David Senra - Scott Wu, Cognition

Episode Date: June 28, 2026

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 Made possible by Ramp: ⁠https://ramp.com AppLovin: https://applovin.com/senra Deel: https://deel.com/senra 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 Learn more about your ad choices. Visit megaphone.fm/adchoices

I want to know why you describe yourself as salty. What does that mean? I've just always been this way. As a kid, I just hated losing. Like, my first competitive memory ever is, like, when I was in second grade, I went to the seventh grade math competition. It was like a middle school competition that was held at the, like, local university or whatever for middle school. But you were seven.

Yeah, yeah. I was like seven or eight years old. I was competing in the, 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. who got third place, you just got second, and it 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 why it is. It doesn't have to be rational. But like, how much of your brain is dedicated to competition? I mean, it's all I do, honestly. I don't know. I think the like, oh, what do you mean it's all you do? Well, I think strategy gain, I don't know.

It's 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 tree search, you know, where you're exploring the different options and the disdain 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. So this is basically just, you don't have memories when you weren't like this, basically.

I think that's right. Yeah, yeah. I was a little brother growing up, and so my older brother was four or five years older than me, and naturally, we would 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. So, like, I spent some time with Demas from DeMind. And what was interesting is I draw a lot of, like, similarities between you too, because I was just 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, right? But then underneath that is, like, this, like, ruthlessly competitive drive. And Demis, I think he said this publicly, but I think he said, like, half his brain is dedicated to competition. And it's, and it A lot of that comes from his early days in chess. Yeah. What were you competing in when you were younger besides math competitions? 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 my 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 really great thing about these competitions, too, is, you know, you compete in your school competition. And if you do well enough than that, then you make it qualify for the local or, like, the city competition and then you can do well in that, then you get to like the regional competition and the state competition, then you get to go to the national thing, the international

thing, right? And so it's like a very nice setup where sooner or later you get to kind of meet people who are like you, basically. When I was a kid doing these competitions, going for that, it was like, oh, 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's like we would hang out online, we'd 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 played a lot of Super Smash Brothers. I used to go to tournaments for Super Smash Brothers. It was a lot of fun. I played melee. And then I played, like, Tetris. I played a lot of poker. I played some chess.

I was okay chess. I was not good. I played some Go. My dad was a competitive Go player. My parents came to the U.S. in some sense because of Go, which was kind of a funny coincidence because my dad was in grad school in China. And he had a professor who really liked him.

The reason he liked him was because my dad was like a really good go player. Like he was like a seven-down at go, which if you were to call it in chess would be like, I don't know, 2,300 or 2400 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 U.S. to come and teach. And at the time, you know, this. was super early on and, you know, immigration from China to the U.S. 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 in the U.S. And that's kind of how we ended up here in the first place. I was, I was born after we moved to the U.S., obviously. So your dad was competitive and go. What was your mom competitive in, though, because I think I read that you said that she might have been the most competitive person of your family.

Yeah, no, she was always, she was definitely the most salty, I would say for sure. I mean, she would, um... What does salty mean? Salty just means that you take offense to the idea of losing. Okay, I love that. Yeah, she would always be, oh, no, no, I'm better at this or I'm very, you know, I can beat you at this, you know? 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. But it was more her personality than any one thing that she really put all of her competitive energy into. I spent a lot of time, obviously, reading the biographies of history's great entrepreneurs. I was fascinated by, like, there's usually two different kind of archetypes for the parents. One, you have like the Larry Ellison and Elon Musk. 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.

Larry's adopted father, which is tell them you're never going to mount to anything, and so they had this inner fire to disprove, you know, saying that basically, no, fuck you, dad, you're wrong about this, right? And then you have like the S.A. 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 it more into like the S.A. Lauder category where she would tell you that, like, hey, these people are doing amazing things. You could do even better than that. Correct?

I think they would have 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 no, they were always very important. But your mom gave you self-confidence.

