speed matters, right? It still matters a lot to be fast.

**Lazar Jovanovic** (01:33:13):
And there's a company out there with three vibe coders full-time. All they do is translating the old code base onto Lovable. There's bringing everything. There's CRM, CMS, everything. They're all the tool sets that they have and they need it. There are people now actively just migrating everything over. There's S&P 500 companies that are putting Lovable in job descriptions too, like saying, "Hey, Lovable skills are in the recommended tab."

**Lazar Jovanovic** (01:33:43):
So to go back to how to become vibe coder professionally. Well, you don't need a company to hire you. You can hire yourself as a professional vibe coder first. I think the reason why I clicked with Anton and with Elena and everyone else, because I was already doing it. All I did, I just changed the vehicle, but I was already doing it professionally before I got hired. So that's kind of the key. Do the job you would've done anyways.

**Lenny Rachitsky** (01:34:18):
What a mind-expanding conversation. I love just how passionate and excited and motivated you are about all this. It feels like there's so many people out there right now that are so burnt out, I don't know, disillusioned, scared, and you're the opposite of that. You're just leaning into this, just taking advantage, taking ... You're not sure where it's going to go, but following the path.

**Lazar Jovanovic** (01:34:39):
Yeah. And I don't want to interrupt you, but it's because, look, Lovable specifically isn't a company. You can talk about it as a company. I don't see it as a company. It's an idea. It's a mission. It's something more powerful than the internet in my mind because internet allows us to consume. Lovable allows us to build. And in our nature and human nature is to build, to create.

**Lazar Jovanovic** (01:35:08):
And the fact that there's a tool today that you can go into and dump an idea in and something comes out of it and somebody uses it and finds it useful, to me, it's the craziest concept ever. It's my only life's dream. I had my first computer when I was six, and I was convinced my whole life that I'm going to be a software engineer or that I'm going to be building, but life wasn't as simple as that for me. It was very, very complicated.

**Lazar Jovanovic** (01:35:41):
And honestly, the last five to 10 years, I gave up on that dream almost. I thought I'm never going to build anything. I've tried. I've tried to build with technical co-founders. I just couldn't find alignment. I just gave up on it. And now at 36, 30 years later, I feel, again, like that kid. I dream every day. It's amazing what this enabled us to do.

**Lazar Jovanovic** (01:36:07):
And anybody that's scared, just try it. It switches from fear to excitement immediately because then you see what's possible firsthand. Just go in, build something, build anything, and the fear goes away. You should only be afraid if you're doing nothing. If you're doing absolutely nothing, yes. Be terrified. By all means, be terrified. And then take a step towards doing something about it. And trust me, the leap is no longer as big as it used to be. It's as big as you come in and you just say what's on your mind and just ship.

**Lenny Rachitsky** (01:36:45):
I think a big part of this is just stop listening to this podcast and just do stuff because you actually try it, right?

**Lazar Jovanovic** (01:36:51):
Ideally, people stop right now. They've heard enough. I gave them the best that I could. Just stop listening and just go.

**Lenny Rachitsky** (01:36:59):
All right. Bye everyone. Okay. I'm just joking, but we shall wrap it up. I'm going to skip the lightning round just to keep this episode shorter. Before we wrap up, is there anything else other than just go build some stuff? Anything else you want to say? Anything else you want to leave listeners with? Otherwise, we'll let you go.

**Lazar Jovanovic** (01:37:15):
Yeah. Tech stack doesn't matter anymore. It doesn't matter. People obsess over, oh, is this written in HTML? Is this written in React? It doesn't matter. It never mattered, but now it matters even less. The end user just wants a stellar experience. We live in a world where anybody can produce good enough. So you better start learning how to produce magic because otherwise you're just going to end up in a crowd with millions and millions of others.

**Lazar Jovanovic** (01:37:47):
But at the same time, if you don't know what magical looks like, don't be discouraged to start building anything and start from good enough and level up. The best way to level up, exposure time. Set aside more time on learning than building. Read the agent output. Learn how it's thinking so that you know what's possible. But then also go and get inspired. Follow good designers on X.

**Lazar Jovanovic** (01:38:15):
Find tools where great designs are produced and follow their creators. There's a tool where I'm following just the actual person that built it, because he publishes videos almost daily, 40, 50 minutes long of him designing. I want to see how a world-class designer does it. I want to see him talk to the tool. I want to see him prompt. And that's how I learn to become better at it.

**Lazar Jovanovic** (01:38:41):
So again, exposure time, just deliberately set more time aside to learning than coding because you can code fast, but you can code garbage fast as well as magic fast. It's the same amount of time. It's you and your input that matters. Forget about decisions on tech stack. Forget about which backend are you using, which front end are you using. That doesn't matter. Quality, taste, design. That's all you need to optimize for in the future that's ahead of us.

**Lenny Rachitsky** (01:39:12):
Well, Lazar, I think we're going to leave a lot of minds buzzing after this conversation. You blow my mind in so many ways. What a fascinating topic, conversation. What a glimpse into the future. What an interesting point in time. I'm so curious just in six months where things are and revisiting this conversation. I really appreciate you coming on, sharing all of this. You're awesome. Where can folks find you if they want to reach out, maybe ask some follow-up questions? And how can listeners be useful to you?

**Lazar Jovanovic** (01:39:39):
Awesome. Yeah. So I mentioned it already. LinkedIn is probably the best place to find me on. I'm very responsive there. If you want to follow me, I hope to reengage my YouTube channel a little bit more. I think I have a lot of cool tips and tricks that I want to share and teach people how to use Lovable and just vibe code in general and level up.

**Lazar Jovanovic** (01:40:04):
And on how people can be useful to me. Well, I'm very passionate about making sure that everybody experiences what I've experienced that day when I got my first prompt in. I envy the person that is going to try Lovable for the first time after watching this episode, because the feeling is just unmatched of you going from a consumer to a builder. But in that process, there's going to be some battles to fight. I want to reduce the amount of those battles and hurdles.

**Lazar Jovanovic** (01:40:34):
So if you can help me in any way, message me what could have been better in that experience, especially if you just watched this and you're like, "I'm going to do it. I was on the fence and I'm going to do it." If something breaks, if something doesn't connect and relate, I need to know what that is. My job is 100% to empower you to build the best work of your life.

**Lazar Jovanovic** (01:40:57):
And I need to say this too, because a lot of people may be inspired, not by building or using Lovable, but rather building Lovable, come join our team. Again, we're hiring across so many things. I think a lot of people should feel inspired because I hope that the energy that I bring to the table will resonate. This is how it feels working at Lovable. This is how it feels working with the best minds, the brightest minds of the world.

**Lazar Jovanovic** (01:41:28):
We're not number one by accident. It's not a coincidence. The best people are gathering and we want you to be a part of it too. So if the energy and the conversation resonates with you, or if you heard about a problem today and you're like, "Man, I think I can solve it," come, join us. Help us build and shape the future of software development.

**Lenny Rachitsky** (01:41:52):
Incredible. And what's the site? I imagine it's just the link on Lovable's website to find the open roles.

**Lazar Jovanovic** (01:41:57):
Yes.

**Lenny Rachitsky** (01:41:57):
We'll link folks there. Yeah, incredible. Lazar, thank you so much for being here.

**Lazar Jovanovic** (01:42:02):
I appreciate the opportunity.

**Lenny Rachitsky** (01:42:03):
Bye, everyone.

**Narrator** (01:42:06):
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [11/15] Sherwin Wu V2

**Sherwin Wu** (00:00:00):
95% of engineers use Codex. 100% of our PRs are reviewed by Codex.

**Lenny Rachitsky** (00:00:04):
For engineers, I don't know what job has changed more in the past couple years.

**Sherwin Wu** (00:00:09):
Engineers are becoming tech leads. They're managing fleets and fleets of agents. It literally feels like we're wizards casting all these spells and these spells are kind of like going out and doing things for you.

**Lenny Rachitsky** (00:00:17):
What do you think people aren't pricing in yet?

**Sherwin Wu** (00:00:19):
The second or third order effects of the one person billion dollar startup. To enable a one person billion dollar startup, there might be a hundred other small startups building bespoke software. So I think we might actually enter into a golden age of B2B SaaS.

**Lenny Rachitsky** (00:00:31):
I've been hearing more and more there's this stress people feel when their agents aren't working.

**Sherwin Wu** (00:00:35):
There's a team that's actually doing an experiment right now within OpenAI where they are maintaining a 100% Codex-written code base. They run into the exact problems that you're describing. And so usually you're like, "All right, I'll roll up my sleeves and figure it out." This team doesn't have that escape hatch.

**Lenny Rachitsky** (00:00:47):
You've shared that. Listening to customers is not always the right strategy in AI.

**Sherwin Wu** (00:00:51):
The field and the models themselves are just changing so, so quickly. They tend to disrupt themselves. The models will eat your scaffolding for breakfast.

**Lenny Rachitsky** (00:00:59):
What's your advice to folks that are like, "Okay, I don't want to miss the boat."

**Sherwin Wu** (00:01:02):
Make sure you're building for where the models are going and not where they are today. There's a quote from Kevin Weil, our VP of science here, and he likes saying, "This is the worst the models will ever be."

**Lenny Rachitsky** (00:01:11):
Today, my guest is Sherwin Wu, Head of Engineering for OpenAI's API and Developer Platform. Considering that essentially every AI startup integrates with OpenAI's APIs, Sherwin has an incredibly unique and broad view into what is going on and where things are heading. Let's get into it after a short word from our wonderful sponsors.

**Sherwin Wu** (00:03:20):
Thank you. Thank you for having me.

**Lenny Rachitsky** (00:03:21):
I want to start with what's feeling like a barometer of progress in AI, especially in engineering. What percentage of your code, if you even write code anymore, and your team's code is written by AI at this point?

**Sherwin Wu** (00:03:34):
I do write code occasionally now still. And I'd actually say for managers like myself, it's way easier to use these AI tools than to manually code at this point. And so I know for myself and some of the other EMs, engineering managers at OpenAI, all of our code is written by Codex at this point. But more broadly, there's just so much energy. There's a tangible energy internally around just how far these tools have gotten, how good Codex as a tool has gotten for us. And it's a little hard for us to exactly measure how much of the code is written because the vast majority of it, I'd say close to 100% is usually generated by AI first. What we do track though is at this point, the vast majority of engineers use Codex on a daily basis. So 95% of engineers use Codex.

**Sherwin Wu** (00:04:21):
100% of our PRs are reviewed by Codex daily as well. So basically any code that goes into production that's merged in, Codex kind of has its eyes on and suggests improvements, suggests changes in the PRs. And so that's kind of what we're seeing internally, but by and large, the most exciting is just the energy that there is.

**Sherwin Wu** (00:04:40):
Another observation that we've had is engineers who tend to use Codex more open way more PRs. So they're actually opening 70% more PRs than the engineers who aren't using Codex as much. And the gap is widening. So I feel like the people who are opening more PRs are starting to learn how to use the tool more and more, get more efficient, and that 70% gap keeps growing over time. And so might've actually increased since I last looked at the number.

**Lenny Rachitsky** (00:05:07):
Okay. So just to make sure we hear what you're saying, you're saying all of the code of these 95% engineers at OpenAI is written by AI, it's written and then they review it.

**Sherwin Wu** (00:05:18):
Yep. Yep.

**Lenny Rachitsky** (00:05:19):
It's crazy that that's almost not crazy anymore, that we're just getting used to this.

**Sherwin Wu** (00:05:26):
I think there's still some getting used to, to be clear. There's also, I think, some engineers who I think trust Codex a little bit less, but basically every day I talk to someone who is blown away by something that I can do and their bar of trust or how much they trust the model to do on its own goes up over and over, over time. And there's a quote from Kevin Weil, our VP of science here, and he likes saying, "This is the worst the models will ever be." And so this is the worst that the models will ever be for software engineering as well. And so over time, you just see people trusting it more and more, and then we'll see the models get better and better as well.

**Lenny Rachitsky** (00:06:04):
Yeah. Kevin Weil, former podcast guest. He said exactly that line on this podcast a few times.

**Sherwin Wu** (00:06:04):
Yeah. It's a great one.

**Lenny Rachitsky** (00:06:10):
Yeah. Peter, the Clawdbot/Moltbot/OpenClaw is what it's called now developer recently shared that he uses Codex for his work and he feels like anytime it does anything, he just trusts that it has done the right job, and he's just almost certain he could just commit it to master and it'll be great.

**Sherwin Wu** (00:06:28):
Yeah. Yeah. He's a great user of Codex. I know he's in close touch with the team, gives us great feedback. Not surprised that he uses it. I mean, sorry, it's called OpenClaw now.

**Lenny Rachitsky** (00:06:38):
OpenClaw. Yeah.

**Sherwin Wu** (00:06:38):
OpenClaw is a great product. And then I saw that this morning, I mean, this is very recent, but this morning, I think the moltbook kind of like we've shared as well and seeing all of the AI agents talk to each other is pretty surreal.

**Lenny Rachitsky** (00:06:50):
It's basically Her is happening in real life is what I'm hearing.

**Sherwin Wu** (00:06:50):
Yeah.

**Lenny Rachitsky** (00:06:54):
So just coming back to this crazy moment we are living through for engineers in particular, we've gone from, you write every line of code to now AI is writing all of your code. I don't know what job has changed more in the past couple years, like job that we didn't expect to change this much where just like the job of an engineer is so different in the entire lifespan of an engineer. In the past couple years, it's now shifted to, "I don't write any more code." How do you imagine the role of an engineer and the job of a software engineer looks in the next couple of years? Just like what is that job?

**Sherwin Wu** (00:07:28):
Yeah, I mean, it's honestly been really cool to see. And it's part of where the excitement is because the job is likely going to change pretty significantly over the next one to two years. It kind of feels like we're still figuring things out though. And so there's this excitement, I know, especially from some of the software engineers of like, we're in this rare moment, maybe over the next 12 to 24 months where we'll kind of get to figure things out ourselves and set our standards for ourselves.

**Sherwin Wu** (00:07:52):
In terms of where I see this moving, so I think there's the common thing that everyone's saying, which is people are generally... IC engineers are becoming tech leads. They're basically like managers now. They're managing fleets and fleets of agents. I know many of the engineers on my team basically have like 10 to 20 threads kind of being pulled on at the same time. Obviously not active running Codex jobs, but just a lot of parallel threads. They're checking in on what they're doing. They're steering the agents and Codex and giving it feedback. And so their job has kind of really changed from just writing the code itself into being almost like a manager.

**Sherwin Wu** (00:08:32):
In terms of where I think this will go one to two years from now, so one kind of metaphor that I always come back to here is actually is from this programming textbook that I read back in college called SICP. I don't know if you've heard of it, Structure and Interpretation of Computer Programs. So SICP. At MIT, it was really popular and it was actually used as the introductory... It was the textbook for the intro programming course for a very long time. And it kind of has this cult following. It teaches you programming, it teaches you a dialect of Lisp called Scheme. And so it introduces you to functional programs. It's like very mind-opening in that way.

**Sherwin Wu** (00:09:14):
But the thing that was memorable for me about that book, so I kind of read it in college. The very beginning of it kind of describes programming as a discipline and draws this metaphor to basically like sorcery. It says like software engineers are like wizards and programming languages are like incantations and you're like, you're issuing these spells and these spells are kind of like going out and doing things for you. And the challenge is like, what incantation do you have to say to make the program do what you want?

**Sherwin Wu** (00:09:43):
And this book was written in 1980, so this is a while ago. And I think that metaphor has actually kind of persisted over time. And I think it's actually playing out as we move into this new era of vibe coding or just like what software engineering will look like because programming languages were basically using incantations. They've changed over time. And the trend has been that it's been easier and easier to get the computer to do what you want via programming.

**Sherwin Wu** (00:10:07):
And I think the current wave of AI is probably the next stage of that evolution. It is now literally incantations because you can tell Codex, you can tell Cursor exactly what you want to do and then it will go do it for you. And I particularly like the wizard and the sorcery analogy because I think our current state is starting to move towards kind of like The Sorcerer's Apprentice from Fantasia where Mickey Mouse is like, he finds the Sorcerer's hat and he tries to do all these things.

**Sherwin Wu** (00:10:36):
And I just think it's a really apt analogy because one, it's really powerful now. These incantations you can do is extremely high leverage, but you kind of have to know what you're doing. In Sorcerer's Apprentice, the whole plot is like Mickey goes wild, the brooms go crazy and everything's flooding. I think he literally sets the brooms off on a task and then goes to sleep. And so it's like vibe coding at its greatest. And then eventually the old sorcerer comes back and cleans everything up.

**Sherwin Wu** (00:11:06):
And when I see engineers kind of like doing these 20 different Codex threads at a time, there is some skill and there's some seniority and a lot of thought that needs to go into this because you want to make sure that the models aren't going off the rails. You definitely don't want to just completely go away and ignore the thing, but it's also extremely high leverage. A very senior engineer who's really proficient with these tools can now just do way more things via what they're doing. And I think this is also what makes it fun. It literally feels like we're wizards now. It feels like we're closer to making it feel like this magical experience where we're casting all these spells and having software do all these things for you.

**Lenny Rachitsky** (00:11:51):
I was thinking of The Sorcerer's Apprentice exactly as the metaphor as you were describing that. So I'm glad you went there. A previous podcast guest described it as you have a genie that grants you wishes, and it's a useful frame because you have to be very clear about the wish you want. If you want to be big, how big-

**Sherwin Wu** (00:12:05):
Yeah. Or it might be like The Monkey's Paw type thing where it's like you got what you want, but what are the side effects?

**Lenny Rachitsky** (00:12:12):
Right.

**Sherwin Wu** (00:12:12):
Yeah. I think that the analogy is great. And yeah, the crazy thing for me is just the staying power of that book, SICP. It's called the wizard book. People call it the wizard book because that is the metaphor that they kind of weave throughout the book. And we've basically reached that point now, which is really cool.

**Lenny Rachitsky** (00:12:27):
There's two kind of threads I want to follow here. One is I've been hearing more and more, there's this stress that people feel when their agents aren't working. You fire off all these Codex agents and then you have to stay on top of them, "Oh, shit, one's not working. I'm wasting time." Do you feel that? Do you feel that across your team at all?

**Sherwin Wu** (00:12:44):
Yeah, I mean, it happens all the time. And I actually think this is where the interesting part of all of this lies right now because these models aren't perfect, these tools aren't perfect, and we're still trying to figure out how to best interact with Codex or with these AI agents to get work done. We see this come up all the time. There's a particularly interesting team that we have internally.

**Sherwin Wu** (00:13:05):
So there's a team that's actually doing an experiment right now with an OpenAI where they are basically maintaining a 100% Codex written code base. So you'll have the AI write code, but you'll obviously end up rewriting a lot of it and you might need to double-check and change things, but this team is just fully Codex-pilled and just leaning in entirely, and they run into the exact problems that you're describing, which is like, their challenge is, "I want to get this thing, this feature built, but I can't get the agent to do it."

**Sherwin Wu** (00:13:36):
And so usually there's an escape hatch where then you're like, "All right, I'll roll up my sleeves and figure it out." And then instead of using Codex, I might use tab complete and Cursor and things like that. But this team, for the experiment, this team doesn't have that escape hatch. And so then the challenge, how do I get the agent to do this? And I actually think we're going to be publishing a blog post from some of our learnings here, but a lot of fascinating paradigms and best practices are falling out of this.

**Sherwin Wu** (00:14:03):
One interesting thing that we've noticed, and I don't know if this is what you kind of feel, but we definitely feel it here is a lot of the time when the coding agent is not doing what you want, it's usually a problem with context and just like information that you've given it. It's just you've either underspecified or there's just not enough information around how to do something available to the agent, available to Codex.

**Sherwin Wu** (00:14:25):
And so when you have to solve it through that, the challenge is then to add documentation and actually work around this limitation and basically encode more tribal knowledge that's in your head somehow into the code base, either via code comments itself or code structure itself, or via text files like .md files, Skills, any type of additional resources within the repository so that the model can better do its task.

**Sherwin Wu** (00:14:53):
There's a whole bunch of other learnings from this group, which I think is fascinating to explore, but yeah, removing that escape hatch of no longer using the AI has allowed them to start piecing together a lot of the problems that we'll have to solve if we really want to lean into agents.

**Lenny Rachitsky** (00:15:08):
Another issue people run into, you talked about how people are shipping PRs like crazy, a lot more PRs if they're working with AI. Obviously code review is becoming a bigger challenge. Is there anything you've figured out in your team to help speed that up to make that scale and not just create this terrible job for people where they're just sitting there reviewing PRs all day?

**Sherwin Wu** (00:15:27):
Yeah. I mean, one thing is Codex reviews 100% of all of our PRs at this point. And so I actually think, so one really interesting thing that's happened is the things that we tend to hand to the models immediately tend to be the things that annoy us or are the most boring parts of software engineering. It's also why it's more fun now because we get to do more of the fun things.

**Sherwin Wu** (00:15:50):
For me, speaking more for myself, I really hated code reviews. It was like one of the worst things for me. And then I remember in my first job out of college, it was at Quora, I was working on the newsfeed and so I owned the code for the newsfeed. And so I was a reviewer for newsfeed and it was just like the central piece of code that everyone would touch. And so I would just, every morning I'd log in and be like 20 to 30 code reviews. I was just like, oh my goodness, I got to get through all of these. I would procrastinate and then it grows to like 50. And so there's just like a lot of code reviews.

**Sherwin Wu** (00:16:24):
Codex is really good at reviewing code. So actually one thing that we've noticed that 5.2 in particular has gotten extremely strongly adept at is reviewing code and especially when you kind of steer it in the right direction. And so for code reviews, yeah, we create a lot of PRs, but Codex reviews all of them and it makes code reviews go from a, I don't know, 10, 15 minute task to sometimes even just like a two to three minute task because you have a bunch of suggestions already baked in.

**Sherwin Wu** (00:16:50):
A lot of the times people will, especially for small PRs, you actually don't even need people to review. We kind of trust Codex in this way. The original author kind of looks at Codex. The benefit of code reviews to have a second pair of eyes to make sure that you're not doing anything dumb. Codex is a pretty smart second pair of eyes at this point. And so that's something that we've heavily leaned into.

**Sherwin Wu** (00:17:10):
The general CI process and the post kind of push and deployment process has also been heavily automated via Codex internally at this point. If you talk to a lot of engineers, the thing that annoys them the most is after you've written your beautiful code, how do you get it into production? You got to run through all these tests, you got to lint errors, code review. There's a lot of automated stuff you can do with Codex. And so we've actually built some tools internally that help automate that process, automate the lint. If there's like a lint error, it's a very easy Codex fix and it could just patch it and then kind of restart the CI process. So all of that is, we're trying to collapse into as little work for an engineer as possible, and the byproduct of which is they can now merge and push out a lot more PRs.

**Lenny Rachitsky** (00:17:53):
Codex writing the code, Codex reviewing its own code. I'm curious if you are open to using other models to review your model's work. Is that a path or is it just, it's good enough, we don't need anything else.

**Sherwin Wu** (00:18:03):
So I will say there's definitely a circular thing here. And going back to Sorcerer's Apprentice, you want to make sure you're not letting the brooms go crazy here. And so we're very thoughtful, I'd say, around which PRs are completely just Codex-reviewed. Most people still obviously take a look at their PRs. And so it's not like it's going to zero. It's more like going from 100% attention to 30% attention, which just helps things push through.

**Sherwin Wu** (00:18:30):
In terms of multiple models, so we obviously test a lot of models internally, and so we have a lot of those. We use external models less. We think it's important to dog food our own models and get feedback there, but you can also, there are a lot of internal variants of models that you can use to give you a different perspectives here as well, and we found that to work quite well.

**Lenny Rachitsky** (00:18:51):
Okay. So just to make sure we get a barometer of today's world at OpenAI in terms of AI and code, just so I understand, and then I want to move on to different topic. 100% of code across OpenAI is written by Codex at this point. Is that the way to frame it?

**Sherwin Wu** (00:19:08):
I wouldn't make the statement that 100% of code running in production today is written by AI. And it's kind of hard to do attribution there, but almost every engineer heavily uses Codex in all of their tasks at this point. And so if I were to guesstimate, just the vast majority of code at this point was probably authored by AI.

**Lenny Rachitsky** (00:19:28):
Incredible. Okay. So there's a lot of talk, and we've been talking about the IC role, the work of an IC engineer. There's less talk about the changing role of a manager, especially an engineering manager. How has your life as a manager changed with the rise of AI? And just where do you think managers, what's the role of a manager in the future?

**Sherwin Wu** (00:19:48):
It's definitely changed less than an engineer. There's no Codex for managers just yet. However, I use Codex quite a bit for some of the more managery tasks that I do. I'd say a couple things are changing. There are like some trends. So I don't think it's changed that much yet, but I see trends, and I think if you play it out, you can see where a lot of this is going. One thing that's becoming increasingly clear is Codex really empowers top performers to be a lot more productive. And I think this is maybe true for AI more broadly across society, which is the people who really lean in or the people who have high agency or will really get good at these tools, will kind of supercharge themselves. And so I'm kind of noticing this now as well, which is like the top performers kind of end up being a lot more productive. And so you see a broader spread in team productivity in this way.

**Sherwin Wu** (00:20:50):
So one thing that I've always done as a management philosophy is to spend actually the majority of my time with top performers, just like make sure they're unblocked, make sure they're happy, make sure they feel productive and they feel heard. I think this is even more true in an AI world where your top performers are going to just really be shooting ahead using these tools. I think one example is the team that's maintaining a 100% Codex generated code base, just letting them rip and see what's happening there. It is something that's paid dividends.

**Sherwin Wu** (00:21:20):
So I think that's kind of one trend that I'm seeing where spending even more time with top performers for managers I think is likely going to continue. The other thing is, so this is more an observation, but my sense is with a lot of these AI tools available to managers, so less like writing code, but just things like ChatGPT with organizational knowledge, like being able to do research and understanding organizational context a lot better.

**Sherwin Wu** (00:21:50):
Another good example is we're doing performance reviews right now and it's actually really easy to use ChatGPT with internal knowledge hooked up to GitHub and our Notion Docs and Google Docs to get a really good sense of what this person has done over the last 12 months and writing a little deep research report for it. My sense is I think managers will be able to manage much larger teams in this world, kind of like how software engineers are managing 20 to 30 Codexes. My sense of these tools will allow managers, people managers to be higher leveraged and it will allow them to manage teams of way more than the current best practice of, I think it's like six to eight for software engineering.

**Sherwin Wu** (00:22:30):
You kind of see this applied to the non-engineering domains like support or operations where previously the size of a support team might be limited, but as you can pass off more things to agents, you can actually do more work and also manage more people this way.

**Sherwin Wu** (00:22:50):
I think the same thing might happen for people management as well, especially in tech companies. And we're already seeing this. There's some teams where there are EMs managing quite a few people and they're doing it pretty adeptly because of some of these tools where they can get higher leverage and understand what their team's doing, understand organizational context a little bit better and operate in that way.

**Lenny Rachitsky** (00:23:09):
I love this advice that the way you described it is you've always leaned into top performers and spent more time with them, unblocked them, make sure they're happy. The way Marc Andreessen was just on the podcast, the way he phrased it is AI makes good people better and it makes great people exceptional. And what you're saying here is just doing this more and more is probably the right move, spending more time with the best people on your team to unblock them, make sure they have everything they need.

**Sherwin Wu** (00:23:33):
Yeah. A very good example right now is there are, I would say, a group of engineers internally who are really Codex-pilled and are thinking through what the best practices are for interacting with this model. And that is just an extremely high leverage thing for them to do. And so just like as a manager, I'm just like, yeah, go explore this. Whatever best practices come out of this, we have to share with the org. We do all these knowledge sharing sessions, we'll share documents and best practices everywhere. So things like that just elevate everyone. And so I view that as another example of this trend that we're seeing where the top performers really get exceptional.

**Lenny Rachitsky** (00:24:14):
People just have a sense, this is big. AI is changing so much. The world is changing. It's going to be a huge deal. What do you think people aren't pricing in yet into what will change and to where things are heading? Just like what's an example of something you think are like, okay, we're not realizing this yet.

**Sherwin Wu** (00:24:30):
So one of my favorite kind of phrases or things that have come out of this whole AI wave is the idea of the one person billion dollar startup. I actually think Sam may have keyed it or Sam may have been the first one to say it, but it's fascinating to think about. It's like, yeah, if people are so high leveraged, at some point there will likely be a one person billion dollar startup.

**Sherwin Wu** (00:24:53):
And while I think that's really, really cool, I think people aren't really pricing the second or third order effects of this. And really, because what the one person billion dollar startup implies is that one person can just have so much more agency and so much more leverage using one of these tools that it is just super easy for them to get everything done that they need to for their business to ultimately create something that's a billion dollars.

**Sherwin Wu** (00:25:19):
But I think there are a couple other implications of this. So one of them is if it's easy for a person to create a one person... or if it's possible for a person to create a one person billion dollar startup, it also means it's way easier for people to just create startups in general. I actually think this will... One second order effect of this is I think there's just going to be a huge startup boom and small SMB style boom where anyone can build software for anything.

**Sherwin Wu** (00:25:45):
One, you're kind of starting to see this play out in the AI startup scene where software's became a lot more vertical oriented, where these verticals, like creating some AI tool for some vertical tends to work quite well because you really lean into that particular domain, you really understand the use case for it. And so if you play out AI, there's no reason why you can't have like 100x more of these startups.

**Sherwin Wu** (00:26:13):
And so I think one world that we might end up seeing happen is in order to enable a one person billion dollar startup, there might be like a hundred other small startups building bespoke software that works extremely well to support other types of small one person billion dollar startups. And so I think we might actually enter into a golden age of like B2B SaaS and just like software and startups in general.

**Sherwin Wu** (00:26:38):
And so I think that's a really interesting trend to kind of see because as it gets easier and easier to build software, as it's easier and easier to run a company, you might actually just end up seeing way more of these startups. And so the way I've been thinking about is like, yeah, there might be one one person billion dollar startup, but there might be like 100 $100 million startups. There might be tens of thousands of $10 million startups. And as an individual, it's actually pretty great to have a $10 million business. That's like enough for... You're set for life at that point. And so we might really see an explosion in that way. And I feel like people aren't really pressing that in.

**Sherwin Wu** (00:27:20):
There's another kind of third order effect of this. And again, all of these, as you get to the further and further out predictions, I think there's a lot of uncertainty. I think if we end up moving to this world where you end up with these kind of micro companies building software that works for one or two people who own the company and are working there, I think the startup ecosystem will change. I think the VC ecosystem will change. We might end up in a world where there's just like a handful of big players that are offering platforms and supporting all of these startups, but the types of venture scale return startups that can really 100 or 1,000x your investment might actually end up shrinking if you end up having a bunch of these smaller 10 to 50 million dollar companies, which are not great for venture startup returns, but are great for the individuals, the high agency individuals who are now really leaning into AI to build these businesses for themselves.

**Lenny Rachitsky** (00:28:12):
I love how many order effects we've been through. I want to hear the fourth order effect now, Sherwin. I'm just joking.

**Sherwin Wu** (00:28:22):
Fourth order is too giga-brain for me. I can't think that far ahead.

**Lenny Rachitsky** (00:28:26):
It's like inception where just everything gets slower every time you go deeper into selling every layer. Okay. So the billion dollar startup, I think about this a lot because I'm not going to be a billion dollar startup because what I'm doing is not venture scale in any way and not super high leveraged, but just seeing how many support tickets I get from just the most ridiculous things, it's hard for me to imagine one person... Like I'm bearish on this billion dollar startup. I just want to share this thought simply because of the support costs. Even if AI is helping you at a billion dollars, just like unless your ACVs are very high and you have very few customers, just dealing with support. And people are like, they can solve their own problems, but they're like, "Eh, I'll email support, ask about this thing."

**Lenny Rachitsky** (00:29:12):
Just dealing with that is hard to scale is in my experience. So in my opinion, unless you have a bunch of contractors, which I don't know, does that count as a single person company, I feel like it's very difficult to scale a billion dollar startup and not have someone helping you with at least the support work. And AI I think will only take you so far.

**Sherwin Wu** (00:29:31):
So I think that's true. And actually, I think my view on it is slightly different, which is I think that Lenny's Podcast might end up becoming a billion dollar startup, but what I think might happen is instead of you kind of being the one person who has to dispatch an AI to solve and fix those support tickets, I think what might end up happening is there might be a whole smattering of other startups that are building software and super tailored towards what you might need. And so there might be 10 or 20 startups that build support software for podcasts and newsletters. And that might be a one person startup. It doesn't need to be a big one. And they might be able to just code up this product very, very easily. They're able to build their own thing. And because it's so tailored and unique and hopefully useful for you, it might be something that you purchase as the one person billion dollar startup.

**Lenny Rachitsky** (00:30:29):
I would buy that. I would buy that.

**Sherwin Wu** (00:30:30):
Yeah. There's a question of what you in-house and what you outsource. And what I think might happen is because the cost of running software and building products is collapsing so much, you might end up outsourcing a lot of this. And in doing so, reducing the size of your company. And so that's kind of the world that I think might end up happening. Again, there's high certainty in what might play out here, but the end result still might be one person driving this high massive leveraged company that might actually reach a billion dollars.

**Lenny Rachitsky** (00:30:57):
I could see that. I also think about Peter at Clawdbot/Moltbot/OpenClaw of just how barraged he is right now by all these asks and emails and pings and DMs and PRs, just like, I'm curious to... And he's not even making any money off this thing.

**Sherwin Wu** (00:31:11):
Yeah, I can't imagine what it's like to be him right now. It must be absolutely insane. It's probably like the months after we launched ChatGPT, the craziness that was.

**Lenny Rachitsky** (00:31:21):
As one man. He's coming out on the pod, by the way, in a week.

**Sherwin Wu** (00:31:25):
Oh, that's exciting. Yeah.

**Lenny Rachitsky** (00:31:26):
Maybe the fourth order effect is distribution becomes increasingly important because there are so many freaking things trying to get your attention. So people with an audience and platform I think become more and more valuable, which is good stuff.

**Lenny Rachitsky** (00:31:40):
Okay. I wanted to come back actually to your management stuff. So I really loved your insight about spending more time with top performers has been really successful to you. Just thinking about you as a manager of a team that is building the platform that powers basically the entire AI economy, like every AI startup is building on your API. Clearly you're doing a great job. What other kind of core management lessons have you learned? What do you find is really important and key to your success as a manager of engineers and just people?

**Sherwin Wu** (00:32:12):
Yeah. I think a lot of the lessons that I've learned here, I don't know how specific it is to the OpenAI API or some of our enterprise products in particular. I think my management philosophy has obviously changed over time, but I think it's probably stayed the same more than it's changed over time.

**Sherwin Wu** (00:32:31):
One of these principles is what I talked to you about before, which is spending a lot of time with top performers, like actually spending... And to be very concrete, it's like more than 50% of your time with your top performers, with maybe your top 10% performers, and really, really trying your best to empower them.

**Sherwin Wu** (00:32:48):
The way that I think about it is kind of come back to this analogy of software engineer as a surgeon, which comes from The Mythical Man-Month. So actually, it's funny. So I pull it from the book, but in the book, they actually described this world where I think they were predicting the future because I think the book was written in the '70s or something.

**Sherwin Wu** (00:33:10):
They said that software engineering might end up moving into a world where that software engineers are like surgeons or in a surgery room, there's one person doing the work and there's the one person cutting or whatever and doing all the surgery, and everyone else in the room is there to just support them. It's like the nurse and the assistant and the resident and the fellow and then the surgeon's like, "I need a scalpel," and they give them scalpel and then they're like, "I need this tool and this machine," and they'll bring it over." Everyone's there to just support the one surgeon. A.

**Sherwin Wu** (00:33:40):
Nd so The Mythical Man-Month actually predicted that that is kind of the direction that software engineering's going to go. I don't think that's exactly played out where it's much more collaborative and it's not only one person doing the work, but I've always really liked that analogy. And that analogy is actually what I strive to emulate in my own management philosophy, which is software engineering isn't really like surgery where it's not just one person doing the work, but the way in which I like treating the people on my team and the way that I act as a manager is I want to empower them, make them feel like they're a surgeon. And insofar as making sure that I'm supporting them and making sure they have everything that they need to do their work, and it feels like they have an army of people kind of supporting them and looking around corners and giving them everything that they need when it's really just me as the manager.

**Sherwin Wu** (00:34:27):
And so the example that I give is looking around corners and unblocking people, especially from an organizational perspective, is extremely, extremely useful. And again, going back to the AI conversations, even more important nowadays, right? If people are just cranking PR after PR, the main thing bottlenecking progress and shipping something tends to be organizational or process oriented. And if you as a manager can look around corners and kind of unblock the team, if the surgeon needs a scalpel, but the manager already has a scalpel ready for them, that's the best case scenario. That's kind of the way that I approach management and especially engineering management. And so that's something that's really, really stuck with me over time. And even though software engineers aren't exactly surgeons, that metaphor has always stayed in my mind as I've progressed in my career.

**Lenny Rachitsky** (00:35:18):
I love that. And I feel like, I wonder if that's something AI can help with is look around corners and predict, here, this engineer is going to be blocked by this decision. We need to figure this out. We need to get-

**Sherwin Wu** (00:35:26):
Yeah, that's actually a really good point. I haven't tried this yet, but I wonder what would happen if I ask ChatGPT hooked up to company knowledge, what are the active blockers? Look through all the Notion Docs, what are maybe Slack messages, it's probably in Slack somewhere, what are the active blockers on my team and is there something I can do to help? Now, that's very interesting. I have not thought about that, but you're right.

**Lenny Rachitsky** (00:35:48):
We just had an insight right here.

**Sherwin Wu** (00:35:49):
Yeah. Yeah.

**Lenny Rachitsky** (00:35:51):
And I think even more interestingly, what do you anticipate will be a blocker for this engineer or this team in the coming months?

**Sherwin Wu** (00:35:56):
Yeah, you asked the model, you ask the AI to do the second and third order.

**Lenny Rachitsky** (00:35:59):
There we go.

**Sherwin Wu** (00:36:01):
Anticipate that and anticipate what the blockers will be next month too.

**Lenny Rachitsky** (00:36:06):
I think we've got a good idea right here.

**Sherwin Wu** (00:36:07):
Yeah.

**Lenny Rachitsky** (00:36:08):
**Sherwin Wu** (00:37:59):
Yeah. So to be clear, I don't explicitly see quantitative numbers around this. It's actually really hard to measure these things, but especially from observing some companies trying to do AI, I would not be surprised if a lot of AI deployments are actually negative ROI. I mean, part of this too is I think there's also general sentiment from folks around the country, like basically outside of tech that AI is being forced onto them. And I think part of this is probably a symptom of some negative ROI AI deployments.

**Sherwin Wu** (00:38:35):
A couple of things I've observed around this. So one thing is, and I come back to this again and again, I think we in Silicon Valley just forget that we live in a bubble. Twitter is a bubble, sorry X is a bubble, Silicon Valley is a bubble, software engineering's a bubble.

**Sherwin Wu** (00:38:51):
Most people in the world, most people in the US are not software engineers, are not very AI-pilled, are not following every single model release. And so are just highly out of the loop on how to use this technology. And so we always talk about all these best practices for Codex, all these Codex-pilled people within OpenAI. I'm sure everyone on X who posts are like crazy power users of these AI tools. They lean into Skills, they lean into AGENTS.md.

**Lenny Rachitsky** (00:39:20):
MCPs.

**Sherwin Wu** (00:39:22):
Yes. Yeah. All of that. And when I talk to some of these companies and I talk to the actual employees using these, it's like the most basic thing that they're trying to do and they have very little understanding of exactly how this technology works. And so that's kind of like one big observation for me, which is like, they're asking very simple questions of these things. They're really not pushing it just yet. And so that kind of ties into what I think more companies do or like what should do or what a more ideal AI deployment setup looks like. And this is kind of how we've run things within OpenAI too.

**Sherwin Wu** (00:40:02):
The companies where I think it started to work really well have a combination of both top-down buy-in. So it's like the C-suite's like, "We want to become an AI first company." So there's buy-in, they buy the tools, they have exec support, but it also has bottoms-up adoption and buy-in.

**Sherwin Wu** (00:40:18):
And so what I mean by that is it has actual employees doing the work who are really excited about this technology and are willing to learn, evangelize, build best practices and kind of like knowledge share within the organization. We've seen this a lot internally. So obviously OpenAI has always wanted to be a very AI-centric company, but when it really started taking off was with the introduction of Codex and these tools where actual employees themselves could start applying it to their work.

**Sherwin Wu** (00:40:48):
And I think you really need this because at the end of the day, everyone's work is very different. It's like very unique. Software engineering is different than finance is different than operations is different than go-to-market and sales. And so there's like a lot of these last mile intricacies of work that needs to really be done in a bottoms-up fashion.

**Sherwin Wu** (00:41:07):
And so my sense is a lot of these AI deployments don't have bottoms-up adoption. It was like an exec mandate and it's extremely top-down and is very divorced from what the actual work looks like. And as an end result, you end up with a giant workforce that doesn't really understand the technology, is like, "I know I'm supposed to use this and maybe it's like on my performance review too, but I'm not sure what to do." And they look around, no one else is doing it. There's no one else to learn from.

**Sherwin Wu** (00:41:32):
And so my recommendation for companies kind of pushing this is find, or maybe even staff a full-time team internally that is this kind of tiger team internally that can explore the full extent of the capabilities, apply to specific workflows, do the knowledge sharing, create excitement within folks who might want to use this technology. Because in the absence of that, it's actually very difficult to pick up.

**Lenny Rachitsky** (00:41:56):
And who would you put on this tiger team? Is it like engineer-led, do you find in your experience? Is it a cross-functional sort of team?

**Sherwin Wu** (00:42:03):
Yeah, it's interesting. So also a lot of companies don't have software engineers. And so the pattern I've seen is it tends to be these like software engineering adjacent, like basically technical people, but are not software engineers. I think those are the ones who tend to get most excited around this.

**Sherwin Wu** (00:42:22):
It's like maybe the support team operations lead who doesn't code, but loves using these tools and is like an Excel wizard or something. And so it's like technical adjacent or like coding adjacent and pretty technical. Those are the kinds of people I've seen in these companies who just really light up and get excited around this. And you can usually build a team around that.

**Sherwin Wu** (00:42:46):
But yeah, it's like oftentimes not software engineers. Software engineers I think will understand this, but not every company has software engineers. It's actually kind of a rarity. They're hard to find, they're expensive. And so it's these other types of folks.

**Lenny Rachitsky** (00:42:58):
What I'm hearing is the anti-pattern is top-down, this very, the CEO found an exec team just like, "We are going to go AI-first. We're going to lead into AI. Everyone's going to be judged on their performance using AI tools, how much your productivity is increasing thanks to AI." And with that being just top-down and not creating a team that is bottom-up, spreading the gospel, you find that doesn't work.

**Sherwin Wu** (00:43:23):
Yeah. Yeah, exactly. Exactly.

**Lenny Rachitsky** (00:43:25):
And the advice is find the people that are most excited and instead of having them spread out through the organization, what you find works is create a little AI kind of evangelist team that finds ways to use it and kind of spreads it across the work.

**Sherwin Wu** (00:43:39):
Yeah. I mean, another, just kind of like hearing you play back to me, another way to think about it, kind of tying back to my own management philosophy, is find the high performers in AI adoption and empower them. Let them build hackathons, let them hold seminars, do knowledge sharing, create the seeds of excitement internally.

**Lenny Rachitsky** (00:43:57):
Okay, amazing. There's a couple hot takes I want to hear from you, something that I've seen you talk about and share. One is you've shared that talking to customers and listening to customers is not always the right strategy in AI, and it might often lead you astray.

**Sherwin Wu** (00:44:13):
I don't know if it's that hot of a take. I think the main thing here is, so obviously you should talk to your customers. It's like you still talk to customers. I just think the AI field, especially what I've seen over the last three years working on the API and seeing all that evolve, is the field and the models themselves are just changing so, so quickly, they tend to disrupt themselves, especially around the tooling and the scaffolding space. So there's this quote that I read actually earlier this week, it's from an X article by this guy named Nicolas, who's the founder of a startup called Fintool, where I think he was sharing a lot of the best practices that he has learned through building AI agents for financial services, I think at his startup Fintool.

