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David SenraDavid Senra

Building The World's First AI Software Engineer | Cognition’s Scott Wu

Scott Wu is the co-founder and CEO of Cognition, the company behind Devin, the world's first AI software engineer. Wu describes himself as "salty," a word he traces to second grade, when he competed in a seventh-grade math competition, lost, and never forgot it. Born in 1997 in Louisiana to a Chinese immigrant family, he grew up the little brother who hated losing at video games and turned that into a career. At the International Olympiad in Informatics he won three gold medals and placed first overall in 2014; he was the 2011 MathCounts national champion. He approaches building a company the way he approaches a strategy game: a tree search, calculating moves, working the decision tree toward victory. By his own account, competition is all he does. He dropped out of Harvard after two years, worked as a founding engineer at Scale AI, and co-founded Lunchclub before starting Cognition in August 2023 with fellow IOI gold medalists Steven Hao and Walden Yan. They built it in a New York apartment. Devin's annualized revenue then climbed from $1 million in September 2024 to $73 million by June 2025. In May 2026, Cognition raised at a $26 billion valuation. Show notes: https://www.davidsenra.com/episode/scott-wu David Senra X: https://x.com/davidsenra Instagram: https://www.instagram.com/davidsenra LinkedIn: https://www.linkedin.com/in/davidsenra Facebook: https://www.linkedin.com/company/senrashow Threads: https://www.threads.com/@davidsenra Spotify: https://spti.fi/TVrr557 Apple Podcasts: https://apple.co/4msoZtb Website: https://www.davidsenra.com Scott Wu X: https://x.com/ScottWu46 LinkedIn: https://www.linkedin.com/in/scott-wu-8b94ab96 Chapters 00:00:00 Scott Wu’s Obsession With Winning 00:02:06 Competitive Programming, Games And Finding His People 00:04:24 Family, Go, And The Roots Of Scott’s Competitiveness 00:08:35 Why Losing Hurts More Than Winning Feels Good 00:09:38 What Winning With Devin Looks Like 00:12:55 Devin Today: The AI Software Engineer 00:13:52 Software As The Human-Computer Interface 00:18:45 Why AI Progress Is Hard To Intuit 00:20:39 Thinking About AI From First Principles 00:22:57 What Happens When Agents Can Work For Months 00:30:18 The Original Thesis Behind Cognition 00:31:12 Launching Devin And Handling Criticism 00:37:17 Finding Product-Market Fit In The Enterprise 00:42:41 How Cognition Deploys Devin Inside Large Companies 00:48:34 Measuring ROI Instead Of Token Spend 00:50:01 Why Cognition Wants To Be Model-Neutral 00:52:18 Why Focus Lets Startups Beat Giants 00:57:14 Independence, Acquisitions, And Building A Generational Company 01:00:27 Why Money Is Not The Goal 01:03:42 One Life: Going For It All

David SenrahostScott Wuguest
Jun 28, 20261h 5mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:002:06

    Scott Wu’s Obsession With Winning

    1. DS

      [pen scratching] I wanna know why you describe yourself as salty. What does that mean?

    2. SW

      I've just always been this way. As a kid, I, I just hated losing. Like, my first competitive memory ever is, like, when I was in second grade, I went to this seventh-grade math competition. It was like a middle school competition that was held at the, like, local university or whatever for middle schoolers.

    3. DS

      But you were seven.

    4. SW

      Yeah, yeah. I was like seven or eight years old. I was competing in s- like, middle school math, and, like, I did the, like, math test and whatever, and then they were calling out the names of the, like, "Here's who got third place. Here's who got second." And I was kind of, like, waiting for my name to get called, and then I was none of them, and I just remember being so pissed about that. Yeah. I can't really give you a rational explanation for, for why it is. I think I just-

    5. DS

      It doesn't have, it doesn't have to be rational.

    6. SW

      Yeah.

    7. DS

      But, like, how much of your brain is dedicated to competition?

    8. SW

      I mean, it's all I do, honestly. I don't know. I think the, like... I, I think-

    9. DS

      Whoa, what do you mean it's all you do?

    10. SW

      Well, I think strategy game... I, I don't know. It's, uh, the, the way-- Even building a company, it feels the same. It's just like you're calculating the moves. You're thinking about, okay, if you do this, and then this happens, and then you do that, and here are the different moves, and you're, like, calculating out what comes out to success, you know? It's like a, it's like a tree search, you know, where you're exploring the different options in the decision tree, and you're trying to figure out how to lead to victory. Like, that's, like, the only thing I do in my life.

    11. DS

      So this is basically just you don't have memories when you weren't like this, basically.

    12. SW

      I think that's right. Yeah. Yeah. I was, uh, I was a little brother growing up, and so my older brother was four or five years older than me, and naturally, we'd play video games, and similarly, I would just always be super salty there as well. I don't know. It's just like... Yeah, it's just always like that.

    13. DS

      So, like, I spent some time with Demis from DeepMind.

    14. SW

      Yeah.

    15. DS

      And what was interesting is I, I, I draw a lot of, um, like, similarities between you two 'cause I also spent some time with you. And I was like, well, they're both really smart. They're both articulate. They have, like, a friendly UI. [laughs]

    16. SW

      [laughs]

    17. DS

      Right? But then underneath that is, like, this, like, ruthlessly competitive drive.

    18. SW

      Yeah.

    19. DS

      And Demis, I think he said this publicly, but I think he said, like, half his brain is dedicated to competition.

    20. SW

      Yeah.

    21. DS

      And a lot of that comes from his early days in chess.

    22. SW

      Yeah.

    23. DS

      What were you competing in when you were younger, besides math

  2. 2:064:24

    Competitive Programming, Games And Finding His People

    1. DS

      competitions?

    2. SW

      Yeah. Well, basically everything. So obviously, the main thing was math and programming competitions. And so ever since I was really young, that was, like, that was, like, my, like, life, you know? My whole goal was to become, like, world champion of competitive programming. I would do that all the time as a kid. I would do... You know, the, the, the really great thing about these competitions too is, is, you know, you, you compete in your school competition, and if you do well enough in that, then you make it qualify for the, the local or, like, the city, um, competition. And then if you do well in that, then you get to, like, the regional competition and state competition, and then you get to go to the national thing, the international thing, right? And so it's, it's, like, a very nice setup where sooner or later you get to kinda meet people who are like you, basically. When I was a kid, doing these competitions, going for that, it was, like, all I really cared about. Those people that I met through these national, international competitions were honestly more, like, they were my childhood friends more than, like, the people around me in Baton Rouge, Louisiana, were. And it was like we would hang out online, we would talk about math, we'd talk about problems. But all the other things too. I mean, I played basically all the different competitive games. So, like, I, I played a lot of Super Smash Brothers. I used to go to tournaments for Super Smash Brothers. It was a lot of fun. Played Melee. And then I played, like, Tetris. I played a lot of poker. I played some chess. I was okay at chess. I was not good. I played some Go. My dad was a competitive Go player. My parents came to the US in some sense because of Go, which was kind of a funny coincidence because my dad, um, was in grad school in China, and he had a professor who, like, really liked him. The reason he liked him was 'cause my dad was, like, a really good Go player. Like, he was, like, a 7 dan at Go, which if you were to call it in chess would be, like, I don't know, 2,300 or 2,400 rating equivalent or something like that. He would play with this professor, you know, on the weekends and stuff, and they were like... You know, my dad would generally win, and they would, like, talk about the games and stuff. And then that professor ended up moving to the US to come and teach. Um, and at the time, uh, you know, this was super early on in, you know, immigration from China to the US, and so it was not a very, like... It wasn't really a path that people knew that you could take. The professor wrote my dad and said, "Hey, like, I came. It's great. Like, there's so much more opportunity. It's so much better. Like, you should obviously come as well. Like, I'll help you with your, like, visa application. I'll help you, like, apply to colleges here and everything." And so my dad applied to grad school, uh, in the US, and that's kinda how we ended up here in the first place. Uh, I was, I was born after we moved to the US obviously.

  3. 4:248:35

    Family, Go, And The Roots Of Scott’s Competitiveness

    1. DS

      So your dad was competitive in Go. What was your mom competitive in, though? 'Cause I think I read that you said that she might have been the most competitive person in your family.

    2. SW

      Yeah. No, she was always... Uh, she, she was definitely the most salty, I would say, for sure. I mean, she would, um-

    3. DS

      What does salty mean?