I think she always told me that I was the best. And she was always extremely proud of the, you know, it's the, I had these, like when you would go to these math competitions. Hold on. So you said, she always told you that you're the best.

Did she say that before there was evidence? Yeah, I think so. I think that's right. I think even when I was, like, tiny, she would tell me that it was extremely talented. You know, she was always a huge source of support me, and she always obviously believed in whatever I wanted to do, you know, because you would compete in math competitions, you would give 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 old math competition trophies. It was like, like, my mom was very intentional about, like, No, no. The thing that we value in this household... Other people's trophies are the ones you want. Ours, are the ones that me and my brother want, of course. We're going to put up pictures of other people's trophies, and you'd better damn sure replace them with your own.

So, like, you know, when I was pretty... My brother as well, you know, we both really like these competitions. We're like, you know, like, accumulate these trophies, and she would always, like, every time we got one, she would hang it up and we're, like, put it up on the, like, the mantel piece and everything. And, no, it's like, it's always, I think, think what she really valued. I think I think education was really important to her. And I think

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What matters to you more? Like, what is a, is it the pain of losing? Is like losing is worse than the love, like the thrill of winning? Yeah. Because that's how you started it. You're just like, I could not stand losing. Let me back up. He's reading another Larry Ellison biography.

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 talk to Michael Dell about it. And he's like, yeah, it's just the fear of losing. The pain of losing is way worse than the good feeling of winning. So I think that's definitely true in terms of how it feels. In order to get anywhere, you've got to lose a lot. If anything, almost like a lot of, you know, I mean, a lot of the guys who are talking about,

if anything like the way they got to where they are is 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 want to stop trying, if that makes sense. Yeah, no, I think it's impossible. I know your personality type, like, there's just impossible for you not to do this. What is, like, let's get into Devon. What is like winning with Devon

look like to you? So again, you know, we're hyper-competitive, 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 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 want to go for all. You know, we want to be a generational business. Like, we want to build a hyperscalor and we want to go and do that. And, like, maybe we'll succeed. Maybe we won't. I don't know. But, like, that's what we're going after. And I think to me, what that means in our field in building software is, like, you know,

people sometimes need the term, like, coding agents. or like, you know, like AI programming or something. I always like, you know, I was here that I think a little bit about like, well, we're not always, I mean, we're not going to be interacting with code for that much longer, you know, our 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 devon is the way that humans can tell their computers what to do.

Because that was the whole point of software. engineering anyway, right? It's just to be able to work with your computer and tell what you want it to do. I mean, I think doing that for the world is like a massive opportunity, and that's what we're really excited to go after. Why is that interesting to you? You just said we all did different things. We came together and we're like, no, this is the big one. Yeah. I mean, the simple answer is, you know, we're a bunch of nerds who were all programmers and built software and so on before, and now this is the idea of teaching AI how to go do that. Wait, say more about that. Well, you just said, we're all nerds. This is the big one because we can actually teach AI how to

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 and have everybody have an AI that can help them make things and feel the wonder of that. I mean, it's pretty sick. Is that the idea, the single idea in the world that animate you the most that gets you the most excited? I think it is incredible. Yeah. I mean, well, I would go further, too, and just say, like, at a zoomed out level, it's, I honestly think that, you know, everyone talks about AGI. I'm talking about what is the future going to be like.

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 want to make in the world. My co-founder has a slime, which I've always loved. Which is, you know, we've been spending all this time living in survival mode as a species. You know, and now we're going to be living in creative mode. And I think that's right.

Like, I think, you know, Minecraft survival mode is where you're like, you know, you're growing food. You're, like, making sure you're safe from the monsters at night and whatever. 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 want to make happen. I think it's going to be amazing. And I mean, I think that is like the world that we're going towards.

And I'm like, we want to be the ones building that. Okay, so talk about what Devin does today. Yeah. And then I want you to flesh out that idea of like where you see in the future. It's just the interface between humans and computers. Sure. Yeah. So Devin is today, you know, what what folks know as an AI software engineer.