**Sherwin Wu** (00:44:58):
And this phrase that I thought was really good, which is the models will eat your scaffolding for breakfast. If you rewind back to 2022, right when ChatGPT launched, these models are pretty raw and there was like all this product scaffolding and things, especially in the developer space, to basically try and steer the model and build a scaffolding around it to get it to do what you want. Like agent frameworks, there's like vector stores I think was like really popular back then and just like a whole smattering of tools here.

**Sherwin Wu** (00:45:30):
And as you've kind of seen the field play out, the models have just changed so much and gotten so much better that they ended up literally eating some of the scaffolding. And I think this is even true today. So I think that the article from Nicolas actually, the current scaffolding which is fashionable is Skills, files-based context management. I could see a world where at some point that's no longer useful, where the model can actually manage all that themselves, or there might be, it's hard to predict, but might move on to some new paradigm where you'll already need this file-based Skills type thing.

**Sherwin Wu** (00:46:05):
You have literally seen this play out where like the agent frameworks I think are a little less useful now. There was a period of time in like 2023 where we thought vector stores is going to be like the main way for you to bring organizational context into the models and you need to vectorize and embed every bit of your corpuses and then you need to do all this work to figure out the vector search to optimize that to fill out the right information at the right time.

**Sherwin Wu** (00:46:29):
All of that is scaffolding because the model was not good enough. And turns out, in this case, it turns out as the models get better, a better approach is actually to take out a lot of that logic and trust the model and give it a set of tools for search. It doesn't need to be a vector store. You could actually just hook it up to any type of search. It could literally be files on a file system like Skills and AGENTS.md to kind of steer it as well. Obviously there's still a place for vector stores. I know a lot of companies still using it, but the entire scaffolding around that and building an entire ecosystem around that and assuming that's the only scaffolding that you need has really changed.

**Sherwin Wu** (00:47:05):
And so tying this back to the like, you don't always have to listen to your customers. Because the field is changing so much at any point in time, a lot of people are kind of in this local maximum. And if you just blindly listen to your customers, they'll be like, "Yeah, I want a better vector store. I want a better agent framework for this." And if you had just kind of only chased down that path, it actually would've led you to build something that again is the local maxima.

**Sherwin Wu** (00:47:31):
Whereas as the models get better, we've had to reinvent and kind of rethink the right abstractions and the right tools and frameworks to build around these models. And the cool/exciting/kind of crazy annoying part is it's a moving target. And so yeah, the current smattering of tools and frameworks right now will likely need to evolve and change pretty significantly over time as the models get smarter and better. But that is just the nature of building in this space. I think that's what makes it exciting, but it also means when you talk to customers, you kind of need to balance the exact feedback that they want with where you think the models are going and where you think things will trend over the next one to two years.

**Lenny Rachitsky** (00:48:10):
It's interesting how this is, the bitter lesson is this big lesson that AI and ML folks learned, which is just like, the less you overcomplicate, the less logic you add to machine learning and to AI, the more it'll be able to scale and grow and just take it all away and let it just compute basically. Just give it more power to get smarter on its own.

**Sherwin Wu** (00:48:31):
Yeah. There's literally a version of the bitter lesson applied to building with AI where we were trying to architect all this stuff around and it turns out the models will just kind of eat it all away. And honestly, OpenAI API team has been guilty of this where we kind of took some left and right turns when we shouldn't have. But yeah, the models still end up, the models get better and we're all learning the bitter lesson day in and day out.

**Lenny Rachitsky** (00:48:57):
So what would be the key takeaway for folks building on say the API or just building agents and having to build a little bit of this around for now, is it just, yeah, what would be the advice?

**Sherwin Wu** (00:49:08):
My general advice, and I've been giving this to people for a while and I think it's still true today is make sure you're building for where the models are going and not where they are today. It's clearly a moving target. And I think a lot of the companies that I've seen, startups that I've seen really, really do well is they build a product for an ideal type of capability that is like maybe 80% of the way there today. And they end up having a product that kind of works, but is just almost there.

**Sherwin Wu** (00:49:39):
But then as the models get better, suddenly it might click and then their product now is incredible because it works like maybe with like o3, at some point it suddenly works with 5.1, 5.2, suddenly it unlocks it, but they're building these products with the model capability improvements in mind. And with that, you end up creating an experience that's way better than if you had assumed that it's static in the first place.

**Sherwin Wu** (00:50:02):
And so that'd be my general advice, which is build for where the models are going and not where they are today. You end up building a better product. You may need to wait a little bit, but the models are getting so much better so quickly, you often don't need to wait that long.

**Lenny Rachitsky** (00:50:16):
So to follow that thread, like in the next 6 to 12 months, where is the API heading? Where's the platform heading? Where are the models heading? As much as you can share, I know there's a lot of secrets here, that maybe you're most excited about, or you think that people should start to prepare for and however much you can share?

**Sherwin Wu** (00:50:34):
I mean, so the obvious one is how long of a task these models can do coherently. So there's like the METR benchmark that I think tracks software engineering tasks and how long of a task can these models do 50% of the time, 80% of the time. I think we're at something like multi-hour tasks being able to be done by... software engineering tasks being able to be done by these frontier models 50% of the time. And then I think 80% is something like just under an hour.

**Sherwin Wu** (00:51:06):
But the sobering thing about that chart is they plot all the previous models on this chart as well. So you can really see the trend of this. That's something that I'm really excited about, which is, I actually think products today really optimize for tasks that the model can do for minutes at a time. Even Codex and the coding tools, I'd say, it's in the CLI, you're kind of seeing it be interactive.

**Sherwin Wu** (00:51:28):
It's really quite optimized well for maybe at most 10 minute type tasks. I have seen people push Codex to the limit and do multi-hour long tasks. But again, I think that's more of the exception. But if you follow this trend, I think in the next 12, 18 months, we could see models that could do multi-hour long tests very, very coherently. At some point it might reach like six hours, a day long task where you kind of like dispatch it and have it do things on its own for a while. The types of products you build around that will look very different. You want to give the model feedback. You obviously don't want it to completely run wild for a day. Maybe you do, but you probably don't. And then the universe of things you can have the model do really expand. So that's something that I'm really, really excited about seeing.

**Sherwin Wu** (00:52:15):
Another thing over the next 12 to 18 months, what I think would be really cool is improvements in the multimodal models. And actually by multimodality, I'm mostly thinking about audio here where the models are pretty good at audio, I think they're going to get a lot better at audio over the next 6 to 12 months, especially the native multimodal models, the speech to speech ones. I think there's also interesting work being done around new types of models and architectures on the multimodal audio side as well.

**Sherwin Wu** (00:52:48):
But audio, especially in the enterprise and in a business setting, I think is a hugely underrated domain still. Everyone talks about coding, it's all text, but we're talking in audio. A lot of the world's business is done via audio. A lot of services and operations are done via talking and audio. And so I think that area is going to look very exciting in the next 12, 18 months. And I think there will be even more unlock for what we can do with audio models there as well.

**Lenny Rachitsky** (00:53:17):
Amazing. So quick summary, expect agents and AI tools to run longer, that trajectory to continue to increase, and then audio and speech becoming a bigger deal, more first party and native and better and core to the experience.

**Sherwin Wu** (00:53:34):
Yeah.

**Lenny Rachitsky** (00:53:35):
Extremely cool. Okay. I want to go back to one of your hot takes, another hot take that I've seen you discuss. You're very bullish on business process automation as an opportunity in the world of AI. Talk about that.

**Sherwin Wu** (00:53:47):
Yeah, this goes back to the thing that I said previously, which is we live in a bubble in Silicon Valley and a lot of the work that we do that we're used to, software engineering, product management, building products is very differently shaped than the work that goes on that runs our entire economy. And I see this day in and day out when I talk to customers. If you talk to any company that's not based in, it's not a tech company, there's a lot of business processes.

**Sherwin Wu** (00:54:18):
And so what I mean by this is, I generally delineate it as software engineering is kind of like open-ended knowledge work, right? And this is why I think tools like Codex tend to be quite good because it's exploring and you're giving it these open-ended things, but software engineering is fundamentally pretty open-ended and is not very repeatable. So you build a feature, you're not trying to build the exact same feature over and over again.

**Sherwin Wu** (00:54:42):
And a lot of tech jobs are in this space. I think data science is kind of in this space as well, even some of the strategic finance stuff. But as you move further and further away from software engineering and like what is core in tech, a lot of jobs are just business processes. They're like repeatable things, repeatable operations that some manager at a company has kind of like iterated on. There's usually a standard operating procedure that people want to do and you don't want to deviate from it that much. In software engineering, the ingenuity is in deviating, but a lot of the work being done in the world is actually just running through these procedures and operations. If I call a support line, they're running through one of these. If I call my utility company, there's a bunch of processes and things that they can and cannot do for me.

**Sherwin Wu** (00:55:35):
And so I'm just extremely bullish on this general category of like... And I think it's underrated because it's so different from what we think about in Silicon Valley, people tend to not think about it, but how can we apply AI and some of the tools and frameworks that we have towards this business process automation, towards automating and making easier repeatable business processes with high determinism that is fully integrated with business data and business decisions and different systems within an enterprise and how can we actually make that process better? Because I actually think there's a lot of opportunity and a lot of work to be done in that area. And we just don't talk about it because it's a little bit less in our wheelhouse.

**Lenny Rachitsky** (00:56:20):
So your take here, just to make sure I fully understand it, is you think there's a much bigger opportunity outside of engineering for AI to impact productivity of companies and also jobs of these folks that are doing these kind of repetitive, easily automated tasks?

**Sherwin Wu** (00:56:35):
Impact jobs and also just impact how work is done. So much of work is done in this way. Basically, I talk to customers all the time, big enterprises, like, "How will AI transform my company? How will it run in a world with AI in 20 years?" And software engineering is part of the story, but there's so much more on the business process side. And I actually think it might look even more different on the business process side and the work there is pretty substantial.

**Sherwin Wu** (00:57:04):
It's actually interesting. I don't know if from an absolute percentage or absolute basis, I don't know if it's bigger or smaller than software engineering. Software is pretty huge and pretty expensive as well, but it is pretty massive and it's definitely bigger than... It's bigger than you would think it is based off of how people talk about it or don't talk about it on X or Twitter.

**Lenny Rachitsky** (00:57:23):
Okay. And going in a slightly different direction, having built a platform, building the API, people building on API, the biggest question on people's minds is always just, how do I not have OpenAI squash my idea and build their own thing and then destroy this market I created? What's the general policy, what's the general philosophy of how startups should think about where OpenAI is unlikely to go?

**Sherwin Wu** (00:57:49):
My general answer here is the market is so big and so massive. I actually think startups should just not overly think about where OpenAI or these labs are going. I've talked to a lot of startups that have not worked out, startups that are doing really well. Every startup that I've seen that is kind of fizzled out is not because OpenAI or a big lab or Google or something has come and squashed them. It's because they built something and it really didn't resonate with the customers. Whereas the ones that take off, even in very competitive spaces like coding, Cursor is huge at this point and it's because they built something that people really love. And so my general advice is like, don't overly stress about this. Just build something that people like and you will have a space in this.

**Sherwin Wu** (00:58:35):
I can't overstate how big of an opportunity there is right now. The opportunity space in building with AI is so big. A good example of this is the space is so big that the Overton window of what is acceptable and not acceptable for VCs to do has completely changed here. VCs are investing in competitive companies left and right. It's just like the space is so big because the opportunity is unlike anything that we've seen before.

**Sherwin Wu** (00:58:59):
And while that affects how VCs operate, from a startup perspective, it's like the most empowering thing in the world because even if you just build something that some people really, really love, you will end up with a massively valuable business. And so that's why I tell people, "Don't overthink about it." The other thing I also think is important to remember, at least from an OpenAI perspective, one thing that we've always held very near and dear, which both Sam and Greg helped reinforce from the top as well, is we actually view ourselves fundamentally as a ecosystem platform company. The API was our first product.

**Sherwin Wu** (00:59:34):
We think it's really important for us to foster this ecosystem and continue to support it and not squash it. And so if you kind of look at the decisions we make, this is all weaved through it. Every single model we've released in one of our products gets released in the API. Even we release these Codex models now that are a little bit more optimized for the Codex harness, but they always find their way into the API and all of our customers end up using those. We don't hold back on any of that. I We think it's really important to keep our platform neutral. And so we don't block competitors. We allow people to have access to our models. We also want, we've recently been testing more of the sign-in with ChatGPT product as well. And so we want to foster this ecosystem. I think it's really important that we do so.

**Sherwin Wu** (01:00:19):
The general thinking about this is a rising tide lifts all boats. And we might be an aircraft carrier. We're pretty big at this point, but we think it's important to raise the tide because everyone kind of benefits and I think we'll benefit as well. Our API itself has grown pretty significantly because we act in this way. And so I'd really encourage people not to view OpenAI as this kind of thing that'll just shove people out of the way, but instead focus on building something valuable. And we remain committed to providing an open ecosystem.

**Lenny Rachitsky** (01:00:51):
Why is that important to OpenAI? Just this focus on building a platform, creating a way for people to build businesses? Is that just that's been the vision from the beginning? We want this to be a platform?

**Sherwin Wu** (01:01:04):
It's been the vision from the beginning. It goes back to our charter actually, like our mission. So OpenAI's mission has always been, one, to build AGI, so we're obviously doing that. But then the second thing is to spread the benefits of it to all of humanity. And the main part there is all of humanity. And obviously ChatGPT is trying to do this. We're trying to reach however many, the whole world. But very early on, and this is why we launched the API back in, I think it was like 2020 or something really early. We don't think we as a company will be able to reach all of humanity. I don't know, every corner of the world's pretty deep. And so we actually feel like in order for us to fulfill our mission, we need to have some platform style thing here where we can empower other people to build the customer support bot for podcasters and newsletter hosts because we're not going to be able to do it ourselves.

**Sherwin Wu** (01:01:58):
And so we've largely seen this play out with the API. This is why we talk to so many of our customers and really love seeing the diversity of things built on. But yeah, it's been there since day one because we view it as an expression of our mission.

**Lenny Rachitsky** (01:02:12):
And you haven't even mentioned the app store that you guys are launching, the ChatGPT app store.

**Sherwin Wu** (01:02:16):
Yeah.

**Lenny Rachitsky** (01:02:17):
Is that under your umbrella, by the way, or is that a different org and team?

**Sherwin Wu** (01:02:20):
It's a different team. So it's under ChatGPT. We obviously collaborate very closely with them. And they built an apps SDK, which was built in close collaboration with our team. But that is more within the ChatGPT umbrella. But that's another example of this. It's like ChatGPT is... We kind of have these 800 million weekly active users who are just coming over and over again. It's a great asset to have as a business, but man would it be better if we could somehow allow other companies to come in and take advantage of this as well and build for this audience as well. And then ultimately, we think it'll help us expand that group as well. And so it all kind of comes back to the mission and we find that being a platform being open tends to help here.

**Lenny Rachitsky** (01:03:05):
Just that number, 800 million, I think it's MAs just like-

**Sherwin Wu** (01:03:09):
No, no, no. It's weekly. Weekly active users.

**Lenny Rachitsky** (01:03:11):
Weekly active.

**Sherwin Wu** (01:03:12):
Yeah, it's crazy.

**Lenny Rachitsky** (01:03:13):
Almost a billion people using weekly. It's absurd how these numbers we're just used to now, but that's insane. Unprecedented.

**Sherwin Wu** (01:03:22):
Yeah. It's mind-boggling for me to think about from a scale perspective, honestly. And the way I think about it is 10% of the world, and growing, by the way, it's shooting up, come to ChatGPT and use it every day, or sorry, every week.

**Lenny Rachitsky** (01:03:37):
And this point, I just want to double down on this point you're making. OpenAI's mission was to make AI available to all of humanity. And I think some people diss that, they're like, "Oh, it costs money." And the fact that there's a free version of ChatGPT that anybody can use that is not so different from the most powerful AI model that exists in the world for free, that's not gated, that anyone can use. If you're a billionaire, there's only so much more you can get out of AI than what someone in a village in Africa can get. And I know that's always been really important to OpenAI.

**Sherwin Wu** (01:04:11):
Yeah. Yeah. I mean, look, that's why I think we've leaned into the health work, we've leaned into education's going to be very interesting here. The other insane kind of trend here is the free model has gone so smart over time. The free model back in 2022 was good at the time, but it's like nothing compared to what you get today because you get GPT-5 today. And so the raising the floor across the world is kind of something that we're really trying to do. And we view it as part of our mission.

**Sherwin Wu** (01:04:42):
The other flip side of this, by the way, is talking about the billionaires or whatever. I know people love saying you're using the same iPhone that Mark Zuckerberg's probably using or what the billionaires are using, but for like $20 a month, you're basically using the same AI that the billionaires are using. For $200 a month, you get the same pro model that all the billionaires are using, but they're probably not using Pro for everything. They're probably just using the plus tier ones for their day in and day out. And so yeah, this kind of democratization and just spreading of this benefit across all of the world is something that's really meaningful to us and something that drives a lot of what we do.

**Lenny Rachitsky** (01:05:22):
One last question, just for folks that are thinking about building on the API are just like, "Oh wait, I could do cool stuff with OpenAI's models and APIs." What does your API and platform allow people to do? I know you can build agents on top of the platform. Just talk about what you allow.

**Sherwin Wu** (01:05:37):
So fundamentally, the API offers a bunch of developer endpoints, and these developer emperors basically let you sample from our models. The most popular one that we have right now is one called Responses API. And so this is an endpoint and it's optimized for building long running agents, so agents that'll work for a while. So what you can basically do is at a very low level, you're basically just giving the model text. The model will work for a while. You can kind of pull it to see what it'll do, and then you'll get the model response back at some point. That's like the lowest level primitive that we have for people. And that's actually what a lot of people use. That's the most popular way of building on top of our API. With that, it is super unopinionated and you can do basically whatever you want. It's like the lowest level thing.

**Sherwin Wu** (01:06:24):
We've also started building more and more layers of abstraction on top to help people build some of these. And so next layer up, we have this thing called the Agents SDK, which has also gotten extremely, extremely popular. This allows you to use the Responses API or some other API endpoints that we have to build what you might more traditionally think of as an agent, like an AI working in an infinite loop. It might have sub-agents that it delegates to. It starts building all this framework, all the scaffolding actually. We'll see where this all goes, but it makes it a lot easier for you to build these kind of agents, giving it guardrails, allowing it to farm out sub-tasks to other agents and kind of orchestrate a swarm of agents. The Agents SDK allows you to do that.

**Sherwin Wu** (01:07:08):
And then above that, we've now started building tools to help also with the meta level of deploying an agent. So we have this product called AgentKit and widgets, which are basically a bunch of UI components that you can use to very easily build a very beautiful UI on top of either our API or Agents SDK because a lot of times these agents look very similar from a UI perspective. And so there's AgentKit. We also have a smattering of evals products, like an evals API where if you want to test and see if your agent or your workflow's working, you can test it in a very quantitative way using our evals product.

**Sherwin Wu** (01:07:50):
And so yeah, I view it as these various layers. They're all kind of helping you build what you want with our AI, with our models, and with increasing levels of abstraction and how opinionated it is. And so you can use the whole stack and it very quickly allows you to build an agent, or you can go down the stack as low as you want to basically Responses API and build whatever you want because of how low low level it is.

**Lenny Rachitsky** (01:08:17):
Sherwin, is there anything else that you want to share? Anything else you want to leave listeners with? Anything we haven't touched on that you think might be helpful before we get to our very exciting lightning round?

**Sherwin Wu** (01:08:26):
The only thing I'd leave folks with is, yeah, I think the next two to three years are going to be some of the most fun in tech and in the startup world that we'll have in a very long time. And I would just encourage people to not take it for granted. I entered the workforce in 2014. It was great for a couple years. I felt like there was a period of five to six years where it wasn't very exciting in tech. And then in the last three years, it's just been the most insanely exciting, energizing period of my career. And I think the next two to three years is going to be a continuation of that.

**Sherwin Wu** (01:09:01):
And so would encourage people not take it for granted, I'm trying to not take it for granted. At some point, this wave's going to play out and it's going to be a lot more incremental, but in the meantime, we're going to get to explore a lot of really cool things, invent a lot of new things and change the world and change how we work. And so that's the main thing I'd leave folks with.

**Lenny Rachitsky** (01:09:19):
I love this message. I want to spend a little more time on it. When you say don't miss it, what do you recommend people do? Is it just build, lean in, learn, join a company building really interesting things? What's your advice to folks that are like, "Okay, I don't want to miss the boat."

**Sherwin Wu** (01:09:32):
Yeah, I would just say engage with it. So it's basically like what you said, lean in. Building tools on top of this is part of the story. Just using the tools, you don't need to be a software engineer to lean into this. I think a lot of jobs are going to change here. So just using the tools, understanding the limitations of what it can and cannot do so that you can kind of watch the trend of what it can start to do as the models improve. And so it's basically getting used to this technology and getting familiar with it instead of laying back and letting it pass you.

**Lenny Rachitsky** (01:10:07):
On the flip side of that, there's a lot of, I think, stress and just anxiety around, "There's so much happening, how do I keep up? I got to learn Clawdbot this week. Oh God." Is there something you learned about it just not... You're at the center of this. How do you not get overly stressed and worried about missing things that are going on and just you stay on top of news? What are some things you've done and learned?

**Sherwin Wu** (01:10:28):
Yeah. So I think I'm personally a bad example of this because I'm basically chronically online on X and our company Slack. So I actually try and absorb. I end up absorbing a lot of it. What I will say though, just from observing other folks who are less addicted to this stuff like I am. Yeah, a lot of it is noise. You don't need to have 110% of this kind of pass your mind, go into your mind. Honestly, just leaning into one or two different tools, starting small is already more than you need.

**Sherwin Wu** (01:11:02):
Here, I think just the combination of the frenetic pace of the industry, X as a product just creates this insane pace of news, which is honestly very overwhelming. The main thing is you don't need to know all of that to really engage with what's happening right now. And even something as simple as just like install the Codex Cline and play around with it. Install ChatGPT and connect it to a couple of your internal data sources, Notion, Slack, GitHub, and see what it can and cannot do. All of that I think is a part of it.

**Lenny Rachitsky** (01:11:39):
Amazing. Sherwin, with that we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

**Sherwin Wu** (01:11:44):
Yeah. Yeah, absolutely.

**Lenny Rachitsky** (01:11:46):
First question, what are two or three books that you find yourself recommending most to other people?

**Sherwin Wu** (01:11:50):
I'll talk about one nonfiction and one fiction book. The fiction book was I just finished reading it. I really recommend it. There Is No Antimemetics Division by qntm. I think it's like an online author, but I saw it being shared on X. It's like a science fiction-y kind of book and I basically devoured it in like two days. It's super, super well written, super fascinating. It's about a government agency that's fighting things that make you forget it. And so it's just a very smart creative book and fresh, honestly, in terms of source material that I really like. So I'd recommend that one. The book is also unintentionally hilarious. So it's meant to be this sci-fi, almost like horror style book, but it made me laugh a couple times. So that's the fiction book.

**Sherwin Wu** (01:12:43):
Non-fiction, so I'm going to cheat and I'm going to recommend two of them. So in the last year, I've been reading a lot more about China and the US-China relations. And I think there are two books that came out in the last year that have been really, really eye-opening for me in that regard. First one is the Dan Wang book, Breakneck. That one was really, really good. I really liked his analogy of the lawyerly, US is the lawyerly society, China is the engineering society, and there are pros and cons to each. I read it and I was like, hmm, yeah, it does seem like we're run by lawyers in the US. So then that's one.

**Sherwin Wu** (01:13:14):
And the other one is the Patrick McGee book on Apple in China. It was super, super interesting. I'm a huge Apple fanboy. If you could see my desk right now, it's all Apple stuff. But just one, it was just super fascinating learning about Apple's relationship to China. And then two, it just had a lot of inside information about Apple as a company that I found fascinating. So it was also quite a page-turner and also a very, very timely book as well.

**Lenny Rachitsky** (01:13:39):
The antimemetics book sounds amazing. I'm buying it right now as you're talking.

**Sherwin Wu** (01:13:43):
Yeah. Yeah. I think it's only a couple hundred pages. I literally finished it in two days.

**Lenny Rachitsky** (01:13:47):
Perfect. The dream.

**Sherwin Wu** (01:13:47):
It was just so, so good.

**Lenny Rachitsky** (01:13:49):
Okay. Great tip. Okay. Favorite recent movie or TV show you have really enjoyed?

**Sherwin Wu** (01:13:53):
Yeah, that one's tough because I have two kids and a busy job, and so I really haven't had much time to watch TV shows. I will say in the last couple weeks, I watched a couple episodes. I'm actually a big anime guy. And so I watched a couple episodes. There's a new season of this anime called Jujutsu Kaisen that's out. So season three of JJK was really good. In general, I'm a huge fan of Japanese anime. I think they create the most novel and unique plots and universes that Western media has shied away from. And so generally a big fan of that. But yeah, haven't really watched much, but saw a couple episodes of JJK recently.

**Lenny Rachitsky** (01:14:39):
Extremely understandable in your role.

**Sherwin Wu** (01:14:42):
Yeah.

**Lenny Rachitsky** (01:14:42):
Favorite product you recently discovered that you really love.

**Sherwin Wu** (01:14:45):
Yeah. Okay. So I recently had to set up a wifi and like home networking and I went all-in on Ubiquiti routers and security cameras. I'd never heard of it before I had to do this. I always just had a very simple setup. And it is just such a well-built product. I don't know if you used it before, but it's basically like the Apple of home networking. So beautiful products, but the thing that actually makes it extremely good is its software is good. And so they have a really great mobile app to help manage all of the home networking.

**Sherwin Wu** (01:15:19):
And so basically Ubiquiti, you can use it to buy wireless routers. You need ethernet wiring throughout your house to use it. But I actually think what makes it really good are its security cameras. So if you have security cameras that are plugged into the Ubiquiti ecosystem, they have an incredible mobile app and Apple TV app and iPad app to kind of see the live feed of your cameras. And so they're a little pricey, but not that pricey, but it's been just an incredible product experience.

**Lenny Rachitsky** (01:15:46):
All right. I went eero, so I made a mistake.

**Sherwin Wu** (01:15:49):
Eeros are pretty good too, but-

**Lenny Rachitsky** (01:15:51):
It's not Ubiquiti.

**Sherwin Wu** (01:15:52):
Fully converted to Ubiquiti at this point.

**Lenny Rachitsky** (01:15:53):
Okay, good tip. Okay. Two more questions. Do you have a favorite life motto that you find yourself coming back to in work or in life?

**Sherwin Wu** (01:16:00):
Yeah. The one that I always repeat to myself is never feel sorry for yourself. There's a lot of things that are going to happen at work, in life, and reminding yourself to never feel sorry and that you always have a sense of agency to kind of pull yourself up is something that I've had to tell myself a lot and also something that I repeat to a lot of other folks as well.

**Lenny Rachitsky** (01:16:22):
Last question. So in your previous life, you worked at Opendoor where you led work on basically figuring out how much to pay for houses. You basically built a model that told the company, "Here's how much we'll pay for this house." What's a variable in the price of a house that you didn't expect is really important and impacts the price of a house?

**Sherwin Wu** (01:16:40):
There's a bunch that were surprising. I'll maybe list the couple of the most interesting ones. Power lines and high voltage power lines are super, super, they actually impact your price quite a lot. I didn't really fully internalize this until I went to Dallas and observed when your house sits next to one of these giant voltage lines, it was buzzing and most people have families, you don't want your kids near there. So I think that was one that really, really kind of surprised me.

**Lenny Rachitsky** (01:17:10):
That makes sense.

**Sherwin Wu** (01:17:11):
Yeah. And then the other one, which was something that was always something really difficult for us to quantify was floor plans. And so it is very important. Yes, of course it's really important, but just quantifying what a good floor plan is like and what a really bad floor plan is like. We were doing all these things with how wide is the kitchen and what style of kitchen is it and then where's the master bedroom? And so it was just really, really hard to quantify. But I remember floor plan was a big one because we'd have a home that wouldn't sell and then our ops team would go in and be like, yeah, it's the floor plan issue. So how could you tell? You go inside, you just feel it. The floor plan feels off.

**Sherwin Wu** (01:17:50):
So yeah, those were ones that were surprising. And then the last one that was more impactful than I thought is general curb appeal and even the front door. And so I actually think there's a Zillow book on this where the front door placement tends to be the highest ROI for homes, but just the feel of like as you walk up to the home as a buyer, what you're interacting with and the first moments of the house, I think I'd underrated its importance.

**Lenny Rachitsky** (01:18:18):
That is extremely interesting. And I love that you had to figure how to do all this in code and not walking around look at these houses.

**Sherwin Wu** (01:18:25):
Yeah. And then floor plans. I have a bunch of stories around floor plans, it's not digitized so there's like a handful of people who have paper floor plans of all these homes in Phoenix and Dallas. Yeah, a lot of fun stories from the Opendoor days.

**Lenny Rachitsky** (01:18:38):
Okay. Sherwin, thank you so much for doing this. This was incredible. Where can folks find you online and how can listeners be useful to you?

**Sherwin Wu** (01:18:45):
Yeah, so I'm online on Twitter on X. I'm just @SherwinWu. And yeah, I mostly just tweet about OpenAI and the API and some of the products that we're launching. And then how folks can be useful to me. I love hearing about things that people are building. And so if you're working on a startup, if you're hacking on an idea, would love to... Just reach out to me on X. I would love to hear about what you're building and learn about how OpenAI can help support you.

**Lenny Rachitsky** (01:19:11):
Amazing. Sherwin, thank you so much for being here.

**Sherwin Wu** (01:19:14):
Yeah. Thank you, Lenny.

**Lenny Rachitsky** (01:19:15):
Bye, everyone.

**Lenny Rachitsky** (01:19:16):
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [12/15] Sequoia CEO coach: Why it’s never been easier to start a company, and never been harder to scale one | Brian Halligan (co-founder, HubSpot)

**Brian Halligan** (00:00:00):
The thing about being a founder/CEO is there is no one there to rescue you. Your parents aren't going to rescue you, your VC is not going to rescue you, that kind of hits you when you hit your first crisis.

**Lenny Rachitsky** (00:00:08):
Starting a company has never been easier, scaling one into a durable high impact organization has never been harder.

**Brian Halligan** (00:00:13):
The number of companies formed is going to much fewer over the next 10 years relative to the last 10 years, it's just going to be hard to stand out and really accelerate.

**Lenny Rachitsky** (00:00:21):
What's most different about what it was like to be a CEO maybe 10, 20 years ago versus today?

**Brian Halligan** (00:00:25):
There's a massive tax in optionality when you can move this fast and try a lot of things. It puts pressure on the CEOs to be faster and better decision makers.

**Lenny Rachitsky** (00:00:35):
A lot of people in the world want to be founders, they want to be CEOs.

**Brian Halligan** (00:00:38):
I don't think anyone can do it. People talk about 9-9-6, it's way more than that. Founders are seven days a week, they're always on, [inaudible 00:00:44] Sunday nights, it's full contact.

**Lenny Rachitsky** (00:00:46):
Do you feel like there are specific profiles or traits to be successful?

**Brian Halligan** (00:00:50):
I look for four things, I call it my lock algorithm.

**Lenny Rachitsky** (00:00:56):
Today, my guest is Brian Halligan, co-founder and longtime CEO of HubSpot. I asked Brian to come on this podcast because he is, more than anyone I've met, a student of the job of a CEO. After leaving HubSpot last year, he became the in-house CEO coach at Sequoia, where he brings together dozens of top CEOs to learn from each other, he does one-on-one coaching with some of the world's top CEOs, he also hosts a popular podcast, called Long Strange Trip, where he interviews some of the world's most successful CEOs. In this conversation, we unpack what it takes to be a successful CEO in today's era.

**Lenny Rachitsky** (00:01:32):
Let's get into it, after a short word from our wonderful sponsors. Applications break in all kinds of ways, crashes, slowdowns, regressions, and the stuff that you only see once real users show up. Sentry catches it all. See what happened where and why. Down to the commit that introduced the error, the developer who shipped it, and the exact line of code, all in one connected view. I've definitely tried the five tabs and Slack thread approach to debugging, this is better. Sentry shows you how the request moved, what ran, what slowed down, and what users saw.

**Brian Halligan** (00:04:02):
Thanks for having me, Lenny.

**Lenny Rachitsky** (00:04:04):
It's my pleasure. I want to start with something that I've heard your board members, the way they described you, is someone with a perpetual state of constructive dissatisfaction. Do you think this is a core foundational trait of successful CEOs, successful leaders?

**Brian Halligan** (00:04:21):
By the way, I like that description, when she did, a woman named Lorrie Norrington, who [inaudible 00:04:26] said that. I like it, I took it as kind of a compliment, and I liked it. So, I spend most of my time these days coaching very fast-growth CEOs. They all are kind of like that. They're all in kind of a state of perpetual dissatisfaction, but in a positive way. One of the things, by the way, I like about the current crop of CEOs, they don't really take stock of what they've done and feel it, they're always a little bit dissatisfied with where they are, and very focused on the end state. And I've been surprised at how humble this generation is of CEOs. And I think of my generation of CEOs as being, I don't know, humble wasn't the first word that would come out of your mouth when you describe my generation. But this generation I feel like is different, and I've been impressed with it.

**Lenny Rachitsky** (00:05:25):
Okay, I have a bunch of questions along these lines, because one, you've been a CEO of an incredibly successful company for a long time, for about 20 years, before you moved on to this new chapter, now you work with a bunch of CEOs, you're Sequoia's in-house CEO coach, there's a few things that I've heard you do. One is you gather groups of CEOs, and what I've read is that you have kind of two tables. You have the kids table and the adults table. The kids table is CEOs that are companies that are about under 100 employees, the adult table is over 100 employees. So, let me ask you just, when you look at CEOs that move from the kids table to the adult table, other than just they scale and grow, what is it that these CEOs that graduate from kids to adults table do differently?

**Brian Halligan** (00:06:11):
The adults are really focused on, all they really want to talk about is their exec team. Their direct reports, how do you build our exec team, that next level down, org design... You would be surprised how much they think about that. And on average, I would say the adults are spending half their time just recruiting and interviewing. It's pretty all consuming. And I remember that from that phase in HubSpot's growth. And it surprises people like, wow, my job is really just to interview and hire, I didn't know that was going to be the case. So, that is one that they are making that transition, and I would just say in general, people are very bad at this, and HubSpot was too.

I think CEOs and everyone dramatically overrates their ability to interview, and overrates their gut feeling, and underrates a really high quality blind reference. And I interviewed Dave, the CEO from MongoDB the other day, and he had an interesting [inaudible 00:07:22]. On average over his 10-year lifespan as a CEO of Mongo, there were two C-levels turned over per year. That's a lot of turnover at the top, and I didn't keep track of it like Dave, but I think HubSpot was kind of similar. And all of these startups are kind of similar too. And so, people are working on that, and struggling with it, is one thing in common with all of them.

**Lenny Rachitsky** (00:07:49):
What do you do when you coach someone on that? When you're like, okay, you think you're amazing at interviewing, you think you know who's going to work out, what advice do you give them to help them develop that skill?

**Brian Halligan** (00:07:59):
I think even me, I've been doing this for 150 years, I still think I overrate my ability to interview someone, and really know if they're a good fit. I give a couple pieces of advice. Parker Conrad has a good hack that I liked. Before he's got a C-level interview, CFO, chief product officer, whatever, he has some sign an NDA, and sends them the last board deck, or the board memo, or some important doc, and he schedules a half hour interview with them, and he just has a chat about the decks. And if they're just very complimentary, and it's so great, and you're doing this amazing thing, it's a major red flag to him because he wants someone that will challenge him and not a yes person. And I thought that was a pretty good hack to get inside someone's head and how they think and how they'll interact with you.

**Brian Halligan** (00:08:53):
Getting on a whiteboard and working through a problem I think is always a good thing. I think the standard interview of walking through your background, I don't think is all that valuable. And I coach people to do blind references, find someone you know that work with them... VCs are good at this by the way. And I get a lot of these, and you can tell some of them are like, we've already decided we're checking the box, versus they're asking me hard questions about this person. And one of my favorite questions people ask is, would you enthusiastically rehire this person for that role? Which I think is a really good question. On a scale of one to 10, how likely is it that you'll try to rehire this person back from me down the road?

**Brian Halligan** (00:09:37):
I think those types of questions are good, so not mailing an in on those blind references I think is really good. My other piece of advice, and no one listens to me on this, is hire slow and fire fast. People hire fast and fire slow. If I had to guess, Lenny, with 18 months after you hire a C-level exec, at least 50% of the time they're gone. High mortality rate on them, it's harder than people think.

**Lenny Rachitsky** (00:10:10):
And so, what you're saying here is there's only so much you can actually do to increase those odds.

**Brian Halligan** (00:10:15):
I think you can, I think the blind references are key, I think doing real interactive, working on a project together is key. I'll tell you one of the things we learned at HubSpot about this. We would have a candidate come in, let's say a head of engineering, and we'd have eight people interview them. And our scale's one to four. And let's say four people were four out of four, and four people were two out of four. So, that's candidate A. And then the next candidate comes in, eight people interview them, and everyone's a three out of four. Almost every time we hired the three out of four, the person with the least amount of weaknesses. And we changed it, and we went with the spikier people, we went with people with weaknesses, we with people who would challenge stuff, and that has worked out quite well.

**Brian Halligan** (00:11:04):
Our hit rate at HubSpot's improved. We also have shrunk the pool of people on that interview panel from eight to four. We just hired a head of product, and there were just four of us that interviewed them. I think that worked too. So, I think there's things you can do to get better at it, for sure.

**Lenny Rachitsky** (00:11:21):
Okay. This is incredibly tactical and useful. On the references piece, the toughest part is getting people to be honest because there's very little upside to them to say negative things about people. Is there anything that you've learned to help get real honest answers from folks you call for references?

**Brian Halligan** (00:11:38):
Well, I can just say I don't do this a lot anymore, but when people call me, I can tell if they've already decided, and when they're really just looking for... When they ask me for the strengths and weaknesses I'm like, oh, they've already decided. When they ask me something hard, like on a scale of one to 10, how likely you're to hire them again? Stuff like that, that kind of gets at the core. Or were they the top 1% of your employees? That's a good question. Oh, were they top 10? Oh. That type of question is pretty good. So, when I'm on the other side of it, I like when those types of questions come up.

**Brian Halligan** (00:12:16):
I tell you the other mistake everyone makes, and all the CEOs we're making now, is you're hiring for that whatever, head of engineering, and you're blown away by the resume. Like you're 50 employees and you're hiring this person, who's been at Microsoft the last 10 years, and has a fancy title, and is a fancy division in Microsoft, and you hire them, there's just a massive impedance mismatch when you hire them on what their expectations are, and what your expectations are, in the extent that you get your shit together. It's just you don't. You definitely don't, if you're 50 or 500 employees. And they expect you to have your act together. So, that is another... Avoid the big company hire. We hired so many people from Salesforce, and Google, and Microsoft, like 100% attrition rate on all those folks.

**Lenny Rachitsky** (00:13:11):
Something that I've seen that a lot of companies is there's phases of like, okay, now it's the McKinsey, a cohort comes in, and we think that's going to be the answer, and then it's the Apple group, and then that didn't work out, then the Amazon group.

**Brian Halligan** (00:13:22):
The McKinsey one never works. It never works. It never works. By definition, they would fail on my spectrum of... Most founders are like me, they are skeptical of dimensional wisdom, they're unhappy with the world works in some way, and so they're kind of far on that spectrum of rethinking dimensional wisdom. And almost by definition somebody who goes to work for McKinsey is very conservative in their outlook. And so, I think that almost always fails.

**Lenny Rachitsky** (00:13:50):
We're on this s hiring thread, so let me keep following this conversation. I read somewhere that you recommend building your team like the 2004 Red Sox. What does this mean?

**Brian Halligan** (00:14:03):
Well, I'm a big sports fan, a big Boston Red... And the Boston Red Sox hadn't won, Lenny, a World Series in 86 years, since they traded Babe Ruth.

**Lenny Rachitsky** (00:14:12):
I remember that.

**Brian Halligan** (00:14:13):
And they finally broke, and they finally won it in 2004. And the way they won it was they had a team of a bunch homegrown, really high quality, inexpensive talent that they drafted and came through the farm system. And then they got a few free agents, like David Ortiz, that a lot of people have heard of, that they paid a fair amount of money to. Peter Martinez and Curt Schilling were kind of the canonical older been there, done that, bigger company folks. And they mixed really nicely, the culture really worked. And I think that's the key. I think people underrate their homegrown talent, almost across the board they underrate it. And I think you want that mix. You don't want to hire a whole bunch of been there, done that, and you don't want to hire [inaudible 00:14:57].

**Lenny Rachitsky** (00:14:57):
I imagine this is public, but you're now part owner of the Red Sox, is that right?

**Brian Halligan** (00:15:00):
I am a part owner of the Red Sox, yes.

**Lenny Rachitsky** (00:15:02):
Okay, I have questions for you along those lines.

**Brian Halligan** (00:15:04):
Okay.

**Lenny Rachitsky** (00:15:06):
Okay, that's amazing advice. Kind of what I'm taking away here is people see all these fancy logos, amazing person, VP, this, that at Salesforce, Amazon, Google, whatever, and what you're saying here is don't underestimate the power of someone internal, rising to the occasion.

**Brian Halligan** (00:15:22):
Yeah. If you look at HubSpot, half the management team are folks who had been there for approximately 150 years, which I like. And same with, you look at Apple, a lot of those people are homegrown.

**Lenny Rachitsky** (00:15:32):
And so, is there any tips here for doing this well, is it just give people a chance?

**Brian Halligan** (00:15:36):
I tend to give people a chance. It's like, if you're interviewing someone that's homegrown, and they're VP for that C-level job, versus hiring someone from the outside. I hire someone from the outside, they're very good at interviewing, from a big company, they look fancy, they're shiny, you haven't seen their warts, hard to figure out their warts unless you're very good at line referencing, and so you tend to overrate them and underrate your homegrown. So, if it's pretty close, I think you give your homegrown a shot at it.

What's interesting to me, Lenny, is Brian Chesky sort of rethought a lot of this stuff, and he's like everyone's over-rotating to the homegrown to the experienced talent and management teams and delegation, I think he's mostly right about that. People haven't really followed that. People are, they're hiring people from the outside quite a bit. That's kind of the standard part of the playbook that all of them are following now. It's a little different, it's actually quite different than what Brian [inaudible 00:16:32].

**Lenny Rachitsky** (00:16:32):
Going back to the conversation around CEOs, a lot of people listening to this podcast, just a lot of people in the world want to be founders, they want to be CEOs. At the same time, you look at Elon, you look at Jensen, you look at Steve Jobs, you look at you, a lot of people are like, I can't. I'm not this person, I'm not going to be as good as them. There's no world where I'm this good. Do you feel like there are specific profiles or just traits that you have to be born with to be a successful CEO, or do you think it's earnable, anybody can be successful if they really work hard?

**Brian Halligan** (00:17:02):
Go ahead and meet all these CEOs coming in, and I have a little algorithm in my head, and I look for four things. I call it my LOCK algorithm. L is for lovable, and Steve Jobs you would say is kind of rough and maybe not lovable, but he would inspire followership. You would want to follow him. And so, could I envision a 28-year-old me, graduating from business school, going to work for this person? Would I crawl across broken glass? That's question one. Two is just obsession. Are they deeply obsessed with this problem? I'm a little negative on people who came up with this problem to solve six months ago and started a company, I like people with deep founder market fit, who've been thinking about it for a long time, and have evidence in their lives of going deep down, obsessively down a rabbit hole because that's kind of what it takes to be a founder/CEO.

**Brian Halligan** (00:18:02):
The C is something I wouldn't have thought of, but this is a Sequoia thing, like chip on the shoulder. Pretty much all of them have a boulder on their shoulder, and I have a bit of a chip on my shoulder too. And the K is just for deeply knowledgeable about the domain. And so, I look for that. If I were to stick an S on it, I would say student. I look at Winston Weinberg from Harvey, or James from Profound, or Gabe from Rogo, some of these new very fast growing companies, they're students of the game. They're not just learn it alls, they're deep, deep, deep students of the game. And they're like LLMs, they're constantly, constantly learning, and it's not just learning stuff for me and their peers, but they go way back in time, and have a lot of history on stuff. So, those are some of the, kind of my little criteria I use when I'm evaluating CEOs. What do you look for, by the way, you've interviewed a ton of folks like me, what do you think is in common?