    4. SW

      [laughs] Salty just means that you take offense to the idea of losing. [laughs]

    5. DS

      Okay, I love that.

    6. SW

      Yeah. She would always be, "Oh, no, no, I'm better at this," or, "I'm very... You know, I, I could beat you at this," you know? And, and I don't think she... I mean, she played ping pong a bunch growing up. Actually, she played on her, like, school ping pong team. Obviously, she studied some amount of math and so on. Um, but, but it was just, it was more her personality than, than any one thing that she really put all of her competitive energy into.

    7. DS

      I spent a lot of time, obviously, reading the biographies of history's greatest entrepreneurs.

    8. SW

      Yeah.

    9. DS

      I'm always fascinated by, like, there's u- usually two different kind of archetypes for the parents. One, you have, like, the Larry Ellison and Elon Musk. Uh, their dads would literally tell them, "You know, you're worthless." There's stories in Elon's biographies where his dad just gets in his fucking face and yells at him for hours.

    10. SW

      Yeah.

    11. DS

      Larry's adopted, uh, father would just tell him, "You're never gonna amount to anything." And so that they had this, like, inner fire to, to pr- to disprove, uh, you know, saying that basically, "No, fuck you, Dad. You're wrong about this," right?

    12. SW

      [laughs]

    13. DS

      And then you have, like, the Estée Lauders who, you know, their uncle or even their father is just like, "You're really special. You have a lot of talents. If you put a lot of effort into this, you can do whatever you want." Your mom falls at more into, like, the Estée Lauder ca- like, category, where she would tell you that, like, "Hey, these people are doing amazing things. You could do even better than them," correct?

    14. SW

      I think they would've been happy enough if I just got, like, a more traditional cushy job and did all of that. Like, I don't know that they specifically steered me towards entrepreneurship and being a founder, but, um, but no, they were always very supportive-

    15. DS

      But your mom, but your mom gave you self-confidence.

    16. SW

      She-- Yeah, I think she, she always told me That I was the best. [laughs] And she was always extremely proud of the, you know, it's, it's the, I had these, like, when we would go to these math competitions-

    17. DS

      Hold up. She told, she, you, so you said she always told you that you're the best. Did she say that before there was evidence?

    18. SW

      Yeah, I think so. I think that's right. I, I think even when I was, like, tiny she would tell me that I was extremely talented, extremely... You know, she was always a, a huge source of support me, and she always obviously believed in whatever I wanted to do. You know, 'cause you would compete in math competitions, you would get these trophies and stuff. And, like, we didn't have, like, you know, growing up, like, we didn't have, like, pictures of our parents, like, on the walls. You know, we just had all the math competition trophies. It was like-

    19. DS

      [laughs]

    20. SW

      Like, my mom was very intentional about like, "No, no, the thing that we value in this household is-

    21. DS

      Other people's [laughs] trophies or the ones you won?

    22. SW

      Oh, ours. Ours.

    23. DS

      Okay.

    24. SW

      The ones that me and my brother won, of course. Of course.

    25. DS

      We're gonna put up pictures of other people's trophies, and you'd better damn sure replace them with your own. [laughs]

    26. SW

      [laughs] Yeah. And so, so, like, you know, when, when, when I was pretty young, my brother as well, you know, we both really liked these competitions, so we like, you know, we're like, we would, like, accumulate these trophies, and, and she would always, like, every time we got one, she would hang it up and really, like, put it up on the, on the, on, like, the mantelpiece and everything. And, and no, it's like it was always, um, I think what she really valued. I think, I think education was really important to her, and I think being the best was, was very important to her as well.

    27. DS

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  4. 8:359:38

    Why Losing Hurts More Than Winning Feels Good

    1. DS

      What matters to you more? Like, uh, what, what is, uh, is it the, the pain of losing? Is, like-

    2. SW

      Yeah

    3. DS

      ... losing is worse than the lo- like, the thrill of winning?

    4. SW

      Yeah.

    5. DS

      'Cause that's how you started it. You're just like-

    6. SW

      [laughs]

    7. DS

      ... I could not stand losing. Uh, let me back up. I was reading another Larry Ellison biography.

    8. SW

      Yeah.

    9. DS

      He says this. He's just like, "Listen, I'm addicted to winning, but I fear losing more than I love winning." And I actually talked to Michael Dell about it, and he's like, "Yeah, it's, it's just the fear of losing is w- the, the pain of losing is way worse than the, the, the f- good feeling of winning."

    10. SW

      So I think that's definitely true in terms of how it feels. In order to get anywhere, you gotta lose a lot. If anything, almost, like, a lot of the, you know, I mean, a lot of the guys we were talking about, if anything, like, the way they got to where they are is by, by losing a lot and having their share of wins along with that. But, like, you just have to put yourself out there and do a lot. So it's kind of an interesting thing to your point of, like, I definitely feel the same way that, yeah, like, losing feels way worse than winning feels good, but not by enough that it makes me wanna stop trying, if that makes sense.

    11. DS

      Yeah, no.

    12. SW

      Yeah.

    13. DS

      I, I think it's impossible.

    14. SW

      So that's the, the-

    15. DS

      I, I've, I know your personality type. Like-

    16. SW

      [laughs]

    17. DS

      ... it's just impossible for you not to

  5. 9:3812:55

    What Winning With Devin Looks Like

    1. DS

      do this.

    2. SW

      Yeah.

    3. DS

      What is... Like, let's get into Devin.

    4. SW

      Yeah.

    5. DS

      What is, like, winning with Devin look like to you?

    6. SW

      So again, you know, we're hypercompetitive, but also, you know, the other thing about us is, like, we all had kind of started our own companies before this. So our founding team is, it was a pretty big founding team. It was nine people, and most of us had already founded our own companies before. We had done different things. It was true for a lot of the early team. And we've always thought about this as, like, this is the big one. And so, like, we wanna go for it all. You know, we wanna be a, a, a generational business. Like, we wanna build a hyperscaler, and we wanna go and do that. And, like, maybe we'll succeed, maybe we won't, I don't know. But, but, like, that's what we're going after. And, and I think to me what that means in our field, um, in building software is, like, you know, people sometimes use the term, like, coding agents or, like, you know, uh, uh, like, AI programming or something. I, I always, like, you know, I, I always hear that and I think a little bit about, like, well, we're not always... I mean, we're not gonna be interacting with code for that much longer, you know? Or programs might not be the right, like, level of abstraction, but I think what is always true probably is that it will be the human-computer interface. And so what I mean by that is, like, like, the way that we think about Devin, if we're successful, is Devin is the way that humans can tell their computers what to do. 'Cause that was the whole point of software engineering anyway, right? Is just to be able to work with your compu- computer and tell it what you want it to do. I mean, I think doing that for the world is, like, a massive opportunity and, and, and, and that's what we're really excited to go after.

    7. DS

      Why is that interesting to you? You just said, "We all did different things."

    8. SW

      Yeah.

    9. DS

      "We came together and we're like, 'No, this is the big one.'"

    10. SW

      Yeah. I mean, uh, the simple answer is, you know, we're a bunch of nerds who, uh, we're all programmers and built software and so on before, and now this is the idea of teaching AI how to go do that.

    11. DS

      Wait, say, say more about that, what you just said. We're all nerds. This is the big one-

    12. SW

      Oh, I'm just saying, like-

    13. DS

      ... 'cause we can actually teach AI how to program

    14. SW

      ... we're all nerds who've spent the last, you know, I don't know, 20 years of our lives just coding and making little things for the world, and the idea that you could teach AI how to go make things, uh, and, and have everybody have an AI that can help them make things and feel the wonder of that, I mean, it's pretty sick.

    15. DS

      Is that the idea, the single idea in the world that animates you the most, that gets you the most excited?

    16. SW

      I think it is incredible, yeah. I mean, it, the, the... Well, I, I would go further too and just say, like, at a zoomed out level, it's, I honestly think that... 'Cause you know, everyone talks about AGI, everyone talks about, well, what is the future gonna be like? What, what are human lives going to be, you know, once AI can do everything? And I mean, to some extent, I think the thing that is most human, obviously, is self-expression, creativity. Like, having things that you want to make happen in the world and being able to go do those things, right? And so, I mean, a lot of how I think about this is basically- We want to build the tool that gives everybody the power to go and make things that they wanna make in the world. My co-founder has this line which I've always loved, uh, which is, you know, we've been spending all this time living in survival mode as a species, you know, and now we're gonna be living in creative mode. And I think that's right. Like I, I think, um, you know, Minecraft survival mode is, is where you're like, um, you know, you're, you're growing food, you're like making sure you're safe from the monsters at night and whatever. Um, and, and like creative mode is just like everything's up to you. You know, you have all the resources at your disposal. If you want something to happen, it'll happen. And the only question for you is like what you wanna make happen. I think it's gonna be amazing. And I mean, I think that is like the, the world that we're going towards and, and like we wanna be the ones building

  6. 12:5513:52

    Devin Today: The AI Software Engineer

    1. SW

      that.