And basically what that means is that Devon is a tool that anyone can use. that will work with them end-to-end on building out software. And so we work with a lot of the biggest companies across the world. We work with Golden Sacks. We work with Mercedes. We work with a lot of areas of the U.S. 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 the thing about it is in the last 20, 30 years, obviously, I mean, software is eating

the world is the famous line. I think it's very much true. It's still got a couple order of magnitudes to go. And in practice, what it looks like is that teams use Devon to show. 10 times faster and to do 10 times more. Okay, so that's where it's at today. Yeah. How do you get to where you're saying,

you're just the interface machine, humans, and computers? So now we're going to get into a philosophical discussion of what it means to be a programmer or a software engineer, right? And I think, like, you know, if you go all the way back, it's, you know, there was a time where programming was like using the vacuum tubes. And you might plugging all those in and having it do the machine do the arithmetic, right? Like the ENIAC was, you know, with the first, in some sense, the first computer out there, although it was obviously very different, or it would have been, like,

filling out the punch cards and like setting up the, or writing, you know, the, like, putting down assembly, you know, or writing in basic or something, right? 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's deep? 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 wanted to do.

Here's how I want it to look. Here's what I want this button to do. Here's how I want to architect it. Every single little decision obviously was made by somebody. But the computer itself is then executing it accordingly to what the wishes of its creators was. Right. And I think what we'll start to see is that abstract.

will continue to climb, right? And like, you know, we kind of see this already. Like at this point, you don't need to know a programming language. It's like 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 want to make a cool website that does this, this and that. Or, for example, in my existing product, you know, 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 going to 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, that the math only really works out to create software if it's going to be used at least like a million times or something.

You know, I'm giving it, maybe it's 10,000 times or whatever. And my point is like, if you want to go build a product today, you need a whole team of engineers. Engineers are expensive. You got to pay salaries, you've got to go build all this out, and you've got to go and do that, right? You need that software to be used enough times or 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 that's what's made that work out and made it feasible, right? But there are so many things out there, which only, you know, very specific things 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-color work that we talk about today, even, is very, all right, you know, wake up in the morning, all right, I'm going to go look through these like 15 LinkedIn profiles, I'm going to look for this and that or whatever, or I'm going to go fill out these forms, or I'm going to 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 hire a whole team of people to go make you that piece of software,

which you're going to use one time and never again versus just having the human go and do that themselves, right? I think what we're going to give. get to is we're going to get to a point where you are just giving your instructions to that agent. And the agent on the back end, you don't even have to look at this, but on the back end, the agent is going to figure out, okay, here's, I'm going to write this code that's going to go do this. I'm going to put a script that automates this part. I'm going to do this and do this. And that's what's going to allow it to actually go and do all these things.

But what you start to get to, as we're kind of saying, it's like, this is really just how you control your computer and how you do what you want to go do. And you wake up in the morning and it's like, here's what I want to 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 the literal, like, all right, put the pen to the paper on writing code. But that task is like, you're the one that's deciding what to do. So this ideal future that's in your mind, right?

How far away do you think we are to that? Yeah. I mean, we've made a lot of steps toward it. I would say we still have got a ways to go. You can use Devon today. You can use all the different kind of coding tools today. And you can do a lot more than you could have done. done 10 years ago, but certainly, or even one year ago or six months ago, but certainly,

you know, it's, it's not at the level that we're talking about of you are neuralinked into the AI and you can tell it exactly what you want to do and what you want to see in the world and just have the AI go and do that. When do we get there? 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. In AI terms, five years is like a century, you know, 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 I really think it will. So this is kind of related to something I heard you say, where you're saying that humans just have a really hard time understanding exponential curves. It's really true.