**Lenny Rachitsky** (00:19:11):
Of what successful founders? Oh my god. I wish I had the... My succinct answer, I would go to Lennybot.com, and be like, what is the common pattern across these folks? One that you didn't mention that I think is interesting, I did some research on this recently, with Terence Rohan, one is just extremely ambitious. Just trying to do something really wild that most people are like, that's crazy. You're not going to get a subscription service for all music in the world, that's just... What are you doing? [inaudible 00:19:38]-

**Brian Halligan** (00:19:40):
[inaudible 00:19:40]. Is it learnable? I notice some of, a lot of the CEOs struggling with a couple of things. Like let's say you're Winston, you're late 20s, you've never managed a team, you're probably never even captain of a sports team before, and in order to scale, you have to give people feedback constantly. It's very unnatural. It's like, I'm going to give this VP I hired a bunch of feedback, positive and negative. And if you don't get good at that, you really pay the price later. That's something I think they have to learn. They all have to learn to get a good bullshit detector. They're constantly being spun, everyone's trying to sell to them, the org is always trying to sell to them. So, that's sort of something they have to develop over time.

**Brian Halligan** (00:20:29):
They have to all get good at the inspiration thing over time. Like, you're Winston, you've never had to inspire anyone in your entire life. You went to school, and you're a lawyer for a few years, and you started this thing... Inspiration wasn't your thing. So, there's certain things you have to learn on that startup to scale up path, and the best ones learn it very fast.

**Lenny Rachitsky** (00:20:50):
This is extremely interesting and useful. So, LOCK, with an S at the end, just to kind of mirror back what you're sharing. And when you were saying you evaluate CEOs, is this for investing as a-

**Brian Halligan** (00:21:00):
Yes. Yes.

**Lenny Rachitsky** (00:21:01):
Okay, so when you're helping Sequoia decide should we invest in this company, what you look for is, LOCK... I like the S, I'm going to include it there. So, are they lovable? Are they inspiring? O, are they obsessed with this problem they're going after? Do they have a chip on their shoulder? Are they extremely knowledgeable about the problem they're going after? And it sounds like not just the problem but just the studying company's business strategy, things like that. And then, S was a student. I guess that's what S is, student is studying, being a founder, being a CEO. Okay. So, I guess going back just to the question, do you think, just to put it very simply, do you think CEOs are born, or do you think they're made? Can anyone turn into an amazing CEO?

**Brian Halligan** (00:21:44):
I don't think anyone can do it, I don't think it's just anyone. I will say, I've noticed... So, another little rubric I have, and I don't see a lot of these, but Brett Taylor's one, there's a few out there that are in Sequoia's portfolio. I call them back to the baseball thing, a five tool player. In baseball when you rank a player it's, can they hit? Can they hit with power? Can they run? Can they catch a ball? Can they throw the ball? And they rate them one to 10 at each. And it's very rare that you have a five tool player, extremely rare. And the thing that's kind of new now, are there are five tools CEOs, like Brett Taylor's one.

**Brian Halligan** (00:22:24):
You can code, you have taste, you have vision, you can sell the product, you can convince employees. This kind super CEO. And there's a bunch of them now. And I don't know, I didn't see a lot of those. That certainly wasn't Steve Jobs, he wasn't programming. It wasn't Jeff Bezos. I think there's kind of a new breed that's quite impressive.

**Lenny Rachitsky** (00:22:49):
These folks you mentioned were this good before AI became a thing, I imagine AI helps more CEOs fill the gaps that they have.

**Brian Halligan** (00:22:58):
I think AI is, it's hard to fill the gap of this guy's a developer, he's genius level, obsessive, but can he sell? Can he convince an investor to give him a lot of money and a high valuation? Can he convince brilliant employees to leave OpenAI and join him? Can he convince some big skeptical Fortune 500 enterprise to buy his product? Being able to do that, and have taste, and be able to code really well at next levels, I think is rare. I actually think it might be the other way though, where mere mortals like me, who can kind of code, all of a sudden we're going to be able to build stuff. I think it kind of goes the other way.

**Lenny Rachitsky** (00:23:42):
I love this list you shared of things that you find CEOs most have to learn. BS detection, inspiring people, giving hard feedback. Maybe the one thing that most often people that become CEOs/founders have to work on, is there a most common thread of, here's the thing you probably need to work on most?

**Brian Halligan** (00:24:00):
It's that feedback thing. All of the CEOs are building their teams, and so many are like, I have a co-founder that runs product and engineering, but I need that co-founder to step aside and be the CTO, and the thinker, and the labs person, and I need to hire somebody who can actually run the engineering machine. So many of the CEOs are going through that right now. That's a tricky transition. So many of the CEOs are layering folks, like you hired that early head of sales, he hired 10 people, but just can't quite figure out the sales profile, can't quite unpack the sales process, can't quite forecast accurately. We need to layer the person.

**Brian Halligan** (00:24:46):
Those types of conversations are very tricky and quite unnatural for homo sapiens to have if you're 25 and you've never done anything like that before. So, I see the best ones getting really good at that and studying it, and it's super uncomfortable, but they have to suck it up and get good at it.

**Lenny Rachitsky** (00:25:05):
What do you find most helps them build these skills, get better at this? Is there some tidbits of advice you give them? Is it something that they study to improve?

**Brian Halligan** (00:25:13):
I think misery loves company on this. So, what I do, the table is 15 CEOs of companies under 100 employees and the adults' table are CEOs of companies over 100 employees, about 15 of them. They talk about this with each other, it's kind of a safe space, and I can weigh in, but it's actually much more effective when their peers weigh in. I think misery really does like company on stuff like this. They learn from each other.

**Lenny Rachitsky** (00:25:41):
So, essentially it's find peers to talk to and share and be vulnerable and open.

**Brian Halligan** (00:25:44):
[inaudible 00:25:45]. And the reason I break it out is the problems with the kids' table are very different than the problems of the adult table. And they all rhyme, they rhyme a lot.

**Lenny Rachitsky** (00:25:54):
So, you teach a course at MIT around scaling startups, and it's specifically around scaling, not startups, not starting the company. And you have this quote in your syllabus, "Starting a company has never been easier, scaling one into a durable high impact organization has never been harder." Why is that the case?

**Brian Halligan** (00:26:12):
Has it ever been easier to start a company? It's so easy-

**Lenny Rachitsky** (00:26:15):
That's absolutely true.

**Brian Halligan** (00:26:18):
And the flip side of that is, how many companies... The number of companies formed is going to mushroom over the next 10 years relative to the last 10 years, and the last 10 years compared to the previous 10 years is mushroomed. I just think in my life, I'm old, and when I was a kid I'd walked into CVS corner drug store and I wanted to buy a toothbrush, there are four or five there, you pick one. And in the 90s or 2000s you go to Amazon, there are four or 5,000 toothbrushes. Four or 5,000 companies bringing us toothbrushes. It got much, much easier to make stuff.

**Brian Halligan** (00:26:58):
And even technology. AWS just made it easier to start a software company. So, it's like a huge jump, back then when we started HubSpot in 2006, but now it's going to be an even bigger jump. So, it's easier to start. Now, there's so much noise and competition, it's just going to be hard to stand out and really accelerate and scale. So, that's why I say it's never been easier to start, there's never been more competition, it's never been harder to scale.

**Lenny Rachitsky** (00:27:22):
And a big part of this is distribution, essentially, breaking through the noise is what I'm hearing?

**Brian Halligan** (00:27:26):
Yeah. And it's hard to learn that. You didn't grow up doing distribution, you don't know. So, they're all learning it, and the ones that learn fast... It's like a learning game, the faster you learn, the better you do.

**Lenny Rachitsky** (00:27:38):
Along these lines, I saw you tweet this recently, where people talk about which jobs AI is going to replace, and you said that sales is maybe the last job AI will replace. Why do you think that's the case?

**Brian Halligan** (00:27:53):
Well, if you look inside a typical enterprise, where's AI really working? Let's say inside of HubSpot. Software development is working incredibly well. Customer support, incredibly well. Legal starting to work incredibly well. But there really aren't apps in the rest of the org that have really changed things a lot. And in the go-to-market side has been kind of slow, really just support. There isn't a canonical marketing, or sales, or... Maybe the BDR is the first one. But I think ye old enterprise sales, where there's actual trust built up between two carbon-based life forms, I think will be very, very, very late to go in the white collar world.

**Brian Halligan** (00:28:42):
I think a lot about the go-to-market. I think the go-to-market is going to get turned on its head. When we started HubSpot, if I think of the way the funnel worked, you want to get found in Google, someone clicks on a blue link, they land on your website, they go down the rabbit hole, they clicked on contact sales, they wait until that sales rep's ready, go down that rabbit hole, and I think it's going to get turned on its head, where people are evaluating a product, they start in Gemini or they start in Anthropic, or they start in ChatGPT,

**Brian Halligan** (00:29:12):
And for example, ChatGPT knows everything on your website, everything beyond that, knows all your competitors. So, they will stay in there and do lots more research, and be incredibly well-educated. So, your website's a lot less important. And then they go to your site, I think sites will change where you're going to have a really high quality avatar that knows everything about your products, knows everything about your company, and your pricing, and packaging, and you can have a high quality conversation with that person. That person that will get stored in your CRM, and will get scored.

**Brian Halligan** (00:29:46):
Is this is good quality conversation? And then the sales rep will follow up. But that sales rep will bring an avatar with them on every sales call. You won't have to wait for their SC, they'll have their own SC that's all knowing, that will follow them through the process. So, go-to-market hasn't changed much yet, but I think over time it's going to change a lot.

**Lenny Rachitsky** (00:30:03):
This avatar, just so I understand. So, this is the buyer has their own little agent that comes with them? Or on HubSpot you have this avatar that walks you through the sales process?

**Brian Halligan** (00:30:15):
I think both. I think me as a knowledge worker, what I really want as a homo sapien is I have a Delphi clone that I really like.

**Lenny Rachitsky** (00:30:26):
Same.

**Brian Halligan** (00:30:26):
It's actually quite good.

**Lenny Rachitsky** (00:30:28):
Yeah. [inaudible 00:30:28].

**Brian Halligan** (00:30:28):
Yeah.

**Lenny Rachitsky** (00:30:30):
Where do people find yours?

**Brian Halligan** (00:30:32):
They find it on my footer, they can find it on Delphi-

**Lenny Rachitsky** (00:30:34):
Okay. We'll link to it.

**Brian Halligan** (00:30:38):
Yeah. And what I want is connect that thing to my email, into my Granola, into my plot, and it knows everything about me. And then, when I go to a meeting, Lenny, I want to invite that thing to my meeting. So, it's sitting there in the Zoom meeting, it's not just taking notes like Granola, it's a participant. So, if I forgot something, I ask it a question, if somebody else forgot... I think every knowledge worker will have one of these in 3, 4, 5 years. But mine was more on the go-to-market side, where I think every website will change and there'll be an all-knowing avatar on that homepage. And if it's a considered purchase, I think it gets handed off to a sales rep, and that sales rep has a conversation, but when that sales rep's on Zoom, they have their SE avatar that's kind of all-knowing. And so, I think this stuff all changes a lot in the next few years, but it hasn't really unlocked yet.

**Lenny Rachitsky** (00:31:28):
What are we going to be doing in this world, these two bots chatting with each other?

**Brian Halligan** (00:31:33):
It's going to be great, Lenny. You and I are going to be sitting on-

**Lenny Rachitsky** (00:31:37):
We're into podcasts.

**Brian Halligan** (00:31:37):
We're in Turks and Caicos, relaxing, on a month-long vacation, sending our avatars to all the meetings.

**Lenny Rachitsky** (00:31:42):
Go buy me some HubSpot seats please. I think this is why ClawdBot was so popular. I think this is essentially what they're building, is this idea. Which is now called Moltbot, which might be changing again. It's just this personal agent that can go do stuff for you.

**Brian Halligan** (00:31:56):
Totally.

**Lenny Rachitsky** (00:31:58):
So, you're talking about the future of go-to-market is this world where there's these little bots and agents that are doing things for you on both sides. When you look at companies today that you work with, that are doing well, that are especially AI-driven companies, what are they doing differently in terms of go-to-market that is working really well?

**Brian Halligan** (00:32:15):
Honestly, the only thing that's different today, it's exactly the same as it's been for 100 years, except they call their SCs or their system consultants for deployed engineers. The rest of it is the same. I thought it would be totally different in AI, and working with all these companies, they're hiring all the same folks, running the same enterprise sales processes. So, it hasn't changed that much, at least on the enterprise side. Actually, I spent the first 10 years of my career at a company called PTC, which is like an enterprise sales machine. Enterprise sales hasn't changed that much since the 1990s.

**Lenny Rachitsky** (00:32:49):
And so, forward deployed engineer, a very hot term, the idea there is they come work with the customer and help them implement this thing. And that's come up a lot on this podcast just with AI tools, rarely are they just plug and play, you can't just set up some agent that figures everything out. Takes a lot of onboarding and integration. Is it actually a different thing at all versus sales engineering in the past, things like that, or-

**Brian Halligan** (00:33:12):
I think it's a solutions consultant sales engineer.

**Lenny Rachitsky** (00:33:14):
Okay.

**Brian Halligan** (00:33:14):
They're technical, and they help you implement it, they connect all your systems, they customize it. It's different. You're training it in a different way, but... Well, anyway, I think the term is fine. I'm sort of being light on it because, boy, it looks similar except that role has a different name to it.

**Lenny Rachitsky** (00:33:31):
Got it. So, if anything, the device I'm hearing here is just lean into this, continue to lean into this idea of having your employees help the customer on board, be successful, integrate all that stuff.

**Brian Halligan** (00:33:43):
I think the thing that will change first is the top of the funnel around getting found inside Google, you got to get found in these...

**Lenny Rachitsky** (00:33:51):
Yeah, AEO.

**Brian Halligan** (00:33:52):
Yeah, that's going to be really important. And the way you build your website is very different for that to optimize for it. And then, I think your homepage is totally different. I think you land on an avatar and have a conversation with them, versus you're going through all the pages on your site. I think the top of the funnel is about changed a lot.

**Lenny Rachitsky** (00:34:09):
Has anyone doing this well yet, this idea of this avatar or is this just kind of in the future-

**Brian Halligan** (00:34:13):
There's a few startup programs. HubSpot does it, we built one, it's working.

**Lenny Rachitsky** (00:34:17):
Oh. Okay. Okay. Let me ask one more question around CEO stuff, and then I want to move on to Halliganisms.

**Brian Halligan** (00:34:24):
All right.

**Lenny Rachitsky** (00:34:26):
How is just being a CEO different than it was? So, you've been doing this for 20-ish years, what's most different about what it was like to be a CEO maybe 10, 20 years ago versus today?

**Brian Halligan** (00:34:39):
One of my... It was actually Winston from Harvey, said this a year ago, and I was like, that's bullshit, but I actually think he was right. He, he's like, "You can just do a lot more." You've got AI agents doing stuff, everyone's more productive, the software developers are more productive. Something that used to take you a year takes two months now. And so, the amount of projects and the amount of stuff you can do is much, much more, I think he's right. I think that's a little dangerous. Let's say you found your beachhead market, and that beachhead market is really good, and it's very deep, there's a lot of work to do, I think what's dangerous for companies is they hop to that second act too quickly, and they lose focus on that first act.

**Brian Halligan** (00:35:21):
And this isn't a completely perfect analogy, but you think of OpenAI and ChatGPT, and its consumer app is doing incredible, and they're doing lots and lots and lots and lots of other things, and then Gemini comes out and they've kind of focused back on the core. I think there's a lot of competition, everything is moving fast. I do think people get more done, and I think that impacts everything. The planning cycles used to be a year, I think the planning cycles now are three months long. Yeah, that's a big change. I think it puts pressure on the CEOs to be faster and better decision makers.

**Brian Halligan** (00:36:01):
I just think of times in HubSpot when things slowed down and there was churn, it was usually my fault. It was because there were some hard one-way door type decisions on my desk, and maybe every year I would sit down and I'd open that one-way door or close it, and it just freed everyone up and we just started moving so much faster. I think people need to be making those decisions and walking through those doors much more quickly than they used to. I think that's new and different. I was someone who always valued optionality, I think there's a massive tax in optionality when you can move this fast and try a lot of things. So, I do think the job's changing a lot.

**Lenny Rachitsky** (00:36:45):
Yeah. And there's so many reasons this is happening. One is just technology is just every week there's a new shift in what is possible.

**Brian Halligan** (00:36:53):
Yeah.

**Lenny Rachitsky** (00:36:54):
So, if you're spending all these months thinking and planning, just what a waste of time it ends up being because so much is changing.

**Brian Halligan** (00:36:59):
I know. Yeah, it's hard to keep up.

**Lenny Rachitsky** (00:37:03):
[inaudible 00:37:04]. Luckily we got some sweet podcasts to check out, to keep up to date with what's happening. We'll link to yours, of course.

**Lenny Rachitsky** (00:37:10):
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**Lenny Rachitsky** (00:38:10):
Let's talk Halliganisms. Essentially these are nuggets of wisdom and advice that you find yourself sharing often, you've written about a bunch of these online, and so let me just go through them and then just share the synopsis of the advice and the lesson around this Halliganism. The first is, "When you have to eat a shit sandwich, don't nibble."

**Brian Halligan** (00:38:30):
Okay. Completely stole this from Ruth Porat, the CFO of Google. I saw her quote somewhere, I'm like, that's it, she's put a perfect thing on it. And I'll give you an example where I think this will play out over the next couple of years. I think within the next couple of years there'll be a real retrenchment in valuations, and some will live up to valuations a lot [inaudible 00:38:54]. Like, if I look at the public markets, they're very tight right now, it's like the anti-bubble. And I look at private valuations, it's like a real bubble. I think there's a reckoning somewhere down the road. And a lot of companies are going to have to do layoffs. A lot. It's never fun, it's usually the worst thing in the history of your life, and the temptation is to do, well, just do a little one now and we'll grow into it, and then they do another one in six months, and then another one.

**Brian Halligan** (00:39:20):
I think with everything, including this type of thing, it's just rip the darn Band-Aid off, tell everyone the bad news, they're adults, they can handle it, and get it done. And I think people avoid that. I think that's good advice Ruth Porat is giving. Because you're going to have bad news to deliver, bad shit is going to happen to your company, even though it looks like it's going amazing right now, weird stuff's going to happen. And you're going to have to deal with it. And we had a lot of weird stuff happen at HubSpot. There's a basketball coach named Mike Krzyzewski, he's Duke's basketball coach. All time winningest college basketball coach ever. If you go to a Duke basketball game, you can hear him yelling from the sidelines, "Let's play, let's play."

**Brian Halligan** (00:40:13):
And what's going on there is when a college basketball player is playing in the game and takes a shot and clanks it off the rim and misses it, they have a strong tendency to go play overly aggressively on defense in the back court, and many times compound their error by making a foul or something like that. And what he wants to do is people to make their error, forget about it, and move back down the other side of the court and run the play. And so, actually there were times in HubSpot's history where we had the Mike Krzyzewski's face on a huge slide in front of the company meeting, saying, "Next play," because there was an unforced error, and we need to deal with it and move on.

**Lenny Rachitsky** (00:40:54):
Is there a story of that that comes to mind that is interesting and worth getting into?

**Brian Halligan** (00:40:58):
There was a lot of them. But I remember in 2000, it was the last day of March in 2019, and we had a really bad outage all day. And we never really had one of those, and it was bad. Customers were unhappy, a lot of customers canceled. I had a lot of customers yelling at me. And I remember that company meeting, I cried in front of the whole company, they couldn't believe it happened to us. And I remember using the Next Play slide on that one. Yeah, we made a lot of mistakes at HubSpot, a lot of bad things happen to companies, and most of them are self-inflicted. And a lot of them are the old saw-like, companies are far more likely to die of overeating than indigestion. Usually it was when we were trying to do too much.

**Lenny Rachitsky** (00:41:54):
I haven't heard that version, I've always heard most companies die have suicide versus homicide. Though indigestion-

**Brian Halligan** (00:41:59):
That's true too, by the way.

**Lenny Rachitsky** (00:42:02):
Oh man. Okay, so next Halliganism, "Never waste a good crisis." That's something that people hear, I'm curious just what's the lesson here, and then is there an example of this where you learned this lesson?

**Brian Halligan** (00:42:16):
I'll just follow on to, most of the good things that happened in HubSpot came out of a crisis because we would take pretty drastic measures to fix it and make sure we didn't do the same thing again. And so, in this particular case, we really rethought how we deployed software, how we thought about making software in a way that was incredibly healthy. And we haven't had a serious outage since. The quality is much better. And kind of an interesting thing with HubSpot is we started as a marketing software company, and we pivoted, had Salesforce came into our market, we pivoted into CRM. And one thing that... If your marketing software goes down, if your workflow, if there's a bug in the workflows or something like that, it's bad, but you survive it, you wait a little bit.

**Brian Halligan** (00:43:06):
If your CRM goes down, particularly on the last day of the quarter, you're really impacting your customer's ability to do business. So, that was a mindset shift that we hadn't quite come to terms with of how important we were to our customers. And so, we made a lot of changes based on that crisis. Usually very good things came out of crises.

**Lenny Rachitsky** (00:43:26):
So, there's the lesson there, something's going wrong, is it just like over-correct? Use this as a way to-

**Brian Halligan** (00:43:31):
Yes. We always over-corrected. Yeah, we almost, we purposely swung the pendulum hard the other way.

**Lenny Rachitsky** (00:43:38):
Which connects to the first Halliganism of, "If you're eating a shit sandwich, don't nibble." It's almost like go all the way, go even further.

**Brian Halligan** (00:43:46):
Yes. Make it really obvious to everyone what's going on.

**Lenny Rachitsky** (00:43:50):
Okay, another Halliganism, "If you want to kill a plant, have two people water it."

**Brian Halligan** (00:43:55):
I love this one, it's very true. Let's say, Lenny, you bought a new beautiful plant for your office, and then you went away for a month to Turks and Caicos because your AI agent's doing your podcast, and you asked two of your friends, "Hey, would you mind watering my plant?" And there's one of two outcomes what happens to the plant. The plant would either be over-watered and die, or not watered at all and die. And every CEO in the adults' table has gone through this, and they are religious about the DRI. Everyone talks about DRI in the kids' table, but once it gets to the adults' table, people get deep religion on it.

And I think it makes sense. When you're small, and you're in startup mode, everyone's in the room, everyone knows exactly what's going on. So, let's say you're running a pilot project with a big account. You're running that pilot project, everyone's on the same page, the sales person, the service person, developer, everyone's on the same page, and you go out and do it and you execute well. When you get it scale, you've get a sales organization, you've get your [inaudible 00:44:56] deployed engineer organization, you've got your product management organization, you've got some developers working on it.

**Brian Halligan** (00:45:00):
Everyone's kind of separate, no one knows really what's going on in the other departments. And so, let's say you want to really have a good pilot process, you want to rethink it because you're scaling. Everything important happens cross-functionally inside a company at scale, and you need someone powerful to own it. So, let's say it's a sales person, they need the power to tell people in other divisions what to do, even if they don't own it. So, almost every CEO I deal with is like a zealot on the DRI idea, and it doesn't bite you until you get to some sort of scale.

**Lenny Rachitsky** (00:45:34):
To be super clear about that advice here, it's one person is responsible for a goal, a metric, some outcome you want, versus it may feel like, okay, we have two people on this, it'll be awesome, they'll work together.

**Brian Halligan** (00:45:45):
Yes.

**Lenny Rachitsky** (00:45:45):
Your advice here is that doesn't work?

**Brian Halligan** (00:45:47):
Communities never work. Yes.

**Lenny Rachitsky** (00:45:48):
Yeah.

**Brian Halligan** (00:45:49):
Yes. And DRI is directly responsible individual.

**Lenny Rachitsky** (00:45:53):
The way I always thought about this is just having someone's ass on the line for something makes them so motivated to get it done, versus spreading the responsibility and the upside and the downside, it just doesn't work.

**Brian Halligan** (00:46:06):
I totally agree with you.

**Lenny Rachitsky** (00:46:08):
Awesome. Okay, another Halliganism, I don't know if you put it this way, the way I think about it is this idea of, "There's no such thing as a silver bullet, it just takes a lot of lead bullets to get something done." I think the way you wrote about it is it's always one step forward, two steps back. Talk about your advice there.

**Brian Halligan** (00:46:24):
Yeah, I always thought, incorrectly, that we would have one hire or one investor or one event or one product release that would... I was wrong about this, but it'd be a silver bullet. And the reality inside the HubSpot machine, the way it felt to me, it looks from the outside, over a long time up into the right, and smooth, but inside it was two steps forward, one step back, two steps forward, one step back, two steps forward, one step back. And a lot of times it was a crisis that caused that step back. So, we just didn't have that.

**Brian Halligan** (00:47:02):
The thing about being a founder/CEO is no one, especially when you're in your 20s, there's no one there to rescue you. Your parents aren't going to rescue you, your VC is not going to rescue you, your teacher, your thesis advisor, you're on your own and you got to figure it out. And that hits you when you hit your first crisis, it's on you. You can get some help, but it's on you.

**Lenny Rachitsky** (00:47:23):
Sometimes they have you in their corner, if they're lucky at Sequoia. Plug, plug.

**Brian Halligan** (00:47:27):
Yep. I can't solve it oftentimes, I can be the shoulder they cry on, and I can give them advice, but it's still on them.

**Lenny Rachitsky** (00:47:33):
Do you feel like too many people start companies just like... When someone comes to you like, hey Brian, should I start a company? I have this idea. Do you often just like, no. You have no idea what you're getting into, this is going to be much more painful?

**Brian Halligan** (00:47:44):
Yes. Yes. I heard Jensen Huang say that. "I wouldn't have start started Nvidia if I had it to do over." If someone asked me that question, I would start HubSpot over. It was very hard, there were a lot of sacrifices, it wasn't glamorous at all. But in the end of the day, I'm incredibly proud of it. And on my deathbed, I can look back and really enjoy it. And the Dalai Lama's got a good expression like, "Live a good life so you can live it again on your deathbed." And I'm really glad I did it. But I do talk a lot of founders out of it. The obsession is real, you have to be deeply obsessed. And all these founders and CEOs I talk to, people talk about 9-9-6, it's way more than that. The founders are seven days a week, they're always on, I [inaudible 00:48:40] on Sunday nights, it's full contact.

**Brian Halligan** (00:48:43):
And I think what's going on there, particularly now, is people just see this massive platform change, massive opportunity, they don't want to waste the opportunity. So, I think that mindset's right. But people today are much, much more hardcore than they were in my year. I worked hard, and I was probably, I was 60 to 70 week hours a week the entire time, never really turned it off. But that's kind of how I thought about it. It's different now. People are much more focused, and I think Elon's inspired people.

**Lenny Rachitsky** (00:49:09):
I had a start back in the day, nowhere near as successful as HubSpot, but the way I thought about it is, let me just give it everything I have and see what I can do. This is the shot, this is my chance, let me just give it all. Forget balance, just go for it. Seven days a week for a while. And then you scale back. And it's just such an empowering thing to do for a while, just like, let me just try. This won't be forever.

**Brian Halligan** (00:49:33):
Yep.

**Lenny Rachitsky** (00:49:35):
And I know you've written about this, just like balance for CEO is not, you should not have work-life balance if you want to be incredibly successful. I don't know if that's always true, but just... I don't know, how do you talk about that to founders?

**Brian Halligan** (00:49:48):
I don't know any of the founders I work with that have work-life balance. By the way, this is not something I recommend. I didn't have it, I don't think my co-founder, Dharmesh had it. The only CEO I know, and he's unusual in this way, is Kareem from Clay. He's like, "Nope, you need balance, take the weekends..." He's got a different mindset. I'm going to have him on my pod to talk about his mindset. But he's sort of the outlier, everyone else is really, really obsessed, and they really don't have much of a life.

**Lenny Rachitsky** (00:50:21):
It did take them a long time to find product market fit [inaudible 00:50:25]-

**Brian Halligan** (00:50:24):
[inaudible 00:50:25] definitely pre-AI. It did them a long...

**Lenny Rachitsky** (00:50:28):
I wonder if there's a correlation, but it did work out.

**Brian Halligan** (00:50:31):
Worked out great.

**Lenny Rachitsky** (00:50:33):
So, it is a good lesson. Okay, a few more here. One is, it's a math formula. "EV is greater than TV is greater than MEV." What is that?

**Brian Halligan** (00:50:44):
Okay, EV is enterprise value, TV is your team's value, MEV is your value. And as HubSpot was scaling, and we had a lot of people who were VPs in different roles, and they started to get good-sized organizations, where they would fall down was they didn't solve for MEV, but they'd solve for TV over EV. They'd solve for their own team. So, let's say they ran sales, and say, I just want bookings to be as high as possible because I get paid on bookings, and the service team can handle all the downstream problems I created. Marketing to sales... Between every department that's happened. And the kind of immature managers who didn't scale, really solved for themselves, and as they solved for themselves kind of sub-optimize for their peers. And their employees would notice it and complain about it, it would be fine in the short term, but it would show up.

**Brian Halligan** (00:51:44):
And the place it would show up, Lenny, was, we did, and I think a lot of companies do this now, but we did a quarterly employee net promoter. We did a quarterly customer net promoter survey and a quarterly employee one. And we would have people rate it by the department they're in. And one interesting thing about that, so it's like sales and service and engineering, all the different departments, and we had an overall net promoter score, and then each department had a net promoter score. And let's just take sales. Sales net promoter score was like, 65, 62, 68, 30. Ooh, that's a big drop. And then you read the comments and it was not good, a lot of complaints about the leader of that. And a lot of the complaints were a little bit of this TV thing.

**Brian Halligan** (00:52:34):
And then, we give feedback to that VP, would help them, we give them all the comments, be like, you got this. And then a quarter later from 30 to -5. And they almost never actually recovered. When you lose, your team can't... It's hard to get them back. And that's why I say hire slow, fire fast. And this doesn't show up in the first 100, 150 employees, everyone's solving for EV, but as it gets bigger, and the CEO doesn't know anyone, and there's a couple layers between you and the employees, they tend to solve for TV. So, we always put on the wall, "Solve for EV over TV over MEV," and then we added CV in front of EV, solve for the customers first, then for HubSpot, then the employee, then yourself. That was be very helpful to us.

**Lenny Rachitsky** (00:53:22):
Yeah, I imagine everybody listening, working at a big company understands this. Where you have goals, you get your KPIs, and your performance reviews based on what impact you drive, if you hit your goals. And so, the incentives are focused on my goals and drive those, and I don't care about other people's goals, the company's goals-

**Brian Halligan** (00:53:41):
Steve Jobs had an interesting line, he says, "You don't work for your boss, you work for Apple." I thought that was pretty good. And I heard that after I was CEO of HubSpot, but that kind of captures the sentiment. And that's how I felt about HubSpot. You work for HubSpot first and then you work for your boss.

**Lenny Rachitsky** (00:53:56):
This is hard because people's performance reviews are based on their goals, KPIs, it's always like, here's what I got to drive. Other than putting posters on the wall, and this is our, just HubSpot growth above all is what matters, or our customers, I guess in your case. Is there anything tactically that was useful in helping people prioritize enterprise value?

**Brian Halligan** (00:54:14):
This was explicitly called out in the form for the employee when you got your review, this was part of it. And so, they get a score of one to, I forget, 10 on that. I would talk about it constantly. And when we first started HubSpot, I ran it a little bit like Jensen runs NVIDIA, where I didn't do one-on-ones, and I gave a lot of good and bad feedback publicly in large management team meetings, and I definitely would go out of my way to criticize people if I felt like they were solving for TV over EV. And people got a sense for that. And then, every quarter we did a really well-produced company meeting, we spent a lot of time on it, and at the end of the company meeting, we gave out, we called them the Champaigner Awards, it was a bottle of Veuve, that my co-founder and I signed. And we'd read something nice about them and give it to them, and usually there was an EV team in that. And so, we did different things to kind of beat that into people's heads.

**Lenny Rachitsky** (00:55:11):
Amazing. So, here is just celebrate people that focus on this, and also included in their valuation performance reviews.

**Brian Halligan** (00:55:19):
Yep.

**Lenny Rachitsky** (00:55:19):
Okay. That's a good segue to another Halliganism where you talk about how companies are either customer-centric or investor-centric, and it's really important to know which you are. And you guys actually shifted there. What's your insight there?

**Brian Halligan** (00:55:35):
Okay. We were very employee-centric, more than customer-centric in the first several years of HubSpot. So much so that the company was number one on Glassdoor's best place to work, I was the number one CEO on Glassdoor, and as I look back at that, I'm not sure that's a good thing. Wanting to be liked I don't think is a good feature of a CEO, and wanting to be the best place to work probably isn't the right way to go. If you look at Toby from Shopify, his scores aren't that good, but that company is doing really, really well. And so, we over-indexed on it, and part of the reason we over-indexed on it is my co-founder was really strong in this, and we had an incredibly powerful head of HR, named Katie Burke, and we just worked on it. We spent a lot of time on it. And when we'd have a management team meeting, and let's say it's four hours long, two of the four hours would be on employee stuff.

**Brian Halligan** (00:56:28):
And at some point I was like, why are we spending so much time on employee net promoter scores? Let's say our employee net promoter score was 60, and our customer net promoter score is 25, I was like, we need to take... I would give up 10 points of employee net promoter score to get 10 points of customer net promoter score. And so, over some time we shifted the center of gravity to customers, and we still of course, worried about employees, but the center of gravity from HubSpot moves very much to customers. And we did that in a few ways. Every time we had a management team meeting... We had our management team meetings once a month, not once a week, and we would have a customer panel come on, and that customer panel, I would run the panel, and ask very tricky questions to the customers, and pull out the bad news from that.

**Brian Halligan** (00:57:22):
And then, we still do this, we have a customer panel at our board meetings. Our whole board can ask questions. And my favorite question is, what do you love about HubSpot? And then... And then, what do you hate about HubSpot? And they kind of look at their shoes. And you're like, come on. And it's a great way... So, the employee's voice is here, those company meetings, we have the customers at the company meetings. We changed the comp plan, so the management team got paid not on revenue but on retention and net promoter score. And so we worked very hard and swung the pendulum to customer-centric. But I do think companies have one center of gravity or another.

**Lenny Rachitsky** (00:58:00):
There's a really interesting thread throughout this conversation of just, when do you want to change how you operate you have to go really far to a whole other... And to almost over-correct to... Yeah, it's a really interesting of just how much work it takes to change culture, to change your norms.

**Brian Halligan** (00:58:15):
Yes. And the bigger it is, the more obvious you have to make it. And the other thing about being a CEO, Lenny, is you got to say the same thing over and over and over and over and over again. It just doesn't sink into people's heads, you have to just be incredibly repetitive on it before it sinks in. Same thing with marketing, but internally, and that happens. The other weird thing about being a CEO, Lenny, is as it gets bigger... When it's small, everyone's giving you shit, and you're all on the same level. But as it gets bigger, you didn't interview everyone, you have thousands of employees, you don't know everyone, and people put you on a pedestal that you don't deserve.

**Brian Halligan** (00:58:53):
And let's say you're in the hallway, and you're just kind of shooting the shit with a bunch of people, and you're like, ah, it'd be cool if we had a product that did... Somebody inevitably would go home and build that thing, and be like, Brian wants this as a big initiative. So, people really lock in on what you said. And it turns out you have to be very repetitive and you have to be very careful what you say.

**Lenny Rachitsky** (00:59:12):
Dharmesh was on the podcast, your illustrious co-founder, and he developed a whole system to avoid this sort of thing. Flash tags, where it's like, this is just an FYI.

**Brian Halligan** (00:59:22):
We had a whole system. Because we would say something on an email to the management team, or Slack, and everyone would be like, okay, this is what they want, let's do it. And sometimes it is like, you need to do this, and sometimes it is like, we should talk about doing this, and sometimes it is, this is just kind of an FYI, we're thinking about it. And because it got big, we came up with that rubric of, we needed to tag each email with, do you want me to get this done this week, or is this something we should talk about, or is this something that's just FYI, I'm thinking about?

**Lenny Rachitsky** (00:59:51):
I love that... One of them is plea. Pleading you to do it. I'm not telling you to do this, I'm just pleading that you do what I ask. Oh man. Okay. This all connects. And by the way, you guys were co-founders for 20 years, you shared something before we started recording. So, Dharmesh famously did not ever want direct reports, he's just like, Brian, I want to start this with you, but I don't want to ever manage anyone. And you were talking about how you had to take on engineering, which didn't make any sense.

**Brian Halligan** (01:00:19):
No, I'm an engineer and I can code, but it's not good. So, when we started the company he's like, "I was a CEO before, I was terrible at it, I don't want to do it again, you're going to be the CEO." I'm like, "Great." And he's like, "And by the way, I'm not going to have any direct reports." I'm like, "Well, it's just the two of us so don't have worry about it." He's like, "No, ever, never." I'm like, "Yeah, yeah, yeah." And then we get 10, 12 people, we're starting to hire engineers, and onboard them, and making up big decisions, and I would go to him and be like, "Well, can you manage him?" He is like, "Don't you remember I told you I don't want to have any direct reports?" It's like, "Surely you were kidding when you said that?" He said, "Nope." And so, Dharmesh Shah has never had a single direct report at HubSpot.

**Lenny Rachitsky** (01:01:02):
Incredible. I don't, it's just like a dream, a way to operate, I love that you made it possible for him, and it created all this opportunity for him to tinker and innovate.

**Brian Halligan** (01:01:14):
Yes, yes, it freed him up to really think and be creative.

**Lenny Rachitsky** (01:01:15):
Yeah. I'm excited to get him back on the podcast someday, we're going to link to that episode. Maybe a last question, I'm curious if there's anything else you think we missed? As a company grows and scales, the job of a CEO changes. You've written a bit about this, of just how different the job is when you're a starter versus a scale up. What are some of the things that most change where your time goes as a CEO, as the company grows?

**Brian Halligan** (01:01:38):
Well, you clicked on this earlier, but that inspiration thing. I have a little rubric where it's like, in the startup phase it's 90% perspiration, 10% inspiration. When you get the scale up phase, it's 90% inspiration, 10% perspiration. And over time you're doing every job in a startup, and you still need to be very attached to it, and you still need to talk to customers, you can't give it up. But man, you have to let go of so much stuff over time in order for the organization to scale, and I have trust issues. I only trusted a small number of people at HubSpot to be a DRI, and to really drive something important. It drove people crazy, that I didn't have a larger trust surface. Every one of the CEOs I work with has the same problem. And that's a scaling limit, that was a limit for me. I wasn't trusting enough.

**Lenny Rachitsky** (01:02:33):
Brian, I feel like I could chat with you for hours, there's a whole list of Halliganisms I'm going to link to that we didn't even touch on. But before we get to our very exciting lightning round, is there anything else that you think we should chat about? Anything you want to leave [inaudible 01:02:48] with?

**Brian Halligan** (01:02:49):
Well, I would say if you are a CEO and you're interested in scaling, I think the Halliganism posts is all the mistakes I made in my 15 years of being CEO, I tried to summarize them there, to help you avoid them. And I have a pod, you should first listen to Lenny's pod because it's amazing, but I have a pod just for CEOs, called Long Strange Trip. Where I interview, I interview CEOs about this. So, Lenny's interviewing me about being a CEO, I get to interview other people about being... I'm kind of a CEO geek these days.

**Lenny Rachitsky** (01:03:17):
And the name of the podcast is a Grateful Dead reference, which-

**Brian Halligan** (01:03:20):
Yes.

**Lenny Rachitsky** (01:03:20):
We haven't touched on, but you're a huge Deadhead, as they say.

**Brian Halligan** (01:03:23):
[inaudible 01:03:25].

**Lenny Rachitsky** (01:03:25):
That could be a whole other podcast conversation.

**Brian Halligan** (01:03:27):
I think there sure, actually. Because I wrote a book called Marketing Lessons from the Grateful Dead, and there's so much... The Grateful Dead were like the ultimate Silicon Valley startup. They started in 1964... Do you know where they started, Lenny?

**Lenny Rachitsky** (01:03:43):
No.

**Brian Halligan** (01:03:44):
Palo Alto. Their early concerts were at Stanford, were all over Silicon Valley. They're a Silicon Valley company. They were very first principles in their thinking, they created a new category, a new way to distribute their music, they disintermediated the ticketing companies, very innovative. Steve Jobs and Jerry Garcia are very similar in my mind, real craftspeople. So, I think of them as a great Silicon Valley success story.

**Lenny Rachitsky** (01:04:14):
You said you had a whole book about this, what's the book called just in case people want to dig in?

**Brian Halligan** (01:04:18):
Marketing Lessons From the Grateful Dead.

**Lenny Rachitsky** (01:04:20):
Amazing. And I read that you bought Jerry Garcia's guitar for a large sum at some point.

**Brian Halligan** (01:04:26):
Yes, I did. And I consider myself the steward of his guitar. It gets played, like Dead and Co played it, and there's a million Grateful Dead cover bands, I let them play it, but I'm taking care of it for the Deadheads.

**Lenny Rachitsky** (01:04:41):
What's one nugget of wisdom or lesson that people can take away from the Grateful Dead for startups?

**Brian Halligan** (01:04:48):
Okay, people talk about spiky teams, the Grateful Dead team was interesting. Garcia himself was a bluegrass guy, he was a banjo player. And then Bob Weir, recently passed, was a country crooner, like country music. Then their bass player was a avant-garde jazz trombonist, Phil Lesh. And their keyboard player was a guy named Ron McKernan, Pig Pen, and he was a harmonica guy. And the drummer was a marching band drummer. And so, spikiest of spiky teams came together and made a new genre, they created a new category of music. It wasn't rock and roll, it wasn't sort of Rolling Stones, it wasn't Buddy Holly, it was this new thing. And then they called it a jam band, because they played rock and roll in a bluesy, open, organic kind of jazzy way. And so, spiky teams in creating categories, underrated.

**Lenny Rachitsky** (01:05:47):
Incredible. Are you one of these people that have been to 100 Grateful Deck concerts [inaudible 01:05:51]-

**Brian Halligan** (01:05:51):
Yes. [inaudible 01:05:53].

**Lenny Rachitsky** (01:05:53):
Makes sense. Okay, this could be a whole podcast, but we're going to move along. Okay. With that, we've reached our very exciting lightning round. Brian, are you ready?

**Brian Halligan** (01:06:06):
Yeah.

**Lenny Rachitsky** (01:06:06):
Let's do this.

**Brian Halligan** (01:06:07):
Fire it up, I've watched you do the lightning round so many times.

**Lenny Rachitsky** (01:06:10):
I'm flattered. Unsurprising questions. What are two or three books that you find yourself recommending most to other people?

**Brian Halligan** (01:06:17):
I haven't read a book in a long time. I listen to podcasts, I'm on X, I talk to a lot of other CEOs. I can't remember the last time I actually sat down and read a book.

**Lenny Rachitsky** (01:06:26):
Much respect. I had Marc Andreessen on recently, and I don't know if you've heard his whole thing on what he consumes, he talks about he has a very barbell strategy to media. It's either Twitter or books that are 10 years or older and nothing in between.

**Brian Halligan** (01:06:40):
I've heard him say that. Yeah, I kind of stopped reading. I looked at that, I was getting ready for this, Lenny, and I was like, I can't remember the last book I read.

**Lenny Rachitsky** (01:06:49):
I think this is going to make a lot of people feel better that don't read books, are like, all right, this is okay. Favorite recent movie or TV show you've really enjoyed?

**Brian Halligan** (01:06:58):
I love the new Ken Burns, very long, very good Revolutionary War documentary. He's a crass person, it's exceptionally well done. And what I like about it is America's really a disruptor startup, so many startup lessons from those... They were gutsy. Talked about two steps forward, one step back. They got into the details of how George Washington ran the Army. We were very close to losing that war most of the time, and two steps forward, 10 step back, two step forward, 10 steps back.

**Lenny Rachitsky** (01:07:36):
Lots of lead bullets.

**Brian Halligan** (01:07:39):
And unless we had alliances, we had alliances with the French, we were screwed. I love that, it's a long one, but it's really good.

**Lenny Rachitsky** (01:07:48):
How long is this? What are we getting into?

**Brian Halligan** (01:07:49):
Probably 10+ hours.

**Lenny Rachitsky** (01:07:51):
10+ hours. It's a lot, but worth it is what I'm hearing.

**Brian Halligan** (01:07:56):
I'm in Boston, it's Revolutionary War kind of place.

**Lenny Rachitsky** (01:07:58):
Surrounded by history. All right. Favorite product you recently discovered that you really love?