    2. DS

      Okay. So talk about what Devin does today.

    3. SW

      Yeah.

    4. DS

      And then I, I want you to flesh out that idea of like where you see it in the future. It's just the, the interface-

    5. SW

      Yeah

    6. DS

      ... between humans and computers.

    7. SW

      Sure. Yeah. So Devin is, uh, today, you know, what, what, what, what folks know as, as an AI software engineer. And basically what that means is that Devin is a tool that, that anyone can use, uh, that will work with them end to end on building out software. Um, and so we work with a lot of the big- biggest companies across the world. You know, we work with Goldman Sachs, we work with Mercedes, we work with, you know, a, a lot of areas of the US government at this point and so on. And we work with their software teams to just help them build more and do much more. And you know, the thing about it is, in the last twenty, thirty years, obviously, I mean, software is eating the world is the, is the famous line. I think it's very much true. It's still like it's still got a couple order of magnitudes to go. And in practice what it looks like is that teams use Devin to ship ten times faster and to do ten times more.

    8. DS

      Okay, so that's where it's at today.

  7. 13:5218:45

    Software As The Human-Computer Interface

    1. SW

      Yeah.

    2. DS

      How do you get to where you're saying you might-- there is no-

    3. SW

      Yeah

    4. DS

      ... like you're just the interface between humans and computers.

    5. SW

      So now we're gonna get into a philosophical discussion of what it means to be a programmer or a software engineer, right? And I, I think like, you know, if you go all the way back, it's, you know, there, there was a time where programming was like using the vacuum tubes, like plugging all those in and having it do the, the machine do the arithmetic, right? Like the ENIAC was, um, you know, when the first, in some sense, the, the first computer out there, although it was obviously very different. Or it would've been like, you know, filling out the punch cards and like setting up the, or, or writing, you know, the, like putting down assembly, you know, or writing in Basic or something, right? So, so we've gone through a lot of generations already is my point. And what does it look like, you know, going forward? When you talk about programming, all it really comes down to is like, how do you tell your computer what to do? And like every single piece of software that you use, if you're using, you know, Instagram or TikTok or YouTube or whatever, like that's a piece of software that somebody or like some, you know, in these cases, some pretty big teams of engineers came together and thought through all these details of, okay, here's what I want it to do. Here's how I want it to look. Here's what I want this button to do. Here's how I wanna architect it. Every single little decision obviously was made by somebody, but the, the computer itself is then executing it accordingly to, to what the wishes of its creators was, right? And I think what we'll start to see is that abstraction will continue to climb, right? And like, you know, we kinda see this already, like at this point, you don't need to know a programming language. It's like you don't need, you don't need to know Python or Java or something like that in order to build your own software, right? And you can just say, "Hey, here's what I want. I wanna make a cool website that does this, this, and that." Or for example, you know, in my existing product, you know, here's, here's what we have today and I want to change this thing or add this new plan or add this new feature, and just have the agent go and do that for you, right? I think we're gonna continue to go further down that axis. One important kind of like distinction I'd make, which is, you know, I think what we'll see a lot more of in the near future is software today, the, the math only really works out to create software if it's gonna be used at least like a million times or something. You know, I'm giving a, maybe it's ten thousand times or whatever. And my point is like, if you wanna go build a product today, you need a whole team of engineers. Engineers are expensive. You gotta pay salaries, you gotta go build all this out, and you gotta go and do that, right? You need that software to be used enough times or to be cr-- to, to create enough value for that to be worth it, right? And you know, something like YouTube passes that test because obviously so many, so many hours have been spent on building YouTube, but way more hours have been spent on using YouTube. And, and that's, that's what's made that, that work out and made it feasible, right? But there are so many things out there which only, you know, very specific things that, that only need to be used a few times or even like only need to be used once, right? And so like all the white-collar work that we talk about today even is very, um, all right, you know, wake up in the morning. All right, I'm gonna go look through these like fifteen LinkedIn profiles. I'm gonna look for this and that or whatever. Or I'm gonna go fill out these forms, or I'm gonna do this data analysis and put this Excel sheet together with this research that I found, right? All of these things are things that could be done with software. It's just, it obviously doesn't make sense to, to hire a whole team of people to go make you that piece of software, which you're gonna use one time and never again, versus just having the human go and do that themselves, right? I think what we're gonna get to is we're gonna get to a point where you are just giving your instructions to that agent and the agent on the back end, you know, you don't even have to look at this, but, but on the back end, the agent is gonna figure out, okay, here's... I'm gonna write this code that's gonna go do this. I'm gonna put a, a, a script that automates this part. I'm gonna do this, I'm gonna do this. Um, and that's what's gonna allow it to actually go and do all these things. But, but, but what you start to get to, you know, a-as we're kind of saying, it's like this is really just how you control your computer and how, how you do what you wanna go do, right? And you wake up in the morning and it's like, here, here's what I wanna go do. You talk to your agent about it, you figure out the task together. Once it has it, it can do the part of, of, of the literal like, all right, put the, put the pen to the paper on writing code. But that task is like, you know, you're, you're the one that's deciding what to do.

    6. DS

      So this ideal future that's in your mind, right?

    7. SW

      Yeah.

    8. DS

      How far away do you think we are to that?

    9. SW

      Yeah. Um, I mean, we've made a lot of steps toward it. I would say we still got a ways to go. You can use Devin today, you can use all the different kind of coding tools today, um, and you can do a lot more than you could have done, you know, ten years ago, um, but certainly, um, or even one year ago or six months ago. Um, but, but, but certainly, you know, it's, it's, it's not at the level that we're talking about of you are neural linked into the AI, and you can tell it exactly what you want it to do and what you wanna see in the world and just have the AI go and do that. When do we get there? Um, it's, it's hard to say, but I honestly, I mean, I think we'll have solved most of that over the next five years or so. And in AI terms, five years is, is like a century, you know, in, in, in the rest of the world terms. Obviously, it's like, it's kind of crazy to imagine that things can change that much in five years, but, but I really think it will.

  8. 18:4520:39

    Why AI Progress Is Hard To Intuit

    1. DS

      So this is kinda related to something I heard you say, where you're saying that humans just have a really hard time understanding exponential curves.

    2. SW

      It's so true. I mean, you see this in, in, in the progress itself. You see this in the scaling laws with the data. You see this in the revenue curves of the companies that are building an AI.

    3. DS

      Including your own.

    4. SW

      [laughs] And, um, a-a-and it's, it's a very, you know, it's like humans aren't really wired for this, right? Like all of our like in-in-inherent like fight or flight response are, are kind of like our ability to kind of like measure things. To vastly oversimplify it, if you, if you're just, you know, fighting out there, you know, foraging for food or whatever it is, like, you know, a good hunt will bring you, you know, a couple days worth of food or something. But, but, but, but obviously, with the kind of exponential curves that we deal with, you know, the equivalent of a good hunt here could be a thousand years' worth of food and, and we don't have that intuitive signal in our brains to really understand that, right? At, at, at a really deep, like native level. People often underestimate, I think, how fast things can change and how fast the world can change. I mean, my parents even like drilled this into me because they grew up in, you know, in communist China, um, and they came to the US and, you know, even that-- Like we're talking about what are, what were ultimately even like much slower scales of progress in some sense relative to, I think, what we're seeing today. But even that was like, as you can imagine, it was incredibly jarring to them. You know, the idea that, uh, like you come to the US and everybody has a car and all of these different... You know, it's like all, every, everyone has all these household appliances. It has all, you know... It, it was a very different life when they grew up, um, in, in like, you know, the sixties in China when they were much, much poorer and it was very, very different, right? And, and it's like, I mean, funnily enough, people got used to all these things pretty quickly and now we can't live without them. But, but I think we'll kind of undergo the same period with AI where five years from now it's gonna be insane to think about all these things that we're gonna have. Ten years from now we're gonna have forgotten that we ever lived without them, honestly.