I mean, you see this 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. Including your own. And it's a very, you know, it's like humans aren't really wired for this, right? Like all of our like inherent like fight or flight response are kind of like our ability to kind of like measure things to vastly oversimplify. 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 know a couple days worth of food or something. But but but obviously with the kind of exponential curves that we deal with, you know, the equivalence of a good hunt here could be a thousand years worth of food.

And we don't have that intuitive signal in our brains to really understand that, right? at a really deep, like, native level. People often understand, I think, how fast things can change and how fast the world could change. I mean, my parents even, like, drilled this into me because they grew up in, you know, in communist China. And they came to the U.S. 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 that, you know, the idea that, like, comes to the U.S. and everybody has a car and all of these different, you know, it's like all, everyone has all these household appliances and has a, you know, it was a very different life when they grew up in, like, you know, the 60s in China.

And they were much, much poorer and it was very, very different, right? And it's like, I mean, funny enough, people got used to all these things pretty quickly. And now we can't live without them. But I think we'll kind of undergo the same period with AI where five years from now, it's going to be insane to think about all these things that we're going to have. Ten years from now, we're going to have forgotten. that we ever lived without them, honestly. What do you think you understand about AI

that other people don't? The reason I ask the question is because we have some mutual friends. I would describe our mutual friends as some of the most AGI-I-pilled people that I know. Yeah. 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 somebody like you.

And so I'm very curious, like, what do you understand about AI that most people don't know? And even people within the tech industry. No, you're way too generous. I don't know that there's, I don't know that I have anything that interesting or that deep of an insight. I mean, I think it's...

You say that because you're used to it. 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 100 years, it's always been like this. And it's probably safe to assume that it will be.

And 99% of the time, that works great, right? And in these particular periods where things that actually move and they're real things that are different. Those are the 1% of times where it truly is different. Now you just kind of, rather than any kind of pattern matching,

like what really matters is just thinking about things from first principles. Like AI, you know, there's the famous like METR report, which was saying, you know, a couple years ago, AI would do about 10 to 20 seconds

worth of human work without interruption. And then you'd have to, you know, guided or directed or it would make a mistake or something like that. 10 seconds, 20 seconds. And that's just doubled every couple months, basically. And now we're talking about like hours of work.

So basically an AI can just take a task that would have taken humans' hours of work to go do. If you go and describe that task, well, and that's 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 principal's 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 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 simple pattern match.

So I think you've even taken this further where you're like, well, what happens when they can work for a year unassisted. Yeah. Yeah. And I think that's true, and I think we will get there. If Devin could work for a year without any human assistance, what would you have it do right now? Destroy your competitor.

All sorts of things. I mean, no, I mean, I still wake up and think about this in every different, like, you know, 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 formatted to work, and it's, like, kind of painful. Some of these things are still like kind of hard to use or it doesn't support a certain like styling of the email that you wanted to go do. I think the thing I like had pasted something with indents

and then it was like, it just couldn't like unindent them because the editor like, I don't know, there were some weird things where the editor like would not allow you to unendent one part but not the other or whatever. And I was just thinking like, it's really crazy that like 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 want to do. 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? And then you get to the point where you actually really just to get to spend all your time thinking about, well, what do you want to do? You know, what do you want to build? What do you want to create?

Like, what are the things that you want to see in the world that aren't there already? But I think what makes it interesting about what you were saying earlier is like, the more you increase the time, the more interesting it gets to me. So, like, there's this guy named Edwin Land, who I won't shut up about. And he was the founder of Polaroid. Yeah, yeah. Steve Jobs' hero, a lot of, what we think of his 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 that Hedlandland used in, like, the 70s of Polaroid.

And he thought of himself as a scientist, not as an entrepreneur, Edwin-Land. He died with, I think, the third most patents. He was like Thomas Edison, some other dude, and then Edwin-Land. And what he did is he couldn't figure out he invented the industry of instant photography. Yeah. Before you took a picture, and you're like, has to look. We'll find out two weeks from now when we get back to Kodak. Like, I have no idea. And he, now he took a picture of Polaroid. He's like, we'll find out 60 seconds when it dries.