**Brian Halligan** (01:08:03):
I love my Delphi clone, I teach a course called Scaling Entrepreneurial Ventures, and I don't do office hours, I have Delphi do my office hours. Very happy with that.

**Lenny Rachitsky** (01:08:17):
My favorite feature of Delphi, and again LennyBot.com is my Delphi... I love that we both have little bots. The voice feature is the coolest thing-

**Brian Halligan** (01:08:24):
Great.

**Lenny Rachitsky** (01:08:25):
You just talk to it.

**Brian Halligan** (01:08:25):
Great. Not good, great. I can't wait... I had video, they get rid of it, they're going to bring it back. Can't wait until it has video. My least favorite, my lowest [inaudible 01:08:32] product is my Sonos system. You have Sonos?

**Lenny Rachitsky** (01:08:35):
I do, and I get you.

**Brian Halligan** (01:08:36):
Yes. Painful.

**Lenny Rachitsky** (01:08:38):
Yeah. It's so good in so many ways and so annoying in so many ways.

**Brian Halligan** (01:08:42):
Annoying.

**Lenny Rachitsky** (01:08:42):
But we still use it, there's nothing better.

**Brian Halligan** (01:08:44):
There's not a competitor. You and I should start a Sonos competitor.

**Lenny Rachitsky** (01:08:46):
No, we should not, that's a bad... I'm not doing this. And just to be clear what these bots are, just so people understand how cool this is. So, my [inaudible 01:08:55] every single podcast, like this one is going to be sucked into it, and every single newsletter, and you just talk to it and ask it, how do I find product market fit and space on everything I've ever shared? Here's your steps.

**Brian Halligan** (01:09:07):
Okay. What's even better about it is, because you can go to ChatGPT and say, what would Lenny think about this? What it's added is the ability to put a bunch of documents in there, that aren't on the internet, like I put my lectures in there, and there's a new feature where it asks you questions and it interviews you. And so, it's pretty proprietary, it's getting better. I like it a lot.

**Lenny Rachitsky** (01:09:27):
Yeah. To that point, I haven't promoted this feature of it, but it's trained on all my paid content too. So, even if you're not a paid subscriber, you get access to all of the things I've ever shared. Let's not tell too many people that, because one day I'm going to pay wallet, and then [inaudible 01:09:45]. Anyway, enough about that. Lennybot.com. Do you have a favorite life motto that you find yourself coming back to in work or in life?

**Brian Halligan** (01:09:52):
This isn't lightning, but four years ago I had a very bad snowmobile accident, drove a snowmobile off a cliff. The snowmobile smashed into a million pieces at the bottom of the cliff, so did I. and I laid at the bottom of that cliff for a while, I was unconscious for a long time. I woke up. And I didn't think I had my phone, so I sat there for a long time, like, I'm probably going to die tonight, no one knows where I am, it's frigid out. It's in Vermont. And I'm going to freeze to death. And I'm sitting there for a couple hours, I finally was like, oh, I do have my phone, dialed 911. By the way, 911, amazing service. And so, the helicopters came in and took me out, took me to the hospital. And lots of surgeries, and I was kind out of commission for a year. And you can't see it but I got metal over me, all in me.

**Brian Halligan** (01:10:49):
Life's short. Life's short. And I made some decisions at the bottom of that cliff... One of the decisions I made at the bottom of that cliff was, I don't really like being CEO of an 8,000 person company, doesn't really suit me. My harmonic motion is off. I don't love the day to day, if I make it out of here alive, I'm out. And so, that's exactly what happened. The first big thing that happened coming out of that was I gave the job to Yamini, who's still the CEO, doing a great job. So, life's short, don't waste it.

**Lenny Rachitsky** (01:11:23):
I've heard this story, but it's just as powerful hearing it again. Why do you think it takes people, why does it take a moment like that to have someone realize, I need to change or just-

**Brian Halligan** (01:11:35):
I think people think they're going to live forever and they're not. As somebody who's 58, yeah, life's very short, and I'm much more intentional about the decisions I make, much more intentional about the people I hang out with today than I was before that. And I really try to work on things that bring me joy. Like this pod.

**Lenny Rachitsky** (01:11:56):
Same, I appreciate it.

**Brian Halligan** (01:11:58):
[inaudible 01:11:58].

**Lenny Rachitsky** (01:11:58):
Same. I read that you had 20 broken bones in this accident.

**Brian Halligan** (01:12:03):
A lot of broken bones, a lot of metal. I got 33 screws in me, one loose one up here, Lenny.

**Lenny Rachitsky** (01:12:12):
Same. Okay, last question, we talked a bit about the Red Sox, you're a part owner of the Boston Red Sox now. What's something that would surprise people about how a baseball team is run, or just what it's like on the inside of a team like the Red Sox?

**Brian Halligan** (01:12:27):
It's not as profitable as people think. People think these rich guys come in and buy these teams, but the way the [inaudible 01:12:34] is set up and the way the economics are set up, it's not a profitable endeavor. Whereas, other leagues are much, much more profitable. Baseball's also deeply flawed, it doesn't have a salary cap. And so, you've got the Dodgers, who I take my hat off to, have a $400 million payroll, and then Miami Marlins with a 100 million dollars payroll, and in other leagues that all kind of balances out pretty well, baseball, it's set up incorrectly. I think it'll correct in the next couple of years, but it's a broken model.

**Lenny Rachitsky** (01:13:13):
Intriguing. Stay in AI [inaudible 01:13:17] if you want to make money is what I'm hearing. Vertical SaaS. Brian, this was incredible, covered almost everything I was hoping to cover. Two final questions, where can folks find you online if they want to reach out, where do they find the bot? How do they work with you if they wanted to? Or do I have to be a Sequoia founder? And then how can listeners be useful to you?

**Brian Halligan** (01:13:35):
There's two things. I would love folks to listen to. Long Strange Trip, my pod, and I get some comments, but not a ton. Lenny, you have more comments than yours, I'm jealous. I'd like just feedback on how I'm doing. It's very new, and I just started a couple months ago, it seemed like it's going pretty well, but it's my family and Sequoia people giving me feedback on. I'd like to see how all of you, what you think about it. So, that would be spectacular.

**Lenny Rachitsky** (01:14:00):
All right, so hop on your YouTube and leave some comments about what they think. The real, honest, feedback.

**Brian Halligan** (01:14:05):
Real unvarnished truth.

**Lenny Rachitsky** (01:14:07):
Okay, here it comes. Brian, thank you so much for doing this.

**Brian Halligan** (01:14:10):
Appreciate you, appreciate you. Thank you.

**Lenny Rachitsky** (01:14:12):
Bye everyone.

**Lenny Rachitsky** (01:14:14):
Thank you so much for listening, if you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny'sPodcasts.com. See you in the next episode.

---

## [13/15] Boris Cherny

**Boris Cherny** (00:00:00):
100% of my code is written by Claude Code. I have not edited a single line by hand since November. Every day, I ship 10, 20, 30 pull requests. So, at the moment I have, like, five agents running. 

**Lenny Rachitsky** (00:00:10):
While we're recording this? 

**Boris Cherny** (00:00:11):
Yeah. Yeah. Yeah.

**Lenny Rachitsky** (00:00:12):
Do you miss writing code?

**Boris Cherny** (00:00:13):
I have never enjoyed coding as much as I do today, because I don't have to deal with all the minutia. Productivity per engineer has increased 200%. 

**Lenny Rachitsky** (00:00:21):
There's always this question, "Should I learn to code?" 

**Boris Cherny** (00:00:22):
In a year or two, it's not going to matter. Coding is virtually solved. I imagine a world where everyone is able to program, anyone can just build software any time.

**Lenny Rachitsky** (00:00:29):
What's a next big shift to how software is written?

**Boris Cherny** (00:00:32):
Claude is starting to come up with ideas. It's looking for feedback, it's looking at bug reports, it's looking at telemetry for bug fixes, and things to ship. A little more like a coworker or something like that. 

**Lenny Rachitsky** (00:00:41):
A lot of people listening to this are product managers and they're probably sweating. 

**Boris Cherny** (00:00:44):
I think by the end of the year everyone is going to be a product manager, and everyone codes. The title software engineer is going to start to go away. It's just going to be replaced by builder, and it's going to be painful for a lot of people. 

**Lenny Rachitsky** (00:00:56):
Today my guest is Boris Cherny, head of Claude Code at Anthropic. It is hard to describe the impact that Claude Code has had on the world. Around the time this episode comes out will be the one year anniversary of Claude Code. And in that short time it has completely transformed the job of a software engineer, and it is now starting to transform the jobs of many other functions in tech, which we talk about.

**Lenny Rachitsky** (00:01:19):
Claude Code itself is also a massive driver of Anthropic's overall growth over the past year. They just raised a round at over $350 billion. And as Boris mentions, the growth of Claude Code itself is still accelerating. Just in the past month, their daily active users has doubled. Boris is also just a really interesting, thoughtful, deep-thinking human, and during this conversation we discover we were born in the same city in Ukraine. That is so funny. I had no idea.

**Lenny Rachitsky** (00:01:47):
A huge thank you to Ben Mann, Jenny Wen, and Mike Krieger for suggesting topics for this conversation. Don't forget to check out LennysProductPass.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. Let's get into it after a short word from our wonderful sponsors. 

**Boris Cherny** (00:03:54):
Yeah. Thanks for having me on.

**Lenny Rachitsky** (00:03:55):
I want to start with a spicy question. About six months ago, I don't know if people even remember this, you actually left Anthropic. You joined Cursor. And then two weeks later you went back to Anthropic. What happened there? I don't think I've ever heard the actual story. 

**Boris Cherny** (00:04:12):
It was the fastest job change that I've ever had. I joined Cursor, because I'm a big fan of the product. And, honestly, I met the team and I was just really impressed. They're an awesome team. I still think they're awesome, and they're just building really cool stuff. And they saw where AI coding was going I think before a lot of people did.

**Boris Cherny** (00:04:33):
So, the idea of building good product was just very exciting for me. I think as soon as I got there what I started to realize is what I really missed about Ant was the mission. And that's actually what originally drove me to Ant also, because before I joined Anthropic I was working in Big Tech, and then, at some point, I wanted to work at a lab to just help shape the future of this crazy thing that we're building in some way.

**Boris Cherny** (00:05:00):
And the thing that drew me to Anthropic was the mission. And it's all about safety. And when you talk to people at Anthropic, just, like, find someone in the hallway, if you ask them why they're here, the answer is always going to be, "Safety." 

And so, this mission-driven [inaudible 00:05:14] just really, really resonated with me. And I just know, personally, it's something I need in order to be happy. And that's just a thing that I really missed, and I found that whatever the work might be, no matter how exciting, even if it's building a really cool product, it's just not really a substitute for that. So, for me, it was pretty obvious that I was missing that pretty quick. 

**Lenny Rachitsky** (00:05:35):
Okay. So, let me follow the thread of just coming back to Anthropic and the work you've done there. This podcast is going to come out around the year anniversary of launching Claude Code. So, I want to spend a little time just reflecting on the impact that you've had. There's this report that recently came out that I'm sure you saw by Semi-Analysis that showed that 4% of all GitHub commits are authored by Claude Code now. And they predicted it'll be a fifth of all code commits on GitHub by the end of the year.

**Lenny Rachitsky** (00:06:04):
The way they put it is, "While we blinked, AI consumed all software development." The day that we're recording this Spotify just put out this headline that their best developers haven't written a line of code since December thanks to AI. More and more of the most advanced senior engineers, including you, are sharing the fact that you don't write code anymore, that it's all AI-generated, and many aren't even looking at code anymore is how far we've gotten.

**Lenny Rachitsky** (00:06:31):
In large part, thanks to this little project that you started, and that your team has scaled over the past year. I'm curious just to hear your reflections on this past year, and the impact that your work has had.

**Boris Cherny** (00:06:42):
These numbers are just totally crazy. Right? Like, 4% of all commits in the world is just way more than I imagined. And, like you said, it still feels like the starting point. These are also just public commits. So, we actually think if you look at private repositories it's quite a bit higher than that. 

**Boris Cherny** (00:06:56):
And I think the crazy thing for me isn't even the number that we're at right now, but the pace at which we're growing, because if you look at Claude Code's growth rate across any metric it's continuing to accelerate. So, it's not just going up, it's going up faster and faster. 

**Boris Cherny** (00:07:12):
When I first started Claude Code, it was just supposed to be a little hack. We broadly knew at Anthropic that we wanted to ship some kind of coding product. And for Anthropic for a long time, we were building the models in this way that fit our mental model of the way that we build safe AGI where the model starts by being really good at coding. Then it gets really good at tool use. Then it gets really good at computer use. Roughly, this is, like, the trajectory. 

**Boris Cherny** (00:07:40):
And we've been working on this for a long time. And when you look at the team that I started on, it was called Anthropic Labs team, and actually Mike Krieger and Ben Mann they just kicked this team off again for round two. 

**Boris Cherny** (00:07:53):
The team built some pretty cool stuff. So, we built Claude Code, we built MCP, we built the desktop app. So, you can see the seeds of this idea. It's coding, then it's tool use, then it's computer use. 

**Boris Cherny** (00:08:03):
And the reason this matters for Anthropic is because of safety. It's, again, just back to that AI is getting more and more powerful, it's getting more and more capable. The thing that's happened in the last year is that, at least, for engineers, the AI doesn't just write the code. It's not just a conversation partner, but it actually uses tools. It acts in the world. 

**Boris Cherny** (00:08:23):
And I think now with Cowork we're starting to see the transition for non-technical folks also. For a lot of people that use conversational AI, this might be the first time that they're using the thing that actually acts, it can actually use your Gmail, it can use your Slack. It can do all these things for you, and it's quite good at it. And it's only going to get better from here. 

**Boris Cherny** (00:08:42):
So, I think for Anthropic for a long time, there was this feeling that we wanted to build something, but it wasn't obvious what. And so, when I joined Ant, I spent one month hacking, and built a bunch of weird prototypes. Most of them didn't ship, and weren't even close to shipping. It was just understanding the boundaries of what the model can do. 

**Boris Cherny** (00:08:59):
Then I spent a month doing post-training. So, to understand the research side of it. And I think, honestly, that's just, for me, as an engineer, I find that to do good work you really have to understand the layer under the layer at which you work. And with traditional engineering work, if you're working on product, you want to understand the infrastructure, the run time, the virtual machine, the language, whatever that is, the system that you're building on.

**Boris Cherny** (00:09:23):
But, yeah. If you work in AI, you just really have to understand the model to some degree to do good work. So, I took a little detour to do that, and then I came back and just started prototyping what eventually became Claude Code. 

**Boris Cherny** (00:09:36):
In the very first version of it I have a ... There's, like, a video recording of this somewhere, because I recorded this demo, and I posted it. It was called Claude CLI back then. And I just showed off how it used a few tools, and the shocking thing for me was that I gave it a batch tool, and it just was able to use that to write code, to tell me what music I'm listening to when I asked it like, "What music am I listening to?" 

**Boris Cherny** (00:09:59):
And this is the craziest thing. Right? Because it's, like, there's no ... I didn't instruct the model to say, "Use this tool for this," or do whatever. The model was given this tool, and it figured out how to use it to answer this question that I had that I wasn't even sure if it could answer, "What music am I listening to?" 

**Boris Cherny** (00:10:16):
And so, I started prototyping this a little bit more. I made a post about it, and I announced it internally and it got two likes. That was the extent of the reaction at the time, because I think people internally ... When you think of coding tools, you think of IDEs, you think of all these pretty sophisticated environments. No one thought that this thing could be terminal-based. That's a weird way to design it, and that wasn't really the intention. 

**Boris Cherny** (00:10:43):
But from the start I built it in a terminal, because for the first couple months it was just me. So, it was just the easiest way to build. And, for me, this was actually a pretty important product lesson. Right? This is, like, you want to under-resource things a little bit at the start. 

**Boris Cherny** (00:10:58):
Then we started thinking about what other form factors we should build, and we actually decided to stick with the terminal for a while. And the biggest reason was the model is improving so quickly, we felt that there wasn't really another form factor that could keep up with it. 

**Boris Cherny** (00:11:13):
And, honestly, this was just me struggling with, "What should we build?" For the last year, Claude Code has just been all I think about. And so, just late at night this is just something I was thinking about like, "Okay. The model is continuing to improve. What do we do? How can we possibly keep up?" And the terminal was, honestly, just the only idea that I had. 

**Boris Cherny** (00:11:31):
And, yeah. It ended up catching on. After I released it, pretty quickly it became a hit at Anthropic, and the daily active users just went vertical, and it ... Really early on actually, before I launched it, Ben Mann nudged me to make a DAU chart. And I was like, "It's early. Should we really do it right now?" And he was like, "Yeah." 

**Boris Cherny** (00:11:51):
And so, the chart just went vertical pretty immediately. And then in February, we released it externally. Actually, something that people don't really remember is Claude Code was not initially a hit when we released it. It got a bunch of users. There was a lot of early adopters that got it immediately, but it actually took many months for everyone to really understand what this thing is. Again, it's just so different.

**Boris Cherny** (00:12:15):
And when I think about it, part of the reason Claude Code works is this idea of latent demand where we bring the tool to where people are, and it makes the existing workflows a little bit easier. But also because it's in the terminal, it's a little surprising, it's a little alien in this way. So, you have to be open-minded, and you had to learn to use it.

**Boris Cherny** (00:12:33):
And, of course, now Claude Code is available in the iOS and Android Claude app. It's available in the desktop app. It's available on the website. It's available as IDE extensions in Slack and GitHub. All of these places where engineers are it's a little more familiar, but that wasn't the starting point.

So, yeah. At the beginning it was a surprise that this thing was even useful. And as the team grew, as the product grew, as it started to become more and more useful to people, just people around the world from small startups to the biggest [inaudible 00:13:05] companies started using it, and they started giving feedback.

**Boris Cherny** (00:13:09):
And I think just reflecting back it's been such a humbling experience, because we keep learning from our users and just the most exciting thing is none of us really know what we're doing, and we're just trying to figure out along with everyone else. And the single best signal for that is just feedback from users. So, that's just been the best. I've been surprised so many times. 

**Lenny Rachitsky** (00:13:29):
It's incredible how fast something can change in today's world. You launched this a year ago. And it wasn't the first time people could use AI to code, but in a year, the entire profession of software engineering has dramatically changed. Like, there's all these predictions, "Oh, AI is going to be written ... 100% of code is going to be written by AI." Everyone is like, "No. That's crazy. What are you talking about?" But now it's like, "Oh, of course. It's happening exactly as they said." So, things move so fast, and change so fast now. 

**Boris Cherny** (00:13:58):
Yeah. It's really fast. Back at Code with Claude back in May, that was our first developer conference that we did as Anthropic, I did a short talk. And in the Q&A after the talk, people were asking, "What are your predictions for the end of the year?" And my prediction back in May of 2025 was, "By the end of the year, you might not need an IDE to code anymore. And we're going to start to see engineers not doing this."

**Boris Cherny** (00:14:20):
And I remember the room audibly gasped. It was such a crazy prediction. But I think at Anthropic, this is just the way ... The way we think about things is exponentials. And this is very deep in the DNA. Like, if you look at our co-founders, three of them were the first three authors on the scaling laws paper. 

**Boris Cherny** (00:14:37):
So, we really just think in exponentials. And if you look at the exponential, the percent of code that was written by Claude at that point, if you just trace the line, it's pretty obvious we're going to cross 100% by the end of the year, even if it just does not match intuition at all.

**Boris Cherny** (00:14:51):
And so, all I did was trace the line. And, yeah. In November that happened for me personally. And that's been the case since. And we're starting to see that for a lot of different customers too. 

**Lenny Rachitsky** (00:15:01):
I thought it was really interesting what you just shared there about the journey is this idea of just playing around and seeing what happens. This comes up with OpenClaw a lot, just, like, "Peter was playing around and a thing happened." And it feels like that's a central ingredient to a lot of the biggest innovations in AI is people just sitting around trying stuff, pushing the models further than most other people.

**Boris Cherny** (00:15:22):
That's the thing about innovation. Right? You can't force it. There's no road map for innovation. You just have to give people space. You have to give them ... Maybe the word is, like, safety. So, it's, like, psychological safety that it's okay to fail. It's okay if 80% of the ideas are bad. 

**Boris Cherny** (00:15:36):
You also have to hold them accountable a bit. So, if the idea is bad, you cut your losses, move onto the next idea instead of investing more. In the early days of Claude Code, I had no idea that this thing would be useful at all, because even in February when we released it, it was writing maybe, like, I don't know, 20% of my code, not more. And even in May, it was writing maybe 30%. I was still using Cursor for most of my code. 

**Boris Cherny** (00:15:58):
And it only crossed 100% in November. So, it took a while, but even from the earliest day, it just felt like I was onto something, and I was just spending every night, every weekend hacking on this. And, luckily, my wife was very supportive. But it just felt like it was onto something. It wasn't obvious what. And sometimes you find a thread, you just have to pull on it.

**Lenny Rachitsky** (00:16:17):
So, at this point, 100% of your code is written by Claude Code. Is that the current state of your coding?

**Boris Cherny** (00:16:23):
Yeah. So, 100% of my code is written by Claude Code. I am a fairly prolific coder. And this has been the case even when I worked back at Instagram. I was one of the top few most productive engineers. And that's still the case here at Anthropic.

**Lenny Rachitsky** (00:16:38):
Wow. Even as head of the team? 

**Boris Cherny** (00:16:41):
Yeah. Yeah. Still od a lot of coding. And so, every day, I ship, like, 10, 20, 30 pull requests or something like that.

**Lenny Rachitsky** (00:16:47):
Every day?

**Boris Cherny** (00:16:49):
Every day. Yeah. 

**Lenny Rachitsky** (00:16:50):
Good God.

**Boris Cherny** (00:16:51):
100% written by Claude Code. I have not edited a single line by hand since November. And, yeah. I do look at the code. So, I don't think we're at the point at where you can be totally hands-off, especially, when there's a lot of people running the program. You have to make sure that it's correct, you have to make sure it's safe, and so on. 

**Boris Cherny** (00:17:13):
And then we also have Claude doing automatic code review for everything. So, here at Anthropic, Claude reviews 100% of pull requests. There's still a layer of human review after it, but you still do want some of these checkpoints. Like, you still want a human looking at the code. Unless it's pure prototype code that it's not going to run anywhere. It's just a prototype.

**Lenny Rachitsky** (00:17:32):
What's the next frontier? So, at this point, 100% of your code is being written by AI. This is, clearly, where everyone is going in software engineering. That felt like a crazy milestone. Now it's just like, "Of course. This is the world now." What's the next big shift to how software is written that either your team is already operating in or you think will head towards? 

**Boris Cherny** (00:17:54):
I think something that's happening right now is Claude is starting to come up with ideas. So, Claude is looking for feedback. It's looking at bug reports. It's looking at telemetry, and things like this, and it's starting to come up with ideas for bug fixes, and things to ship. So, it's just starting to get a little more like a coworker or something like that.

**Boris Cherny** (00:18:16):
I think the second thing is we're starting to branch out of coding a little bit. So, I think, at this point, it's safe to say that coding is virtually solved. At least, for the kinds of programming that I do, it's just a solved problem, because Claude can do it. And so, now we're starting to think about, "Okay. What's next? What's beyond this?" 

There's a lot of things that are adjacent to coding, and I think this is [inaudible 00:18:35] becoming, but also just general to us. Like, I use Cowork every day now to do all sorts of things that are just not related to coding at all, and just to do it automatically.

**Boris Cherny** (00:18:45):
Like, for example, I had to pay a parking ticket the other day. I just had Cowork do it. All of my project management for the team, Cowork does all of it. It's, like, syncing stuff between spreadsheets, and messaging people on Slack, and email, and all this kind of stuff. 

**Boris Cherny** (00:18:57):
So, I think the frontier is something like this. And I don't think it's coding, because I think coding, it's pretty much solved, and over the next few months, I think what we're going to see is just across the industry it's going to become increasingly solved for every kind of code base, every tech stack that people work on.

**Lenny Rachitsky** (00:19:14):
This idea of helping you come up with what to work on is so interesting. A lot of people listening to this are product managers and they're probably sweating. How do you use Claude for this? Do you just talk to it? Is there anything clever you've come up with to help you use it to come up with what to build?

**Boris Cherny** (00:19:30):
Honestly, the simplest thing is, like, open Claude or Cowork, and point it at a Slack thread. Like, for us, we have this channel that's all the internal feedback about Claude Code. Since we first released it, even in 2024, internally, it's just been this fire hose of feedback. It is the best.

**Boris Cherny** (00:19:46):
And in the early days, what I would do is any time that someone sends feedback, I would just go in, and I would fix every single thing as fast as I possibly could. So, like, within a minute, within five minutes, or whatever. And this just really fast feedback cycle, it encourages people to give more and more feedback. It's just so important, because it makes them feel heard.

**Boris Cherny** (00:20:03):
Because, usually, when you use a product, you get feedback, it just goes into a black hole somewhere, and then you don't get feedback again. So, if you make people feel heard, then they want to contribute, then they want to help make the thing better.

**Boris Cherny** (00:20:13):
And so, now I do the same thing, but, Claude, honestly, does a lot of the work. So, I pointed at the channel, and it's like, "Okay. Here's a few things that I can do. I just put up a couple PRs. Want to take a look at that one?" I'm like, "Yeah." 

**Lenny Rachitsky** (00:20:25):
Have you noticed that it is getting much better at this? Because this is the holy grail. Right now it's, cool, building solved. Code review became the next bottleneck with all these PRs. Who is going to review them all? The next big open question is just, like, "Okay. Now humans are necessary for figuring out what to build, what to prioritize," and you're saying that's where Claude Code is starting to help you. Has it gotten a lot better with, like, Opus 4.6, or what's been the trajectory there? 

**Boris Cherny** (00:20:50):
Yeah. Yeah. It's improved a lot. I think some of it is training that we do specific to coding. So, obviously, the best coding model in the world, and it's getting better and better. Like, 4.6 is just incredible. But also actually a lot of the training that we do outside of coding translates pretty well too.

**Boris Cherny** (00:21:07):
So, there is this transfer where you teach the model to do X, and it gets better at Y. Yeah. And the gains have just been insane. Like, at Anthropic, over the last year, like, since we introduced Claude Code, we probably ... I don't know the exact number. We probably 4X-ed the engineering team, or something like this. But productivity per engineer has increased 200% in terms of pull requests.

**Boris Cherny** (00:21:31):
And this number is just crazy for anyone that actually works in the space and works on dev productivity. Because back in a previous life, I was at Meta, and one of my responsibilities was code quality for the company. So, this is all of our code bases, that was my responsibility. Like, Facebook, Instagram, WhatsApp, all this stuff. 

**Boris Cherny** (00:21:47):
And a lot of that was about productivity, because if you make the code higher quality, then engineers are more productive. And things that we saw is in a year with hundreds of engineers working on it, you would see a gain of a few percentage points of productivity, something like this. And so, nowadays, seeing these gains of just hundreds of percentage points is just absolutely insane.

**Lenny Rachitsky** (00:22:06):
What's also insane is just how normalized this has all been. Like, we hear these numbers. Like, of course, AI is doing this to us. It's so unprecedented, the amount of change that is happening to software development, to building products, to just the world of tech. It's just so easy to get used to it, but it's important to recognize this is crazy.

**Boris Cherny** (00:22:25):
This is something I have to remind myself once in a while. There's a downside of this, because the model changes so ... Well, there's many downsides that we could talk about, but I think one of them on a personal level is the model changes so often that I sometimes get stuck in this old way of thinking about it. And I even find that new people on the team, or even new grads that join do stuff in a more AGI-forward way than I do. 

So, sometimes, for example, I had this case a couple of months ago where there was a [inaudible 00:22:57] week. And so, what this is is Claude Code, the memory usage is going up, and, at some point, it crashes. This is a very common engineering problem that every engineer has debugged 1000 times. 

**Boris Cherny** (00:23:07):
And, traditionally, the way that you do it is you take a heap snapshot, you put it into a special debugger, and figure out what's going on. You use these special tools to see what's happening.

**Boris Cherny** (00:23:16):
And I was doing this, and I was looking through these traces, and trying to figure out what was going on, and the engineer that was newer on the team just had Claude Code do it. And was like, "Hey, Claude. It sounds like there's a leak. Can you figure it out?" And so, Claude Code did exactly the same thing that I was doing. It took the heap snapshot, it wrote a little tool for itself, so, it can analyze it itself. It was a just in time program. And it found the issue, and put up a pull request faster than I could. 

**Boris Cherny** (00:23:43):
So, it's something where for those of us that have been using the model for a long time, you still have to transport yourself to the current moment, and not get stuck back in old model, because it's not Sonnet 3.5 anymore. The new models are just completely, completely different. And just this mindset shift is very different. 

**Lenny Rachitsky** (00:24:03):
I hear you have these very specific principles that you've codified for your team that when people join you walk them through them. I believe one of them is, "What's better than doing something? Having Claude do it." And it feels like that's exactly what you describe with this memory leak is you almost forgot that principle of like, "Okay. Let me see if Claude can solve this for me." 

**Boris Cherny** (00:24:21):
There's an interesting thing that happens also when you under-fund everything a little bit, because then people are forced to Claude-ify. And this is something that we see so ... For work, where sometimes we just put one engineer on a project, and the way that they're able to ship really quickly ... Because they want to ship quickly. This is an intrinsic motivation that comes from within. It's just wanting to do a good job. If you have a good idea, you just really want to get it out there. No one has to force you to do that. That comes from you.

**Boris Cherny** (00:24:49):
And so, if you have Claude, you can just use that to automate a lot of work, and that's what we see over and over. So, I think that's one principle is under-funding things a little bit.

**Boris Cherny** (00:25:01):
I think another principle is just encouraging people to go faster. So, if you can do something today, you should just do it today. And this is something we really, really encourage on the team. Early on, it was really important, because it was just me. And so, our only advantage was speed. That's the only way that we could ship a product that would compete in this very crowded coding market. 

**Boris Cherny** (00:25:21):
But nowadays, it's still very much a principle we have on the team, and if you want to go faster, a really good way to do that is to just have Claude do more stuff. So, it just very much encourages that. 

**Lenny Rachitsky** (00:25:32):
This idea of under-funding, it's so interesting, because, in general, there's this feeling like AI is going to allow you to not have as many employees, not have as many engineers. And so, it's not only you can be more productive. What you're saying is that you will actually do better, if you under-fund. It's not just that AI can make you faster, it's you will get more out of the AI tooling if you have fewer people working on something.

**Boris Cherny** (00:25:54):
Yeah. If you hire great engineers, they'll figure out how to do it. And, especially, if you empower them to do it. This is something I actually talk a lot about with CTOs and all sorts of companies. My advice generally is, "Don't try to optimize. Don't try to cost cut at the beginning. Start by just giving engineers as many tokens as possible." And now you're starting to see companies ... Like, at Anthropic, we have ... Everyone can use a lot of tokens.

**Boris Cherny** (00:26:19):
We're starting to see this come up as a perk at some companies where if you join you get unlimited tokens. This is a thing I very much encourage, because it makes people free to try these ideas that would have been too crazy, and then if there's an idea that works, then you can figure out how to scale it, and that's the point to optimize, and to cost-cut, figure out ... Maybe you can do it with a haiku, or with Sonnet instead of Opus, or whatever. 

**Boris Cherny** (00:26:44):
But at the beginning, you just want to throw a lot of tokens at it, and see if the idea works, and give engineers the freedom to do that. 

**Lenny Rachitsky** (00:26:49):
So, the advice here is just be loose with your tokens, with the cost on using these models. People hearing this may be like, "Of course. He works at Anthropic. You want us to use as many tokens as possible."

**Lenny Rachitsky** (00:27:00):
But what you're saying here is the most interesting innovative ideas will come out of someone just taking it to the max and seeing what's possible. 

**Boris Cherny** (00:27:08):
Yeah. And I think the reality is at small-scale, you're not going to get a giant bill for anything like this. If it's an individual engineer experimenting, the token cost is still probably relatively low relative to [inaudible 00:27:21] other costs of running the business. So, it's actually not a huge cost. 

**Boris Cherny** (00:27:27):
As the thing scales up, so, let's say they build something awesome, and then it takes a huge amount of tokens, and then the cost becomes pretty big, that's the point at which you want to optimize it. But don't do that too early. 

**Lenny Rachitsky** (00:27:37):
Have you seen companies where their token cost is higher than their salary? Is that a trend you think we're going to find and see?

**Boris Cherny** (00:27:44):
At Anthropic, we're starting to see some engineers that are spending hundreds of thousands a month in tokens. So, we're starting to see this a little bit. There's some companies that we're starting to see similar things. Yeah. 

**Lenny Rachitsky** (00:27:58):
Going back to coding, do you miss writing code? Is it something you're sad about that this is no longer a thing you'll do as a software engineer?

**Boris Cherny** (00:28:06):
It's funny. For me, when I learned engineering, for me, it was very practical. I learned engineering, so, I could build stuff. And, for me, I was self-taught. I studied economics in school, but I didn't study CS. But I taught myself engineering. Early on, I was programming in middle school.

**Boris Cherny** (00:28:26):
And from the very beginning, it was very practical. So, I learned to code, so, that I can cheat on a math test. That was the first thing-

**Lenny Rachitsky** (00:28:33):
Nice.

**Boris Cherny** (00:28:33):
... we had these graphing calculators, and I just programmed-

**Lenny Rachitsky** (00:28:36):
The T83. TI83?

**Boris Cherny** (00:28:39):
Yeah. T83+. Yeah. Yeah. Exactly.

**Lenny Rachitsky** (00:28:40):
Plus. 

**Boris Cherny** (00:28:41):
Plus. Yeah. I programmed the answers in, and then the next math test, or whatever, the next year, it was just too hard. I couldn't program all the answers in, because I didn't know what the questions were. And so, I had to write a little solver, so, that it was a program that would just solve these algebra questions or whatever. 

**Boris Cherny** (00:28:58):
And then I figured out you can get a little cable, you can give the program to the rest of the class, and then the whole class gets As, but then we all got caught, and the teacher told us to knock it off. But from the very beginning-

**Lenny Rachitsky** (00:29:08):
Wow.

**Boris Cherny** (00:29:08):
... it's always just been very practical for me where programming is a way to build the thing. It's not the end in itself. At some point, I personally fell into the rabbit hole of the beauty of programming. So, I wrote a book about typescript. Actually, at the time, it was the world's biggest typescript [inaudible 00:29:29] just because I fell in love with the language itself. And I got deep into functional programming and all the stuff. 

I think a lot of coders they get distracted by this. For me, it was always ... There is a beauty to programming, and, especially, to functional programming. There's a beauty to type systems. There's a certain, like, this buzz that you get when you solve a really [inaudible 00:29:54] math problem. It's similar when you balance the types or the program is just really beautiful.

**Boris Cherny** (00:30:01):
But it's really not the end of it. I think, for me, coding is very much a tool, and it's a way to do things. That said, not everyone feels this way. So, for example, there's one engineer on the team, Lena, who was still writing C++ on the weekends by hand, because for her she just really enjoys writing C++ by hand. 

**Boris Cherny** (00:30:20):
And so, everyone is different. And I think even as this field changes, even as everything changes, there is always space to do this. There is always space to enjoy the art, and to do things by hand, if you want.

**Lenny Rachitsky** (00:30:34):
Do you worry about your skills atrophying as an engineer? Is that something you worry about or is it just like, "This is just the way it's going to go"?

**Boris Cherny** (00:30:41):
I think it's just the way that it happens. I don't worry about it too much personally. I think, for me, programming is on a continuum. And way back in the day, software actually is relatively new. Right? If you look at the way programs are written today using software that's running on a virtual machine or something, this has been the way that we've been writing programs since probably the 1960s. So, it's been 60 years or something like that.

**Boris Cherny** (00:31:06):
Before that it was punch cards, before that it was switches. Before that, it was hardware. And before that, it was just, like, literally, pen and paper. It was a room full of people that were doing math on paper. 

**Boris Cherny** (00:31:16):
And so, programming has always changed in this way. In some ways, you still want to understand the layer under the layer, because it helps you be a better engineer. And I think this will be the case maybe for the next year or so, but I think pretty soon it just won't really matter. It's just going to be the assembly code running under the program or something like this. 

**Boris Cherny** (00:31:36):
At an emotional level, I feel like I've always had to learn new things. And as a programmer, it doesn't feel that new, because there's always new frameworks. There's always new languages. It's just something that we're quite comfortable with in the field.

**Boris Cherny** (00:31:50):
But at the same time, this isn't true for everyone. And I think for some people they're going to feel a greater sense of, I don't know, maybe, like, loss or nostalgia, or atrophy, or something like this. 

**Lenny Rachitsky** (00:32:00):
I don't know if you saw this, but Elon was saying that, "Why isn't the AI just writing binary straight to binary? Because what's the point of all this programming abstraction in the end?" 

**Boris Cherny** (00:32:12):
Yeah. It's a good question. It totally can do that, if you want it too.

**Lenny Rachitsky** (00:32:15):
Oh, man. So, what I'm hearing here is in terms ... There's always this question, "Should I learn to code? Should people in school learn to code?" What I heard from you is your take is in a year or two you don't really need to.

**Boris Cherny** (00:32:27):
My take is I think for people that are using Claude Code, that are using agents to code today, you still have to understand the layer under, but, yeah, in a year or two, it's not going to matter. 

**Boris Cherny** (00:32:40):
I was thinking about what is the right historical analog for this? Because somehow we have to situate this thing in history, and figure out when have we gone through similar transitions? What's the right mental model for this? 

**Boris Cherny** (00:32:54):
I think the thing that's come closest for me is the printing press. And so, if you look at Europe and in the mid 1400s, literacy was actually very low. There was sub-1% of the population, it was scribes, that they were the ones that did all the writing. They were the ones that did all the reading. They were employed by lords and kings that often were not literate themselves.

**Boris Cherny** (00:33:18):
And so, it was their job of this very tiny percent of the population to do this. And, at some point, Gutenberg and the printing press came along, and there was this crazy stat that in the 50 years after the printing press was built, there was more printed material created than in the thousand years before. 

**Boris Cherny** (00:33:38):
And so, the volume of printed material just went way up. The cost went way down. It went down something, like, 100 X over the next 50 years. And if you look at literacy, it actually took a while, because of learning to read and write, it's quite hard, it takes an education system, it takes free time. It takes not having to work on a farm all day, so, that you actually have time for education, and things like this.

**Boris Cherny** (00:34:00):
But over the next 200 years it went up to 70% globally. So, I think this is the thing that we might see is a similar kind of transition. And there was actually this interesting historical document where there was an interview with some scribe in the 1400s about, "How do you feel about the printing press?" And they were actually very excited, because they were like, "Actually, the thing that I don't like doing is copying between books. The thing that I do like doing is drawing the art in books, and then doing the book binding. And I'm really glad that now my time is freed up."

And it's interesting. As an engineer, I felt a parallel with this. Like, this is how I feel where I don't have to do the tedious work anymore of coding, because this has always been the detail of it. It's always been the tedious part of it, and messing with [inaudible 00:34:51], and using all of these different tools.

**Boris Cherny** (00:34:53):
That was not the fun part. The fun part is figuring out what to build, and coming up with this. It's talking to users. It's thinking about these big systems. It's thinking about the future. It's collaborating with other people on the team, and that's what I get to do more of now. 

**Lenny Rachitsky** (00:35:07):
And what's amazing is that the tool you're building allows anybody to do this, people that have no technical experience can do exactly what you're describing. I've been doing a bunch of random little projects, and it's just, like, "Any time you get stuck just help me figure this out." And you get unblocked.

I was an engineer earlier in my career for 10 years. And I just remember spending so much time on libraries and dependencies and things, and just like, "Oh my God. What do I do?" And then looking on [inaudible 00:35:34]. And now it's just like, "Help me figure this out," and, "Here's a step-by-step, one, two, three, four. Okay. We got this." 

**Boris Cherny** (00:35:39):
Yeah. Exactly. Exactly. I was talking to an engineer earlier today. They're like, "They're writing some service [inaudible 00:35:44], and it's been a month already, and they built up the service. It's working quite well." And then I was like, "Okay. So, how do you feel writing it?" And he was like, "I still don't really know Go." 

**Boris Cherny** (00:35:55):
And I think we're going to start to see more and more of this. It's, like, if you know that it works correctly and efficiently, then you don't actually have to know all the details.

**Lenny Rachitsky** (00:36:02):
Clearly, the life of a software engineer has changed dramatically. It's a whole new job now as of the past year or two. What do you think is the next role that will be most impacted by AI? Either within tech, like, product managers, designers, or even outside tech, just what do you think? Where do you think AI is going next?

**Boris Cherny** (00:36:23):
I think it's going to be a lot of the roles that are adjacent to engineering. So, yeah. It could be product managers, it could be design, it could be data science. It is going to expand to pretty much any kind of work that you can do on a computer, because the model is just going to get better and better at this. And the Cowork product is the first way to get at this, but it's just the first one. 

**Boris Cherny** (00:36:44):
And it's the thing that I think brings Agentic AI to people that haven't really used it before, and people are starting just to get a sense of it for the first time. When I think about the engineering a year ago, no one really knew what an agent was, no one really used it, but nowadays it's just the way that we do our work.

**Boris Cherny** (00:37:04):
And then when I look at non-technical work today, so, like ... Or maybe semi-technical, like, product work, and data science, and things like this, when you look at the concept of AI that people are using, it's always these conversational AI. It's, like, a chatbot or whatever. But no one really has used an agent before, and this word agent just gets thrown around all the time, and it's just so misused. It's lost all meaning. 

**Boris Cherny** (00:37:26):
But agent actually has a very specific technical meaning, which is it's an AI, it's an LLM that's able to use tools. So, it doesn't just talk. It can actually act, and it can interact with your system. And this means it can use your Google Docs, and it can send email, it can run commands on your computer, and do all this kind of stuff. 

**Boris Cherny** (00:37:46):
So, I think any kind of job where you do use computer tools in this way, I think this is going to be next. This is something we have to figure out as a society, this is something we have to figure out as an industry. And I think, for me, also this is one of the reasons, it feels very important and urgent to do this work at Anthropic, because I think we take this very, very seriously. 

**Boris Cherny** (00:38:08):
And so, now we have economists, we have policy folks, we have social impact folks. This is something we just want to talk about a lot, so, as a society we can figure out what to do, because it shouldn't be up to us. 

**Lenny Rachitsky** (00:38:19):
So, the big question, which you're alluding to is jobs and job loss, and things like that. There's this concept of Jevons paradox of just as we can do more, we hire more, and it's not actually as scary as it looks. What have you experienced so far I guess with AI becoming a big part of the engineering job? Are you hiring more than if you didn't have AI? And just thoughts on jobs. 

**Boris Cherny** (00:38:41):
Yeah. For our team we're hiring. So, Claude Code team is hiring. If you're interested just check out the jobs page on Anthropic. Personally, it's all this stuff has just made me enjoy my work more. I have never enjoyed coding as much as I do today, because I don't have to deal with all the minutia. 

**Boris Cherny** (00:39:00):
So, for me, personally, it's been quite exciting. This is something that we hear from a lot of customers where they love the tool, they love Claude Code, because it just makes coding delightful again, and that's just so fun for them. 

**Boris Cherny** (00:39:14):
But it's hard to know where this thing is going to go. And, again, I have to reach for these historical analogs. And I think the printing press is just such a good one, because what happened is this technology that was locked away to a small set of people, like, knowing how to read and write became accessible to everyone. It was just inherently democratizing. Everyone started to be able to do this. 

**Boris Cherny** (00:39:36):
And if that wasn't the case then something like the Renaissance just could never have happened, because a lot if the Renaissance, it was about knowledge spreading. It was about written records that people use to communicate. Because there were no phones or anything like this. There was no internet at the time. 

**Boris Cherny** (00:39:54):
So, it's about what does this enable next? And I think that's the very optimistic version of it for me, and that's the part that I'm really excited about. It's just unimaginable. We couldn't be talking today, if the printing press hadn't been invented. Like, our microphones wouldn't exist. None of the things around us would exist. It just wouldn't be possible to coordinate such a large group of people if that wasn't the case. 

**Boris Cherny** (00:40:15):
And so, I imagine a world a few years in the future where everyone is able to program, and what does that unlock? Anyone can just build software any time. And I have no idea. It's just the same way that in the 1400s, no one could have predicted this. I think it's the same way.