  9. 20:3922:57

    Thinking About AI From First Principles

    1. DS

      What do you think you understand about AI that other people don't?

    2. SW

      Um-

    3. DS

      The reason I ask the question is 'cause we have some mutual friends.

    4. SW

      Yeah.

    5. DS

      I would describe the-- our mutual friends as some of the most AGI-pilled people that I know.

    6. SW

      Yeah.

    7. DS

      And I feel every time I have a conversation with them, I'm like, oh, even though I pay attention to this stuff, I feel like a toddler compared to-

    8. SW

      [laughs]

    9. DS

      ... like somebody like you.

    10. SW

      [laughs]

    11. DS

      And so I'm very curious, like what do you understand about AI that, that most people don't?

    12. SW

      Yeah.

    13. DS

      And even people within the tech industry.

    14. SW

      No, I, you're way too generous. I don't know that there's... I don't know that I have that, anything that interesting or that deep of an insight. I mean, I think it's, it's-

    15. DS

      You, you say that 'cause you're used to it.

    16. SW

      Here's what I'd say, is the way that folks typically kind of predict the future or think about what happens next is they pattern match based on what they've seen historically, and they say, "Okay, well, for a hundred years it's always been like this, and it's probably safe to assume that it will be." And ninety-nine percent of the time that works great, right? Um, and in these particular periods where things that actually move and, and there are real things that are different, those are the, the one percent of times where it truly is different. Now you just kind of, you know, rather than any kind of pattern matching, like what, what really matters is just thinking about things from first principles, like AI. You know, there's, there's the famous like METR report, which was saying, you know, a couple years ago, AI would, would, would do about ten to twenty seconds worth of human work without interruption and then you'd have to, you know, guide it or direct it or it would make a mistake or something like that, right? Ten, ten seconds, twenty seconds, and that's just doubled every, you know, every couple of months basically. And now we're talking about like hours of work. So, so basically an AI can just take a task that would've taken humans hours of work to go do. If you go and describe that task well enough to the AI, it will just go and do the whole thing and come back to you with the result, and then you give it the next thing and the next thing, right? And if you just ask from a first principles question, well, why can't that be days or why can't that be weeks or months of work? And then what does the world look like if everybody has an agent that can just do months of work for them at a time? Then you get to a pretty different conclusion from, from what we've all seen and what we've all lived for the last several years. And I think that that kind of first principles thinking is as different as it sounds and as crazy as it sounds, you know, this is one of those times where it's actually more correct than the, the, the, the simple pattern match, if

  10. 22:5730:18

    What Happens When Agents Can Work For Months

    1. SW

      that makes sense.

    2. DS

      So you-- I think you've even taken this further where you're like, "Well, what happens when they can work for a year unassisted?"

    3. SW

      Yeah. Yeah. Yeah. Um, yeah, I think, I think that's true, and I think we will get there.

    4. DS

      If Devin could work for a year without any human assistance-

    5. SW

      Yeah

    6. DS

      ... what would you have it do right now?

    7. SW

      [laughs]

    8. DS

      Destroy your competitor? [laughs]

    9. SW

      [laughs] All, all sorts of things. I mean, no, I mean, I, I, I still wake up and think about this in every different like, you know, e-every little thing that I run. You know, dumb example. Yesterday I was sending out like a bulk email and I was like trying to get the like email formatter to work and it's like kinda painful. You know, some of these things are still like kinda hard to use or it doesn't support a certain like styling of the email that you want it to go do. I think the thing I like had pasted something with indents and then it was like just couldn't like un-indent them, [laughs] um, because the editor like... I don't know, there were some weird things where the editor like would not allow you to un-indent one part but not the other or whatever. And I was just thinking like it's really crazy that like, that I'm- Still doing this basically, right? Like, in as much simplicity or honestly more as it would take you to explain this to another person. Like, "Hey, here's what I wanna do. I just like, I'm just trying to make it look like this, and then this, and then that." Like, the rest of that execution should just be done for you, you know? [laughs] And then you get to the point where you actually really just get to spend all your time thinking about, well, what do you wanna do? You know? Well, what do you wanna build? What do you wanna create? Like, what are the things that you wanna see in the world that aren't there already?

    10. DS

      But I think what makes it interesting about what you were saying earlier is like y- the, the more you increase the time-

    11. SW

      Yeah

    12. DS

      ... the more interesting it gets to me.

    13. SW

      Yeah, yeah.

    14. DS

      So like, there's this guy named, uh, Edwin Land, who I won't shut up about.

    15. SW

      Yeah.

    16. DS

      And he was the founder of Polaroid.

    17. SW

      Yeah, yeah.

    18. DS

      Steve Jobs' hero. A lot of Ste- what we think of as Steve Jobs' ideas literally just came from Edwin Land, down to, like, the chairs and the table he would use for his presentations. It's like the same thing-

    19. SW

      Yeah

    20. DS

      ... that Edwin Land used in, like, the '70s at Polaroid. And he thought of himself as a scientist, not as an entrepreneur.

    21. SW

      Sure.

    22. DS

      Edwin Land, he died with, I think, the third-most patents. He's, he's like Thomas Edison, some other dude, and then Edwin Land.

    23. SW

      Yeah.

    24. DS

      And what he did is he couldn't figure out, he invented the, the, the industry of instant photography-

    25. SW

      Yeah

    26. DS

      ... uh, before you took a picture, and you're like, "Hey, how's it look?" They're like, "We'll find out two weeks from now-"

    27. SW

      [laughs]

    28. DS

      "... when we get it back from Kodak." Like, I have no idea. And he's, now we take a, he took a picture of a Polaroid, he's like, "We'll find out in 60 seconds when it dries." But that was black and white forever. When I read that part where you're like, "Well, we're gonna have agents that can work on a system for a year-"

    29. SW

      Yeah

    30. DS

      ... I didn't think of what I would do, which is the question I just asked.

  11. 30:1831:12

    The Original Thesis Behind Cognition

    1. DS

      When you started Cognition, did you know that you were gonna try to make an automated software engineer? Was that your first idea?

    2. SW

      I'll say it was, it was always two things. One, it was always related to code and software, which again, probably has something to do with us all being programming nerds. Um, and then two is it was always around the idea that these would be like real multi-step iterative processes, um, which was, uh, I would say was, was a real hot take at the time. Like this was twenty twenty-three, end of twenty twenty-three.

    3. DS

      Okay. So this is when I met you guys.

    4. SW

      Yeah, yeah.

    5. DS

      And, uh, I was on a walk with a mutual friend of ours, and I remember first hear-- this is-- you guys scaled from what, like a million in revenue to like five hundred million or something like that in what, like twenty months or something?

    6. SW

      Yeah. Yeah, a lot more than eighteen months.

    7. DS

      I remember before you had no revenue, and I heard the idea, and it was pitched as like essentially like an automated software engineer.

    8. SW

      Yeah, yeah.

    9. DS

      And I was like, "Holy shit."

    10. SW

      Yeah.

    11. DS

      This is like a huge-- You're going after labor. This is a huge market. And then you released.

  12. 31:1237:17

    Launching Devin And Handling Criticism

    1. SW

      Yeah.

    2. DS

      I remember the demo video.

    3. SW

      Yeah.

    4. DS

      And then you got a lot of shit.

    5. SW

      Yeah, yeah, yeah.

    6. DS

      Is that, is that a good description?

    7. SW

      Well, it's a mix of-- I think it was the, the full polar ends of the spectrum, right? So there were some people who were like, "This is the coolest thing ever," and like, you know, all this. And then there were some people who were like, you know, "This is like the worst," you know, "thing that you could pos-" But, but so similarly, like in terms of the capabilities, there were some people who were like, "Oh my God, everybody's gonna lose all their jobs tomorrow," which is not what we've ever really believed, uh, [chuckles] or thought of this as. Um, to also like, "Dude, there's no way this is ever gonna work, and like this is totally a, a scam," you know? Um.

    8. DS

      So what was the criticism? Did you, did you release the product earlier than you wanted to?

    9. SW

      Well, look, it, it's, it's-- It wasn't even a product at the time, to be honest. I mean, it was more just like a prototype or like a demo of what was possible. And, and, you know, we had been working with it and playing with it for a few months at that point, and the-- kinda just wanted to show people some of the examples of what it was capable of, because there was a real like-- like it was pretty, [chuckles] it was pretty insane for us to see as well. Like, I remember the first time that it did like a real task. Like, I could not sleep that night. Uh, and, and so it was just like showing that, um, you know-

    10. DS

      Wait, wait. Say more about that.