But that was black and white forever. When I read that part where you're like, well, we're going to have agents that can work on a sister for a year. I didn't think of what I would do, which is a question I just asked. I thought of Edwin Land hiring this guy. He's like, I want you to think about how we turn this from black and white into color. And before he could begin, the guy worked there and just thought for two years. I'm like, that would be very convenient if I could have an agent, attacked this problem. While I'm working in the background because I can't figure it out,

they're just thinking about how to attack a single problem for two, in his case, this guy that fucking solved instant color photography. It just took him two years of thinking to do it. So this is like,

I'm going to push you on this a little bit more because it's like, I don't want your year-long agent to send bulk email. Oh, I agree. I can do it just to be clear. So I, and I think at some point it's kind of, you know,

you see this, right, where it's like, you know, when you're talking about seconds, you're literally talking about just like a specific command, right? When you're talking about hours, you're talking about giving it a task and having it do the task, right? And I agree with you.

Obviously, bulk email is not, you know, for years, what you're talking about is like, you're giving the agent permission, basically, you know? And it's like, this is, way more fun. Yeah, exactly. And this is like, what do I care about? And, you know, the answer might be, look, I want to give you a million different example. You know, the answer might be, like, there's this, like, one, you know, societal problem, which is really important to me.

And I think there's, like, I think it's a solvable problem. I think we can all be happy. but like, you know, we really need to spread awareness about it. We really need to get folks to understand the points of it. And we, you know, we need to figure out how everybody should, you know, work together and coordinate on. That's your point. That's a problem that an agent can think about, right?

Or even, you know, some of the kind of like sillier things too. Like, yeah, you know, there's this video game that I really like, but I wish, you know, if I were making the game, here's exactly how I would think about all these things. And I feel like there's, like, this really cool idea if, you know, you could combine elements from this one game that I really like, but then incorporate some of the elements from this other game and set the agent off on that mission of like, look,

we're going to go and like make the coolest thing ever and the coolest game ever. And like, we're going to incorporate these elements. We're going to think about how those like, you know, nicely intertwined and work together, right? Or if it's like, here's this, this like piece of just like novel science, which I'm just like really passionate about. You know, materials have been created this way for years and years. But like, here's this like avenue of attack. which I've been wondering about and thinking about,

of maybe there's a different, like, novel construction of materials. This way, set your agent to go work on that for years, you know, or months, right? And, like, have it to study that and explore these things and run its own experiments and try all these. I think all of these are, I think, soon going to be very possible, and to your point, very different. Yeah, like the framing of we're sending them on missions. Yeah.

I would have one that would pick the missions that I need to send other agents on. Yeah, yeah, yeah. Then you'll have the AI, you know, which is like the manager AI, of the missions, yeah. And I think it's like, I think we will continue. You know, it's my example of this is like, I always joke about how, you know, if you think about our ancestors from, you know, hundreds of years ago or thousands of years ago, imagine them looking at us and what we do.

And it's like, you know, you're pushing buttons, you know, and you're like sitting in a room and talking with other people and you call that a meeting. And that's like, those things, that's work for you guys, you know. And it's like, what do you mean that's work? You know, like, I'm in the fields. Like, I'm doing this every day. I'm going in farming, I'm making sure, you know, making all of our, like, you know, clothes by hand or taking care of all these things. And that's, like, work, you know, but like, how can you guys call?

You know, and my point is just, I think what we will have going forward is going to look that different from what we have today. We will look at people who, as we say, just, like, have these really interesting curiosities that they want to pursue. They have, like, causes that they're passionate about. They have, like, fun ideas or, like, art that they want to create. And, like, they're sending off their agents in pursuit of those missions. And they will think of that as work. And we will look at them and be like, wow, it's kind of crazy. That's what you get up and think about all day.