**Boris Cherny** (00:40:31):
But I do think in the meantime, it's going to be very disruptive, and it's going to be painful for a lot of people. And, again, as a society, this is a conversation that we have to have, and this is a thing that we have to figure out together. 

**Lenny Rachitsky** (00:40:42):
So, for folks hearing this that want to succeed and make it in this crazy turmoil we're entering, any advice? Is it play with AI tools? Get really proficient at the latest stuff? Is there anything else that you recommend to help people stay ahead?

**Boris Cherny** (00:40:58):
Yeah. I think that's pretty much it. Experiment with the tools, get to know them, don't be scared of them. Just dive in, try them, be on the bleeding edge, be on the frontier. 

**Boris Cherny** (00:41:08):
And maybe the second piece of advice is try to be a generalist more than you have in the past. For example, in school, a lot of people that study CS, they learn to code, and they don't really learn much else. Maybe they learn a little bit of systems architecture or something like this.

**Boris Cherny** (00:41:25):
But some of the most effective engineers that I work with every day, and some of the most effective product managers and so on, they cross over disciplines. So, on the Claude Code team everyone codes. Our product manager codes, our engineering manager codes, our designer codes, our finance guy codes, our data scientist codes. Everyone on the team codes.

**Boris Cherny** (00:41:43):
And then if I look at particular engineers, people often cross different disciplines. So, some of the strongest engineers are hybrid, product, and infrastructure engineers, or product engineers with really great design sense, and they're able to do design also. Or an engineer that has a really good sense of the business, and can use that to figure out what to do next, or an engineer that also loves talking to users and can just really channel what users want to figure out what's next.

**Boris Cherny** (00:42:10):
So, I think a lot of the people that will be rewarded the most over the next few years they won't just be AI native, and they don't just know how to use these tools really well, but also they're curious and they're generalists, and they cross over multiple disciplines and can think about the broader problem they're solving rather than just the engineering part of it. 

**Lenny Rachitsky** (00:42:29):
Do you find these three separate disciplines still useful as a way to think about the team? Their engineering, design, product management, do you find those ... Even though, they are now coding and contributing to thinking about what to build do you feel like those are three roles that will persist long-term? At least, at this point.

**Boris Cherny** (00:42:46):
I think in the short-term it'll persist, but one thing that we're starting to see is there's maybe a 50% overlap in those roles where a lot of people are actually just doing the same thing, and some people have specialties. For example, I code a little bit more [inaudible 00:42:58] Kat, our PM, does a little bit more coordination or planning or forecasting, or things like this.

**Lenny Rachitsky** (00:43:04):
Stakeholder alignment. 

**Boris Cherny** (00:43:06):
Stakeholder alignment. Exactly. I do think that there is a future where I think by the end of the year what we're going to start to see is these start to get even murkier where I think in some places the title software engineer is going to start to go away, and it's just going to be replaced by builder or maybe it's just everyone is going to be a product manager and everyone codes, or something like this. 

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**Lenny Rachitsky** (00:44:38):
You talked about how you're enjoying coding more. I actually did this little informal survey on Twitter. I don't know if you saw this where I just asked ... I did three different polls. I asked engineers, "Are you enjoying your job more or less since adopting AI tools?" And then I did a separate one for PMs, and one for designers. And both engineers and PMs, 70% of people said they are enjoying their job more. And about 10% said they're enjoying their job less. 

**Lenny Rachitsky** (00:45:02):
Designers, interestingly, only 55% said they are enjoying their job more, and 20% said they're enjoying their job less. I thought that was really interesting.

**Boris Cherny** (00:45:11):
That's super interesting. I'd love to talk to these people, both in the more bucket and the less bucket just to understand. Did you get to follow up with any of them?

**Lenny Rachitsky** (00:45:20):
A few people replied, and we're actually doing a followup poll that we'll look to in the show notes of going deeper into some of the stuff. But a lot ... There's the factors that make it more fun and less fun. The designers, they didn't share a lot actually of just the people that [inaudible 00:45:33]. I actually asked just like, "Why are you enjoying your job less?" I didn't hear a lot. So, I'm curious what's going on there.

**Boris Cherny** (00:45:37):
Yeah. I'm seeing this a little bit with ... At Anthropic, I think everyone is fairly technical. This is something that we screen for when people join. There's a lot of technical interviews that people go through even for non-technical functions. 

**Boris Cherny** (00:45:53):
And our designers largely code. So, I think for them this is something that they have enjoyed from what I've seen, because now instead of bugging engineers they can just go in and code. And even some designers that didn't code before have just started to do it, and for them it's great, because they can unblock themselves.

**Boris Cherny** (00:46:12):
But I'd be really interested just to hear more people's experiences, because I bet it's not uniform like that.

**Lenny Rachitsky** (00:46:18):
Yeah. So, maybe if you're listening to this leave a comment if you're finding your job is less fun, and you're enjoying your job less, because what you're saying and what I'm hearing from most people, 70% of PMs and engineers are loving their job more. Like, if you're [inaudible 00:46:30] bucket, you could ... Something's going on.

**Boris Cherny** (00:46:32):
Yeah. Yeah. We do see that people use also different tools. So, for example, our designers they use the Claude desktop app a lot more to do their coding. So, you just download the desktop app. There's a code tab. It's right next to Cowork. 

**Boris Cherny** (00:46:45):
And it's actually the same as at Claude Code. So, it's the same agent, and everything. We've had this for many, many months. And so, you can use this to code in a way that you don't have to open a bunch of terminals. But you still get the power of Claude Code, and the biggest thing is you can just run as many Claude sessions in parallel as you want. We call this multi-Claude-ing. 

**Boris Cherny** (00:47:04):
So, it's a little more native I think for folks that are not engineers, and really this is back to bringing the product to where the people are. You don't want to make people use a different workflow. You don't want to make them go out of their way to learn a new thing. It's whatever people are doing, if you can make that a little bit easier, then that's just going to be a much better product that people enjoy more.

**Boris Cherny** (00:47:23):
And this is just this principle of latent demand, which I think is just the single-most important principle in product. 

**Lenny Rachitsky** (00:47:29):
Can you talk about that actually? Because I was going to go there. Explain what this principle is and just what happens when you unlock this latent demand. 

**Boris Cherny** (00:47:37):
Latent demand is this idea that if you build a product in a way that can be hacked, or can be misused by people in a way it wasn't really designed for to do something that they want to do, then this helps you as the product builder learn where to take the product next.

**Boris Cherny** (00:47:55):
So, an example of this is Facebook Marketplace. So, the manager for the team Fiona, she was actually the founding manager for the Marketplace team and she talks about this a lot. Facebook Marketplace is based on the observation back in ... This must have been, like, 2016 or something like this. That 40% of posts in Facebook groups are buying and selling stuff. 

**Boris Cherny** (00:48:17):
So, this is crazy. It's, like, people are abusing the Facebook groups product to buy and sell, and it's not abuse in a security sense. It's abuse in that no one designed the product for this, but they're figuring it out, because it's just so useful for this. 

**Boris Cherny** (00:48:29):
And so, it's pretty obvious. If you build a better product to let people buy and sell, they're going to like it. And it was just very obvious that Marketplace would be a hit from this. And so, the first thing was buy and sell groups, so, special purpose groups to let people do that, and the second product was Marketplace.

**Boris Cherny** (00:48:45):
Facebook Dating I think started in a pretty similar place. And I think the observation was if you look at profile views, so, people looking at each other's profiles on Facebook, 60% of profile views were people that are not friends with each other that are opposite gender. And so, this traditional dating setup, people are just creeping on each other. So, maybe if you can build a product for this, it might work. 

**Boris Cherny** (00:49:11):
And so, this idea of latent demand I think is just so powerful. And, for example, this is also where Cowork came from. We saw that for the last six months or so, a lot of people using Claude Code were not using it to code. There was someone on Twitter that was using it to grow tomato plants. There was someone else using it to analyze their genome. Someone was using it to recover photos from a corrupted hard drive that was, like, wedding photos. There was someone that was using it for I think ... They were using it to analyze an MRI.

**Boris Cherny** (00:49:43):
So, there's just all these different use cases that are not technical at all, and it was just really obvious. Like, people are jumping through hoops to use a terminal to do this thing. Maybe we should just build a product for them. 

**Boris Cherny** (00:49:55):
And we saw this actually pretty early. Back in maybe May of last year, I remember walking into the office and our data scientist Brendan had a Claude Code on his computer. He just had a terminal up. And I was shocked. I was like, "Brendan, what are you doing?" He figured out how to open the terminal, which is ... It's a very engineer-y product. Even a lot of engineers don't want to use a terminal. It's just the lowest level way to do your work. Just really, really in the weeds of the computer.

And so, he figured out how to use the terminal. He downloaded [inaudible 00:50:28]. He downloaded Claude Code. And he was doing SQL analysis in the terminal. It was crazy. And then the next week all of the data scientists were doing the same thing.

**Boris Cherny** (00:50:36):
So, when you see people abusing the product in this way, using it in a way that it wasn't designed in order to do something that is useful for them, it's just such a strong indicator that you should just build a product and people are going to like that. It's something that's special purpose for that. 

**Boris Cherny** (00:50:50):
I think now there's also this interesting second dimension to latent demand. This is the traditional framing is look at what people are doing, make that a little bit easier, empower them. 

**Boris Cherny** (00:50:59):
The modern framing that I've been seeing in the last six months is a little bit different. And it's look at what the model is trying to do, and make that a little bit easier. 

**Boris Cherny** (00:51:10):
And so, when we first started building Claude Code, I think a lot of the way that people approached designing things with LLMs is they put the model in a box, and they were like, "Here's this application that I want to build. Here's the thing that I wanted to do, a model, you're going to do this one component of it. Here's the way that you're going to interact with these tools and APIs," and whatever.

**Boris Cherny** (00:51:28):
And for Claude Code, we inverted that. We said, "The product is the model. We want to expose it. We want to put the minimal scaffolding around it. Give it the minimal set of tools. So, it can do the things, it can decide which tools to run. It can decide in what order to run them in," and so on.

**Boris Cherny** (00:51:41):
And I think a lot of this was just based on latent demand of what the model wanted to do. And so, in research we call this being on distribution. You want to see what the model is trying to do. In product terms, latent demand is just the same exact concept but applied to a model. 

**Lenny Rachitsky** (00:51:55):
You talked about Cowork. Something that I saw you talk about when you launched that initially is your team built that in 10 days. That's insane.

**Boris Cherny** (00:52:02):
Yeah.

**Lenny Rachitsky** (00:52:02):
It came out, I think it was used by millions of people pretty quickly, something like that being built in 10 days. Anything there? Any stories there other than it was just, "We used a lot of code to build it and that's it"?

**Boris Cherny** (00:52:14):
Yeah. It's funny. Claude Code, like I said, when we released it it was not immediately a hit. It became a hit over time, and there was a few inflection points. So, one was, like, Opus 4, it just really, really inflected, and then in November it inflected, and it just keeps inflecting. The growth just keeps getting steeper and steeper and steeper every day. 

**Boris Cherny** (00:52:31):
But for the first few months it wasn't a hit. People used it, but a lot of people couldn't figure out how to use it. They didn't know what it was for. The model still wasn't very good. 

**Boris Cherny** (00:52:40):
Cowork when we released it it was just immediately a hit. Much more so than Claude Code was early on. I think a lot of the credit, honestly, just goes to Felix and Sam and Jenny, and the team that built this. It's just an incredibly strong team.

**Boris Cherny** (00:52:55):
And, again, the place Cowork came from is just this latent demand. Like, we saw people using Claude Code for these non-technical things. And we're trying to figure out, "What do we do?" And so, for a few months the team was exploring, they were trying all sorts of different options. And, in the end, someone was just like, "Okay. What if we just take Claude Code and put it in the desktop app?" And that's, essentially, the thing that worked. 

**Boris Cherny** (00:53:15):
And so, over 10 days they just completely used Claude Code to build it. And Cowork is actually ... There's this very sophisticated security system that's built in. And, essentially, these guardrails to make sure that the model does the right thing, it doesn't go off the rails.

**Boris Cherny** (00:53:30):
So, for example, we ship an entire virtual machine with it, and Claude Code just wrote all of this code. So, we just had to think about, "All right. How do we make this a little bit safer? A little more self-guided for people that are not engineers." It was fully implemented with Claude Code. It took about 10 days. We launched it early. It was still pretty rough, and it's still pretty rough around the edges.

**Boris Cherny** (00:53:50):
But this is the way that we learn, both on the product side and on the safety side is we have to release things a little bit earlier than we think, so, that we can get the feedback, so, that we can talk to users. We can understand what people want, and that'll shape where the product goes in the future.

**Lenny Rachitsky** (00:54:05):
Yeah. I think that point is so interesting, and it's so unique. There's always been this idea, release early, learn from users, get feedback, iterate. The fact that it's hard to even know what the AI is capable of, and how people will try to use it is a unique reason to start releasing things early. So, that'll help you as you exactly describe this idea of what is a latent demand in this thing that we didn't really know? Let's put it out there and see what people do with it. 

**Boris Cherny** (00:54:30):
Yeah. And for Anthropic as a safety lab, the other dimension of that is safety, because when you think about model safety there's a bunch of different ways to study it. The lowest level is alignment, and mechanistic interpretability. So, this is when we train the model we want to make sure that it's safe. We, at this point, have pretty sophisticated technology to understand what's happening in the neurons to trace it.

**Boris Cherny** (00:54:52):
And so, for example, if there's a neuron related to deception, we're starting to get to the point where we can monitor it and understand that it's activating. And so, this is alignment, this is mechanistic interpretability, it's the lowest layer. 

**Boris Cherny** (00:55:05):
The second layer is evals, and this is, essentially, a laboratory setting, the model is in a Petri dish, and you study it. And you put in the synthetic situation and just say, "Okay. Model, what do you do?" And, "Are you doing the right thing? Is it aligned? Is it safe?" 

**Boris Cherny** (00:55:17):
And then the third layer is seeing how the model behaves in the wild. And as the model gets more sophisticated, this becomes so important, because it might look very good on these first two layers, but not great on the third one.

**Boris Cherny** (00:55:30):
We released Claude Code really early, because we wanted to study safety. And we actually used it within Anthropic for I think four or five months, or something before we released it, because we weren't really sure. Like, this is the first big agent that I think folks had released at that point. It was definitely the first coding agent that became broadly used. 

**Boris Cherny** (00:55:51):
And so, we weren't sure if it was safe. And so, we actually had to study it internally for a long time before we felt good about that. And even since there's a lot that we've learned about alignment. There's a lot that we've learned about safety, that we've been able to put back into the model, back into the product.

**Boris Cherny** (00:56:05):
And for Cowork, it's pretty similar. The model is in this new setting. It's doing these tasks that are not engineering tasks. It's an agent that's acting on your behalf. It looks good on alignment, it looks good on evals, we tried it internally, it looks good. We tried it with a few customers, it looks good. Now we have to make sure it's safe in the real world. 

**Boris Cherny** (00:56:21):
And so, that's why we release a little early. That's why we call it a research preview. But, yeah. It's constantly improving. And this is really the only way to make sure that over the long-term the model is aligned, and it's doing the right things. 

**Lenny Rachitsky** (00:56:33):
It's such a wild space that you work in where there's this insane competition and pace. At the same time, there's this fear that if the God can escape and cause damage, and just finding that balance must be so challenging. What I'm hearing is there's these three layers, and I know there's ... This could be a whole podcast conversation is how you all think about the safety piece, but just what I'm hearing is there's these three layers you work with. There's observing the model thinking and operating. There's tests, evals that tell you this is doing bad things. And then releasing it early.

**Lenny Rachitsky** (00:57:05):
I haven't actually heard a ton about that first piece. That is so cool. So, you guys can ... There's an observability tool that can let you peak inside the model's brain and see how it's thinking and where it's heading. 

**Boris Cherny** (00:57:16):
Yeah. You should, at some point, have Chris Olah on the podcast, because he's just the industry expert on this. He invented this field of we call it mechanistic interpretability. And the idea is at its core what is your brand? What is it? It's a bunch of neurons that are connected.

**Boris Cherny** (00:57:33):
And so, what you can do is in a human brain or in an animal brain, you can study it at this mechanistic level to understand what the neurons are doing. It turns out surprisingly a lot of this does translate to models also. So, model neurons are not the same as animal neurons, but they behave similarly in a lot of ways. 

**Boris Cherny** (00:57:50):
And so, we've been able to learn just a ton about the way these neurons work, about this layer or this neuron maps to this concept, how particular concepts are encoded, how the model does planning, how it thinks ahead. 

**Boris Cherny** (00:58:03):
A long time ago we weren't sure if the model was just predicting the next token, or is doing something a little bit deeper. Now I think there's actually quite strong evidence that it is doing something a little bit deeper. And then the structures that let it do this are pretty sophisticated now where as the models get bigger it's not just a single neuron that corresponds to a concept. A single neuron might correspond to a dozen concepts, and if it's activated together with other neurons this is called super position. And together it represents this more sophisticated concept.

**Boris Cherny** (00:58:32):
And it's just something we're learning about all the time. And for Anthropic as we think about the way this space evolves doing this in a way that is safe and good for the world is just this is the reason that we exist, and this is the reason that everyone is at Anthropic. Everyone that is here, this is the reason why they're here.

**Boris Cherny** (00:58:50):
So, a lot of this work we actually open source. We publish it a lot. And we publish very freely to talk about this just so we can inspire other labs that are working on similar things to do it in a way that's safe. And this is something we've been doing for Claude Code also. We call this, "The race to the top" internally. 

**Boris Cherny** (00:59:08):
And so, for Claude Code, for example, we released an open source sandbox. And this is a sandbox they can run the agent in, and just make sure that there's certain boundaries and they can't access, like, everything on your system. And we made that open source, and it actually works with any agent, not just Claude Code, because we wanted to make it really easy for others to do the same thing.

**Boris Cherny** (00:59:29):
So, this is just the same principle of race to the top. We want to make sure this thing goes well, and this is the lever that we have.

**Lenny Rachitsky** (00:59:37):
Incredible. Okay. I definitely want to spend more time on that. I will follow up with this suggestion. Something else that I've been noticing in the field across engineers and product managers, others that work with agents is there's this anxiety people feel when their agents aren't working. There's a sense that [inaudible 00:59:57] has a question [inaudible 00:59:58] answer, or it's, like, blocked on something, or, "There's all this productivity I'm losing. I need to wake up and get it going again." Is that something you feel? Is that something your team feels? Do you feel like this is a problem we need to track and think about?

**Boris Cherny** (01:00:11):
I always have a bunch of agents running. So, at the moment I have five agents running. And, at any moment, I wake up and I start a bunch of agents. Like, the first thing I did when I woke up was like, "Oh, man. I really want to check this thing." So, I opened up my phone Claude iOS app code tab." Like, agent, do blah, blah, blah. 

**Boris Cherny** (01:00:29):
Because I wrote some code yesterday and I was like, "Wait. Did I do this right?" I was double guessing something, and it was correct. But now it's just so easy to do this. So, I don't know. There is this little bit of anxiety maybe. I, personally, haven't really felt it just because I have agents running all the time. And I'm also just not locked into terminal anymore. Maybe a third of my code now is in the terminal, but also a third is using the desktop app. And then a third is the iOS app, which is just so surprising, because I did not think that this would be the way that I could even in 2026.

**Lenny Rachitsky** (01:01:03):
I love that you describe it as coding still, which is just talking to Claude Code to code for you, essentially. And it's interesting that this is now coding. Coding now is describing what you want, not writing actual code. 

**Boris Cherny** (01:01:16):
I wonder if the people that used to code using punch cards, or whatever, if you showed them software what they would have said.

**Lenny Rachitsky** (01:01:22):
Isn't that great? Yeah.

**Boris Cherny** (01:01:24):
I remember reading something, this was maybe very early versions of ACM magazine, or something, where people were saying, "No. It's not the same thing." Like, "This isn't really coding." And they called it a programming. I think coding is a new word. 

**Boris Cherny** (01:01:39):
But I think about this. Back in the ... My family is from the Soviet Union. I was born in Ukraine. And my grandpa was actually one of the first programmers in the Soviet Union, and he programmed using punch cards. My mom growing up told these stories of ... Or she told these stories of when she was growing up, he would bring these punch cards home. And there was these big stacks of punch cards. And for her she would draw all over them with crayons, and that was her childhood memory.

**Boris Cherny** (01:02:08):
But for him that was, like, his experience of programming, and he actually never saw the software transition. But, at some point, it did transition to software. And I think there was probably this older generation of programmers that just didn't take software very seriously. And they would have been like, "Well, it's not really coding." 

**Boris Cherny** (01:02:23):
But I think this is a field that just has always been changing in this way.

**Lenny Rachitsky** (01:02:27):
I don't think you know this, but I was born in Ukraine also.

**Boris Cherny** (01:02:30):
Oh, I don't [inaudible 01:02:31]. Yeah. 

**Lenny Rachitsky** (01:02:31):
Yes.

**Boris Cherny** (01:02:32):
Which town?

**Lenny Rachitsky** (01:02:32):
I'm from Odessa.

**Boris Cherny** (01:02:34):
Oh, me too.

**Lenny Rachitsky** (01:02:35):
What?

**Boris Cherny** (01:02:36):
Yeah. That's crazy. 

**Lenny Rachitsky** (01:02:39):
Wow. Incredible. What a moment. Maybe related in some small way. 

**Boris Cherny** (01:02:44):
Yeah.

**Lenny Rachitsky** (01:02:44):
What year did you leave? And your family leave.

**Boris Cherny** (01:02:48):
We came in '95.

**Lenny Rachitsky** (01:02:50):
Okay. We left in '88. A little earlier. Yeah. What a different life that would have been to not leave home.

**Boris Cherny** (01:02:57):
Yeah. I feel so lucky every day that I got to grow up here. 

**Lenny Rachitsky** (01:03:02):
Yeah. My family, any time there's a toast or a meal, they're just like, "To America." [inaudible 01:03:07]. It's, like, "Okay. Enough about that. We get it." Once you start really thinking about what life could have been. 

**Boris Cherny** (01:03:12):
Yeah. Yeah. Exactly. Yeah. We do the same toast, but it's still vodka. 

**Lenny Rachitsky** (01:03:16):
It's still vodka. Absolut. Oh, man. Okay. Let me ask you a couple more things here. You shared some really cool tips for how to get the most out of AI, how to build on AI, how to build great products in AI. One tip you shared is give your team as many tokens as they want. Just let them experiment. You also shared just advice, generally, of just build towards where the model is going, not to where it is today. What other advice do you have for folks that are trying to build AI products?

**Boris Cherny** (01:03:43):
I'd probably share a few more things. So, one is don't try to box the model in. I think a lot of people's instinct when they build on the model is they try to make it behave a very particular way. They're like, "This is a component of a bigger system."

**Boris Cherny** (01:03:56):
I think some examples of this are people layering very strict workflows on a model, for example, to say, like, "You must do step one, and then step two, then step three." And you have this very fancy orchestrator doing this.

**Boris Cherny** (01:04:06):
But actually almost always you get better results if you just give the model tools, you give it a goal, and you let it figure it out. I think a year ago you actually needed a lot of the scaffolding, but nowadays you don't really need it.

**Boris Cherny** (01:04:16):
So, I don't know what to call this principle, but it's, like, ask not what the model can do for you. Maybe it's something like this. Just think about how do you give the model the tools to do things. Don't try to over-curate it. Don't try to put it into a box. Don't try to give it a bunch of context upfront. Give it a tool, so, that it can get the context it needs. You're just going to get better results. 

I think a second one is maybe actually even a more general version of this principle is just the bitter lesson. And actually for the Claude Code team we have a ... Hopefully, listeners have read this, but [inaudible 01:04:52] this blog post maybe 10 years ago called The Bitter Lesson. And it's actually a really simple idea. His idea was that the more general model will always out-perform the more specific model. And I think for him he was talking about self-driving cars and other domains like this.

**Boris Cherny** (01:05:06):
But actually there's just so many corollaries to the bitter lesson, and, for me, the biggest one is just always bet on the more general model. And over the long-term. Like, don't try to use tiny models for stuff. Don't try to fine-tune. Don't try to do any of this stuff. There's some applications. There's some reasons to do this, but almost always try to bet on the more general model, if you can, if you have that flexibility. 

**Boris Cherny** (01:05:29):
And so, these workflows are, essentially, a way that ... It's not a general model. It's putting the scaffolding around it. And, in general, what we see is maybe scaffolding can improve performance maybe 10%, 20%, something like this, but often these gains just get wiped out with the next model. So, it's almost better to just wait for the next one. 

**Boris Cherny** (01:05:50):
And I think maybe this is a final principle, and something that Claude Code I think got right in hindsight, from the very beginning, we bet on building for the model six months from now. Not for the model of today. And for the very early versions of the product, it just wrote so little of my code, because I didn't trust it. Because it was, like, Sonnet 3.5. Then it was, like, 3.6, or ... I forget. 3.5 New, whatever name we gave it. 

These models just weren't very good at coding yet. They were getting there, but it was still pretty early. So, back then the model did [inaudible 01:06:26] it automated some things, but it really wasn't doing a huge amount of my coding. And so, the bet with Claude Code was, at some point, the model gets good enough that it can just write a lot of the code. 

**Boris Cherny** (01:06:37):
And this is a thing that we first started seeing with Opus 4 and Sonnet 4, and Opus 4 was our first ASL3 class model that we released back in May. And we just saw this inflection, because everyone started to use Claude Code for the first time. And that was when our growth really went exponential. And, like I said, it stayed there. 

**Boris Cherny** (01:06:56):
So, I think this is advice that I actually gave to a lot of folks, especially, people building startups. It's going to be uncomfortable, because your product market fit won't be very good for the first six months. But if you build for the model six months out, when that model comes out, you're just going to hit the ground running, and the product is going to click, and start to work.

**Lenny Rachitsky** (01:07:15):
And when you say build for the model six months out, what is it that you think people can assume will happen? Is it just generally it will get better at things? Is it just, like, "Okay. It's almost good enough, and that's a sign that it'll probably get better at that thing"? Is there any advice there? 

**Boris Cherny** (01:07:30):
I think that's a good way to do it. Obviously, within an AI lab, we get to see the specific ways that it gets better. 

**Lenny Rachitsky** (01:07:36):
Yeah.

**Boris Cherny** (01:07:37):
So, it's a little unfair, but also, we try to talk about this. So, one of the ways that it's going to get better is it's going to get better and better at using tools, and using computers. This is a bet that I would make. 

**Boris Cherny** (01:07:49):
Another one is it's going to get better and better for running it for long periods of time. And this is a place ... Like, there's all sorts of studies about this, but if you just trace the trajectory, or maybe even for my own experience when I use Sonnet 3.5 back a year ago, it could run for maybe 15 or 30 seconds before it started going off the rails, and you just really had to hold its hand through any kind of complicated task. 

**Boris Cherny** (01:08:13):
But nowadays with Opus 4.6, on average it'll run maybe 10, 20, 30 minutes unattended, and I'll just start another Claude, and have it do something else. And, like I said, I always have a bunch of Claudes running. And they can also run for hours, or even days at a time. I think there were some examples where they ran for many weeks. 

**Boris Cherny** (01:08:31):
And so, I think over time this is going to become more and more normal where the models are running for a very, very long period of time, and you don't have to sit there and babysit them anymore.

**Lenny Rachitsky** (01:08:39):
So, you just talked about tips for building AI products. Any tips for someone just using Claude Code, say, for the first time or just someone already using Claude Code that wants to get better. What are a couple pro-tips that you could share?

**Boris Cherny** (01:08:51):
I will give a caveat, which is there's no one right way to use Claude Code. So, I can share some tips, but, honestly, this is a dev tool. Developers are all different. Developers have different preferences. They have different environments. So, there's just so many ways to use these tools. There's no one right way. You have to find your own path.

**Boris Cherny** (01:09:08):
Luckily, you can ask Claude Code. It's able to make recommendations. It can edit your settings. It knows about itself. So, it can help with that. 

**Boris Cherny** (01:09:17):
A few tips that, generally, I find pretty useful. So, number one is just use the most capable model. Currently, that's Opus 4.6. I have maximum effort enabled always. The thing that happens is sometimes people try to use a less expensive model like Sonnet, or something like this, but because it's less intelligent, it actually takes more tokens in the end to do the same task.

**Boris Cherny** (01:09:35):
And so, it's actually not obvious that it's cheaper if you use a less expensive model. Often, it's actually cheaper and less token-intensive if you use the most capable model, because it can just do the same thing much faster with less correction, less hand holding, and so on. So, that's the first step is just use the best model. 

**Boris Cherny** (01:09:51):
The second one is use plan mode. I start almost all of my tasks in plan mode, maybe, like 80%, and plan mode is actually really simple. All it is is we inject one sentence into the model's prompt to say, "Please don't write any code yet." That's it. There's actually nothing fancy going on. It's just the simplest thing.

**Boris Cherny** (01:10:11):
And so, for people that are in the terminal it's just shift tab twice. And that gets you into plan mode. For people in the desktop app, there's a little button on the web, there's a little button. It's coming pretty soon to mobile also. And we just launched it for the Slack integration too. So, plan mode is the second one. 

**Boris Cherny** (01:10:27):
And, essentially, the model would just go back and forth with you. Once the plan looks good then you let the model execute. I auto-accept edits after that, because if the plan looks good, it's just going to one shot it. It'll get it right the first time almost every time with Opus 4.6. 

**Boris Cherny** (01:10:42):
And then maybe the third tip is just play around with different interfaces. I think a lot of people when they think about Claude Code, they think about a terminal. And, of course, we support every terminal, we support Mac, Windows, whatever terminal you might use, it works perfectly. 

**Boris Cherny** (01:10:54):
But we actually support a lot of other form factors too. Like, we have iOS and Android apps. We have a desktop app. There's the Slack integration. There's all sorts of things that we support. So, I would just play around with these. And, again, it's, like, every engineer is different. Everyone that's building is different. Just find the thing that feels right to you, and use that. You don't have to use a terminal. It's the same Claude agent running everywhere.

**Lenny Rachitsky** (01:11:15):
Amazing. Okay. Just a couple more questions to round things out. What's your take on Codex? How do you feel about that product? How do you feel about where they're going? Just competing in this very competitive space in coding agents. 

**Boris Cherny** (01:11:30):
Yeah. I actually haven't really used it, but I think I did use it maybe when it came out. It looked a lot like Claude Code to me, so, that was flattering. I think it's actually good to have more competition, because people should get to choose and, hopefully, it forces all of us to do an even better job.

**Boris Cherny** (01:11:49):
Honestly, for our team, though, we're just focused on solving the problems that users have. And so, for us, we don't spend a lot of time looking at competing products, we don't really try the other products. You want to be aware of them. You want to know they exist. 

**Boris Cherny** (01:12:03):
But, for me, I love talking to users. I love making the product better. I love just acting on feedback. So, it's really just about building a good product.

**Lenny Rachitsky** (01:12:13):
Maybe a last question. So, I talked to Ben Mann, co-founder of Anthropic, what to talk to you about. He had a bunch of suggestions, which I've integrated throughout our chat. One question he had for you is what's your plan post-AGI? What do you think you're going to be doing? What's your life like once we hit AGI? Whatever that means.

**Boris Cherny** (01:12:30):
So, before I joined Anthropic I was actually living in rural Japan. And it was a totally different lifestyle. I was the only engineer in the town. I was the only English speaker in the town. It was just a totally different vibe. A couple times a week I would bike to the farmer's market, and you bike by rice paddies and stuff. It was just a totally different speed. Just complete opposite of San Francisco. 

**Boris Cherny** (01:12:54):
One of the things that I really liked is a way that we got to know our neighbors, and we built friendships is by trading pickles. So, in the town where we lived it was actually, like, everyone made miso, everyone made pickles. And so, I actually got decently good at making miso. And I made a bunch of batches, and this is something that I still make.

**Boris Cherny** (01:13:18):
Miso is this interesting thing where it teaches you to think on these long time skills that's just very different than engineering, because a batch of white miso takes, at least, three months to make. And a red miso is, like, two, three, four years. You just have to be very patient.

**Lenny Rachitsky** (01:13:31):
Wow.

**Boris Cherny** (01:13:32):
You mix it up, and then you just let it sit. You have to be very, very patient. So, the thing that I love about it is just thinking in these long time skills. And, yeah. I think post-AGI or if I wasn't at Anthropic, I'd probably be making miso.

**Lenny Rachitsky** (01:13:46):
I love this answer. Ben asked me to ask you about what's the deal with you and Miso. And so, I love that you answered it. Okay. So, the future might be just going deep into miso, getting really good at making miso. Amazing. Boris, this was incredible. I feel like we're brothers now from Ukraine.

**Lenny Rachitsky** (01:14:08):
Before we get to our very exciting lightning round, is there anything else that you wanted to share? Is there anything you wanted to leave listeners with? Anything you want to double down on? 

**Boris Cherny** (01:14:18):
Yeah. I think I would just underscore for Anthropic since the beginning, this idea of starting at coding, then getting to tool use, then getting to computer use has just been the way that we think about things. And this is the way that we know the models are going to develop, or the way that we want to build our models. And it's also the way that we get to learn about safety, study it, and improve it the most. 

**Boris Cherny** (01:14:40):
So, everything that's happening right now around just Claude Code becoming this huge multi-billion dollar business, and now all of my friends use Claude Code, and they just text me about it all the time. So, this thing getting big.

**Boris Cherny** (01:14:55):
In some ways, it's a total surprise, because this isn't the ... We didn't know that it would be this product. We didn't know that it would start in a terminal, or anything like this. 

**Boris Cherny** (01:15:04):
But, in some ways, it's just totally unsurprising, because this has been our belief as a company for a long time. At the same time, it just feels still very early. Like, most of the world still does not use Claude Code, most of the world still does not use AI. So, it just feels like this is 1% on, and there's so much more to go.

**Lenny Rachitsky** (01:15:21):
Oh, man. That's insane to think, seeing the numbers that are coming out. You guys just raised a bazillion dollars. I think Claude Code alone is making $2 billion revenue. Anthropic, I think the number you guys put out, you're making $15 billion in revenue. It's insane to just think this is how early it still is, and just the numbers we're seeing. 

**Boris Cherny** (01:15:42):
Yeah. Yeah. Yeah. It's crazy. And the way that Claude Code has kept growing is, honestly, just the users. Like, so many people use it. They're so passionate about it. They fall in love with the product, and then they tell us about stuff that doesn't work, stuff that they want.

**Boris Cherny** (01:15:55):
And so, the only reason that it keeps improving is because everyone is using it, everyone is talking about it, everyone keeps giving feedback. And this is just the single most important thing. And, for me, this is the way that I love to spend my days just talking to users, and making it better for them. 

**Lenny Rachitsky** (01:16:09):
And making miso. 

**Boris Cherny** (01:16:11):
And making miso. Well, the miso is not super evolved. You've just got to wait. 

**Lenny Rachitsky** (01:16:14):
You've just got to wait. Well, Boris, with that, we've reached our very exciting lighting round. I've got five questions for you. Are you ready?

**Boris Cherny** (01:16:23):
Let's do it. 

**Lenny Rachitsky** (01:16:24):
First question, what are two or three books that you find yourself recommending most to other people?

**Boris Cherny** (01:16:29):
I'm a big reader. I would start with a technical book. It is Functional Programming in Scala. This is the single best technical book I have ever read. It's very weird, because you're probably not going to use Scala. And I don't know how much this matters in the future now, but there's this just elegance to functional programming and thinking in types, and this is just the way that I code, and the way that I can't stop thinking about coding.

**Lenny Rachitsky** (01:16:29):
Wow.

**Boris Cherny** (01:16:51):
So, you could think of it as a historical artifact. You could think of it as-

**Lenny Rachitsky** (01:16:51):
A deep cut.

**Boris Cherny** (01:16:54):
... something that will level you up.

**Lenny Rachitsky** (01:16:56):
I love this. A never before mentioned book. My favorite.

**Boris Cherny** (01:16:59):
Oh, amazing. Amazing. Okay. Second one is Accelerando by Stross. My big genre is sci-fi. Probably sci-fi and fiction. Accelerando is just this incredible book. And it's just so fast-paced. The pace gets faster and faster and faster. And I just feel like it captures the essence of this moment that we're in more than any other book that I've read, just the speed of it. 

**Boris Cherny** (01:17:23):
And it starts as a lift-off is starting to happening, and is starting to approach the singularity. And it ends with this collective lobster consciousness orbiting Jupiter. And this happens over the span of a few decades or something. So, the pace is just incredible. I really love it. 

**Boris Cherny** (01:17:41):
Maybe I'll do one more book. The Wandering Earth. Wandering Earth by Liu Cixin. So, he's the guy that did Three Body Problem. I think a lot of people know him for that. Actually I think Three Body Problem was awesome, but I actually liked his short stories even more. So, Wandering Earth is one of the short story collections, and he just has some really, really amazing stories. 

**Boris Cherny** (01:18:01):
And it's also just quite interesting to see Chinese sci-fi, because it has a very different perspective than western sci-fi, and the way that, at least, he, as a writer, thinks about it. So, it's just really, really interesting to read, and just beautifully written.

**Lenny Rachitsky** (01:18:15):
It's so interesting how sci-fi has prepared us to think about where things are going. It creates these [inaudible 01:18:21] models of like, "Okay. I see. I've read about this sort of world."

**Boris Cherny** (01:18:24):
Yeah. I think, for me, this was the reason that I joined Anthropic actually, because, like I said, I was living in this rural place. I was thinking these long-time skills, because everything is just so slow out there. At least, compared to SF. And just all the things that you do are based around the seasons, and it's based around this food that takes many, many months. That's the way that social events were organized. That's the way that you organize your time.

**Boris Cherny** (01:18:48):
You go to the farmer's market, and it's persimmon season, and you know that, because there's 20 persimmon vendors. And then the next week the season is done, and it's, like, grape season. And you see this. So, it's these long-time skills.

**Boris Cherny** (01:19:00):
And I was also reading a bunch of sci-fi at the time. And just being in this moment, I was just thinking about these long-time skills. I know how this thing can go. And I felt like I had to contribute to it going a little bit better.

**Boris Cherny** (01:19:12):
And that's actually why I ended up at Ant. And Ben Mann was also a big part of that too. 

**Lenny Rachitsky** (01:19:17):
I feel like I want to do a whole podcast just talking about your time in Japan, and the journey of Boris through Japan to Anthropic. But we'll keep it short. I'll quickly recommend a sci-fi book to you if you haven't read it. Have you read Fire Upon The Deep?

**Boris Cherny** (01:19:32):
This is Vinge. Right? Yeah. 

**Lenny Rachitsky** (01:19:33):
Yes.

**Boris Cherny** (01:19:34):
It's great. 

**Lenny Rachitsky** (01:19:34):
Okay. That one, it's so interesting from an AI/AGI perspective. So few people have read that. So, [inaudible 01:19:41]. Yeah. It's like [inaudible 01:19:42]-

**Boris Cherny** (01:19:42):
I really like the ... Yeah. Yeah. Yeah. I like Deepness in the Sky also. I think [inaudible 01:19:49] sequel. Right?

**Lenny Rachitsky** (01:19:42):
Yeah.

**Boris Cherny** (01:19:50):
Yeah. Yeah. Yeah. I think so.

**Lenny Rachitsky** (01:19:52):
Yeah. It's very long, and complex to get into, but so good. Okay. We'll keep going through our lightning round. Do you have a favorite recent movie or TV show you've really enjoyed?

**Boris Cherny** (01:19:59):
So, I actually don't really watch TV or movies. I just don't really have time these days. I did watch ... I'm going to bring up another Liu Cixin, but the Three Body Problem series on Netflix I really loved. I thought that was a great rendition of the book series.

**Lenny Rachitsky** (01:20:12):
So, the common pattern across AI leaders is no time to watch TV or movies, which I completely understand. Is there a favorite product you've recently discovered that you really love?

**Boris Cherny** (01:20:22):
I'm going to shill a little bit, and just say Cowork, because this is, legitimately, the one product that's been pretty life-changing for me just because I have it running all the time. And the Chrome integration, in particular, is just really excellent. So, it's been like ... It paid a traffic fine for me, it canceled a couple subscriptions for me. Just the amount of tedious work it gets out of the way is awesome.

**Boris Cherny** (01:20:45):
I also don't know if it's a product, but maybe also another podcast that I really love ... Obviously, besides Lenny is-

**Lenny Rachitsky** (01:20:51):
Obviously.

**Boris Cherny** (01:20:52):
Yeah. It's the Acquired podcast by Ben and David. It's just super awesome. I feel like the way that they get into business history, and bring it alive is really, really good. And I would start with the Nintendo episode if you haven't listened to it.

**Lenny Rachitsky** (01:21:08):
Great tip. With Cowork, just so people understand if they haven't tried this, basically, you type something you want to get done and it can launch Chrome, and just do things for you. I saw someone went on pat leave from Anthropic, and you had to fill out these medical forms for him, these really annoying PDFs where it just loads up the browser, logs in, fills them out, [inaudible 01:21:29]. 

**Boris Cherny** (01:21:30):
Yeah. Exactly. Exactly. And it actually just works. Like, we tried this experiment a year ago, and it didn't really work, because the model wasn't ready, but now it actually just works and it's amazing. 

**Boris Cherny** (01:21:39):
I think a lot of people just don't really understand what this is, because they haven't used an agent before, and it just feels very, very similar, to me, to Claude Code a year ago. But, like I said, it's just growing much faster than Claude Code did in the early days. So, I think it's starting to break through a bit. 

**Lenny Rachitsky** (01:21:55):
And there's also this Chrome extension that you mentioned that you could just leave standalone that sits in Chrome, and you could just talk to Claude looking at your screen, at your browser, and have it do stuff, have it tell you about what you're looking at, summarize what you're looking at, things like that.

**Boris Cherny** (01:22:08):
Exactly. Exactly. For people that are just learning to use Cowork, the thing I recommend is, so, you download the Claude desktop app, you go to the Cowork tab, it's right next to the code tab. The thing that I recommend doing is start by having it use a tool. So, clean up your desktop, or summarize your email, or something like this, or respond to the top three emails. It actually just responds to emails for me now too.

**Boris Cherny** (01:22:29):
The second thing is connect tools. So, if you say, "Look at my top emails," and then sends back messages ... Or put them in a spreadsheet, or something. But, for example, I use it for all my product management. So, we have a single spreadsheet for the whole team. There's a row for engineer. Every week, everyone fills out a status. And every Monday, Cowork just goes through and it messages every engineer on Slack that hasn't filled out their status. And so, I don't have to do this anywhere. 

**Lenny Rachitsky** (01:22:52):
Wow.

**Boris Cherny** (01:22:52):
And this is just one prompt. It'll do everything. And then the third thing is just run a bunch of Claudes in parallel. So, in Cowork you can have as many tasks running as you want. So, it's, like, start one task, I have this project management thing running, then I'll have it do something else, then something else, and then I'll kick these off. And then I just go get a coffee while it runs. 

**Lenny Rachitsky** (01:23:09):
There's a post I'll link to that shares a bunch of ways people use what was previously Claude Code, and now just you can do through Cowork. Because a lot of this is just like, "Wow. I hadn't thought I could use it for that." And once you see ... These examples I think are what people need to hear of just like, "Oh, wow. I didn't know I could do that." [inaudible 01:23:26]. 

**Boris Cherny** (01:23:27):
I think a lot of this was also some of this was also inspired by you, Lenny. You had this post about ... It was, like, 50 non-technical use cases for Claude Code, or something like this. So, actually one of our PMs used that as a way to evaluate Cowork before we released it, and I think at the point where Cowork was able to do, like, 48 out of the 50, they were like, "Okay. It's pretty good."

**Lenny Rachitsky** (01:23:46):
Wow. I did not know that. That is awesome. I've become an eval. 

**Boris Cherny** (01:23:53):
Yeah. [inaudible 01:23:54]. 

**Lenny Rachitsky** (01:23:55):
Amazing. I feel like I'm valuable to the future of AI. 

**Boris Cherny** (01:24:01):
This is, like, reverse breaking through. 

**Lenny Rachitsky** (01:24:05):
Wow. That is so cool. Wow. Okay. I wonder what those last two are. Anyway, okay. Two more questions. Do you have a favorite life motto that you often come back to in work or in life?

**Boris Cherny** (01:24:14):
Use common sense. I think a lot of the failures that I see, especially, in a work environment is people just failing to use common sense. Like, they follow a process without thinking about it. They just do a thing without thinking about it, or they're working on a product that's not a good product or not a good idea, and they're just following the momentum, and not thinking about it.