    11. SW

      The first task that Devin did was it like set up MongoDB for us, and it's like, you know, it's a, it-i-it's, it's a standard thing. It's issues that a lot of people run into all the time when they're going and getting their kind of like initial, um, you know, DB set up or whatever. Uh, but we would just like run into errors. You have this whole flow where you like, um, you find an error, you Google the error, you find some message on Stack Overflow or whatever, or you ask ChatGPT, and it tells you, "Okay, here's, here's what you should try." You try that thing, you run into a different error, [chuckles] and you like paste that in, and then, you know. And, and, and like for this one, we kinda like at some point we're just like, "Okay, Devin, just, just try to go fix it. Just go run commands. Like do whatever you need to go do it." And then it worked.

    12. DS

      And you couldn't sleep.

    13. SW

      It was truly like-- Because again, it's, you know, it's, it's like just seeing the exponential curve ahead, because this was a very, you know, very specific case. It was the one success that we had. It was very much like a, you know, like, like, uh, definitely a way better than average run. But no, there was this feeling of like, why shouldn't all software and all products be built this way now? Like, you can just tell it what you want it to do and have it go do it. I mean, it's kind of funny because, yeah, to your point, like we did get hate in the beginning. We did not feel motivated at all from the hate to be like, "Oh yeah, no, maybe you guys are right. Like, maybe we should go and like, you know, focus on the more kind of like chatbot Q&A style product experiences." Like m-maybe more than we should have. Maybe, maybe we should have done something in the middle. You know, I-I don't know. But, but like I, I think for us, like when we had seen that and when we had done these different things, like all of those like demos that we showed, you know, in, in, in our launch announcement were like actual runs of Devin that we had done ourselves and like run into and been like, "Holy shit, this is insane." For us, it was always kind of like, look, you can, you know, we can debate when or, or, or, or, or like what level of effectiveness or whatever, but like it's just, it's gonna happen, and that's how we always felt about it.

    14. DS

      So explain the process of iteration to go from that product, you're getting a lot of hate. Well, uh, actually, you know what? I called Jeremy Stern-

    15. SW

      Yeah

    16. DS

      ... who wrote this, uh, excellent profile of you-

    17. SW

      Yeah

    18. DS

      ... in Colossus, which there's a lot of parts I was just laughing my ass off, by the way.

    19. SW

      [laughs]

    20. DS

      And I was asking, I was like, "Tell me the stuff that didn't make, uh, the profile."

    21. SW

      Yeah.

    22. DS

      And he said there wasn't that much because you're kinda like an open book, and a lot of people like, um, manage their media.

    23. SW

      Yeah.

    24. DS

      And you know, you could have to talk about certain stuff off the record or whatever, and you were just like saying everything. But you did say something about like you made the point where like when you released the first product, what was the benchmark?

    25. SW

      SuiteBench. Yeah, yeah.

    26. DS

      And it was at like-

    27. SW

      Thirteen percent.

    28. DS

      That was Devin.

    29. SW

      Yeah, yeah. And it-

    30. DS

      And your point was that that's already better than...

  13. 37:1742:41

    Finding Product-Market Fit In The Enterprise

    1. DS

      So when did you have this idea where you guys are gonna take a run at very, I would say, ferociously to like these giant like Fortune five hundred-

    2. SW

      Yeah

    3. DS

      ... companies or even the, like the US Army uses you.

    4. SW

      Sure, yeah.

    5. DS

      Like, where did that strategy come from?

    6. SW

      So funnily enough, so that launch was in March of twenty twenty-four. And to your point, it went very viral. But again, we didn't have any customers, we didn't have a revenue, we didn't really have like-- We had-

    7. DS

      I thought your first iteration of the business model was like five hundred dollars a month or something, wasn't it? Am I, am I misremembering this?

    8. SW

      So, so we had that. That was actually later on, actually.

    9. DS

      Okay.

    10. SW

      That was, that was end of twenty twenty-four.

    11. DS

      Okay.

    12. SW

      But you know, initially what we started was just like, you know, a bunch of people came, asked us for the product. We were like, "Guys, [laughs] I don't know if this is a product that's ready for prime time, but if you re-- you know, well, you can try it and like, just let us know." And so like, people ran. You know, we had to go and scramble to build a system of like a, "Oh, okay, let's do like a pilot or like a POC," you know? And like, you know, we tried our best to like not overpromise and be like, "Guys, like really it's like very early, but if you wanna try it, you know, you can try it." And we did all this and perhaps unsurprisingly, I mean, they were all just like failing, [laughs] um, which is kind of natural. Like it's like you could do some pretty cool things with it. You could, you could do some pretty interesting like toy demos or projects or whatever. But it was certainly not ready to work on like a actual company's like real code base and so on, right? And so from that point, this would've been like April or May of twenty twenty-four, we were make, making agents work with like GPT-four. You know, this was like a very different era, [laughs] so they, they were much more primitive agents. We kind of talked about this and got to this question of like, "Okay, well, what do we think it actually does look like when the agents start to get good enough for adoption?" And I think what we kinda came to was, well, different tasks are different, right? And so, so like there are some tasks out there that are just really mundane and really repetitive, and you're just doing the same tedious thing over and over and over again. And there are some tasks on the other end of the spectrum that are like really tough, like architecture problems or like deep like, you know, deep issues that you have to like really understand all the context and have all the know-how to, to know how to fix. And like people were trying to use Devin for all of this and were failing at it, understandably. And so then the question was like, okay, what are the, the, the natural tasks that are like if there's like a first task that is gonna have PMF, you know, and real value from these kind of agent experiences where it can do the whole thing end to end, what does that gonna be? And we kinda said, "Okay, well, it should be some of these like really repetitive, tedious ones." It's not so cut and dry that you can just like have an automated script that does the exact thing every time. So it does take obviously some intelligence and some amount of meandering, but it is like repetitive enough and scoped enough and like on a tight enough feedback loop that you can have an agent do it and it would be able to kind of go and diagnose and like fix that problem, right? And so that's kind of what brought us to some of these like naturally to, to some of these like, you know, initial use cases which were things like migrations or version upgrades or, you know, helping people upgrade from like Java seven to Java eight or something like that, which were kind of like, you know, as you can imagine, enterprise had-- enterprises had these massive code bases where they would go and do all that, you know, and it's like a fifty thousand file code base where you have to go, you know, it's like the same eight things that you need to change in each one. You have to be a little bit thoughtful about how you'd make the trade-offs, but like it's a very repetitive task, right? Our first success ended up being with a company called Nubank, uh, biggest bank in Brazil, you know, by market cap at the time, and the use case was like one of these big migrations, and we had, uh, kind of a custom Devin that was like extremely, extremely optimized, um, for doing that. And as we grew from there, you know, later on we had kind of, uh, uh, as things got a little bit more mature, you know, we had both self-serve business and enterprise business and so on. But I think from the beginning we had always seen this value that building software in the real world and managing massive, massive products that like millions of people use every day was pretty substantially different from, you know, just building a cute website or a cute demo from scratch, right? And I think we really, really leaned into that. And naturally, it's, you know, all the Fortune five hundred or all the biggest companies in the world are software companies in twenty twenty-six. You know, even the ones that, that folks don't necessarily think of that way, right? And like Walmart or CVS or JPMorgan or, um, or, or Mercedes or whatever, you know, they're, they're all software companies, right? They have massive, massive teams of software engineers. They have tons of things that they're building and shipping and, um, and maintaining. And that kind of became like a natural thing for us to work on because like I, I think we had, had learned very early on that we wanna work on real problems and we wanna work on things that, that, that matter and that people actually care about.

    13. DS

      So wait, what percentage of your revenue is coming from enterprise then?

    14. SW

      Today, around seventy-five, eighty percent.

    15. DS

      What are people using on the self-serve? Like what are examples of-

    16. SW

      Yeah. So we have a lot of teams who use it in self-serve. I mean, that's grown actually, you know, that's grown quite a bit as well lately. Um, but um, you know, we have startups who, who, you know, we have Exa, um, who uses it a ton, or Open Router or Built. You know, I've, I've, I, I ran into someone in, in, in my apartment in the elevator the other day, like, "Oh, you're the Devin guy," like, "we use Devin," you know. Uh, the, the- [laughs]

    17. DS

      Are you Devin? [laughs]

    18. SW

      Hey, I've, I've gotten that as well. "Hey, are you Devin?" And I was like-

    19. DS

      [laughs]

    20. SW

      ... "You know, I'm actually not, but that's, that's, that's okay." Uh-

    21. DS

      Why didn't we call it Scott? [laughs]

    22. SW

      [laughs] Um, you know, there, there's kind of both self-serve and enterprise. With that said, it is entirely in both sides it is still like actual engineering teams building real output. And so, so we don't focus at all on like, you know, individual hobbyists who are just like trying to make a cool thing or something like that. We focus on like real teams who are building real products that they want people to use and, um, and getting output out of that.