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So you want to get started quickly before all of your competitors are on Apploven. And you can do that by going to apploven.com. That's apploven.com. When you started Cognition, did you know that you were going to try to make an automated software engineer? Was that your first idea? I'll say 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.

And then two is it was always around the idea that these would be like real multi-step iterative processes, which was a, I would say, was a real hot take at the time. Like this was 2023. Okay. So this is when I met you guys. and I was on a walk with a mutual friend of ours. And I remember first here, you guys scaled from what, like a million in revenue to like 500 million or something in what, like 20 months or something? Yeah, more than 18 months.

I remember before you got no revenue. And I heard the idea. And it was pitched as like essentially like an automated software engineer. And I was like, holy shit. It's like a huge, you're going after labor. This is a huge market. And then you released. Yeah.

I remember the demo video. Yeah. And then you got a lot of shit. Yeah, yeah, yeah. Is that a great. I think is the full polar ends of the spectrum, right? So there are some people who are like, this is the coolest thing ever.

And like, you know, all this. And then there are some people who are like, you know, this is like the worst, you know, thing that you could buy. Similarly, like, in terms of the capabilities, there are some people who are like, oh my God, everybody's going to lose all their jobs tomorrow, which is not what we've ever really believed or thought of this. As to also like, dude, there's no way this is ever going to work.

and like this is totally a scam, you know. So what was the criticism? Did you release the product earlier than you wanted to? Well, look, 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, you know, we had been working with it and playing with it for a few months at that point. And the kind of 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, 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. And so it's just like showing that. Wait, we'll say more about that. The first task that Devin did was, like, set up MongoDB for us. And it's like, you know, 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, you know, DB set up or whatever. But we would just, like, run into errors.

You have this whole flow where you like, you find an error, you Google error, find some message on Stack Overflow or whatever. or you ask Chatchee to me, and it tells you, okay, here's what you should try. You try that thing. You run into a different error. You know, like, paste that in. And, you know, and like for this one, we kind of like, at some point we're just like, okay, Devin, just try to go fix it.

Just go run commands, like, do whatever you need to go do it. And then it worked. And you couldn't sleep. It was truly like, because, again, it's, you know, 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 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. is like maybe more than we should have. Maybe we should have done something in the middle, you know? I don't know. But like 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 our launch announcement were like actual runs of Devon that we had done ourselves and like run into it 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 like what level of effectiveness or whatever. But like, it's just, it's going to happen. And that's how we always felt about it. So explain the process of iteration to go from that product. You're getting a lot of hate. Actually, you know what?

I called Jeremy Stern. He wrote this excellent profile of you in Colossus, which there's a lot of parts I was just laughing my ass off, by the way. And I was asking, I was like, tell me the stuff that didn't make the profile. Yeah. And he said there wasn't that much because you're kind of like an open book. And a lot of people, like, managed their media. Yeah.

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, you release the first product, what was the benchmark? Sweet bench. Yeah. And it was like... 13%.

That was Devin. Yeah, yeah. And your point was that that's already better than... Yeah, at the time, the best known was like three or four percent or something like that. But obviously, yeah, 13%. It still means you fail, you know, 87% of the time. Was there a pronounced benefit from releasing early like that? For sure.

Yeah. I mean, so for us, by the way, you know, if you kind of think about this overall AI ecosystem, I mean, a simple way to put it, it's like, dude, we relate by a lot. You know, it's like, Open AI started, Google Deep Mind or Google Brain. I mean, these are obviously, you know, more than a decade old already. Open AI started like end of 2015 or even, you know, like the anthropic or, you know, other that folks talk about in the world, like, had been around already for years.