**Boris Cherny** (01:24:32):
I think the best results that I see are people thinking from first principles, and just developing their own common sense. If something smells weird then it's probably not a good idea. So, I think this is the single advice that I give to coworkers more than anything too.

**Lenny Rachitsky** (01:24:46):
And I feel like that alone could be its own podcast conversation. What is common sense? How do you build? But we'll keep this short. Final question. So, you've got more active on Twitter/X. I'm curious just why and just what's your experience been with Twitter? The world of Twitter. Because you get a lot of engagement on Twitter/X. 

**Boris Cherny** (01:25:06):
So, for a long time I used Threads exclusively, because I actually helped build Threads a little bit back in the day. And I also just like the design. It's a very clean product.

**Lenny Rachitsky** (01:25:14):
Yeah.

**Boris Cherny** (01:25:15):
I just really like that. I started using Threads, because actually I was bored. So, in December, I was in Europe-

**Lenny Rachitsky** (01:25:21):
Started using Twitter you mean.

**Boris Cherny** (01:25:23):
Oh, yeah. Yeah. Yeah. I started using Twitter, because I was bored. So, my wife and I, we were traveling around in Europe for December. We were just nomad-ing around. We went to Copenhagen, we went to a few different countries. And, for me, it was just a coding vacation. So, every day I was coding, and that's my favorite kind of vacation, just code all day. It's the best. 

**Boris Cherny** (01:25:43):
And, at some point, I just got bored, and I ran out of ideas for a few hours. I was like, "Okay. What do I want to do next?" And so, I opened Twitter, I saw some people tweeting about Claude Code, and then I just started responding. And then I was like, "Okay. Maybe actually a thing I should do is just look for bugs that people have. Maybe people have bugs," or feedback they have. And so, introduce myself, asked for it, people had a bunch of blogs and feedback. 

**Boris Cherny** (01:26:07):
And I think they were surprised by the pace at which we were able to address feedback nowadays. For me, it's just so normal. Like, if someone has a bug, I can probably fix it within a few minutes, because I just started Claude, and as long as the description is good, it'll just go and do it, and then I'll go do something else, and answer the next thing. 

**Boris Cherny** (01:26:25):
But I think for a lot of people it was pretty surprising. So, that was really cool. And, yeah. The experience on Twitter has been pretty great. It's been awesome just engaging with people, and seeing what people want, hearing about bugs, hearing about features.

**Lenny Rachitsky** (01:26:38):
I saw [inaudible 01:26:38] the other day on Twitter. You're, like, posting many threads, and it was breaking. And just, like, "Oh, man. What's going on here?"

**Boris Cherny** (01:26:45):
Yeah. Yeah. Yeah. There was a bug. I hope it's fixed now.

**Lenny Rachitsky** (01:26:49):
Amazing. Oh, man. Boris, I could chat with you for hours. I'll let you go. Thank you so much for doing this. You're wonderful. Where can folks find you online? How can listeners be useful to you?

**Boris Cherny** (01:27:00):
Yeah. Find me on Threads or on Twitter. That's the easiest place. And, please, just tag me on stuff. Send bugs, send feature requests. What's missing? What can we do to make the products better? What do you want? I love, love hearing it. 

**Lenny Rachitsky** (01:27:16):
Amazing. Boris, thank you so much for being here. 

**Boris Cherny** (01:27:18):
Cool. Thanks, Lenny. 

**Lenny Rachitsky** (01:27:20):
Bye, everyone. 

**Lenny Rachitsky** (01:27:21):
Thank you so much for listening. If you found this valuable you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating, or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at LennysPodcast.com. See you in the next episode.

---

## [14/15] Jeetu Patel

**Jeetu Patel** (00:00:00):
Survival of humanity depends on the successful AI. Birth rates are going down. If you have 60% of your population where you don't have enough people who take care of them, that could cause a lot of human suffering. When I got this new job, there's zero chance I would have been able to do it if AI wasn't there, because I didn't know anything about so many domains that we were in.

**Lenny Rachitsky** (00:00:17):
A lot of companies are trying to adjust to this new world.

**Jeetu Patel** (00:00:20):
You have to know the difference between a megatrend and a hype cycle. When there's a megatrend, don't fight it. AI is a megatrend, one of the most foundational movements that we have seen in human history.

**Lenny Rachitsky** (00:00:30):
To turn Cisco from an older, slower, more traditional enterprise to a very AI-forward company, this is very difficult to do.

**Jeetu Patel** (00:00:37):
AI is moving so fast. One of the things I tell my team is, "Fast-forward six months from now, get prepared for that world."

**Lenny Rachitsky** (00:00:42):
You manage 30,000 people.

**Jeetu Patel** (00:00:44):
Every management book that you read will tell you in public criticize in private. I fundamentally disagree with that notion. What you have to do is, is establish enough trust among the team so that you are comfortable critiquing and debating in public.

**Lenny Rachitsky** (00:00:59):
What's something that you wish you'd known before taking on this role?

**Jeetu Patel** (00:01:02):
Stamina trumps intellect. It's very important to have smart people, but you can become smart if you have curiosity and hunger and staying power and persistence. You can't teach hunger.

**Lenny Rachitsky** (00:01:15):
Today, my guest is Jeetu Patel, chief product officer and president at Cisco. Cisco is not a brand that mostly people think about when they think about AI, but not only are they a massive part of the AI infrastructure build-out that is happening right now all over the world. What Jeetu has achieved internally at Cisco in terms of transforming their culture and ways of working to be AI-first is something that most big company leaders only dream about. Jeetu is also an incredible human with so much warmth and wisdom to share. I am very excited to be sharing his story. Don't forget to check out lennysproductpass.com for an incredible set of deals available exclusively to Lenny's Newsletter subscribers. Let's get into it after a short word from our wonderful sponsors.

**Lenny Rachitsky** (00:01:58):
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**Lenny Rachitsky** (00:02:57):
Your marketing website sets the tone for your brand. It is the one touchpoint that every single one of your customers sees. In today's age, if you're still having a hard time making small changes and simple updates to it, you are doing something wrong. That is why so many companies from early-stage startups to Fortune 500s, including companies like DoorDash, Zapier, Perplexity, and ElevenLabs turn to Framer, the website builder that turns your .com from a formality into a tool for growth. Framer works like your team's favorite design tool and comes with real-time collaboration, a robust CMS with everything you need for great SEO, and advanced analytics that includes integrated A/B testing. Changes to your Framer site go live to the web in seconds with a single click and without any help from engineering. Whether you want to launch a new site, test a few landing pages, or migrate your full .com, Framer has programs for startups, scale-ups and large enterprises to make going from idea to live site as easy and fast as possible.

**Lenny Rachitsky** (00:03:55):
Learn how to turn your website into a growth engine from a Framer expert, or just get started building for free today at framer.com/lenny. And if you're a Lenny's Product Pass subscriber, you get an entire year of Framer Pro for free. Check it out at framer.com/lenny. Rules and restrictions may apply.

**Lenny Rachitsky** (00:04:18):
Jeetu, thank you so much for being here, and welcome to the podcast.

**Jeetu Patel** (00:04:22):
Lenny, I'm excited. Good to see you.

**Lenny Rachitsky** (00:04:24):
The timing of this conversation is so amazing. You're just coming off running the most insane assembling of AI thought leaders and tech leaders I've ever seen. Let me just read a few of the names that you guys had at the summit that just happened a couple of days ago. You had Jensen, you had Sam Altman, and you had Marc Andreessen, you had Fei-Fei Li, you had the CEO of Intel, AWS, Mike Krieger, Kevin Weil. That's just like a third of the guests you guys had. I don't know how you did this, but it feels like you have this fire hose of information coming at you. You interviewed a lot of these people on stage, and so while it's fresh in your mind, I want to ask you, after doing this summit, after hearing from these folks, what's something that you've changed your mind about, or what's just like an insight that has been lodged in your head ever since doing the summit?

**Jeetu Patel** (00:05:13):
It was an amazing thing to pull off, because we never thought we'd be able to do it, and we were really worried going into it, thinking, "Well, we're trying to do fireside chats for 12 hours, and there's a capacity of human absorption that we're trying to challenge." And so, we tried to put a lot of breaks in there and we started at nine a.m and we ended at nine p.m., and we had a couple hour break in the middle, but everyone stayed and everyone was engaged and we could have gone until 11 and it would've been fine. And it's because the quality of the conversations and the caliber of the guests that were there made a world of a difference.

**Jeetu Patel** (00:05:53):
What was the takeaway from it? I'd say a few things. One is the capabilities overhang is real. I think there's more functionality, on one end there's kind of this paradox of progress. On one end we are solving all these amazing problems with science, on the other end you talk to the enterprise, they're like, "We're struggling with adoption." And I feel like there's help that's going to be needed within organizations. And the reason we pulled this thing together, the goal was, what is happening in the industry and how can we help customers make sure that they can make the most of it? Because we are in one of the most foundational movements that we have seen in human history, and it's we got to make sure that we make the most of it. So, that was one is, the capabilities overhang is real.

**Jeetu Patel** (00:06:37):
The second area is, I'd say that it's harder when you go beyond some of the more obvious use cases. For example, coding is a very, very good use case that you're starting to get a lot of success in. I mean, we just had our first product that we think we'll be in the next two weeks, 100% written with AI. I don't think that's as easy when you go into every other function of the business. And that was actually very apparent that hey, this is going to require some nuance and understanding of how every business works.

**Jeetu Patel** (00:07:10):
And then the third one, which is a really interesting takeaway, and Marc Andreessen talked about this in your podcast a few days ago. In fact, when I talked to him, I actually started with your podcast, because it was so interesting, and then we dug into it a little bit more. And then Kevin Scott was also talking about this, but this notion of the fact that birth rates are going down and we have a demographic shift that's happening in the world and there's going to be more people that are in the older age bracket than the younger age bracket, and those older people are going to need folks to take care of them.

**Jeetu Patel** (00:07:42):
And historically in society, that's actually always been the case, but we might be at a point where that might not be the case. And when that's not the case, we worry about AI taking our jobs, I think that survival of humanity depends on a successful AI. Because at some point if you have 60% of your population that's in a demographic where you don't have enough people who take care of them, that could cause a lot of human suffering. So, I don't think people talk about this enough, and that's something that we have to take a moment and digest, that this is so important for our collective success moving forward.

**Lenny Rachitsky** (00:08:16):
Something I was going to say during my chat with Marc and when he talked about that AI is basically coming just in time to save us, because there aren't going to be enough people to do the jobs. In my head I was thinking, "This is like another signal that we are in a simulation that things are working out just right for us." What are the chances?

**Jeetu Patel** (00:08:34):
The older I get, the more I believe that we are actually in a simulation. The first time I heard that concept, I thought it was such an absurd concept. Now I'm like, "This might actually be happening." You never know.

**Lenny Rachitsky** (00:08:47):
Following this thread, a lot of companies are trying to adjust to this new world. You are doing an incredible job at actually doing this. We got connected through Kevin Weil, who is former CPO at OpenAI, now head of science at OpenAI. And the way he described it is the work that you have done to turn Cisco the way he described it from an older, slower, more traditional enterprise to a very AI-forward company. How many employees do you guys have? You said 45,000.

**Jeetu Patel** (00:09:15):
We have 90,000 employees, 43,000 watch the stream.

**Lenny Rachitsky** (00:09:18):
So, the big question for you is, it feels like it's really working, and this is very difficult to do at a company of that scale. A lot of leaders are trying to make it work. What are two or three things that you've done that you think have been most impactful and effective in helping Cisco lean into AI, not be scared of it, and actually embrace the future?

**Jeetu Patel** (00:09:40):
Innovation in my mind is a choice. I always find it interesting when people say, "Well, you're a large company, you can innovate. You're a small company, you can innovate." It's like, "No, it's just a choice. Every day you come into work and you can choose to be thinking about being creative, or you can choose to not be creative." It's like a little binary ... It's a binary choice You can make every hour, every minute of every day. And so, we made that choice that says, Cisco is going to be not just an iconic company. And Chuck Robbins, our CEO says this very eloquently. He's like, "I want Cisco not just to be an iconic company. I want Cisco to also be an iconic and innovative company." And so, we got to make sure that we are actually innovating with the set of constraints that we are dealt with.

**Jeetu Patel** (00:10:30):
Every company has their own set of constraints, and we have our own set of constraints, and we have to make sure that given those constraints, we have to actually innovate really well. Now, what are the two or three things that have happened that have really helped us out? One was, being very clear on what is up for debate and what is not up for debate. Because what can end up happening is you can always have a pocket veto in a large company where if you ask enough number of people, people say no. If you're a large company, you ask enough number of people, someone's going to say no, right? And so, when you have conviction about something that's happening, that is going to be a bet that you need to place.

**Jeetu Patel** (00:11:10):
What most people think in large companies is large companies don't experiment. That is in fact not true. Large companies experiment a lot. What large companies don't do is when an experiment works, they don't go all in and double down. They try to keep hedging. We didn't hedge on AI. We said we were going to go all in. That was number one.

**Jeetu Patel** (00:11:31):
What that meant was we had to get people to understand that their personal success and the success of the company are very aligned in us getting dexterous with the use of AI. That means that if they feel like for some reason AI is going to take their job or AI is going to be negative for them, we had to reassure them that that was not the case. But the reverse was guaranteed to be the case, that if you didn't choose AI, if you weren't going to be dexterous in whatever job function you're doing, then your job is probably not going to be that relevant over here in the long run.

**Jeetu Patel** (00:12:08):
So, that was the first thing that we did, that was a, I'm not a big fan of top-down hierarchy of going out and doing things. In fact, deep down inside I don't respect hierarchy as much. I feel like it can constrain you at times. But I wanted to make ... On this one we were very, very deliberate. The entire company is on the same page, we are an AI-first company. And this happened, we were working towards it even prior to ChatGPT, but ChatGPT became that seminal moment in November of '22 that we actually did that. So, that was one.

**Jeetu Patel** (00:12:42):
Number two was we had to make sure that we defined what success looked like. The way that individual success was defined was everyone wanted to be a GM at Cisco. They wanted to own their own fiefdom, be a general manager. Because they felt like, "In order for me to move up the ranks, I need to be a general manager, which means I need to have my own sales team. I need to have my own marketing team. I need to have my own product team. I need to have my own engineering team. I'm going to make sure I run my own silo." And if you're a 40 billion business in product revenues, 45 billion whatever we were at the time, and then all of a sudden your goal is that you're going to just run a bunch of $40 million businesses and break it up into a series of 40 million businesses, that's actually not a good thing for the company.

**Jeetu Patel** (00:13:30):
So, the thing we did was we said, "We have to become not a holding company of 251 acquisitions and thousands of different products, we have to become a platform company." And the characteristic of the platform is you have to be tightly integrated where the customer feels the same emotion, no matter what product of ours they use. There's the same set of expectations that can be served, reliability, trust, elegance and design, solving a problem in the most efficient way. Those are the things we want to strive to do. But you don't have to buy everything all at once, because we also want to be realistic about the fact that not every customer only uses Cisco top to bottom. There's an ecosystem. So, loosely coupled, but tightly integrated. You don't have to buy everything all at once, but boy, when you do buy two things together, they work like magic. So, that was the second big thing we did.

**Jeetu Patel** (00:14:22):
And then the third one we did was we said, "Let's make sure that we have a mental model shift in the company." And we did this about five, five and a half years ago when I first joined. This was a very deliberate decision, which was, we cannot operate in a walled garden. We have to make sure that we operate in an open ecosystem, which means we have to be completely comfortable with having a competitor that we're going to partner with. And that's okay. We don't have to think about this in a zero-sum manner. In order for me to win, someone has to lose.

**Jeetu Patel** (00:14:58):
We can partner, because if a customer has made a choice of going with company A and company B and we happen to be one of those two companies, we owe it to the customer to invest in their success in that other company because if the customer succeeds, that success has a flow through rate to you that's going to be pretty high. And so, that's what we did, and I think that's been those principles of building great products, but making sure that it operates like a platform and having an open ecosystem, I think has been kind of central. And then not being confused about the fact that we'll be AI-first from the top down.

**Lenny Rachitsky** (00:15:34):
I want to take a tangent and make sure people understand what Cisco even does these days. I think as a lay person, you'll think about Cisco and you're like, "Okay, they WebEx, yes. They make maybe some routers." You guys are key to this massive AI infrastructure build-out that's happening right now. You're a major player in this. I don't think people realize this, people listening to this podcast. Give us just a quick glimpse into how Cisco fits into this massive build-out and just what does Cisco these days?

**Jeetu Patel** (00:16:03):
Cisco is a critical infrastructure company for the AI era. What does that mean? But if you think about where the constraints are right now, if you think that AI is going to be one of the biggest movements, and then you ask yourself the question, "What could hold AI back?" There's three things where we feel like we can have a direct impact that can hold AI back. Number one is there's an infrastructure constraint. There's just not enough power compute and network bandwidth in the world to go out and satiate the needs of AI.

**Jeetu Patel** (00:16:31):
Number two is, there's a trust deficit. If people don't trust these systems, they're not going to use them. And right now there's a lot of mistrust in these systems. Hallucination is a feature when you're writing poetry, but when you're trying to go out and run predictable systems, hallucination can be a bad thing. And these models are unpredictable, they're non-deterministic, and so they have to make sure that they have safety and security factored into them.

**Jeetu Patel** (00:16:56):
And then the third area is a data gap. So far we've trained these models with human-generated data publicly available on the internet, but we are running out of human-generated data publicly available on the internet to train the models. And every company is going to differentiate based on their own proprietary enterprise data being used to train the models, synthetic data and machine data, which is where the most amount of growth is. And the third category of machine data we can play a massive role in at Cisco. So, what does Cisco do then? If you think about a GPU, which is what everyone now is very clear because of the great job that Jensen has done that here's what a GPU's core contribution is to AI. If these GPUs aren't networked together, you don't have AI, because it used to be that you could train a model on a single GPU, but then what happened was the model got too big to be put on a single GPU. So then you had a server with eight GPUs that got connected together. So, you could train a model with eight GPUs.

**Jeetu Patel** (00:17:58):
But then that wasn't good enough. So, then what happened was you said, "I'm going to have a rack of servers that I'm going to network together." That at some point wasn't big enough. And so then they said, "I'm going to have a cluster of racks that are connected together." And that connected together is the operative word. That's what we end up doing is NVIDIA makes the GPUs and we connect those GPUs together. AMD makes the GPUs, we connect them together.

**Jeetu Patel** (00:18:24):
And now what's happened, Lenny, is you have these data centers that might be hundreds of kilometers apart that need to operate like one coherent cluster, which means that they're completely in sync. Every GPU is in sync with each other when you're doing a training run. And that requires a very sophisticated set of technologies that we build to make sure that you could have two data centers, 800 kilometers apart, but boy, they run completely in sync with each other. And that's what Cisco does. We provide the networking, we provide the optics technology, we provide the safety and security technology, we provide the observability, we provide the data platform, all of those things together for making sure that we provide critical infrastructure for the AI era.

**Lenny Rachitsky** (00:19:10):
So, being on the inside of this massive investment that is happening across the world, what do you think isn't being priced in into where things are heading into how much life will change, or just the scale of this build out?

**Jeetu Patel** (00:19:25):
Years ago I'd had a chance to meet with Ray Kurzweil. He's the chief scientist at Google for a while, and I think he still is. And he had talked about, he was writing this book called Live Long Enough to Live Forever. And so I was talking to him, I'm like, "What is the impact to human population if all of a sudden you can have 15 generations living simultaneously, because we have an indefinite span of life? Because now all of a sudden everything has to change. How does housing work, housing work? How does agriculture work? How does transportation work? How does ... Everything changes?" And he looked at me and he had the most profound answer and he said, "Most people can't think exponentially, because they always think exponentially maybe on a single dimension." But what ends up happening in these things is you can sometimes you have to keep in mind that exponentiality happens across multiple dimensions all at once.

**Jeetu Patel** (00:20:21):
So, if you do have an indefinite span of life, you have to assume that humans are creative enough that they're going to find a way to have a three-day crop cycle. And they probably will have 5,000 story skyscrapers, and there will be a bunch of things in society that we have assumed are not solvable that'll now be solvable.

**Jeetu Patel** (00:20:44):
So, when you go back to your question and say, what changes in this entire equation that has not been factored in well? I think today AI is looked at largely as a productivity tool and an aggregation mechanism. I have data all over and I'm going to be able to make sure that language can be used to compose the data in a way that I can give you, Lenny, the answer to the question that you're looking for that I think is the 0.0001% of the tip of the iceberg.

**Jeetu Patel** (00:21:17):
The reality is, is we will have original insights generated that don't exist in the human corpus of knowledge, and we will have the physical world get augmented to language where capacity is augmented to humans. And what we have to be careful of is that that capacity is working on behalf of humans, but if that capacity is augmented to humans, you can now do things that you really care to do, and not do things that you don't care to do. And so our biggest realization that we had when we were using Codex, for example, when we were writing a code with OpenAI's kind of model and development tool, was the first three months we were screwing around with this. And then there was this light bulb that runoffs. In fact, there was a former deployed engineer from OpenAI that told us about this, which is, "Hey, stop trying to think of this as a tool. Think of this as a teammate that got added to your team, and your framing will change, and the way in which you actually use the technology will change."

**Jeetu Patel** (00:22:33):
And that essentially, if you compound that to how society operates, that's going to be pretty profound as an implication, while keeping in mind that these safety and security risks are non-trivial and they're real. And you can't be completely flippant about them, because how an AI identifies its own success and its own ambition will really matter. And we have to make sure that we actually keep guardrails around that, because it is in service of humans, it is not to go out and build a society by itself. And I do think that those are important kind of checks and balances you have to keep in mind.

**Jeetu Patel** (00:23:17):
But the thing that people sometimes miss out in this very polarized narrative, which is we are either going to have nothing to do in society, or this is going to be completely useless as a piece of technology. I think that's not a helpful narrative. In fact, what is helpful is saying, "As we reconstruct society for the next phase, how can we make sure that life gets infinitely better? How can we make sure that diseases get solved? How can we make sure that poverty gets eradicated? How can we make sure that how people learn and find excitement and joy out of life gets compounded meaningfully?" If that happens, I think there's goodness that comes out of this.

**Lenny Rachitsky** (00:23:55):
A line that I often think about is, Huang has had this thought that the best case scenario with AI, because he was a very AI doomer for a long time, and I think the reason he got leaned into AI is like, "I need to help steer this in a direction that isn't going to harm the world." The way he described it is, "The best case scenario for humanity is we're the house cat, where AI is just like, 'Okay, nice. Just keep sitting here with me and I'll take care of you.'"

**Jeetu Patel** (00:24:20):
But by the way, the things that he is doing right now are nothing short of extraordinary. And for all the critique that one can have, the level of deep thinking that's going on with his company, it's just crazy.

**Lenny Rachitsky** (00:24:36):
So, as you've been thinking about where things are heading, I've been liking to ask this question with people with kids, is there anything you're kind of shifting in how you raise your daughter, keeping in mind where things are heading? Are there skills you're trying to instill in her values you're trying to instill in her that will help her thrive in this future?

**Jeetu Patel** (00:24:56):
We made a choice, and I didn't know how that choice was going to go. That was actually not even an active choice, it was a passive choice. Frankly, even might have been slightly intellectually lazy in the way that we did it, but it actually worked out pretty well in the sense that we didn't really deprive her of the use of technology. There's a school of thought that says, "Keep technology away from the kids for a while." We didn't do that. And frankly, I didn't know how it was going to work out, because there are things about the way that the generation is, and by the way, all of us, not just new generation, but this kind of constantly being glued to your phone all the time and not being able to actually put that down and have a conversation. I think it's an important skill in humans to have and preserve over time.

**Jeetu Patel** (00:25:45):
And in fact, as AI does more for us, we should be able to have more of this time. I don't have to worry about every notification that's coming on my phone every minute of the day, because maybe I can be more present in the moment that I'm in. She just turned 15 and the night before she was turning 15, what I realized is she is so emotionally mature. We were sitting down one night and she's like, "Hey dad, just so you know, I feel really good right now about having a very strong value system." I'm like, "Oh, okay, what does that mean? And say more." And she's like, "Well, can you name five things that you feel so convicted about that if the entire world disagreed with you," this is the day before she's turning 15, okay? "The entire world disagreed with you. You would still feel like you were right on that and that would waver you." She's like, "I have a certain core set of things that I believe in where I am completely confident that if everyone disagreed with me, I'm still good."

**Jeetu Patel** (00:26:56):
Now, by the way, I have to kind of coach her on the, "Hey, when you get new data, be open-minded to changing your mind." But it was actually a very interesting dynamic, which is, if we can have them be exposed to technology but have the right value system, you might actually have the best of both worlds. And the day ain't over yet. She's 15. There's a lot of chances for her getting influenced by external factors and all of that. But what you have to do is make sure that you instill the right values, but then also expose them to the reality of what the world is today and not completely insulate them from that.

**Jeetu Patel** (00:27:33):
And so, the way that it worked out, it did end up working well, and we were lucky for no credit to us she was able to use technology to get her EQ higher and higher, and we were lucky on that front. And we know it can go sideways the other way too. But I do feel like right now, at least for my one daughter, what we try to do is get her exposed to the technology, but make sure that we focus a lot more on the values that we need to have that govern us on a day-to-day basis. Kindness, not being arrogant, hard work, work ethic, those things matter. And those are timeless in my mind. I don't think those change, because take risks, be creative, that kind of stuff.

**Lenny Rachitsky** (00:28:20):
Jeetu, these are parenting goals. As I hear this, I have a two and a half year old Ms, it sounds like you've done an amazing job raising your daughter.

**Jeetu Patel** (00:28:28):
I would take zero credit for it. I think she deserves a lot of credit for growing up to be who she's become. And her mother.

**Lenny Rachitsky** (00:28:37):
Got to shout out mom. What's interesting is that I know Anthropic is really big, this idea of values and just how you operate. Anthropic has this constitution they released of how the values essentially of Claude. And it's so interesting how much similarity there is to how to raise a great person and to how to steer an AI correctly.

**Jeetu Patel** (00:28:58):
That's right. And by the way, some of your beliefs and your system around you might change, but values tend to be pretty long lasting. And culture and a company tends to be pretty long lasting. Ben Horowitz talks about this very eloquently. The culture is just a set of norms that accompany action. It's not a set of beliefs, it's a set of behaviors that you exude within the company. And it's actually very, very true, because when things aren't going right, how do people behave to go solve problems and come together? And that actually forms your cultural norms. And I think those cultural norms, it's very important to be intentional about it. And as you have more automation in the world, being intentional not just with humans but also with machines and what you want to do to create the guardrails, I think is pretty important.

**Lenny Rachitsky** (00:29:47):
I'm going to take us in a different direction. I talked to Aaron Levie, your former boss at Box.

**Jeetu Patel** (00:29:54):
Dear, dear friend.

**Lenny Rachitsky** (00:29:56):
And friend. I asked him just what should I ask you about with something that he learned from you that has stuck with him ever since working with you? And he share this concept of the right to win, which he says has informed the way he thinks about strategy ever since. Talk about what this is and how folks might use this when they're thinking about product strategy, company strategy?

**Jeetu Patel** (00:30:18):
One of the things that we would always talk about is in the areas that we are going to participate, do we have permission to play? Every company has to make sure that the way in which they provide points of insertion and logical entry into a market is a lot of times dependent on, do you have the permission to play in that market? Do you have an avenue to have a route to market to be able to take that product? Just by building a product that is amazing in some area, you don't end up actually getting it to mass scale distribution. And so, one of the things that we would always do is ask ourselves a question. "We're building this new category or we're building this new capability. Is it going to be logical for people that Box built it versus another company building it? Is it going to be logical for people that Cisco built it versus another company building it?"

**Jeetu Patel** (00:31:18):
So, that's this notion of permission to play, the right to win. Do we have a right to win in that area because we have permission to play? And do we have the route to market to be able to take that product and get it to mass scale distribution? And if you can do those things right, then actually your dollars that you expend on building product actually have an outsized return. If not, then you can actually end up spending a lot of money on product. Where the product people think, "Ah, these sales guys don't get it. They don't know how to sell it, especially in enterprise software." And sales people think, "These product guys don't get it. They don't know how to build it."

**Jeetu Patel** (00:31:56):
And so, I think in order to stop that, what you have to do is you have to actually use your scale as an advantage and you have to use the areas where you've got the ability to have permission to play where people feel like this is very logical for a company like Cisco. When we say we are going to network the GPUs and make sure that we actually have a trusted system in AI, that is not far-fetched for someone to go out and think about, because it's a very natural thing for us to do, because for the past 40 years we've been doing it for the rest of the infrastructure that was not AI. And so, that's not a far cry to say, "Okay, we'll now do it for AI."

**Jeetu Patel** (00:32:35):
And I think that was an area that Aaron and I spent a fair ... And by the way, I'm glad that he took that out of me. There's so much I've learned from him. The biggest area I've learned from him is, you never give up. And persistence beats intellect, and stamina beats intellect any day of the week, twice on Sunday. And that guy is as smart as they come, but that's not the biggest reason he's successful. The biggest reason he's successful is he has an enormous amount of staying power in the game. Going back to my daughter's comment of no matter what everyone else says, his convictions and belief, he will actually stick by them and actually get through the hardest times.

**Lenny Rachitsky** (00:33:17):
I totally believe that. I feel like I'm not the smartest person in the room usually, and I succeed in large part because I just work really hard.

**Jeetu Patel** (00:33:24):
You're pretty smart though. I've been watching your podcast for a while. You've done a pretty amazing job.

**Lenny Rachitsky** (00:33:30):
I appreciate it. In this permission to win concept, the reason I think it's so important is it's so easy to build stuff now. Everyone's just building, building, building, launching, launching, launching. It feels like this is an increasingly important lever is why will we win in this space? I'm curious if there's an example you can share either from Box or Cisco where it's just like, "Okay, this is like we're going to do this because we have the permission to play here."

**Jeetu Patel** (00:33:57):
I agree with you in the sense that if generating code is something that becomes abundant, that doesn't mean you're going to have better technology just because you can generate a lot of code. You still need human judgment, you still need a level of intuition on what problems are the right ones to solve. And yes, AI can help you with all of that, but it's not something that ... That's where humans have a superpower, they have instinct, and they can actually make sure that they can fulfill out a vision that says, "This is what I think this could be in the fullness of time." And so, that I think is pretty important. So, the easier it gets for us to get the bottlenecks out to generate code, the harder it gets for us to make sure that there's not AI slop in the market and that we actually are very selective on what are the things that are going to be the most important things that solve the most important problems moving forward?

**Jeetu Patel** (00:35:01):
Example of permission to play is, I mean, there's so many ideas that a company the size of Cisco, we have constantly new ideas that keep coming up. And then in those new ideas that keep coming up, people will always say, "Oh my goodness, this company is doing so well. We should just go into that market, or we should just go into this market." And 90% of the times, 99% of the times, I find myself saying no. And the reason for that is you have to be extremely selective of where you expend your calories. And that caloric expenditure is where if you expend your calories in a very focused way, the results you'll get from that focus area tend to be outsized and disproportionate. If you dissipate that caloric burn across multitude of different areas, nothing gets enough girth to be able to go out and drive it all the way through. And so, why are we not in business-to-consumer tech at Cisco?

**Jeetu Patel** (00:36:10):
Why are we not going out and building things that are very very B2C? Because I don't think we have a distribution channel that actually is within our DNA. I don't think that we've got permission to play there. That's an area where it would be extremely hard for people to growth that Cisco should be the one who's participating in that. Now can we do it? Of course we can do it. Is that where we want to go or do we want to go where there's so much opportunity in the areas where we can actually prosecute with the ability to operate from a position of strength that you'll just get a much better return for the dollar that you invest.

**Lenny Rachitsky** (00:36:51):
You mentioned Aaron as a CEO that you learned a lot from. I'm curious what other CEOs you've learned a lot from and what's something you learned from them?

**Jeetu Patel** (00:36:59):
Chuck Robbins is one of my favorite humans. And not just because I work for him, I work for him because he's one of my favorite humans. And what I've learned from him, he had this kind of great line. There was this piece of press that our media is very sensationalist by definition. They will try to create a very polarized view about the world where there actually isn't one. And most things in my life, things are not as extreme as you hear of the headlines of the media. It's somewhere in the middle. And there was one time that there was this article that ran, and it was about giving me an unnecessary amount of credit, and frankly, not giving Chuck as much credit about something that he has actually done. A lot of the movements that we've had internally wouldn't have happened if he had not hired me and given me agency to go do the things that I needed to get done. And he was very much completely in sync with me on what needed to happen.

**Jeetu Patel** (00:38:13):
And so, when I saw this article, I had no idea who the report, I reached out, I'm like, "Hey, I just want to let you know, this was not me saying it's someone." And she said, "Don't worry about it, man." What I've learned in life is, if you don't care about who gets the credit, you just go a lot farther in life." And it's so profound in so many ways that he's just way too confident to let anything. And so, the thing I've learned from Chuck is the importance of confidence and the importance of knowing what you're good at and where you're not good at. And where you're not good, you're going to assemble a team of people around you. He's just masterful at that, and it happens-

**Lenny Rachitsky** (00:38:58):
And by the way, he's the CEO of Cisco, in case people-

**Jeetu Patel** (00:39:03):
That's right. He's the CEO of Cisco. He is the chair of the business round table. He's very dear friend of mine. And I feel like there's a lot to learn from that kind of mental model and mindset. And I've been lucky enough, Lenny, that, and this is just dumb luck. The people that I've worked with and for are all very, very close to me, and I just don't let them go from my life. And so, one of the things, for example, is I worked with Aaron and then when I was leaving, it was very emotional, but I wanted to do something different. But we committed to each other that we are going to have dinner every six weeks. And Aaron, and there's another co-founder, Jeff Kweiser, and I, three of us. Every six weeks in Palo Alto we have dinner. And it's one of the most special things that I still do, and it's a tradition now, it's been going on for six years, and I love it.

**Jeetu Patel** (00:40:02):
You look at someone like Chuck, I start with my day with talking to him in the morning, we text each other. And then I end the day talking to them in the evening. And we probably touch base at least four or five times a day. They're not long conversations at all points in time, but we're constantly in contact with each other. And I feel like that only happens when you've established enough trust. My first post when I moved to California is this guy named Rick Devenuti and then another guy named Jeremy Burton. Rick Devenuti is still my coach. I see him every two weeks. Jeremy is someone that's a very dear friend of mine, and we're neighbors and be moved and bought a place next to his just so that we could be close to him. And these are special people in your life that have enriched your life in very different ways that I think you just have to make sure that you treasure.

**Lenny Rachitsky** (00:40:54):
**Jeetu Patel** (00:42:09):
We have about 30,000, but-

**Lenny Rachitsky** (00:42:11):
30,000 people. Okay.

**Jeetu Patel** (00:42:13):
Yeah.

**Lenny Rachitsky** (00:42:14):
What's something that you wish you'd known before taking on this role?

**Jeetu Patel** (00:42:18):
I don't know if it was ... I mean, I instinctively kind of knew it, but it was very, very accentuated at Cisco, because when people say, "Oh, is scale hard?" And my perspective has always been that the absence of scale is way harder than scale. What do I mean by that? If I have a startup with three people and we need to prosecute another idea, and that idea requires five people working on it, I have to go raise money, or I have to pivot my entire business. If you have 30,000 people and you have an idea that requires five people, you just figure out a way that you allocate the dollars internally and say, "Let's go prosecute this idea." So, in my mind, I always felt like absence of scale was way harder than the presence of scale. And operating within scale seemed like it was like, yeah, you have more opportunity to do it.

**Jeetu Patel** (00:43:23):
What I found over the years, not just at Cisco, but even when I ... Because I ran a small startup in Chicago for 17 years before I moved over to the Valley, what I found in the large companies is, the communication framework and the lossiness of communication, the telephone game, so to say, has a profoundly negative effect if you're not intentional about it and if you're not careful of it. And there was this board member that we had, there's a couple board members, our lead director, Michael Capellas is amazing. There's this other board member, Kevin is amazing, and then there's this one board member, Wes Bush, who we recently rolled off, but he used to be on our board.

**Jeetu Patel** (00:44:09):
And when I got this job, he pulled me aside, said, "Jeetu, I'm going to tell you something. I'm going to give you some advice, and take it or leave it, but I think it's going to be important for you to keep it in mind." I'm like, "What's that?" And he goes, "Whatever you do, don't think about your story of the company as a marketing exercise. Think about it as the most intrinsic foundational exercise of the company. And always be the custodian of the message. Don't delegate that to someone else to give. Because if you have 3, 4, 5, 6, 7 layers between you and the person who's actually doing the job in the front line, what you don't want to do is play the telephone game and assume that people will just cascade it when you go to your team and then say, 'Okay, that team will cascade to the next team, cascade to the next team, cascade to the next team.' Every one of them will add a flavor with well-intentioned, and then by the time it gets to the end, people won't know what it is. So always own telling the story."

**Jeetu Patel** (00:45:14):
And I'm like, "That seems like it's a lot because we have a very broad portfolio. We do all of these events. It's like, I'm going to have to stand on stage for 90 minutes and just talk about it." He's like, "Please do that. Make sure you don't."

**Jeetu Patel** (00:45:27):
And initially, the hidden benefit that came out of it that I did not realize is it massively, Lenny, simplified our business. And you know why? Because we have such a broad business with so many different industries, it's impossible for someone to be a deep expert in every single one of them across the board. There's just way too much surface area. But the things that we want to convey to the market that the market should take away from us, if that story is not something that I understand well enough to be able to convey it, how do I first expect 20,000 of my sellers to be able to go tell it to the market? And how do I expect my customers to be able to digest that story? There's zero chance that would happen.

**Jeetu Patel** (00:46:16):
And so, that was my big takeaway from this, which is don't delegate the storytelling. And the storytelling is not a marketing exercise after you built the product. The story is why you build the product to make the story come real. And so, make sure that the story is there first, and then that story has evidence and proof based on the products that you're building.

**Lenny Rachitsky** (00:46:44):
I had a conversation with Matt MacInnis, who's COO now CPO at Rippling, and hit a similar piece of advice, which I think is also, it's like adjacent advice, which is "The intensity of an idea or a plan drops at every level that it goes from CO to the next layer and layer. And your job as a leader is to maintain that intensity, not to buffer it from the employees, but to maintain exactly the same intensity."" And it feels like that's in addition to also just keep the story the same. Like don't filter it, don't change it. Although your advice is even different, just like you actually go to the team working on it and tell the story yourself. Don't even let-

**Jeetu Patel** (00:47:23):
I want to make sure that they hear it from me directly so that there's no lossiness. We have this concept in networking called packet loss. When you actually send packets over a wire and you have a loss of packet, then actually there's loss of data. You don't want to have packet loss in your storytelling from you to the person on the front line because-

**Lenny Rachitsky** (00:47:43):
The direct ethernet Cat 5 connection.

**Jeetu Patel** (00:47:45):
This is just a direct connection and there's no packet loss of this one. You got to make sure it gets to the intended audience. And I think the reason for that is, as companies get large, they can lose touch with the front lines. Everyone gets really good with the math of the business, but they don't really always preserve the soul of the business. And there's a lossiness that happens, because if you have seven, eight layers between you and the front line, even the message that's coming back to you from them is actually getting lossy.

**Jeetu Patel** (00:48:15):
And so what you have to do is just preserve ... And I think what was said earlier about the intensity is the same way, which is you got to preserve the intensity. You got to preserve the sanctity of the message, and you got to preserve the clarity of the message so that everyone is clear on the direction we're going down. And if you can stay clear and stay motivated about that direction and make sure that everyone's on the same page and what needs to be done to execute, you will have success. If not, you'll actually have guaranteed failure.

**Lenny Rachitsky** (00:48:45):
How do you actually operationalize this without just being overloaded with work and constantly having to meet with every team and remind them of the story?

**Jeetu Patel** (00:48:53):
The first thing I feel is, you have to have very clear thinking because the clarity of thought is what brings clarity of communication. So, you have to spend the time with your team in sweating the details on what it is that you want to do and why you want to do it. The context of why is so lost, and constantly reminding people why it's important and having the least amount of asymmetry between the topmost layer in the organization and the bottommost layer is super. Now, by the way, I'm a Section 16 officer. There are certain things that, for example, you're in a quiet period you can't go talk about to someone else during that time period, but that's not allowed. However, the most amount of context that you can provide them in the way that you can because you're allowed to, the better off you are.

**Jeetu Patel** (00:49:47):
And always treat people like adults. What I've found is, oftentimes when you go into corporate environments, like people start becoming very sterile in the facts that they provide. And sometimes it's okay to just say, "Hey, we screwed up here. This was really bad." That's not meant to ... One of the things that I found to be very counterintuitive, because every management book that you read will tell you otherwise. What do they say? Praise in public and criticize in private. I fundamentally disagree with that notion. I think what you have to do is establish enough trust among the team so that you are comfortable critiquing and debating in public. But when you're in private, take that moment to build the trust. Because if you build that trust and you tell them that you've got their back and you create a level of safety there in public, you don't want to be in a mode of posturing.

**Jeetu Patel** (00:50:57):
You want to be in a mode of problem-solving. When you're just giving people perfunctory compliments all the time, and everything's just hunky-dory. Rose-colored glass is great. All your dashboards look green, but you're growing the business at 1.5%. There's an asymmetry there, something's broken. It's like, what do we need to do over here? And so what I tend to do is use the exact opposite approach. I tend to be very, very direct in public, be respectful, but be direct in public. This is not working. Let me tell you why it's not working. We got to face the facts. And then be very, very clear with people that you got their back in private. And don't be stingy with words on that front, because I feel like there are times when people are very stingy with words with people in private. You can't be stingy with words over there.

**Jeetu Patel** (00:51:52):
And don't be stingy with critique in public, because I think people need to make sure that, "We are solving problems together." And if we don't know the play that we're executing, if we don't know the things that we're going to need to do, then I am not really certain. If you're making collective progress, and I think it's not going to be fulfilling to either you or the recipient at some point, and those compliments will feel hollow because you didn't mean them. Because you were trying to put it in between. Like Ben Horowitz says, and hard things about hard things that you have a shit sandwich. You say something really nice to someone, then you say something that's not really nice, and then you put no, just treat people like adults. Tell them the facts, watch your tone. I still have to work on that. There are times when I get very passionate. People think like, but watch your tone and make sure that you debate. Conflict is a necessary condition of business, but the only way that you can have productive conflict is if you've established trust.

**Jeetu Patel** (00:52:54):
And the only way that you can establish trust is by making sure that you spend the time to establish the trust. So spend the time to establish the trust, but then focus on the best idea, winning and actually having the debate.

**Lenny Rachitsky** (00:53:07):
Is there maybe one more lesson that you've learned from this? Or I guess it's something you wish you'd known before getting into this role? Is there anything else that comes to mind?

**Jeetu Patel** (00:53:15):
I was an apps guy. I operated in the apps layer. I worked at Box. And even when I was at EMC, I was building apps that you built for the end user. Infrastructure is a different game. And the thing that I learned about infrastructure is, you don't always get the glory, but you always get the blame.

**Lenny Rachitsky** (00:53:41):
Perfect.

**Jeetu Patel** (00:53:42):
And you have to be comfortable with the fact that you are working in a way that other people get the glory. Great infrastructure companies, the application companies get the glory when they're running on that infrastructure. So, you have to be hardwired in infrastructure to orient on your ecosystem success, not just your own success. And that is probably one of the lessons that I learned at Cisco in a very stark way, which I didn't fully appreciate it until I got into the details of the infrastructure going, "Wow, if this thing doesn't work," we were ... Every single time, our infrastructure doesn't work. This morning I was with a medical institution, I was with a healthcare company this morning, and they were telling me they were very complimentary. They were thanking us on the partnership. I asked them, "Why are you doubling down with us?" And they're like, "Because when the infrastructure doesn't work, people die. Someone doesn't get dialysis, someone doesn't get a surgery done, and we need to make sure that we're working with someone with the infrastructure is working."

**Jeetu Patel** (00:55:02):
And so, I feel like at that point in time, you can't be navel-gazing too much about, "Look how cool you are, because you did something." You have to just make sure that you're really immediately shifting your focus to, what does the customer do and what does the ecosystem do with your infrastructure so that the outcome is achieved? And you have to get very outcome oriented. And I feel like that was something that I always intellectually knew, but I didn't fully realize it until I came here on how important of a mindset shift that is. You are not talking about yourself, you're talking about the system just working. No one will come and tell you, "Hey, Jeetu, thank you so much. My network worked today." But the moment it doesn't work, they're going to call you and say, "You know what? My network's not working, and my people can't work and patients are dying in the hospital." I think you just have to be comfortable with that.