  14. 42:4148:34

    How Cognition Deploys Devin Inside Large Companies

    1. DS

      Okay. Can you walk us through... I'm very curious, like I'm a big enterprise.

    2. SW

      Yeah.

    3. DS

      I contact you

    4. SW

      Yeah

    5. DS

      Walk us through what happens and to being a customer.

    6. SW

      You know, it starts with education. Um, and everyone's gone crazy over AI and agents and so on, and those are the buzzwords of the last six months, obviously. And so they want to know more about this, but there's still a lot of detail in, like, what does it actually look like to deploy them? What does it... So, so, you know, we'll, we'll show them what this looks like. We'll talk them through, like, how we work with, with teams and how we partner, you know, how we direct them to the right use cases, how we give them guidance on, like, how, how, you know, to maximize their ROI or, like, what projects are or are not feasible with agents. And then from there, you know, enterprises typically, obviously, have some very messy processes. And so, you know, for most software or most, you know, just generally, like, vendors that they'd wanna work with, it's often, I mean, for, as you can imagine, for a massive bank, you know, adopting software, giving it access to all of their, you know, um, their, their repos, getting through security or whatever, that's like a usually, um, uh, for, for typical companies, it's like a twelve to eighteen-month cycle. The thing that we do naturally is, is, is we just work with them to figure out how we go and do that as fast as is humanly possible.

    7. DS

      Did you send employees down to South America for Nubank?

    8. SW

      Well, so Nubank, I mean, the first one, honestly, our entire team was the foreign deployed team. Like, we all flew to Brazil.

    9. DS

      [laughs] What?

    10. SW

      Like, I mean, it was, it, it's, like, the first case, you know, it's like, it's like, let's be real here, okay? Agents did not work generally, okay? And so there was, it was a lot of, like, how do you make it work for these very specific-

    11. DS

      [laughs] Oh, my...

    12. SW

      Like, we all flew to Brazil. I was there. The whole team was there. We were sitting there with their engineers understanding, "Okay, so this is what you do in that case. This is what you do in that case, and this is what Devin needs to know, and Devin needs to be able to read these things," and, like, going and debugging their, like, exact problems. Now, obviously, it's not like that at all because, you know-

    13. DS

      Hold on. We, we'll get to where it is now.

    14. SW

      Okay.

    15. DS

      But the idea of, like, "No, we didn't deploy a t- a team, we deployed the whole company." [laughs]

    16. SW

      Oh, yeah. Yeah, no, it was... I, I mean, it was, uh-

    17. DS

      That's hilarious

    18. SW

      ... getting the first customer obviously was, like, a, a real, you know, it's like, uh, I, I, I mean, I wonder what they thought of that. [laughs]

    19. DS

      [laughs]

    20. SW

      But, but, but like, yeah, it was, like, uh, literally like, "Okay, let's go through all these different things that you guys think could make sense. Let's go through each one. Let's try some of it manually ourselves to understand what the task looks like. Let's see if we can teach Devin to do this correctly and build in the right kind of like, um, you know, the, the, the right orchestration for Devin to be able to do this," and, like, let's just, like... Basically, building the product was, like, almost like building for, for one company. And yeah, it was fun. [laughs] Um-

    21. DS

      So what do you do today?

    22. SW

      So today it's, it's obviously much more, um, uh, it's, it's, it's, it's much more self-start and, you know, the agents are, are, are so much more capable obviously. Uh, and we've, we've filled, figured out a lot more things with the onboarding experience. But, but a lot of it is, you know, we're saying that these, these cycles typically take twelve to eighteen months. We try to get deployed with folks, you know, within, like, three months, and a lot of that requires folks to, obviously, first of all, it requires them to really appreciate that it's a priority. Um, I mean, if you have twenty-five thousand software engineers that you're, you know, uh, in an org that's, that's, that's running that, that, that, that, that costs, you know, ten billion a year or something that you think can move three times faster, you know, usually it is a priority. But then it's, like, figuring out how we get through the security reviews. You know, we, we have all the, you know, we can deploy in their private cloud. We have very strict data agreements. We have, like, tight air walls on, on, on, on all these things, obviously. Um, you know, we're pulling through all their-

    23. DS

      Do, do employees physically have to go? Are they, are they working with-

    24. SW

      Yeah

    25. DS

      Like, are, are these deals so big that the, that they're working with a specific company and only that company for a period of time or no?

    26. SW

      Typically, no. Um, we have a, you know, we have a, a, a full, like, foreign deployed motion, but a lot of what that looks like, as we're saying, is, is, uh, a little bit more like user education and guidance. And so we will fly, you know, we, we still do this, where we'll fly out and kind of like go and see customers and work with them, point them to the right use cases, teach them how to make... get, get success, help them with their, like, setup and their playbooks, like all these things for how to use Devin. But it's much more a kind of like, "Look, we're here to assist you guys. We're, we're giving you a lot of this kind of like direction and so on, but, like, you are yourself, you know, and your team is the one that's using Devin and running all of that," right? So that's a lot of what that looks like. We're set up to be as incentive aligned with our customers as possible, and so a lot of it is like, is not just like, "Oh, here's this tool. Like, hand it over to your engineers and, like, find out what they say about it." It's like, "Okay, well, let's figure out, like, what are the initiatives that you guys really care about, and, like, we will point you to for those initiatives. Here are each of the projects that we think Devin right now can make you ten times faster on. And we'll tell you for the ones that, that it's not, and here's, here's what workflows you should use instead, or here's how you should get to value instead." Um, but it looks more like that than like a, um, than, than like a literal like, you know, we are using Devin on your, on your, on your behalf or something.

    27. DS

      Deel is how the best founders turn the world into their talent pool. I've been studying how history's greatest founders operate for a decade, and one thing they all have in common is they understand that recruiting and hiring the very best talent is your most important priority. A players recognize other A players, which is why top companies like Ramp, Shopify, Eleven Labs, Uber, and DoorDash all use Deel. Many of the top founders I know have personally invested in Deel after using their product, and what they discovered is that Deel is the best company in the world at building infrastructure for global hiring. Deel will help your business hire, pay, and manage any worker anywhere in the world so you can retain the best talent anywhere and spend the rest of your time focusing on what you do best, delivering value to your customers. The founder of Eleven Labs has a great description of the value Deel can give your company. He said, "We built Eleven Labs to break down language and communication barriers. With Deel enabling us to hire and support exceptional talent anywhere, we can accelerate our innovation and bring more voices, stories, and ideas to every corner of the world." Deel is trusted by over forty thousand businesses. Learn how they can help your business today by going to deel.com/senra. That is deel.com/senra. Wait, so tell more about how you align the incentives between your company and then your customers.

  15. 48:3450:01

    Measuring ROI Instead Of Token Spend

    1. SW

      Yeah. So a couple things here, uh, which I think are really important. One obviously is, is just, like, really working with them on the, the just very clearly defining the ROI, um, which I think is a very important... I mean, it's like pe- everyone's talking about this, right? Like, over the last few months, like, people are going crazy on their token budgets and, you know, one engineer can spend so much and, you know, you wanna know that that's actually doing real value for you, and you wanna be able to identify where you are on the-

    2. DS

      Do you think all this stuff is a little crazy?

    3. SW

      I think it is directionally correct, but I think there's some, you know, there, there, there, there are definitely some places where people have gotten carried away. You know, people talk about, oh, like, yeah, like we rank our engineers by how many tokens they're spending. Well, let's, let's try and rank people by how much output they're actually producing or how much good work, you know, is actually getting done, you know? But, but obviously, I think the like the math works out in terms of like, you know, the GPUs are expensive, but like if your engineers are actually able to ship three times more, you know, then it's very clearly worth it. You just wanna make sure you're doing it the right way, right? Obviously a lot there on just like tying to specific outcomes, tying to, okay, what are the actual tickets that are getting done? What are the, what are, what are, what are the projects and the initiatives and like this project, which was scoped out for eighteen months and was gonna be handed off to, you know, an outsourced contractor, um, and, and was gonna cost you fifteen million. Like, let's just talk about how that you do this all internally with your own team, and you do it for one million, and you get it all done in three months, you know? Uh, stuff like that, uh, which I think is super important. The second thing which I'll call out especially is just being neutral. A lot of discussion obviously about-

    4. DS

      What, what do you mean by that?