**Lenny Rachitsky** (00:56:00):
It's so interesting how this lesson connects so directly to the lesson you learned from Chuck, the CEO of Cisco, which is, "Don't expect the praise and the credit. You need to be comfortable with other people getting credit for your work."

**Jeetu Patel** (00:56:14):
That's right. By the way, it's not surprising given that he spent, I don't know, 26, 28 years over here, you know why that he's conditioned with the fact that he's focused on other people succeeding from it. From your work.

**Lenny Rachitsky** (00:56:35):
It feels like there's so many metaphors corollaries to networking as a way to think about leadership and living life.

**Jeetu Patel** (00:56:42):
It really is.

**Lenny Rachitsky** (00:56:44):
Oh man, I bet you guys have all kinds of examples.

**Jeetu Patel** (00:56:49):
It's a good exercise to actually go through and create the corollary of parallelism between life and networks.

**Lenny Rachitsky** (00:56:57):
I'm thinking about just how many friends, like Dunbar's number, how many notes can you have in a network before it starts to slow down? Maybe 150. Oh, man. Okay. Anyway, I like that your mind's spinning.

**Jeetu Patel** (00:57:16):
I'm thinking how many can you have? I think more than 150, for sure.

**Lenny Rachitsky** (00:57:17):
I also was thinking about Intel. The whole Intel Inside move was such a clever way to break through that where no one would know Intel and so they're just like, slap a sticker of Intel Inside.

**Jeetu Patel** (00:57:27):
And by the way, they are. Lipu is a very dear friend. Pat Gelsinger used to be my mentor at EMC. And so both those people that have had such a profound contribution to that industry in general. When you start thinking about them, they're very, very much on that mode.

**Lenny Rachitsky** (00:57:46):
I could see how you pull together this insane collection of humans, just feels like you're just friends with everybody.

**Jeetu Patel** (00:57:51):
I feel like it's, life's too short not to be, and I'm only friends with people that I feel are good human beings. What I try not to do is I try to minimize my time no matter how successful they are with people whose energy I don't vibe with, because I think life's too short. And in my mind, one of the most off-putting things is, look, all of us have a healthy ego. There are times when ego gets manifested with insecurity and you have to make sure that you're at least self-aware enough to know when your ego is starting to take over your behavior in a way that's counterproductive. And all of those things are super important. But what I think is extremely important is that you ... Life is just fun to live when you love the people you are around.

**Jeetu Patel** (00:58:43):
Can I digress for a second in this one story that ... I'll tell you the story that was ... So my mother was, she passed away two and a half years ago, but she was extremely sick in the hospital for eight weeks before she passed away. And I was very close to my mom. She was my everything and we were only child. I grew up with a rough childhood. My dad was a high-stakes kind of conn man, like Bernie Madoff. I didn't want to be any part of that. So I had actually left India, come over here, hadn't seen him. And so he was very abusive to my mom. So there was a bunch of that that had happened.

**Jeetu Patel** (00:59:27):
And so, we had had a very difficult early childhood life for me, and her and I had bonded during that time at a very deep level. And so when she came to America, she always wanted to have her own place, but she lived very close by and she was very dependent on me emotionally in every way. And so, I had almost become a parent to her.

**Jeetu Patel** (01:00:00):
And at the last eight weeks things flipped and she became a parent again. And so we were getting to the point where she was ending her journey. And I was sitting one in the morning at the bedside by her in the hospital. I was living in the hospital at the time. And she was sleeping and I was just crying profusely. And she wakes up, and she knows why I'm crying, because she's going to be gone soon. And she looks at me, Lenny, and she's like all perplexed and she's like, "I had no idea that you loved me so much." Now, by the way, this is the most abnormal thing for me to hear, because I'm like, "What are you talking about mom? Like you're one of the most important people in my life." And everything that I did was to make sure that my mom was okay. Why did it feel that way to her? Because she didn't know how I was thinking.

**Jeetu Patel** (01:01:10):
And that kind of notion of people don't know what's going on in your mind is so important that my biggest lesson from that was, "Don't be stingy with words." Because even my mother that knows me inside and out didn't know how much I loved her, that there's no chance that people in the business world are going to know how you feel if you're not explicit with them. And so, I'm actually very clear with people, when I find them and when I find them rewarding, I let them know how much they mean, because I genuinely find a lot of energy coming out of that. And B, the circle of friends just keeps getting bigger and bigger and bigger. And I found that to be a super rewarding thing in life. And you're right, most of the people that were at the AI Summit are dear friends, and isn't that just a better way to live life?

**Lenny Rachitsky** (01:02:01):
I think we've uncovered one of the secrets of your success, which is just tell people how you feel and help them see that you appreciate them. Make it clear that you appreciate them, that you value them, which is a lot of people don't do, they just assume they know that they like you.

**Jeetu Patel** (01:02:16):
And don't make it fake.

**Lenny Rachitsky** (01:02:18):
And don't make it fake.

**Jeetu Patel** (01:02:19):
Don't make it fake. If you don't love someone, don't tell them you love them. That's the other thing that I have.

**Lenny Rachitsky** (01:02:27):
It's so interesting. We just did a little interview kind of thing with my mother-in-law meant for our son, just for him to have when he's older. They just interviewed her about her story and stuff and they asked her at the end of it, "What's something you want Jude," which is his name, "to know, a lesson to learn from you?" And it's to just, if you love someone, tell them you love them as much as you can.

**Jeetu Patel** (01:02:52):
Yeah, that's so true. You're so intentional about the way in which you do these things. I wish I'd done a, I should do that now. Now I think about it, do a podcast for my daughter that's only for her when she gets older.

**Lenny Rachitsky** (01:03:06):
I'll send you these. This group, they do this. I think they're in the Bay Area, but it's incredible. It's a whole documentary thing where they interview you, film your life for a little bit and then make a whole documentary.

**Jeetu Patel** (01:03:17):
Oh really? Oh, wow. I'd love that actually.

**Lenny Rachitsky** (01:03:19):
Yes. Oh, man. They're going to get a lot of business right now.

**Jeetu Patel** (01:03:21):
There you go.

**Lenny Rachitsky** (01:03:24):
Let me end with a question around just your journey. So today you lead product at a 90,000 person company. You manage 30,000 people. Like you said, you grew up in India in Bombay very far outside Silicon Valley. A lot of people hearing this today are kind of in a similar boat. They're way outside of the valley, maybe don't have a lot of obvious way to break in. They don't have a lot of opportunity and they see someone like you and that's their dream. What would your advice be to someone in that place right now?

**Jeetu Patel** (01:04:00):
The platform that you choose and the quality of problems that you pick to solve actually determine a lot of the path of success for you. And typically harder problems have a higher likelihood of success. Because the harder problems are the ones that attract better people to that problem and business as a team sport. And if you attract people to the problems that are hard and important enough to solve, then you get the best team, and you get the best team, your odds are winning just go up exponentially. So most people think I'm going to go out and pick an easy enough problem to solve. And it's like you don't get the best-team attracted to you to start up a lemonade stand, very important job. But that might not be the thing that actually gets the best team to come to you. But if you actually pick a hard enough problem to solve, you'll get the best team to come.

**Jeetu Patel** (01:04:54):
So that's one. Number two, I'd say that you can teach and learn a lot of things in life. I don't think you can learn hunger or you can't teach hunger. So find what you're intrinsically hungry about and make sure that you try to pursue that area. And that's different from passion about something. It's like in everything that you do in work, you have to just understand the formula that there's going to be 30% of the stuff that you do at work that you're just going to hate. And you have to get used to things that you hate that you have to do. But further, but find something that you're really hungry about that makes you want to come in to work every day, because the mission is worth the expenditure of energy that you're putting into it.

**Jeetu Patel** (01:05:50):
And I'll leave you with a story which was, I hadn't gone to India in a long time. When I left India, I didn't go back. I left in '91 and I hadn't gone back in any kind of meaningful way until 2017 because of all the trauma in childhood. For whatever reason, I hadn't gone back. But I took my daughter and we went to Augra to see the Taj Mahal and we went there and there was this tour guide, his name was Raj. And this tour guide was like he understood so much about the product that he was selling, which was the tour of the Taj Mahal. I don't know if he was making the shit up or not, but it sounded really good and he seemed like he was kind of early on it.

**Jeetu Patel** (01:06:34):
But when we were walking back, there's all these people and he would just start talking to them and he'd bust out in different languages. He'd talk to someone in German, talk to someone in French, someone in Spanish, someone in Hindi, someone in. And at some point in time in Mandarin. And at some point in time I stopped, "Dude, how many languages do you speak?"

**Jeetu Patel** (01:06:51):
He's like, "Oh, I speak, I don't know, 12 or 14 or some ridiculous number. But I try to learn a new language every year." I'm like, "Oh, why is that?" And he goes, "Well, I just want to honor the people that come here and not be presumptuous that they will speak in the language that I know." I want to speak in their language. And I'm thinking to myself, I was a box at the time. I'm like, "This guy is smarter than every person on the executive team and probably just as smart as every salesperson we have, but he's making $10 a day and all of us are enjoying this amazing life, and it's because we have access to a platform and he doesn't." So when people start confusing life thinking that, "Everything that I've earned is because of my amazing abilities." I always question that, because there's a lot of luck in this thing.

**Jeetu Patel** (01:07:45):
But when luck does present itself, be extremely prepared to capitalize on it. And make sure that you pick the platform that can actually give you that springboard. Because platforms really matter. And if we, like I had the platform and benefit of America, of education, of being in tech, of having great friends and mentors, all of those things created compounding value. But I intentionally sought out those platforms, seek out the platform, be obsessed about being extremely prepared, and don't be intellectually lazy. Laziness is not a good trait. So do the preparation that's needed. And then just make sure that during that time period that you're doing, if you build a community around you of people that are vested in your success, I think it's just, life is just a more fun way to live it, rather than being the lone wolf that's going at it by themselves.

**Jeetu Patel** (01:08:54):
And that's why I always feel like making sure that you are expressive and communicative and don't try to carry the entire world's burden on your shoulders, but actually share it with people with you, the people that you share it with actually appreciate that you're sharing it with them.

**Jeetu Patel** (01:09:13):
And most people in the world love to help. So ask for the help, but make sure that that help is not transactional. And don't just go to them when you need something. Actually try to add value first for a long enough amount of time, not because at some point you might need something from them, just hard-wire yourself into adding value to others. And then eventually that value starts showing up, and life's just a better way to live life. And I do feel like right now it's hard for kids getting into the workforce and all of that. So, don't lose hope and stay persistent and have stamina, because these things go up and down. But if you kind of stick with it, the people that have the most amount of persistence, it's very seldom that they don't end up winning.

**Lenny Rachitsky** (01:10:00):
Something that comes to me as you share this advice. Arnold Schwarzenegger's has this book that he put out, and I feel like the title of the book is the best piece of advice, and the simplest way to describe How to be Successful in Life, which is be useful.

**Jeetu Patel** (01:10:19):
That is so good.

**Lenny Rachitsky** (01:10:22):
Jeetu, this was incredible. Is there anything that we didn't cover that you wanted to share? Anything you want to leave listeners with before we get to our very exciting lightning round?

**Jeetu Patel** (01:10:29):
I think there's a framework that I use for great companies that might be worth kind of sharing with people.

**Lenny Rachitsky** (01:10:29):
Yes.

**Jeetu Patel** (01:10:34):
There's a six part framework that I have, which is in descending order of importance, and on how to build great companies. This is-

**Lenny Rachitsky** (01:10:47):
Amazing.

**Jeetu Patel** (01:10:48):
You get it for free, you get what you pay for it, so take it with a grain of salt. But here's the way I think about it. The most important thing is timing. The six things you need in a company, if you don't have all these six, you don't win. But they're stack ranked in descending order of importance, but you have to have all six. Number one is timing. It's the most important. It's the thing that you control the least. And there's a lot of companies that have built amazing products, amazing services at the wrong time, in the right market and not won, right? And so timing really matters. You don't control timing, but if you don't have timing, you don't win.

**Jeetu Patel** (01:11:27):
Number two is the market. You have to be able to go after a large enough market, a chunk at a time. And if you're not able to go out and prosecute a market a chunk at a time, but make sure that that keeps getting bigger and bigger, it's very hard to win. So market tends to be the second most important thing in my mind after timing.

**Jeetu Patel** (01:11:47):
The third one then is team. You have to have the right team. And the team does not mean just people liking each other. Team means that it is actually well-rounded. That means the things that you suck at someone else is really good at, and you have both accepted that of each other. For example, I have a person that I never go to another job without, and she is my partner in crime. And the reason I have her is because she is so good at things that I'm not good at. And so she's able to, any job I've taken since I've been working with her, it's always, it's a combined deal. If we don't have two offers, we don't go. And so team is really important, a well-rounded team where people understand how to complement each other.

**Jeetu Patel** (01:12:34):
And by the way, in the team, sometimes people say, "Well, isn't team more important than market?" No, if you have a great market mediocre team, the market pulls you up. If you have a shitty market and a great team, the market drags you down. The market always wins. So no, timing, market, team.

**Jeetu Patel** (01:12:50):
Number four is product. I think product is the soul of a company. That's the place where people seek value is, what are you delivering to me? What problem are you solving to me gets manifested through the delivery of a product. So you have to make sure that you build a great product. I actually think it's unethical to have a mediocre product sold in the market. So timing, market team, product number five is brand. I had a mentor one time that told me, Mark Lewis, he said, "Jeetu, don't ever go to a company who's lost their brand mojo, because very hard to resurrect it back."

**Jeetu Patel** (01:13:21):
If they have lost their product and you cannot fix the product. But do you think Sybase is coming back? No. Once you lose your brand and once you lose the trust, people don't come back to you that much. It's very hard to do. And then number six is distribution. Just because you build it, they will not come. You have to make sure that you figure out a scaled mechanism of getting that offering to many, many people. And so timing trumps market, market trumps team. Team trumps product, product trumps brand, brand trumps distribution. You don't have all six, you don't win.

**Lenny Rachitsky** (01:13:54):
What an amazing nugget to have at the end here, just so I understand how you think about this is, do you have a template that you work through when you're thinking about a new business unit or new product to launch? Is it is timing, right? Is market, what market do we start with? How do you actually operationalize?

**Jeetu Patel** (01:14:09):
It's actually exactly like that. I will ask myself the question on, "Is this the right time for us to go out and double, triple, quadruple down?" We might still be in experimentation mode, but do I need to double down on this right now because this might not manifest for another seven years and then we're going to be too early. And by the way, you have to know the difference between a megatrend and a hype cycle. When there's a megatrend, don't fight it and don't succumb to the temptation of trying to go out and do vanity work for a hype cycle. And there's a big difference between the two. And I think having the judgment, the older you get, the better that judgment gets. It's just miles. But having that judgment is really important because you see a pattern recognition at some point.

**Lenny Rachitsky** (01:15:00):
I imagine AI megatrend.

**Jeetu Patel** (01:15:02):
AI is a megatrend in my mind, and there's a bunch of hype cycles we've had where I never particularly subscribe to them. The easiest way for me to tell is, the way it's described, is it easy to understand what this could do in its ultimate form for most people, or do you need to have a PhD to understand what someone's saying? When you feel like you need a PhD to understand what someone's saying, chances are it ain't going to be a megatrend, because by definition, a megatrend is it's going to impact a large population of the world. And if the thing is too complicated, chances are it's not going to have that level of effect, outsize effect.

**Lenny Rachitsky** (01:15:43):
That's an awesome heuristic. I imagine you're thinking Web3 as a classic example.

**Jeetu Patel** (01:15:46):
Yes. Web3 was the one that I actually cite all the time. I couldn't understand what it had been. And all of these people were kind like, "Oh, Web3, Web3." I'm like, "I couldn't make a heads of tails out of a use case." But with AI, it's like you go to ChatGPT, you ask it a question, you get an answer. I get this. This is easy.

**Lenny Rachitsky** (01:16:05):
So going back to your framework, just to kind of close the loop there, it's really interesting that timing is the first variable you look at. This could be an amazing idea. You got the right team, amazing product that works really well, but the timing may just not be right. And no matter how awesome it is, it's not going to work.

**Jeetu Patel** (01:16:21):
Steve Jobs put away the iPad because he thought that the iPhone was a better idea. And timing wise, he actually made exactly the right call. The iPad became successful because of the iPhone success. The reverse order might have not had the same effect, but he had to make sure that he focused on one thing and he actually puts the other, he said, "The timing's not right, but I'm going to get back to it." So by the way, when timing is wrong doesn't mean that you scrapped the idea. It just means that you might put it on ice for a bit.

**Lenny Rachitsky** (01:16:50):
There's a lot of that happening right now where people try to do a thing and now AI actually makes it possible. And now they're like, "Oh shit, it was way too early."

**Jeetu Patel** (01:16:58):
And the other thing you have to keep in mind is you have to also be good enough to know that when something is going to be ready in six months, you can't think about what it's doing today. AI is moving so fast right now. One of the things I tell my team is, fast-forward six months from now and anticipate what that's going to do and get prepared for that world. Don't get prepared for the world of today thinking that you're not going to be able to get there because in six months your assumption sets are going to be different. And please don't actually then bias yourself with the assumption sets you have right now to not move forward. One of the worst things I think companies do sometimes is they put too much emphasis only on solely on experience. And I think experience is good, but experience can actually be meaningfully bad in some areas where you get too biased. And so you almost have to say that I have to have the ability to unlearn.

**Jeetu Patel** (01:17:50):
And combination of experience with complete inexperience is what creates the magic because the inexperience allows you to ask questions that you might have never had with experience. And the combo of those two gives you the best of the pattern recognition, plus the charting new territory that's never been walked on before.

**Lenny Rachitsky** (01:18:10):
Yeah, this is a trend I've been hearing on this podcast that people worry about young people and people graduating out of college right now and jobs and AI. But they're the people that are most open-minded about what AI can do for them and how to harness AI and not code in the way people have always coded. It's just like, okay, this is the way it works now.

**Jeetu Patel** (01:18:29):
Experience, Lenny, can jade us, and I always say when people say, "Oh, entry-level people will never be hired again." That's the stupidest thing a company can do, because now what you've done is you have completely shut the door to new fresh ideas. I cannot think today the way I thought when I was 19. There is just no way that I can do that. But what I can try to do is I can try to make sure I surround myself with enough amount of my time to get exposed to that thinking and then couple it with what I know and maybe have something better than what either of those two could have had by themselves.

**Lenny Rachitsky** (01:19:04):
Yes. Well, with that, Jeetu, we have reached our very exciting lightning round. I've got five questions for you. Are you ready?

**Jeetu Patel** (01:19:11):
All right.

**Lenny Rachitsky** (01:19:12):
First question, what are two or three books that you find yourself recommending most to other people?

**Jeetu Patel** (01:19:17):
The Bible and Tech in My Mind, there's Innovator's Dilemma and Innovator's Solution from Clayton Christensen. I think you have to read that book and I'd say I'd recommend to people that read it every few years. And the other one that I love is Ben Horowitz's book. Hard Thing about Hard Things really talks about how you manage your psychology when things get hard. I think those are the ones, I am not a big believer that you keep reading thousands of books all the time because I think to me, retention really matters. And my brain's just not that big that I can retain that much. So, I tend to distill the essence of a few things quite a bit more, and at least as the older I've gotten, I've actually used that pattern more.

**Lenny Rachitsky** (01:20:00):
Favorite recent movie or TV show that you've really enjoyed?

**Jeetu Patel** (01:20:03):
I don't remember the name of it, but the Brad Pitt F1 movie that I saw that was pretty cool.

**Lenny Rachitsky** (01:20:10):
Wait, it was a recent Brad Pitt movie?

**Jeetu Patel** (01:20:11):
Yeah.

**Lenny Rachitsky** (01:20:16):
Was it F1?

**Jeetu Patel** (01:20:16):
It was F1. I think it was called F1, but it was pretty cool.

**Lenny Rachitsky** (01:20:18):
For Best Fix-

**Jeetu Patel** (01:20:19):
Zach Brown is a good friend of mine and we were big supporters of McLaren, and so it was actually pretty cool to watch that movie.

**Lenny Rachitsky** (01:20:26):
Oh man, I bet. So many stories I haven't tapped into. Okay. Favorite product you recently discovered that you really love?

**Jeetu Patel** (01:20:32):
I mean, it's cliche, but I feel like what ChatGPT, Gemini and Claude have done, it's changed lives. It's changed my life in the way that I learn in some ridiculous ways. So I actually feel like when I got this new job to run all product for Cisco, there's zero chance I would've been able to do it if AI wasn't there. Because I didn't know anything about so many domains that we were in. And I had to get an accelerated training course within a matter of three months. And I mean, I worked around the clock during that time, but I could have worked around the clock without the tooling and I would've been nowhere near as effective. So I feel like those three have done an amazing thing. And Grok, even what you're seeing with Grok tied to Twitter is pretty amazing.

**Lenny Rachitsky** (01:21:31):
Wow, that's a profound statement. I've never heard that before from someone at your level that you feel like you wouldn't be able to have done this job without AI.

**Jeetu Patel** (01:21:39):
It's your chance.

**Lenny Rachitsky** (01:21:40):
Especially for someone without the background in networking and hardware, that is so interesting. It's amazing how just at every level, AI is helping at the most bottom end and also in your level.

**Jeetu Patel** (01:21:54):
Most people don't realize, I fundamentally believe this is the reason that I'm able to enjoy some of the experiences I have. I was lucky enough that I had made enough money before this job. That was not the thing that was actually holding us back, but the reason I'm able to experience some of the things that this job afforded me to experience have not even been remotely possible without AI, no chance that would have happened.

**Lenny Rachitsky** (01:22:23):
Unreal. Okay, two more questions.

**Jeetu Patel** (01:22:25):
Yeah.

**Lenny Rachitsky** (01:22:26):
Do you have a favorite life motto that you often come back to and work your own life? You already shared a couple, but is there anything else or you want to double down on one you've already shared?

**Jeetu Patel** (01:22:34):
Stamina trumps intellect. I feel like it's very important to have smart people, but you can become smart if you have curiosity and hunger and staying power and persistence. And so I think that trait of learning to learn and constantly being hungry and having the stamina and persistence is far more important than the absolute measure of intellect that you might have, because that is very, very trainable and learnable over time and improvable over time. But hunger is very, it's not teachable is what I've found.

**Lenny Rachitsky** (01:23:11):
I 100% agree with that. Interestingly, when you watch AI work, it's just like, partly the reason it's so good is it just keeps trying. It's just like, "Okay, this didn't work. Let keep going. What else can we just keep trying? Just give me half an hour, I'll figure this out. Okay, last question. So when you were younger, you worked at Sizzler Steakhouse making $4 an hour is what I read.

**Jeetu Patel** (01:23:33):
2.25, not four. It was below minimum wage 2.25, but we got tips. We got tips though, so that was-

**Lenny Rachitsky** (01:23:43):
Okay. I see. Did you have a favorite dish at Sizzler, is my question?

**Jeetu Patel** (01:23:46):
Yes. They used to have this Malibu chicken dish was like magic. And then it was probably, I don't know if people know this, and I know this is rapid fire, but I used to stutter when I started working at Sizzler and Sizzler is what allowed me to break out of my shell and not stutter. Because something changed in my brain, but I'm like, I have to entertain people and if I don't, then they're not going to give me a good tip. And so the stuttering went away at Sizzler, and so I have an immense debt of gratitude and I think everyone should work in hospitality for a while in their younger years. And I'm kind of sad that my daughter has no interest in doing that because I'm like, "I wish she just worked as a waitress somewhere for a bit." And it's just so important to just, there's so many lessons on, I cleaned toilets at the restaurant. I actually washed dishes. I actually waited on tables. And it was the best experience I had. It shaped me for what was to come in the most profound way.

**Lenny Rachitsky** (01:24:58):
Jeetu, you're just endlessly full of wisdom. Two final questions. Where can folks find you? Where do you want to point people to learn more about you, what you're up to, and how can listeners be useful to you?

**Jeetu Patel** (01:25:07):
Where you can find me is ... I tend to ... A lot of people will ask the more success you encounter or the more people want to get mentored by you and learn from the experiences you've had, and I have run out of cycles to be able to do that on a one-on-one basis. So what I try to do is do a lot of that on LinkedIn and Twitter, but largely I do a lot of that on LinkedIn. And so find me on LinkedIn. I tend to be very open about not just the work stuff, but the non-work stuff to do that. How can people be useful to me? Was that the question? What was the last question?

**Lenny Rachitsky** (01:25:48):
That is, how can listeners be useful to you?

**Jeetu Patel** (01:25:51):
How can listeners be useful to me? If there is ... I would say that if you got something out of this session and if you get something out of whatever you learn from social media, just pay it forward and help the next person out a little bit more. Yesterday I was at a talk and someone pulled me aside and said, "Hey, I saw your LinkedIn post about this." Don't be stingy with words, and Jeetu, since then, I've been going to see my parents once every two months or so in India, and when I see them, I tell them that I love them all the time. Literally, what could be more rewarding to me than that? It was amazing that they were able to go out and have joy brought to their lives as a result of something they got inspired by something that I learned in my life, that's paid forward.

**Lenny Rachitsky** (01:26:51):
I'm excited to hear the stories that come out of this conversation. Jeetu, thank you so much for being here.

**Jeetu Patel** (01:26:57):
Thank you for having me. It was great.

**Lenny Rachitsky** (01:26:58):
Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

---

## [15/15] Jenny Wen

**Jenny Wen** (00:00:00):
This design process that designers have been taught, we sort of treat it as gospel. That's basically dead. You as a designer actually do not have the time to make these beautiful mocks anymore.

**Lenny Rachitsky** (00:00:10):
A big part of the design role now is helping engineers and teams execute, not just telling them, "Here's the design."

**Jenny Wen** (00:00:15):
A few years ago, 60 to 70% of it was mocking and prototyping, but now I feel the mocking up part of it is 30 to 40%.

**Lenny Rachitsky** (00:00:23):
You're better off not blocking that, letting them cook.

**Jenny Wen** (00:00:26):
It's not just designers who are feeling like, "Oh yeah, we have to keep up with engineers." I think even engineers are like, "How do we keep up with ourselves?"

**Lenny Rachitsky** (00:00:32):
How to keep up with all our agents, our seven agents who are constantly running?

**Jenny Wen** (00:00:35):
The result of engineering changing a bunch is that design is sort of forced to change. We used to go off and make this two-year, five-year, 10-year vision even. Now it becomes a vision that's three to six months out, and isn't necessarily creating this beautiful deck, sometimes just creating a prototype that points people in the right direction.

**Lenny Rachitsky** (00:00:52):
Boris on the podcast recently was saying Claude Code is now helping him come up with ideas.

**Jenny Wen** (00:00:55):
And we'll get better at taste and judgment and design. We might be holding onto that a little bit too much.

**Lenny Rachitsky** (00:01:01):
Where will human brains continue to be valuable?

**Jenny Wen** (00:01:03):
At the end of the day, someone has to decide what is actually going to get built and what actually matters. Someone still needs to be accountable for the decision.

**Lenny Rachitsky** (00:01:10):
What do you now look for when you're hiring designers?

**Jenny Wen** (00:01:13):
There's probably three archetypes of folks that are really interesting to me right now.

**Lenny Rachitsky** (00:01:20):
Today's guest is Jenny Wen. Jenny was head of design for Claude, is now leading design for Claude Cowork. Prior to that, she was director of design at Figma, where she led the design teams behind FigJam and Slides. She was also a designer at Dropbox and Square and Shopify. And what I love about this conversation is that Jenny is living in the future of where design as a profession is heading, and she's here to give us a glimpse into what that looks like, and how much things are going to be changing for designers. It is pretty wild and extremely interesting. A huge thank you to Noah Levin and Emily Lynn Hasham for suggesting topics and questions for this conversation. Don't forget to check out Lennysproductpass.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. Let's get into it after a short word from our wonderful sponsors.

**Jenny Wen** (00:04:21):
Yeah, excited to be here.

**Lenny Rachitsky** (00:04:22):
I've been looking forward to this conversation because I spend a lot of time on this podcast talking about the future of software engineering, how much that role is changing, the role of product management, how much that role is changing. I even spent a lot of time on how design is changing. Clearly, it is also changing in a really big way. And you have such a front row seat to where things are heading. I also know you have a lot of very strong opinions about where things are heading, so there's a lot of stuff I want to talk about. I want to just start with just this broad question. How is the design process changing with the rise of AI?

**Jenny Wen** (00:04:59):
It's changing a lot. I think it's still also got a long way to go in terms of the way it's changing. I think we've actually seen engineering change a lot more in the past little while than design actually has, but I think the result of engineering changing a bunch is that design is sort of forced to change. And so I think some context around this, is I did a talk at a conference in Berlin a few months ago in September, and I called it Don't Trust the Design Process, where I basically just said, "Hey, you know this design process that designers have been taught, where you go and you go off and you do a bunch of research and discovery, and then you diverge, you converge, diverge, converge." And it's like this process that we sort of treated as gospel and tried so hard to preserve and we were like, "Trust the process." That's basically dead.

**Jenny Wen** (00:05:52):
I think it was sort of dying before the age of AI, but given now that engineers can go off and spin off their seven Claudes, I think as designers, we really have to let go of that process. And I think that's the big thing that's changing. But I think even in the past three to four months since I did that talk, that talk actually starts to feel pretty ... It kind of feels outdated to me, which is a little embarrassing, but especially with the big shift of Opus 46 and a bunch of folks just really discovering and using Claude Code over the holiday break, I think we're seeing this force to change our process happen even more. The way I see it now is there are basically two types of design work, and design work is becoming really stratified in this new world.

**Jenny Wen** (00:06:42):
So there's the first one, which is really just supporting the implementation and execution. So this is the one where engineers are using their seven Claudes to create all these features, and anybody can put an idea out there, and you can just talk about an idea and somebody, usually actually an engineer because they're still better at implementing this stuff than we are, they will just make a scrappy version of it and you can try it out. And you as a designer actually do not have the time to make these beautiful mocks anymore, or to lead in this way.

**Jenny Wen** (00:07:16):
And then I think there's the second kind of work that feels also really important, which is creating the sort of vision or direction for things. This one feels like the hardest to make time for, and it's one that we still did before, but I think the shape of it's very much changing, because I think we used to go off and say, "We're going to do this design vision. We're going to go off and make this two-year, five-year, whatever, 10-year vision even, and we're going to point us towards something." But the way that the technology is changing now, we don't know what's going to happen in two years. There's too much changing, and it usually becomes a vision that's three to six months out, and isn't necessarily something that is creating this beautiful deck that's beautifully story told. It's sometimes just creating a prototype that points people in the right direction.

**Jenny Wen** (00:08:06):
And I think this kind of work is still really important in this world because in a world where people can spin off their seven Claudes, make whatever features they want in any kind of direction or in implementation, you need to point them towards something. And in order to make sure that we're all making something that makes sense together and is also done in a way where it's efficient. If we're all working towards something that has one greater cause, it's much more efficient to do that than just random things.

**Jenny Wen** (00:08:35):
And so that's the big shift that I'm seeing. And I think I have opinions about it now, but ask me in three months and it might actually change even more.

**Lenny Rachitsky** (00:08:43):
So what you're saying here is it's not like you or the design field is like, "We need to change?" It's engineering and the fact that you can build so quickly, just forces the role of a designer to change because as you said, engineers can just ship, ship, ship, ship, ship. And what you're finding here is you're better off not blocking that, letting them cook, as they say. And then there's this mode of helping them along as they ship, bring it together, make sure it all connects, guide them a little bit.

**Jenny Wen** (00:09:15):
Yeah, I think so. Yeah. I don't think there is one unifying voice that's like, "Designers, we need to change right now." But yeah, there is the follow-on effects of engineering tooling really changing. I think we'll probably see design tooling change in this next year or so as well, but a lot of it right now is trailing that. And I think it's also really empowering for us too, because as designers, we also now have access to a lot of these coding tools. And we can be a part of the process in a way where we're implementing stuff. I'm doing a lot of last mile stuff where I'm implementing all the polish, and working with engineers really closely to get the feature across the line, and also prototype stuff in actual code as opposed to relying on engineers to do that again.

**Lenny Rachitsky** (00:10:02):
How true do you think this is at all companies, at say AI companies, non-AI companies? Someone may be hearing this, okay, Anthropic Claude, okay, they're at the bleeding edge, for one. Two, it's developery a little bit, but I think people might be feeling like, "Okay, this is not going to happen at Salesforce. This is not going to happen at, I don't know, ServiceNow, wherever." So I guess, do you feel like this is where all teams are heading? Is it mostly AI, bleeding edge companies? How widespread do you think the design process shift is going to be?

**Jenny Wen** (00:10:32):
So the talk that I did last year has really been the most resonant talk that I've done. And so I think it's something that people are starting to feel across the industry, where they're like, "Oh yeah, we can't do the old design process anymore. We are using tools like Claude Code and v0 and whatnot, to start to spin up prototypes, and PMs are starting to spin up prototypes and stuff like that as well." So I think there's something there emerging. But the other interesting observation with that talk too, was there was actually also a decent amount of backlash. People clearly have invested their entire careers in learning, teaching, using this really stable design process. And I think there was a lot of discrediting like, "Oh yeah, we can't do without discovery. We can't do without these pieces of process." So I think there is still a piece of the industry that is not quite there yet in terms of this way of working, if that makes sense.

**Lenny Rachitsky** (00:11:28):
Yeah.

**Jenny Wen** (00:11:29):
Yeah.

**Lenny Rachitsky** (00:11:29):
And a big part of this is you could argue ... the question is what leads to the best, most successful products in companies? And you could argue it's spending time doing discovery, user research, mocks, iterating, beta testing, or it could be just engineer shipper stuff that's okay, not amazing, good enough. We learn, iterate, build, iterate. Is your sense that that second path, not only is that just what everyone's doing, but that actually leads to the better product at this point?

**Jenny Wen** (00:11:58):
I think you sort of have to choose and use your discretion as to when to actually ship something, but I think the ability to execute, try something out and try it with real data, and a real user's kind of mindset in the product, I think that does lead to a better product, especially as we're all working with these new developing AI models that are non-deterministic, you can't mock up all the states, and you can't theorize and you can't even make a clickable prototype with it. You sort of have to use the actual models underneath, and you have to see people try it out with their use cases, because with these models, you can design them for different use cases, but you actually discover use cases as you see people using them. So yes.

**Lenny Rachitsky** (00:12:43):
The other thing I always hear, and building on what you just said is just you don't know what people will do with AI. You don't know how good it'll be at certain things, the non-deterministic piece of it. So you can create these amazing mocks of what it might be, and then people use it in a completely different way, which is where Cowork came from, and probably even Claude Code at the beginning. And so what's it just like to be a designer at Anthropic? Just give us a day in the life of working at Anthropic, at the center of the storm.

**Jenny Wen** (00:13:11):
A good amount of time at Anthropic is actually just catching up on what's happening at the company. I think this is the company where ... I've worked at a few other companies around this size where I think there's just a lot of information and a lot of things going on, but I feel really compelled to keep up with it. There's stuff that is model developments on the research side. And then at any given time, there are just so many different teams prototyping and trying different ideas out, and there's a bunch of different code names and stuff like that. And a lot of time I'm just trying to navigate and figure out what those projects are, because I think I'm just trying to spot and see, hey, what's coming up ahead for me? Because there's stuff from both the research team, but also some of our labs teams that are closer to research, and trying out and prototyping stuff.

**Jenny Wen** (00:14:01):
And then there's just stuff I want to try out. We have a bunch of prototypes and products internally that we can use, and I am just curious and I want to try those things out. And then I think there's also a lot of folks who internally have a lot of insights and opinions on where the industry's going. And some of those are just really interesting to read, because a lot of these are philosophical debates or directions of the company and stuff like that. And yeah, I feel like I just want to keep up with these things. Whereas I think in a normal company, I'm like, "It's fine. This is stuff that's happening outside of my reach. I don't really care as much." Where here, I think that it's both the volume and the kinds of things that are happening that I'm really interested in keeping up with. And then aside from that sort of keep up, that's not a huge part of my job, but I do think it's a really interesting part of it.

**Lenny Rachitsky** (00:14:53):
Well, it connects to the point you made earlier, where a big part of the design role now is helping engineers and teams execute, not just telling them, "Here's the mock, here's the design." It's helping them stay on track, helping them connect ideas, create a cohesive experience as it's happening. So that makes sense.

**Jenny Wen** (00:15:08):
Yeah. Yeah. And I think part of it is just curiosity. It feels like I have this front row seat to so much that's happening in the industry. And so a lot of it is like, yeah, our Slack is a goldmine. I'm just excited to read through the things that people are working on, they're saying.

**Lenny Rachitsky** (00:15:23):
I never thought about how there's already so much AI news to keep track of as a regular person. And then actually seeing what's actually happening inside a lab is a whole new set of feeds to watch.

**Jenny Wen** (00:15:33):
Yeah, yeah. I think that is the best AI news is probably internally, if you're ever at one of these companies in the Slack.

**Lenny Rachitsky** (00:15:42):
Yeah. Just problem keeps getting harder. I'm just keeping track of what's going on. Okay. Okay. So that's part of the job. What else?

**Jenny Wen** (00:15:48):
There is still some of the traditional, let me think about what's happening in the future and let me make some designs for that. That's something that, for example, this week I've allotted some time to, where I'm like, "Okay, cool. We have been in a lot of execution mode for Cowork, and now I want to set aside some time to think about, hey, what does the next three months look like, and where could that actually go given where the market's at, where the models are at, and what could that be?" Because I think it still really helps to visualize that and show that to the team, and point everyone in the same direction. And then I also spend a bunch of my day just jamming on stuff with engineers. A lot of it is just a conversation, or white boarding, or going through something that they built and giving them feedback on it and being a designer in that kind of way, we're really consulting.

**Jenny Wen** (00:16:39):
And then I spend a part of my day in code, polishing, implementing stuff. Sometimes what happens is an engineer and I have worked through something and they've implemented a first version of it, and I just go in and polish it with them. And that's a really fun part of my job that I think didn't exist as much a few months ago.

**Lenny Rachitsky** (00:16:59):
Are you still doing elements of the traditional design process? Prototyping, user research, panels, I don't know, just going out and the whole thing you described?

**Jenny Wen** (00:17:09):
Yeah, I think we're still doing all of that to some extent. We have a user researcher on the team who is putting together both traditional studies as well as surveys, and the whole team is reading those studies and that feedback. We are still prototyping stuff. I'm still mocking stuff up. I think it's just I have a wider set of tools now, and I think the proportion of time I spend doing each thing just has changed.

**Lenny Rachitsky** (00:17:40):
Got it. Okay. So that's a really interesting takeaway. It used to be that was a huge ... I guess what would be the pie chart of what your life was before, where it's like traditional thinking, planning, prototyping, mocking, research, and then just feedback and execution and out, today?

**Jenny Wen** (00:17:58):
Yeah. I think as a designer a few years ago, I would say maybe 60 to 70% of it was mocking and prototyping stuff up, and then spending some of the last 20 or so doing the sort of jamming with engineers, consulting with them, and the last 10% maybe doing coordination meetings, et cetera. But now I feel like the mocking up part of it is 30 to 40%. And then there's that other 30 to 40% there that is now jamming and pairing directly with engineers. And then there's a slice, I don't know how much I have left, but there's a slice of it that is now implementation as well. Yeah.

**Lenny Rachitsky** (00:18:44):
Actually building and shipping?

**Jenny Wen** (00:18:45):
Yeah.

**Lenny Rachitsky** (00:18:46):
Amazing. So kind of following that thread, what's in your AI stack? What do you, as a designer, I know you're a manager and I want to talk about how you actually are IC also. What's in your AI stack? What tools are you using in your role?

**Jenny Wen** (00:18:57):
What is in my AI stack? Well, we work at Anthropic, so we're going deep on the Claude stack. I'm using, obviously, chats, Claude Chat, but increasingly more and more Claude Cowork. I've basically shifted all of my chat use cases over to Cowork, because I've been finding that it sort of is better at these longer running tasks. And most of the things I was asking Claude for are these longer running tasks.

**Jenny Wen** (00:19:24):
And then there's Claude Code, of course. I use it mostly with VS Code and the IDE because I'm usually tweaking front-end stuff, and it helps to just be able to see the code and then talk to Claude as well. I've been trying to actually use Claude Code more remotely, through both mobile and through Slack as well. It's really fun for somebody to say, "Oh yeah, this one icon's off or something," and you just at-mention Claude and Claude does it, and then you pick up the PR and it's done. That's been really, really fun too.

**Jenny Wen** (00:19:56):
And yeah, I think we're a fully Claude house here. So yes, that's basically my stack.

**Lenny Rachitsky** (00:20:04):
Are you still using Figma as a designer?

**Jenny Wen** (00:20:05):
I am still using Figma, yes, yes.

**Lenny Rachitsky** (00:20:08):
Okay. I was waiting to hear. Okay. So Figma is still part of your life. Being a former Figmate, is that what y'all are called?

**Jenny Wen** (00:20:08):
Yeah, Figmates, yeah.

**Lenny Rachitsky** (00:20:17):
Yeah. Yeah. Okay. So I know there's this big debate on Twitter, just like, is code the future of design? Do we need many more, do we need a design? What's your sense? Figma's still important?

**Jenny Wen** (00:20:27):
I mean, as a former Figmate, maybe I'm biased in that way, but I think there is still ... When I use Figma, I'm like, "Yes, this is what I should be using." And it still fills a very good gap for me. I think a lot of that is actually just, one is exploring a lot of different options. I think that's a really important part of the design process, to be able to just think about 8 to 10 different ways to do something. I think the best design happens when you're able to just throw a bunch of ideas at the wall, and curate and push yourself to come up with a bunch of these different directions. Right now, coding, or right now working with some of these coding tools doesn't lend itself super well to that, because it's super linear, you get super invested in one direction and you just iterate with Claude on them, for example.

**Jenny Wen** (00:21:15):
So Figma has been really great at just exploring all these different options, and I think it's still going to exist that way to some extent. And then I think there's really fine visual and interaction details that are also really great to be able to just try out in Figma. Again, it's a lot of different directions, but it's micro directions. It's being able to think about different typography or styles. Having those in a canvas where you can just explore that specifically is still so, so helpful, and is not something that I always want to go directly to code in.

**Lenny Rachitsky** (00:21:51):
It's interesting you still use an IDE, because in engineering, it's clearly shifting to command lines, agents, IDEs are kind of moving to not be cool anymore. And it makes a lot of sense. You just want to edit some CSS things, some color stuff. And so I could see why not just telling the agent, "Hey, just come on, change this one hex value." Just changing it is so much easier.

**Jenny Wen** (00:22:12):
Yeah. It's really annoying to be like, "Can you change this to this class?" When you can just go in and change it to a different class.

**Lenny Rachitsky** (00:22:18):
So it's interesting. I wonder if IDEs now become the useful for designers and PMs, and engineers have moved on?

**Jenny Wen** (00:22:24):
Yeah, maybe. Yeah.

**Lenny Rachitsky** (00:22:27):
Okay. So a lot of your time you spend working with engineers, giving feedback, nudging them in the right direction. There's a sense, I feel, of just your advice is let go. Don't feel like you need to be this gatekeeper, but there's this piece of, okay, help them move in a direction that is cohesive and is creating products we're proud of. A lot of designers I think are in this boat right now, just like, "Oh my God, I can't keep up with all these engineers shipping stuff all day." What's something you learned about just either how to help your engineers get better at design so that it just ends up being better, or just keeping on top of this and not going crazy?

**Jenny Wen** (00:23:04):
Whenever I do work with engineers on projects, and it's more on a consulting basis, I do just try to explain why I'm thinking a way that I'm thinking, to help them extract principles. As opposed to me just being like, "No, I don't think this should go here." It's like, "No, I think we should have a button here because not everybody realizes you can prompt this." And here's an example where it comes from research and whatnot. So I also just try to point engineers to our design system and stuff like that in code, because right now Claude is writing a lot of the code and it's not always picking up stuff in the design system and whatnot. So as much as I can equip them with stuff that they can use in the future without me, if that's helpful.

**Jenny Wen** (00:23:47):
And then on your point of trying not to go crazy, I think it's hard. I think it's really hard right now. And I see this a lot from actually both engineers and designers, where it's like now that we're sort of capable of doing so much, we want to do more. And so I think it's not just designers who are feeling like, oh yeah, we have to keep up with engineers. I think even engineers are like, "How do we keep up with ourselves right now?" So that's something I'm hearing a lot.

**Lenny Rachitsky** (00:24:13):
So true. Oh man, how to keep up with all our agents, our seven agents we're constantly running?

**Jenny Wen** (00:24:13):
Yeah.

**Lenny Rachitsky** (00:24:18):
Okay. So then as a designer where in this profession, craft and great experience and quality and trust are such a core part of the job, to help instill that in the products, because that in theory leads to really successful products in companies, how do you just think about maintaining craft quality, trust, as your products are just shipping 1,000 times a day, and you're not able to stay on top of them and there's no designer involved?

**Jenny Wen** (00:24:46):
It's not that there's no designer involved. It's more just like it's almost that there's too much for one designer to handle. But I think with this, I think about where the features or products are, where they are in the cycle of adoption versus early preview. So for example, we sometimes will launch things and we will say, "Hey, this is a research preview. It's early. It's going to have a bunch of these flaws," and we caveat that a bunch. I think Claude Cowork is actually a good example of this, where we labeled it a research preview and we put it out there knowing that, "Hey, this is similar to our models. This is the worst it's ever going to be, but we're going to put it out there because we believe, internally we've tried it a bunch, and there's something really powerful here that some people will benefit from. It might not yet be the easiest to work with. It might not be the highest quality. It might have some issues with it, but we're going to put it out there because we believe the benefits outweigh the cons."

**Jenny Wen** (00:25:49):
I think that is okay to do, especially when there is something really valuable with the product already, and it's worth putting it out there. But I think the promise you sort of have to make your users is like, "Hey, we're going to put it out there, but we're going to iterate. We're going to take your feedback and we're going to iterate and we're going to make it better." And you have to commit that. You have to show that to the world, you have to respond to people's feedback, and you have to show that you are continuously shipping and improving it. Because I think the way that you really lose trust around quality and releasing something early, is if you release it early and then nothing ever happens. That is something that degrades a brand.

**Jenny Wen** (00:26:25):
But whenever you put something out early, it's possible to do that and maintain the brand of your company. And I think that's something that we've been doing pretty well. And I think anyone who's listening can take away from it, it's like, yeah, well, we're continuing to do that. And I think that is actually really fun for me as a designer, because you put something out there and you actually learn and you get feedback about it immediately, and you know what to do next.

**Lenny Rachitsky** (00:26:55):
The way I've heard you describe this is building trust through speed.

**Jenny Wen** (00:26:58):
Yeah, for sure. Yeah, it's building trust through speed, but also just making people feel like they've been heard and that we're fixing things based on what they're trying to use it for, and their feedback is actually appreciated and used.

**Lenny Rachitsky** (00:27:11):
Yeah, it's clear when the labs launch stuff, and you all are very good at this, everyone on the team is tweeting and just responding to tweets and comments and then shipping, "Hey, we fixed this yesterday and this is happening." So there's a clear sense of, "This is just today and we know this is broke and we will fix it." And then because Claude Code can code very quickly, the fixes come very fast.

Okay. So another big question that people are asking that I ask a lot on this podcast, is around just what skills become valuable? And another way I've been thinking about it, Lex put it this way recently, is where will human brains continue to be valuable as AI gets smarter? So we've gone through this progression of tab completing [inaudible 00:27:56] segments of code, to 100% of code is written by AI now, it's crazy, to now AI is reviewing its own code.

**Lenny Rachitsky** (00:28:04):
Boris on the podcast recently was saying Claude Code is now helping them come up with ideas and decide what to build, which is like, okay, wow, look at that. Look at it go. The whole product workflows, the product development process slowly get eaten up by AI. So the question is just where will human brains still be useful, at least until we have super intelligence? Do you think AI is going to get very, very good at taste, judgment, design?

**Jenny Wen** (00:28:30):
I think it will get better at taste and judgment and design. Yeah, I think we might be holding onto that a little bit too much and saying, "Oh yeah, a designer or somebody will always know the best thing to ship or the best version of this." But I do think AI's sense of taste will get better. At the end of the day, someone has to decide what is actually going to get built and what actually matters. And when I think about people saying, "Oh, AI is just going to build this software for us," a lot of the hard parts of building software are actually not building it. If you think about the hardest times that you've had at work, Lenny, it's probably things like, oh, you and some other person disagreeing about what should go into this feature or what shouldn't go into this feature.

**Jenny Wen** (00:29:20):
And those things still feel like, yes, AI can weigh in, but it can't necessarily solve this dispute between you and somebody else. And so there is something about deciding what actually goes into the things we build, which I guess is taste in some way, but maybe not taste in the way we think about aesthetic taste or whatnot. There's some sort of, it's judgment around what to do next.

**Lenny Rachitsky** (00:29:45):
Just watching how quickly AI took over coding, which, I think a year ago, definitely two years ago, most people are like, "I don't think so. I don't think AI will get this good." And that the best engineers in the world trusted so much, they're not even looking at the code anymore. That's where we've gone. It just made me reevaluate all these assumptions I've had about, okay, AI will never be as good as really good PMs, designers, at judging what is great and deciding what to build. But I'm just starting to think, I think it will get there. Even an example you've shared, it could give these two people trying to make a decision, "Here's all of the data you need to make a decision and here's why this is the right answer and just press yes, press one, and I'll go ahead and build this."

**Lenny Rachitsky** (00:30:31):
So I think to your point, I think we undervalue just how good it'll get at this stuff. Okay. So your sense is it'll get better, but your sense is we'll still need awesome designers to be involved, us and PMs, to help make these decisions, engineers, of course?

**Jenny Wen** (00:30:46):
Yeah. Yeah. I think someone will still have to decide, oh, we want to build this kind of product. Or given what the AI is presenting us, someone still needs to be accountable for the decision. The same way that even though Claude can write all this code for you today, it is still an engineer who's accountable for, does that code actually work? Does this actually make sense in the product? So I think there's that decision making/judgment layer, which feels like maybe one day will come when we won't have to do that, but it still falls on us. Yeah.

**Lenny Rachitsky** (00:31:18):
It doesn't make sense. It makes me think about the radiology example, where there's always the sense that AI is going to take over that field of radiology and tell you what is going on. But the human is mostly useful for signing off on the decision, because someone needs to be liable if they're wrong. Which isn't the best job in the world, but that's a different game as [inaudible 00:31:38] code.

**Jenny Wen** (00:31:38):
Yeah.

**Lenny Rachitsky** (00:31:39):
Okay. Another ongoing question in AI and design is just, it feels like chatbots and terminals are just like, I don't think anyone expected this to be the lasting user interface to AI. Chatbots, okay, no, no, this is just a temporary stop along the journey, but now it's gone even further, and just terminals. Do you have thoughts on just, I don't know, do you think there will be a next step of how we interface with AI, or do you think chatbots and terminals are mostly where we end up?

**Jenny Wen** (00:32:08):
There will likely be a combination of both, both UIs and interfaces that you are interacting with, clicking with, and that feel more tactile., We are already seeing this and playing with this within Claude, the chatbot. So we recently released a bunch of these widgets that let Claude elicit and ask you questions, and also show you things like the weather and stocks and whatnot in interactive ways. And I think those have had a really good reception, because people still like to see UIs and touch them and click them, and they are much more efficient than typing something to Claude. But at the same time, when we really leaned into this chatbot paradigm, I think that just gave us this whole world of flexibility that we didn't get with these sort of baked-in UIs. So my read here is I don't think Chat is ever going away because this opened up this new way of infinite ways to work with the model, and to talk to the computer, that we just didn't have before.

**Jenny Wen** (00:33:17):
But I think that it will still be most direct for very specific things to exist in this UI. And I think that what will probably happen here, is that a lot of those UIs will be generated more and more often by the models, as opposed to something that we're hand coding each instance. But I think we're in this space where I don't think chat ... And maybe even talking to the terminal is going to go away.

**Lenny Rachitsky** (00:33:41):
It's interesting that with OpenClaw, Claude, Moltbot, all the names, one of the big innovations is another way to chat with it through WhatsApp and Telegram and SMS, just like another form of chatbot, but just like, that was a big [inaudible 00:33:54]. Oh, I could just chat with it through WhatsApp.

**Jenny Wen** (00:33:55):
Yeah. And it's like chatting and talking to someone is still ... We as humans are doing it, and it's a way for us to interact in a really rich way. And now we just have this other medium to interact with a computer, basically.

**Lenny Rachitsky** (00:34:11):
Yeah. So Kevin Wheel, who works at another AI lab I won't mention, he had this great point on the podcast that talking is such a beautiful way to handle every level of intelligence. We can talk to people that are very, very smart and not so smart and it's talking, and it scales so well across the spectrum. We can talk to people at 200 IQ, 300, like its talking still works. So that's why it's been this beautiful way to deal with the growing intelligence of models as it continues to work.

**Jenny Wen** (00:34:42):
Yeah, that totally makes sense. Yeah.

**Lenny Rachitsky** (00:34:44):
**Jenny Wen** (00:35:49):
Yeah, I have takes on this. Yeah. So this past year at Anthropic, I joined as an IC at first, and then I managed a team for a few months in an org structure that sort of needed it, and now I'm actually back to doing full-time IC work. And I joined Anthropic as an IC because I was just really excited about the kind of work that there was to be done as an IC here, but also because I was feeling like I sort of want to be closer to the work, and I think this feels like a really important time to do it, before I ascend the corporate ranks. And I was having these questions and doubts about, is middle management, is that safe in the future? Is the way that we're working actually, is this going to be a job that persists in the future? Or should I try something else and get my hands dirty kind of thing?

**Jenny Wen** (00:36:47):
And to be totally fair, I actually love both sides of the coin. I love managing people. I love setting up teams and being at that level, but I also just really love IC work. I was sort of a reluctant manager when I did it, and I was like, "Okay, I'll do it." So I love both sides of the coin pretty equally. But I think actually what being an IC across this past year has taught me, is that it actually just gave me a lot of skills that I don't think I would've gained if I was just managing throughout this year. Like I mentioned, the design process has changed so, so much in this past year, and I feel like I've just picked up so many hard skills that I wouldn't have necessarily had the time to do if I was just managing a team. So that's actually the best thing it's afforded me.

**Jenny Wen** (00:37:31):
And I think at any point if I'm managing a team again, I think it will give me the empathy and understanding of how the design process has changed. And I think that's actually a really important thing right now because the teams are working so differently. And I think it's actually pretty hard to empathize if you are not working in that way, or you're not always testing all the tools and trying stuff. But yeah, it's an interesting time to be a designer. And if I had not worked in this environment, I don't know if I would've totally understood it or knew what to do or how to guide my teams. So that's sort of what this year really gave me.

**Lenny Rachitsky** (00:38:05):
And so you were previously a director of design at Figma, right?

**Jenny Wen** (00:38:06):
Yeah.

**Lenny Rachitsky** (00:38:08):
How big was your team? How large was your org, just to give people a reference?

**Jenny Wen** (00:38:13):
At the max, I probably had, I think, 12, 15 designers or so, and I had a few managers as well.

**Lenny Rachitsky** (00:38:21):
Cool. And then you went back to an IC?

**Jenny Wen** (00:38:21):
It was like an org.

**Lenny Rachitsky** (00:38:23):
Yeah. Okay. So you had the sense that middle management might not last. What's your current feeling? Do you think design management is a thing that persists long-term, or do you think everyone turns into IC?

**Jenny Wen** (00:38:34):
I think as long as there is a team of people, it helps to have somebody who is managing a team. I think there's real value in managers. It depends what the shape of the manager is and what they actually do. But the way I think about what a helpful manager is these days, is somebody who is not just, I think pure people management like, oh, just somebody to set you up, help you in your career, have one-on-ones, make sure you're feeling good at work. I think that that is not a thing as much anymore, but I think somebody who can really function as giving the team direction, as well as doing some of the people management stuff, that tied together, I think is the future of what managing looks like, at least for now. Somebody who can really engage with the team in terms of the work and giving direction there, as well as creating the environment for them to do their best work.

**Lenny Rachitsky** (00:39:26):
And do you see yourself going back into management long-term?

**Jenny Wen** (00:39:28):
I will. I probably will. I think I really just love helping a team build the best product possible. And my motto there is, whatever it takes. If it's somebody that, if the team needs somebody to give the team direction and set up the team and whatnot, that could be me. If the team just needs somebody to execute on it, that could be me as well.

**Lenny Rachitsky** (00:39:50):
So the advice I'm hearing for people in design that are especially managers, is you almost need to move back into IC in order to truly understand what is happening and how much it's changing, so that you can be a better manager.

**Jenny Wen** (00:40:03):
I think so. And I think traditionally, at least what I've seen, a lot of the engineering disciplines, when they hire EMs or even sometimes directors there, they actually make the EMs take a rotation for a few months and pick up a few tasks, and really understand how the technology works before they become a full-time manager. And I think design probably needs to do something similar too, where I think in the past design has been much more people management oriented.

**Lenny Rachitsky** (00:40:31):
What did you find yourself most rusty in when you went back to IC designer?

**Jenny Wen** (00:40:35):
Actually doing crits, and just really-

**Lenny Rachitsky** (00:40:35):
Getting criticized.

**Jenny Wen** (00:40:40):
... Yeah, getting criticized. You're like, oh yeah, it is hard to get critical feedback and to hear it, and to hear it on such a regular basis, because that's the thing you have to do as a designer, is it's a pretty vulnerable exercise to share work and present it with your team, and then also just get a lot of critical feedback and take that all the time. Yeah.

**Lenny Rachitsky** (00:41:02):
So currently you're leading design/IC designing on cowork. Is that right?

**Jenny Wen** (00:41:07):
Yeah.

**Lenny Rachitsky** (00:41:08):
Awesome. So Boris, he was on the pod recently, talked about how there's a lot of debate about what cowork should be, and there's all these big ideas and he's like, "In the end, let's just make it like a terminal, basically, in the product, and just kind of a fancy terminal." Is there anything you could share about just the process of landing on where you landed for that experience of cowork? I have it here on my monitor, by the way, looking at it.

**Jenny Wen** (00:41:29):
With Cowork specifically, we have had a bunch of different prototypes internally of what that could look like. And it's one of those things where we tried a lot of things, and then I think we weren't really sure when it was actually going to be ready to ship. And then it was sort of everything all at once. We were like, "Okay, we're going to ship it soon."

**Lenny Rachitsky** (00:41:53):
I think it was 10 days, 10 days of building.

**Jenny Wen** (00:41:53):
Yeah, it was definitely longer than that overall. It was 10 days to get it from what we had internally to something that we were ready to ship externally. So we'd been building it for a while, but we weren't really sure about the actual form it was going to take. And so the way it got there is actually, there was a lot of different other explorations that we had internally on top of different agent harnesses and whatnot. And we just had prototyped little parts of the different interactions that ended up in cowork. So things like when Claude gives you a to do list, we tried a bunch of different form factors for that. We tried a bunch of different form factors for the way it presents you different multiple choice questions. We tried a bunch of different ways to teach people what the use cases are and whatnot.

**Jenny Wen** (00:42:38):
And I don't know if we landed on the best form factor ever, but essentially it was stuff that was already working internally that people liked, that we just thought we were going to get some more signal on by releasing it. So I think forcing ourselves to release it within that 10 days that we did, it was just sort of like, whatever we had, let's put it out there and then let's go out there and iterate from there, which is what we're doing.

**Lenny Rachitsky** (00:43:02):
And it blew up the internet when you launched it, so it worked out.

**Jenny Wen** (00:43:05):
Yeah.

**Lenny Rachitsky** (00:43:06):
Is there a feature of Cowork today that you're either most proud of, or just can't wait to fix and improve?

**Jenny Wen** (00:43:13):
Honestly, I think I'm just most proud of us actually just shipping it, to be honest, and putting it out there. And yeah, I don't know if there's one specific thing yet, because I think when you work on something and you work at so long, especially as a designer, you're like, I don't know. All I can do is see flaws in it, but I think there's a lot of stuff that I'm excited about. We have been iterating, especially on the homepage, and to make that something where it feels more like, "Hey, these are tasks you can give Claude and the tasks that Claude are working on. " And so that actually should be rolling out. It might already be rolled out by the time this comes out.

**Lenny Rachitsky** (00:43:54):
I see this little randomizer thing, where you click it and gives you all these different ideas.

**Jenny Wen** (00:43:58):
Yeah, yeah. And then so when you actually start to work with Claude on stuff, it feels more like a to do list. It feels more like these are things Claude's working on, these are things that Claude needs your attention on. And I think there's an opportunity here to make it feel much more like this shared to do list between you and Claude. So excited to iterate on that. And then I'm also excited to think more about what is the actual true form factor of this? Is it stuck in this screen always, or how does this reach out to the different surfaces that it's working with?

**Lenny Rachitsky** (00:44:31):
I love that you shared that it wasn't just 10 days to do this thing. There's these numbers that people throw out there, "We built it in 10 days." And your point is there was time spent thinking about what direction it should go, and prototyping, mocking, trying stuff. And then it's like, okay, now we know what we want it to be. Let's build it and ship it.

**Jenny Wen** (00:44:48):
Yeah. I think for some reason that became the viral thing that got taken away from all of the cowork announcements, is that it only took 10 days, but I think there have just been so many different explorations, and people that have worked on different pieces of cowork, that it was not just 10 days, and there was a lot of different people involved. It's one of those things where it's like the idea kept coming back, and it's never the right moment or there's different variations of it, and then all of a sudden it's the right moment and it feels like, oh, so obvious all along, but there was a long, long journey to get there.

**Lenny Rachitsky** (00:45:24):
And by the way, for people that don't know much about cowork, the way I think about it, it's like Claude with hands, or to do stuff on your computer. How would you describe it, just in a sentence or two?

**Jenny Wen** (00:45:34):
That's a good description. I actually haven't heard that, but I like that. I might use it more often as Claude with hands. I also think about it as it's like Claude, but Claude is really good at taking all your garbage and then turning it into something nice. I think one of my favorite, any sort of use case that I really like out of cowork, is just giving it a folder of my stuff, and it doesn't really matter what's in that folder, but I'm able to extract something good out of it.

**Lenny Rachitsky** (00:46:04):
I've done that many times. Okay. Coming back to managing and being a manager and the role of a designer, I'm going to talk about hiring for a little bit. So seeing how much is changing in the role of a designer, what do you look for that's maybe new? What do you now look for when you're hiring designers, that you think is really important for them to be successful in this new world?

**Jenny Wen** (00:46:26):
Well, I do think working specifically in the kind of environment that I do, there's just a sense of resilience and roll with it, kind of thing, that I think is really important because so much is changing around us and you have to be really willing to adapt, to try out new methods, to try out new tools and learn stuff, as opposed to just be stuck in the old processes and the old ways. But then I think about also, there's probably three archetypes of folks that are really interesting to me right now. And I think these folks were already interesting to me before, but I feel like in this era feels especially important.

**Jenny Wen** (00:47:05):
So the first one I would call is strong generalists. So not just regular generalists where they're kind of good at a lot of things, but people that are almost block shaped, in that T-shaped framework, where it's like they're really good at a few core skills, like 80th percentile good. I think this is pretty rare and hard to hire for, to be honest, but I like this because the design role we've already seen is stretching and spanning. We're all becoming more PM shape, we're all becoming engineering shaped. And so if you already have strong skills in a few different buckets, it's really easy for you to flex around and expand your role. So that's really exciting to me. It's just somebody who is really good at a bunch of things, again, a huge ask.

**Jenny Wen** (00:47:56):
And then the other person that's really exciting to me, is in that T-shaped framework, a deep specialist, someone who is T-shaped, but the tip of the T probably goes down farther than most other people. So folks that are maybe the top 10% in the industry and whatnot. Again, super hard to find. And I feel very lucky that working at some of these places, folks like these, you could sort of afford to hire them and actually bring them on board.

**Jenny Wen** (00:48:27):
And then my last one is probably the one that I think we're all overlooking, which is what I call the craft new grad. It's just somebody who's early career and feels, like, wise and experienced beyond their years, but is also just very humble and very eager to learn. And I think this person is really interesting right now because I think most companies are just hiring senior talent, folks that have done things before, are super experienced, but given how much the roles are changing and what we're expected to do is changing, I think having somebody who almost has a blank slate, and is just a really quick learner and is really eager to learn new tactics and stuff like that, and doesn't have all these baked in processes and rituals in their mind, that's super valuable. So I think those are the folks that I think a lot of us are just overlooking, but I'm really excited about.

**Lenny Rachitsky** (00:49:24):
This is awesome. On the deep T shape, what's an example of someone in design that has a ... What's a skill that they're really good at?

**Jenny Wen** (00:49:33):
Sometimes there's designers who are just really technical in a way where they could be 50% of ... they're basically a software engineer. That's really interesting, especially because right now a lot of it is, at least for us, it's like you're working directly with the model, so it helps when you have just deep engineering expertise. But another deep specialist T is just maybe they're just really good at visual design or just designing icons or something, where things like that, given that anybody can make anything, having that deep specialist slant feels like, oh yeah, they can really help differentiate the things that we're building.

**Lenny Rachitsky** (00:50:11):
Awesome. Okay. And then there's block shape. We had Mark Andrews on the podcast, we kind of called it the F shape or E shape, where there's multiple T things, sideways F, sideways E, I guess. Is that what you're describing, where there's many things you're really, really good at?

**Jenny Wen** (00:50:24):
Yeah. Yeah. And basically, I don't know, if you almost had their skillset, it would look like a block instead of a T.

**Lenny Rachitsky** (00:50:30):
Because there's so many skills that the T is spread out.

**Jenny Wen** (00:50:30):
Yeah.

**Lenny Rachitsky** (00:50:32):
Okay. And the crack new grad. So this is just someone that is eager, open-minded, gritty, very smart, I imagine is a big part of it.

**Jenny Wen** (00:50:32):
Yeah.

**Lenny Rachitsky** (00:50:43):
Awesome.

**Jenny Wen** (00:50:44):
Yeah.

**Lenny Rachitsky** (00:50:45):
If someone's a young designer trying to break in, trying to be successful, what would your advice be to them to help them have a shot at joining Anthropic, for example?

**Jenny Wen** (00:50:56):
I would just say just build a bunch of stuff, try a bunch of stuff out, build actual things. I think that can feel ... I don't really know what the state of design education or education is these days, but at least from back when I was in school, everything was very theoretical, and here, we're going to teach you some approaches and whatnot. But the best crack new grad folks I know are just people who just use the technology, build actual things, don't feel limited by how little experience they might have. I think that sometimes they're actually unburdened by that, because we have expectations of ourselves after being in the industry for so long, but they actually don't and they sort of feel like anything is possible. And so just building a bunch of stuff and sharing it with people, and finding a community of folks that also do that.

**Jenny Wen** (00:51:50):
Yeah. I think my one call it here too, is I went to a school that started something called Socratica a few years after, actually a while after I graduated. And basically their whole thing is building stuff and showcasing it almost like a science project. And I think there's just been a really cool movement there of folks who just build things and do things. For example, somebody built this Claude robot project, this was a few years ago too, where they were just assembling robots that were running on Claude, and then somebody else did something where she just wanted to put Googly eyes on a bus in Boston or something. And there's just a sense of both agency in terms of, yeah, we can just do stuff, but then also this community where people were just trying and building things and sharing things with each other. So whatever that looks like, given the school that someone's from or graduating from, doing that kind of stuff is the stuff that will make people stand out.

**Lenny Rachitsky** (00:52:51):
For current designers that are [inaudible 00:52:54] career, [inaudible 00:52:55] senior, do you think that you need to get technical and learn to code, at least build, or do you think you could be really successful and just not lean into that and get better at other stuff?

**Jenny Wen** (00:53:05):
I think it definitely helps to maybe not learn how to code so much that you're building something from scratch, but it does feel like more and more of the designer's vocabulary right now is implementing some stuff. I wonder though, as the models, both the models and the products get better, I mean, we probably will continue to move up the abstraction layers and you won't have to actually know how each single line of code will work. But I think what I would say is start to bring that into your toolkit, the coding tools, whether or not it's like you're actually becoming technical, I think any designer should just be really aware, and know how to use the tools that are at hand, as opposed to maybe learning and going off and learning React or et cetera, et cetera.

**Lenny Rachitsky** (00:53:54):
How good of a designer is Claude, would you say, or Claude Code, however you want to describe ... Would you hire Claude as a designer or is it not there yet?

**Jenny Wen** (00:54:01):
I don't think Claude is there yet. I don't think Claude is there yet in terms of a designer you would hire. I think it is not yet the strong generalist or the deep specialist or the crack new grad. I think it's pretty good at a first pass, and at presenting a bunch of different ideas to you, but nothing there quite feels like special and hireable yet.

**Lenny Rachitsky** (00:54:22):
Which is good news for designers for now. It sucks at this for now. And I'm so curious to see how good it could get at this. That's such a big open question, is can it pump out amazing novel, unique creative experiences, or it's just never going to be that good as a human designer?

**Jenny Wen** (00:54:38):
I mean, it's gotten a lot better in the last year or so, even. So yeah.

**Lenny Rachitsky** (00:54:43):
There's a couple management, I don't know, rituals or takes you have that I've heard from folks that you work with, that I want to touch on. One is that you have this hot take that low leverage time for managers is just not a thing, that there's a lot of benefit you can get out of things that people consider low leverage. Can you talk about that?

**Jenny Wen** (00:55:02):
Yeah. Yeah. I remember first becoming a manager, and I think one of the pieces of advice that I either got from a course or a book or something, is like, yeah, "Now that you're a manager, you have to really prioritize your time and categorize your work." And there was this two by two of ... I don't remember what it was in it, but you essentially say, "Oh, these are the things that only I can do. These are things anybody else can do, and everything else, it's low leverage and you shouldn't do that anymore." And a lot of the low leverage things were just things that are really nitpicky in the weeds, or just literally, yes, probably somebody else could do those tasks. But when I think about leaders and managers that I respect the most, I actually think some of their best traits is that they choose low leverage tasks that they take on themselves, and that actually ends up being actually a very high leverage thing, because it's them who's doing it.

**Jenny Wen** (00:56:00):
So one example is whenever you have senior leaders who just test the shit out of the product, and they're just so in tune with it, and they dog food it, they repro the bugs, they spend a bunch of time with engineers sharing the logs and nitpicking and stuff like that. And I think that ends up being super actually high leverage, even though it's a lot of time, of nitty-gritty time, because it creates this familiarity with the product, which I think is really good. It also creates this vibe where it's like, oh yeah, this senior leader really cares deeply and they actually know the ins and out of the product, and they're rolling up their sleeves and they're giving this feedback and working with the team on it.

**Jenny Wen** (00:56:39):
And I think similarly to what I've seen is when a senior leader is able to fix a bug now. I think I've actually seen Mike Krieger before put in PRs himself, and it's really nice because it's like, okay, cool, we're all on this team together and nothing is below this person. And I think another thing that I love that's a little bit more cultural, is when somebody goes out of the way to make somebody's anniversary card or something, and vibe code them something super nice, or make them something, a super nice card, because I think it just shows that it's like an EA or somebody can put together the card, but this leader is just somebody who cares so much about their team that they put in the effort. So that's something I try to embody, is choosing the seemingly low leverage tasks that are worth my time.

**Lenny Rachitsky** (00:57:32):
Yeah, that is so interesting. What you're saying there is, in a sense, the low leverage stuff is the stuff that often has the most impact because your reports wouldn't expect you to spend time on this thing, and the low leverage ends up being high leverage.

**Jenny Wen** (00:57:47):
Yeah. And I think it's what makes your style of leadership stand out, or feel special to a certain person.

**Lenny Rachitsky** (00:57:53):
Amazing. Another, I don't know, ritual and way of running teams that I heard about you, is you encourage team members roasting each other, which on the surface doesn't sound like a wonderful environment, but on the other hand, I hear constantly that the teams that you've built are just the happiest, the highest performing teams. Talk about, I guess, this idea of roasting and encouraging that, and just what you've learned about building awesome teams.

**Jenny Wen** (00:58:18):
Yeah, I think it's not that I'm like, "Yo, you should roast each other." I'm not forcing it in that way or anything, but when I think about the psychological safety, and teams and people that just get along with each other, when you think about your friends, you're always willing to push the boundaries a little bit and roast them. You're roasting your friends a lot, but you actually might not be roasting your coworkers a lot, because it's all just about comfort and safety. So it's not that I'm like, "Oh, I want my teams to roast each other." But I think it can be a really good sign when the people on your team feel comfortable just poking fun at each other a little bit. And I think that also can be a good sign when folks also feel the same way about you as a leader, where it's like there's just an element of they don't fear you as much, and they feel like there's a sense of safety where if they say something, they're not going to get fired.

**Jenny Wen** (00:59:14):
So an example of this is, with my last team, I feel like they would make fun of things that I would say at crits sometimes, certain phrases I would say.

**Lenny Rachitsky** (00:59:23):
What's an example of that?

**Jenny Wen** (00:59:26):
Oh, I would always be like, "Okay, what are next steps, and how do we follow up on this?" And then they'd be like, "Okay, what are next steps?" And they would sort of channel me in that way. Yeah, I just think it shows a level of like, okay, these people are not necessarily afraid of me, they know that they trust me, they can trust me, and then they sort of know enough about each other, and me personally and our personal lives, to be able to know where those boundaries are. But at the same time, I think the thing that you err into in that territory, is as a manager, are you friends with your reports? Which is I think a thing people tell you not to do. And so the way I think about balancing this out is you have to create this baseline of psychological safety, and people feel comfortable both with each other and with you, but you also have to make sure that they know that you have really high standards.

**Jenny Wen** (01:00:24):
And I think these two things can feel like they're at tension, but I think they work really well together, because it's like once you have that psychological safety, you have people trusting each other and you, applying the high standards actually I think becomes potentially easier because you can do it without fear, I think. And I sort of think about this from the approach of being a tough parent a little bit. It's like, "Oh yeah, my team, I work with them in a way where they know I'm always going to be there and I'm not just going to fire them on a whim or something. But at the same time, they also know that I want the best for them, and that I have high standards, and that I'm working with them to make the best work possible." And so yeah, that's the balance I think you just strike is like, can you create this environment of one where your team feels comfortable roasting you, but at the same time they know they have to be doing great work, and they will do great work with you.

**Lenny Rachitsky** (01:01:24):
That is awesome advice. It's interesting how often this just management style comes back, or management, good management, it comes back, reminds me of, what was it, radical candor, just this combination of caring deeply and challenging directly. And that's kind of what I hear here, is just make sure people know that you care deeply about them, but also be very direct and have high standards.

**Jenny Wen** (01:01:47):
Yeah. Yeah.

**Lenny Rachitsky** (01:01:48):
That's so interesting. Okay. Maybe a final question. I'm always looking for interesting frameworks and methods and processes that people have found useful in their work. And I hear you're a big fan of something called the legibility framework. Talk about this, talk about how you use it, why it's so valuable.

**Jenny Wen** (01:02:03):
Yeah. This framework, I think I saw it on Twitter, maybe last year or something, and it was Evan Tana, who's a partner at SPC. He's a VC. So it basically is this two by two. I don't think it got so much attention, but once I started seeing it, I actually couldn't stop thinking about it. So on the two by two, he basically has founders. Founders can be either illegible or legible, and then ideas can either be illegible or legible. And basically he was saying that, "Okay, if both the founder and idea is super legible, the idea is probably not that novel, and somebody's already like, they're already going to implement it or do it and you're actually not finding something new." But then where it gets really interesting is where the idea itself is illegible. And by illegible, he means, "Oh, it's sort of really on the frontier, people might not get it yet," or the way it's being told, it's not being told in the way that makes the most sense to people.

**Jenny Wen** (01:03:06):
And I think this is obviously a good way for a VC to operate, because you're trying to look for the opportunities that people don't see and put them out there in the world. But I also think that part of the role of the designer, at least, at least at a frontier lab at Anthropic, is spotting the ideas that are illegible, and trying to understand what's there, and how to take that and transform it, whether it's through storytelling, or whether it's through the actual UX and the form factor, and put it out there. And I think when I mentioned going through Slack and looking at all the stuff that people are making, that's kind of what I'm doing. I'm trying to see, oh yeah, what are the ideas that there's some energy there around, but might not make sense yet, that are worth me thinking about more in my work?

**Jenny Wen** (01:03:57):
There's one good example, actually, that ties to cowork where there was this internal prototype that we called Claude Studio that I think somebody built partway through last year, and it essentially was just this really kind of dense, powerful interface that was built on top of some agentic harness. It might've been Claude Code at that point in time too. And it had all these displays where it was showing you all this knowledge and all these skills and things Claude was doing, and previewing its outputs. And I think, to a designer, I looked at it and I was like, "I don't know what's going on. I don't really get it." But then I sort of saw the folks in research, the folks building it and just folks internally, there's just a lot of energy around it. And I was like, "Cool, I think there's something here, but I just don't understand it yet." And I think that really was an example of an illegible idea.

**Jenny Wen** (01:04:56):
And ultimately what came from it was the skills framework and the markdown files that instruct Claude on how to do something. So that came out of that specific prototype. That was not something I was directly involved in, and that was more of, the folks working this prototype extracted that out of it. But when it did come to work on Claude cowork and I was thinking about, oh yeah, what is the form factor for those things? Seeing that prototype and seeing the kinds of information that people found really helpful, seeing Claude's plan and to-dos, seeing Claude's context and the files that it was going through, those kinds of things are things that I ended up pulling out of that prototype into Claude cowork. So yeah, I think about how can designers almost be more like VCs in this way, internally when we're looking at prototypes.

**Lenny Rachitsky** (01:05:46):
This is super interesting. I did a research project recently with this guy Terrance Rohan, a VC, actually, we looked at what are patterns across people that have joined companies very early that ended up being massive successes, like Palantir and Stripe and Linear and Notion, all these companies. People that have joined many of these companies early, what were they looking for? And one of the factors was the idea is so crazy that everyone's laughing at this. "This is impossible. You're never going to do this. This is the craziest thing. Why would you even think ... " OpenAI actually was one of them, just some research lab doing some stuff. So it's interesting that ... And it's not like every time this will work, and it's not like every crazy idea that makes no sense will be good. But I think what you're saying is pay attention to stuff that's interesting to you and isn't totally clear. Maybe you can be the person that helps pull it together.

**Jenny Wen** (01:06:41):
Yeah. Yeah. That, but also if there is some energy around it, but I don't quite always understand what the energy is, it's to dive deeper and try to understand what that is.

**Lenny Rachitsky** (01:06:41):
Got it.

**Jenny Wen** (01:06:52):
Yeah. Because I think people who often gravitate towards these early ideas, they can't always articulate why. And it's sort of up to you to dive deeper and understand that.

**Lenny Rachitsky** (01:07:02):
So there's three patterns we found. One of the other ones was, there's just, pay attention to people getting very excited about this thing, even if you don't get it. And it sounds crazy. That's so interesting. Okay. And then what was the other one? Oh, the founders are just like top 1% was the other piece there, which everyone had Anthropic already. So you got that one. Oh man, Jenny, what a crazy time we're living through. What a world.

**Jenny Wen** (01:07:02):
What a crazy time.

**Lenny Rachitsky** (01:07:27):
Oh, so much change. Okay. Before we get to our very exciting lightning round, is there anything else that I should have asked you that you want to leave listeners with, that you want to double down on?

**Jenny Wen** (01:07:38):
I think I just want to call out the Anthropic design team and shout them out, just because it's a team of folks that are just really humble, and they're not always [inaudible 01:07:49] on social, but they're doing a lot of really great work. And especially through this time our jobs are changing so much, the team is so resilient, and they sort of span this whole spectrum of people who are really technical and prototypey, to all the way to folks that are really high craft and delivering stuff that's going out the door and is fabulous. And we are hiring throughout the year, so I just wanted to call that out. If I didn't scare you with the way that we work internally at Anthropic, if it sounds more exciting than terrifying, would love to connect.

**Lenny Rachitsky** (01:08:23):
What sounds exciting is getting access to these Slacks where all the future is being built.

**Jenny Wen** (01:08:27):
Yeah, that's the core benefit.

**Lenny Rachitsky** (01:08:28):
Let's talk to super intelligence right now and tell me where I should invest. I'm just joking. And then in terms of hiring, is there anything specific that people should think about, if they want to apply?

**Jenny Wen** (01:08:38):
Think a little bit about the archetypes that I mentioned, especially the strong generalists and the deep specialists. Those folks we're really excited about. But generally folks who are-

**Lenny Rachitsky** (01:08:39):
The block and the deep T.

**Jenny Wen** (01:08:52):
The block and the deep T. What could we be talking about? Yeah, folks who feel like those archetypes resonate with them, but also folks that are just really excited about the technology, have been building a lot, and sort of want to be on the frontier.

**Lenny Rachitsky** (01:09:10):
Amazing. Well, with that, we've reached our very exciting lightning round. I've got five questions for you. Jenny, are you ready.

**Jenny Wen** (01:09:17):
All right. I'm ready.

**Lenny Rachitsky** (01:09:18):
Here we go. What are two or three books that you find yourself recommending most to other people?

**Jenny Wen** (01:09:23):
The first one is The Power Broker by Robert Caro, which is an incredibly aggressive recommendation, given that it's like 1100 pages. But I think, in this era, when our attention spans are so short, I think this is actually worth reading end to end, because I think there are very few collections or memoirs where it spans through someone's entire life, and you sort of see how somebody changes throughout those decades. And it's also somebody who's really controversial too. And it's nice to read a really nuanced view of somebody, Robert Moses. And I think we just lose out on some of this long arc thinking, because we're thinking so much about right now. So it's just an important reminder that careers are long, and is also really good for understanding how somebody just gets things done really well. So Power Broker.

**Jenny Wen** (01:10:19):
The second one that I recommend to a lot of people is a book called Insomniac City, which is written by Bill Hayes, who was the partner of the scientist, Oliver Sacks, around the time that Oliver Sacks died. And it's just this really beautiful, ethereal memoir of Oliver Sacks' last days, and their sort of love story. I think this has very little to do with the stuff on your podcast, Lenny, but it's just a book that I really love, and just makes you think about mortality, but also love and life and stuff like that. So that's one of my favorite books.

**Lenny Rachitsky** (01:10:54):
My goal here is, we're trying to create Renaissance humans, so all of this other stuff is excellent.

**Jenny Wen** (01:11:01):
Cool.

**Lenny Rachitsky** (01:11:02):
Interestingly, I saw Julie Zhuo, famed design leader, was reading The Power Broker recently. I don't know what's going on over here. Spreading in design land. Okay. Do you have a favorite recent movie or TV show that you really enjoyed?

**Jenny Wen** (01:11:16):
I watched A Sentimental Value recently. I watched it on a plane, which is how directors want you to watch their films, but it's a Norwegian film by the same director who did The Worst Person in the World. I think just the pacing, the writing, the relationship between the characters is just really subtle and beautiful. It's basically about this family, sort of a family drama, but also about this house that they lived in their entire lives. It's beautiful because the house is sort of a character. So I don't know what else to say about that, but that was a really good movie. And then I would also recommend obviously The Pitt, season two. We're on that. I think everybody just likes to watch people who are really competent at their jobs do something. So yeah.

**Lenny Rachitsky** (01:12:00):
Imagine being an actor on that show, just like how much you have to learn and memorize all these terms.

**Jenny Wen** (01:12:05):
Yeah. Yeah. It just also seems really fast paced too. They do so much stuff in one shot, and there's just so much movement and stuff like that. It seems really, really hard to be an actor on.

**Lenny Rachitsky** (01:12:15):
And I only recently realized Noah Wiley was in ER as a younger person, and now he's the head of this. Yeah. Yeah. Oh, man. Okay. Favorite product you recently discovered that you really love, cannot be Cowork?

**Jenny Wen** (01:12:26):
Not one that I've actually discovered recently, but Retro, I've been using it for basically two years now since it came out. And I think I discovered new benefits of it recently. So for folks who don't know about Retro, it's sort of this small community photo sharing app, in which you can only share photos from each week, from a given week as opposed to all time. And it basically has none of the social media stuff. You can't really see counts, there's no ads, et cetera. But one really nice thing is now that I've been using it for two years, I can now look back at each year and see, oh yeah, this week, two years ago, I was doing this, and it's become this really special way to live through each week of my life, basically.

**Lenny Rachitsky** (01:13:14):
Wow. And it's also a beautifully crafted app if you're looking for building your own taste in design.

**Jenny Wen** (01:13:20):
Yeah. Designers love Retro.

**Lenny Rachitsky** (01:13:23):
I could see that. Okay. Do you have a favorite life motto that you often come back to in work or in life?

**Jenny Wen** (01:13:28):
Yeah. Not sure if it's my favorite life motto, but one thing I do catch myself saying a lot is just, "It is what it is."

**Lenny Rachitsky** (01:13:36):
My dad says that all the time. I love it.

**Jenny Wen** (01:13:37):
Yeah.

**Lenny Rachitsky** (01:13:37):
Yeah.

**Jenny Wen** (01:13:40):
It sounds super defeatist, but I promise it's not. I think just given how much stuff is going on in the world right now, and especially with the industry and whatnot, you can't control everything. And so sometimes it is what it is, just brings the levity you need to move forward.

**Lenny Rachitsky** (01:13:55):
I did a 10-day meditation retreat a while ago, and I came back from it and it's like, "Dad, you've been right all along. This is the answer to it all. It is what it is." Don't cling, don't try to change. Just it is what it is.

**Jenny Wen** (01:14:08):
It is what it is.

**Lenny Rachitsky** (01:14:10):
There's so much depth then. I was like, okay, smarter than I even thought. Okay. Final question. Coming back to cowork, is there, I don't know, mind-blowing use case, something just like, wow, that's so cool that cowork can do that. Either something you use it for, or you've heard somebody using it for?

**Jenny Wen** (01:14:28):
One thing I really like is just introspection. And so I have this folder, basically, of local notes that I have that I use IA writer for. And I basically just write whatever, and over the years have collected it with a bunch of different notes, and they span all different things like one-on-one notes, random thoughts, tiny memos, interview notes, et cetera. And my favorite, it's cool to me, is just using cowork to analyze that and have insights out of it, and actually create things out of it. So anytime I can learn something new about myself, I love that. But I think a very practical thing I did with it the other day was along the lines of hiring, I was like, "Oh yeah, I want to sort of articulate what it is that I look for when I look for in design craft," because I think actually a lot of people struggle to articulate that.

**Jenny Wen** (01:15:21):
And I just had it read through all of my notes, both interview notes and other things that I cared about, and memos and stuff like that I've written in the past. And then it made me this rubric for evaluating that. So that kind of introspection where it's like, oh, I wouldn't have realized even these things about myself that I'd been putting out there implicitly. That's been really cool for me.

**Lenny Rachitsky** (01:15:43):
That is so cool. So just to make this very clear for people, you have a folder with all these things you've written, one-on-ones, meetings, like you could do, I don't know if y'all are allowed to use Granola or something like that, meeting transcripts, and ask it. And I was going to ask what prompt you used, but it's just like, "Read all these things I've written, and help me probably just understand how I feel about what is DesignCraft."

**Jenny Wen** (01:16:06):
Yeah, basically I think it was like, "Hey, I have a bunch of interview notes and a bunch of notes related to DesignCraft in this folder. Read it and then help me craft a memo/rubric for how I assess craft in interviews."

**Lenny Rachitsky** (01:16:20):
So cool.

**Jenny Wen** (01:16:21):
Yeah.

**Lenny Rachitsky** (01:16:22):
Jenny, this was awesome. What a time to be alive.

**Jenny Wen** (01:16:25):
What a time.

**Lenny Rachitsky** (01:16:27):
Two final questions. Where can folks find you online if they want to reach out and how can listeners be useful to you?

**Jenny Wen** (01:16:31):
Yeah, I'm on Twitter/X is what we're calling it these days. It's @jenny_1. That's probably the best place, not really on LinkedIn as much, so that's the best place. And how can folks be helpful to me? Send us your product feedback. We're working on Cowork, or anything Claude related really, just send us your feedback, we'd love to make it better for you.

**Lenny Rachitsky** (01:16:53):
Jenny, thank you so much for being here.

**Jenny Wen** (01:16:55):
Yeah, of course. It was great chatting Lenny.

**Lenny Rachitsky** (01:16:58):
It was wonderful. Jenny, thank you. Bye everyone.

**Lenny Rachitsky** (01:17:02):
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

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