  16. 50:0152:18

    Why Cognition Wants To Be Model-Neutral

    1. SW

      Being, being neutral with respect to all the labs, the models themselves, et cetera.

    2. DS

      Yes, I think you said you, you like being Switzerland.

    3. SW

      Yeah, we like being Switzerland, exactly. And so I think it's like an important thing of, of, you know, we are just as incentivized as they are to figure out how to make their token spend efficient, right? And so, so Devin is purposefully meant to be, uh, you know, a compound model system. And so all of these different things, you know, for the right task or even the right sub-task of a task or something like that-

    4. DS

      Describe what a compound model system is.

    5. SW

      Yeah, yeah. So, so for each different part of your use case, I mean, imagine s- let, let's say you tell Devin, "Hey, um, customer just reported this bug. Uh, you know, there's a Jira ticket. Can you take a look at the ticket and, like, go and solve the whole thing?" Right? What does Devin actually go do, right? First of all, Devin's probably going and, like, investigating, understanding, like, what does the report say? What's going on? Second of all, probably, you know, as, as any engineer should, like, it's gonna go and try to reproduce the bug itself, right? So it'll s- it'll say, "Okay, let me spin up the product locally." We click around, try to follow the same steps that they followed and see if I can make the bug happen as well, right? Then if you did that, then like, okay, now you're looking at the logs, you're trying to figure out what went wrong. You're pinpointing what, what are the, the particular files or what was the flow of, you know, what was the command flow that led to this. Then you do the debugging, then you go and test it again, make sure, you know, all, all these steps, right? Then you put it up for review. And it turns out that there are different models that are good for different parts of these tasks, right? And even for across different tasks, obviously also, it's like some tasks are these really crazy hard ones where you wanna actually use max thinking, and you wanna use the, the, the very best models you can get your hands on in the world, right? And then many other tasks are, you know, boilerplate enough or repetitive enough that what you care about is just getting it done really fast, getting an, a, an immediate answer, ha-having the ability to verify that it was correct. But then beyond that, making sure that it was, like, as cheap and fast and efficient as possible, right? And so Devin, rather than being pegged to one model and saying, "Oh, we're only gonna serve you GPT for this," or, "We're only gonna serve you Opus for this," Devin can use any of the different models it has in its arsenal, which include all of these models from Anthropic, OpenAI, Google, et cetera. Um, but, but also our own models, right? Or open source models out there, and it will, you know, dynamically go and choose these models for these tasks.

    6. DS

      How do you think about this? Like, you're a customer of them-

    7. SW

      Yeah

    8. DS

      ... but also competitor with.

    9. SW

      [chuckles]

  17. 52:1857:14

    Why Focus Lets Startups Beat Giants

    1. SW

      Yeah. No, I mean, look, I, I think in, in practice, there's a lot of positive sum work for us to do together. Um, and so, like, you know, the way that we think about ourselves, a couple things. One, software is the only thing w- that we care about, obviously. Um, and there's a lot of value in focus and in building products and building our home- whole motions specifically around that.

    2. DS

      Say more about how you think about focus and the, and the value in it.

    3. SW

      You know, back to what we were saying about startups, right? Like, why do startups ever win at all? And if you do everything, you will lose to Microsoft or Google, who does everything, but also has, like, trillions of dollars more resources and a hundred thousand more people than you, right? And, and infinitely more brand name, right? Um, and, and, like, the way that you build, you know, like a real kind of like, um, you, you know, a, a, a real, like, lasting business or lasting product is by really, really focusing and narrowing on one specific thing, making a very concentrated bet, and then obviously, you know, your bet has to end up being right. But, but, but like, you know, from there, it's like a lot of just, like, really tight execution. Um, and so in software, you know, I love, um, you and I have talked about this. I love the quote from, from Daniel Ek, uh, in Spotify, right? People were saying, so, like, you know, there's, there's like, like YouTube's trying to go and do this, and Apple has Apple Music and like, why, why should there be like a... He was like, "You know, I can give you all the other reasons, but the truth is we're just gonna care way more about music than they are." And I think for us, it's true. It's like we're, we are just gonna care so much more about, like, what does it look like to build software end to end at Goldman Sachs or at, like, Mercedes-Benz or something like that, you know? A-and, and there's a lot of nuance in that, and there's, like, a lot of messiness in that, right? It's, it's not as simple as like, "Oh, here's a sandbox algorithms problem. Go and code me the correct, you know, thirty-line program that solves this problem or something." It's like, how do you work with all the messiness of the real world? How do you understand the code base as it exists today? How do you, like, collaborate with all the humans on it? How do you plug into their ticketing system? How do you give the agent the ability to test its own code and run everything locally, right? Like, all of these are, are obviously super hairy, messy problems. Uh, and that's, that's, that's what we care a lot about, right? And so from that perspective, it's, it's a very kind of nice, like, you know, the labs have their own products. I'm sure they'll continue to do more, but, but in practice, like there are a lot of nice ways for us to cooperate. Obviously, like on top of that, like being the Switzerland means that, um, you know, folks can work with us and kind of like trust in us that, that, that we will kind of route them to the right models, that we will optimize the price performance for them, that we will kind of like direct them to the right use cases and so on because- You know, we're not incentivized for them to spend more on the models either. You know, we're incentivized for them to get value and to get output out of it.

    4. DS

      I love that you use the example of the war between Spotify and the rest of the-

    5. SW

      Yeah

    6. DS

      ... like Apple Music, for example. Jimmy Iovine, who also came on this show, and now is actually a friend of mine, actually called me yesterday about this 'cause anything, anytime anything happens with AI music-

    7. SW

      Yeah

    8. DS

      ... and Spotify, he calls me. But-

    9. SW

      [laughs]

    10. DS

      ... we talked about this because he's like, "You don't understand." Like, he had, like, forty years of experience in the music industry, had all the relationships. He's like, "Apple bought me for three billion dollars." Spotify at the time, he's like, "We're gonna go head to head with them." Spotify only had three million paid subscribers at the time. Jimmy's gonna fight the war, and he's like, "I have one of the biggest companies in the world."

    11. SW

      Yeah.

    12. DS

      And then he talked about it. He's like, that wind up being a huge... He thought it was an asset. It was a huge liability.

    13. SW

      Yeah.

    14. DS

      Because they're like, "We don't give a shit about getting a couple million more, tens of millions of more of subscribers."

    15. SW

      Yeah.

    16. DS

      "We invented the most successful consumer product of all time."

    17. SW

      Yeah.

    18. DS

      And he said something like they just clipped his wings.

    19. SW

      Yeah.

    20. DS

      And he's like, he would just try to do something, they're like, "Oh, this isn't important to us."

    21. SW

      Yeah.

    22. DS

      Where Daniel was going to die if he was not successful.

    23. SW

      Yeah, yeah.

    24. DS

      He just cared about it a lot more.

    25. SW

      Dude, even in the two years that we've been around, you know, I've heard so many different versions of the same argument. 'Cause when we started, as you can imagine, this was true for us, it was true for everybody in the space, building the space, the, the, the number one pushback that you would always hear from investors, from other people, people was like, "But, like, Microsoft already has GitHub Copilot. Like, isn't that-- Everybody's just gonna use that, right?" And it's like, it's a very reasonable thing to say in some sense because, you know what? They did have all, you know, they did own all of GitHub, and they did have a partnership with OpenAI, and they did have, like, VS Code and so on. I think in practice, the reality is there's so much more innovation and so much more to build that there was a lot of, like, positive so- You know, Microsoft's like a great partner of ours, and we do a lot of things together, and we build even more together, right? And, and I think the reality is, like, people have said this forever, like, oh, like, yeah, like startups versus, you know, like, why should... You can give all the rational arguments.

    26. DS

      Ten, ten years ago, it's like Google will do this-

    27. SW

      Yeah

    28. DS

      ... or Facebook will do this.

    29. SW

      Yeah, yeah, for sure. It's like, oh, why should Datadog exist? Or why should Snowflake or Databricks exist? Or what-- You know, this is the clouds. The clouds have all of it. You know, the clouds care about observability too. The clouds care-- And, and the reality is, like, it, it's, it's in some sense a bit of an uncreative way to think about things, I think. Like if, if there was like one thing that, okay, here's what we all know is gonna be the end state future, and everybody's just working toward it, and whoever has the most resources toward it wins. And of course, yeah, you know, it's like we know who has the most resources today, right? But if there are millions of problems out there to solve, there's lots of different things, the world is dynamic, things change all the time, there's lots of new, you know, ideas or opportunities or, or, or, or ways to, to, to build new products, um, then the reality is like,

  18. 57:141:00:27

    Independence, Acquisitions, And Building A Generational Company

    1. SW

      of course there's, there's, there's lots of different niches to own. There's different things to really bet on. There's different focuses to, to, to spend your time on, and I think that will continue. Yeah. I mean, I, I, I think it's like, uh, for better or for worse, after the last couple months of news, you know, Cognition, I think has been a bit more of the like... We've become a bit more known as, as the, you know, the folks betting on independence in some sense, because obviously there have been some high-profile acquisitions.

    2. DS

      It's funny that you said that-

    3. SW

      Yeah

    4. DS

      ... 'cause I was, I, I wanna, I wanna [chuckles] one of the questions I'm gonna ask you, which you probably won't answer, is like-

    5. SW

      Yeah

    6. DS

      ... how many different acquisition offers have you had?

    7. SW

      I will not answer that question. [laughs]

    8. DS

      Dozens? More?

    9. SW

      Dozens is a lot, dude. I don't know about dozens, but- [laughs]

    10. DS

      The time. It's probably, uh, only a handful that could actually, uh, have, afford to buy you if you would sell. But the amount of times they keep coming back.

    11. SW

      Oh, I see. Well, yeah, it's definitely, um... Anyway. [laughs] Um-

    12. DS

      No, this, this independence part is really interesting to me. We were on the phone, me, you-

    13. SW

      Yeah

    14. DS

      ... and a mutual friend on three-way, like, I don't know, I think it was actually last summer. And, um, we're not gonna say the company. Uh, [laughs] but I was like, "Scott, what do you, what do you, what do you think about, uh, you know, an acquisition with X?" Not X the platform. X the, the blank company. And you're like, "I don't know how we'd be able to afford them." [laughs] You just assumed I meant you buying them. [laughs]

    15. SW

      [laughs]

    16. DS

      And I just fucking cracked up laughing. I was like, "This is hilarious." [laughs]

    17. SW

      No. So, so I, I, I think like, um, yeah, yeah, and, and, and it's like folks-- like there, there are folks taking acquisitions and doing things. There's some big high-profile ones. I, I think those are great, to be clear, and I, I think it's a, a very reasonable path to exit. As mentioned, for us, it's like we, we've, we've all come into this like we wanna build a generational business. It's something we're really excited about. And, and I think people, I think today sometimes I, I've, I've seen some more of this nihilism where they say, "Oh, like, yeah, maybe it's just, maybe it's too late, and maybe it's not possible anymore," and like-

    18. DS

      What, what does that mean? What's not possible?

    19. SW

      Like maybe it's not possible to build a new independent business 'cause everything else, you know, it's like all the opp-

    20. DS

      'Cause the labs are gonna do everything?

    21. SW

      'Cause all the opportunities taken, you know? And it's like, guys-

    22. DS

      Those people aren't founders.

    23. SW

      [laughs]

    24. DS

      Founders are rationally optimistic.

    25. SW

      Yeah.

    26. DS

      They're just not founders.

    27. SW

      Yeah.

    28. DS

      They, they believe even there's no evidence that they should succeed-

    29. SW

      Yeah

    30. DS

      ... that they will succeed.

  19. 1:00:271:03:42

    Why Money Is Not The Goal

    1. DS

      And you know, I don't mean to push you on this, but like I am cur- personally curious though, because like I know you're already rich. Uh, I don't think money is your North Star based on the conversations we had in the past, but like there's gotta be some crazy number somebody can throw at you where you're just like, "Fuck, I have to take this."

    2. SW

      [laughs] Not really, hon- I mean, it's like the... You know, people have asked me sometimes before, they've asked me like, um, okay, but, like, really, the... Would you guy- Like-

    3. DS

      This is what I'm doing right now.

    4. SW

      Yeah. A- a- a- and the way that I say it sometimes, um, is, like, we would sell if we thought it was the most ambitious thing to do. It's kind of an oxymoron 'cause obviously [laughs] I mean, it is. But, but, but, you know, it's, it's like my genuine answer in the sense that, like, that's, like, what we care, we care about, you know? It- it's, it's like the... I, I mean, it's funny you talk about money. Like, uh, uh, I mean, I, I don't even... Dude, I don't have... Like, I don't have a car. [laughs]

    5. DS

      You live in an apartment, I just-

    6. SW

      I have a... Yeah.

    7. DS

      I just realized.

    8. SW

      I have a nice apartment. So yeah, it's like, I don't know. I, I think I, I like eating sushi. That's fun. Sushi doesn't cost that much. [laughs] You can do that off of an engineer's salary as well.

    9. DS

      The... Just so you know, if this is true-

    10. SW

      Yeah

    11. DS

      ... like, then you are the, the type of entrepreneur that I find the most fascinating in the world. 'Cause, like, when startup founders talk, talk, uh, come talk to me, it's just like, I don't give a shit about your startup. I like, I ask the same question. It's like, "Is this your last business?"

    12. SW

      Yeah.

    13. DS

      Right? I asked Karim from Ramp like this-

    14. SW

      Yeah

    15. DS

      ... before we did this, like, deep partnership.

    16. SW

      Yeah.

    17. DS

      And it's just like, it's like, okay, you could sell it. Like, the Zuck example where-

    18. SW

      Yeah

    19. DS

      ... you're like, "Why didn't you take a billion dollars? I think you own 25% of the company. You would've made 250 million. You're, like, 22." He's like, "Well, the... What would I do with the money? I would just start another social network. I kinda like the one I have." Like-

    20. SW

      Yeah.

    21. DS

      "I just wanna build shit anyways."

    22. SW

      Yeah.

    23. DS

      So, like, what do I do?

    24. SW

      Yeah.

    25. DS

      And then the other element of this, which I think is almost tied into you, where it's like, I feel like you're just having a lot of fun. Like, even being around here, it's just like there's... Your company's weirder in the composition of the people-

    26. SW

      Yeah

    27. DS

      ... because it's literally just all nerds.

    28. SW

      [laughs]

    29. DS

      Like, and I think you like-

    30. SW

      Thank you. [laughs]

  20. 1:03:421:05:09

    One Life: Going For It All

    1. SW

      Again, I don't... This is not rational, uh, but, but, like, the way that I would describe it, it's like I feel, and I think all of us do, like, that it'd be one thing if we tried and we gave it our all and we just weren't good enough. That'd be fine. Like, okay, it wouldn't be that... Dude, I'd be, I'd be salty as hell. But, you know, I'd be [laughs]

    2. DS

      [laughs] It wouldn't be fine.

    3. SW

      But, but, like-

    4. DS

      [laughs]

    5. SW

      But, like, it would be, like-

    6. DS

      I see

    7. SW

      ... it would be an outcome, it would be, like, an outcome that I could live with, you know? But if we felt like, you know, we, we could have gone for it all, we could have pushed harder, and we didn't.

    8. DS

      Mm.

    9. SW

      Like, that I think is, like, I just, I, I don't think we would, like, live with ourselves in that outcome. And that's, like, the if you have me explain it, it's, it's almost, it's kinda circular. I don't know. But, but it's like, why are, why are we so excited to do this? Why do we do... You know, let's spend all... It's like we wanna achieve our potential and, and build what we were meant to build, you know? And, and, and maybe that's something or maybe that's nothing, but, like, you'd rather find out than see. You know?

    10. DS

      I think this idea of you have one life, go-

    11. SW

      Yeah

    12. DS

      ... is a perfect place to end. Thanks for the time, Scott.

    13. SW

      Yeah. Thanks for having me.

    14. DS

      I hope you enjoyed this episode. Please remember to subscribe wherever you're listening and leave a review, and make sure you listen to my other podcast, Founders. For almost a decade, I've obsessively read over 400 biographies of history's greatest entrepreneurs, searching for ideas that you can use in your work. Most of the guests you hear on this show first found me through Founders. [outro music]

Episode duration: 1:05:10

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