Like, we were getting started in early 2024. There's like a year plus out from the chat GPT launch. A lot of the existing players were already there, you know. The same was true. And code specifically, I mean, there was, you know, GitHub co-pilot, which had people had already, engineers had already used for years, and that was very much the like Q&A, like the auto-complete style experience of, you know, working with AI. You know, when you start a company, you kind of have nothing. Like, you have no right to exist is maybe one way to put it. It's like an interesting

truth about the world, which is obviously startups succeed. You know, a startup succeed all the time. And the startup versus big company has been played out for, you know, for forever. But like, there's no reason, you know, in terms of resources. in terms of people, in terms of brand awareness. Like, you have none of the things that, you know, the big guys have. And so from that perspective, you have no right to win, you know, over, over, over what they're doing, you know. And the reason that you are sometimes able to anyway is if you really, like, you know, plant your flag on the ground and put a stake into, like, what you think the future is, and you run like hell towards that. And if you, like, turn out to be right on some of the core.

things. And like, I think we were wrong on a lot of things, to be clear. And a lot of things, we were, you know, definitely we were early, which is a very fair criticism. I think a lot of the details and the nuances, like, we learned and adjusted over time. But the idea that, like, you would work with AI as a coworker, you know, rather than as like a tool or a chatbot, I think was, I mean, over the last couple years, that's obviously, like, really grown. And I think it was, like, very important for us, for our brand, for recruiting, for, you know, customer work, everything, for us to be, the first ones that actually planted that flag in the ground had said that.

So when did you have this idea where you guys are going to take a run at very, I would say, ferociously to like these giant like Fortune 500 companies or even the like the US Army uses you? Sure. Like where did that strategy come from? So funnily enough, so that launch was in March of 2024. 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. I thought your first iteration in the business model was like $500 a month or something. Wasn't it? Am I missed for what we're going to? So we had. So we had that, that was actually later on, actually. That was end of 2024.

Okay. But, you know, initially what we started was just like, you know, a bunch of people came, asked us for the products. We were like, I don't know if this is a product that's ready for prime time, but if you really, you know, 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 the system of like, oh, okay, let's do like a pilot or like a PSC, you know, and like, try to our best to like not overpromise and go like, guys, like, really, it's like very early.

But if you want to try it, you know, you can try it. You know, you can try, and we did all these, and perhaps unsurprisingly, I mean, they were all just, like, failing, which is kind of a natural. Like, it's like, you could do some pretty cool things with it. You could do some pretty interesting, like toy demos or projects or whatever, but it was certainly not ready to work on, like, actual companies, like real code base and so on, right? And so from that point, this would have been, like, April and May of 2024. We were making agents work with, like, GPT4. You know, this was like a very different era. So 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 kind of came to was, well, different tasks are different, right? And 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 know how to fix. And like, people were trying to use Devon for all of those and we're failing at it, understandably. And so then the question was like, okay, what are the natural tasks that are like, if there's like a first task that is going to

have PMF, you know, and real value from these kind of agent experiences where it can do the whole thing end-to-end, what is that going to be? We kind of said, okay, well, it should be some of these, like, really repetitive TDS wants. 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 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 7 to Java 8 or something like that, which were kind of like, you know, as you can imagine, enterprises had these massive codebuses where they would go and do all that, you know, and it's like a 50,000 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 tradeoffs, but like, it's a very repetitive task, right? Our first success ended up being with a company called New Bank, 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 a kind of a custom devon that was like extremely, extremely optimized for doing that. And as we grew from there, you know, later on, we had kind of, as things got a little bit more mature, you know, we've had both self-served 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. a cute demo from scratch, right? And I think we really, really leaned into that. And naturally, it's, you know, all the Fortune 500 or all the biggest companies in the world are software companies in 2026, you know, even the ones that folks don't necessarily think of that way, right?

And like Walmart or CVS or JPMorgan or Mercedes or whatever, you know, 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 maintaining. And that kind of became like a natural thing for us to work on because we had learned very early on that we want to work on real problems and we want to work on things that matter and that people actually care about. So wait, what percentage of your revenue

is coming from enterprise on? Today, around 75, 80%. What are people using on the self-serve? Like, what are examples of? Yeah. So we have a lot of teams who use it in self-serve. I mean, that's grown actually, that's grown quite a bit as well lately.

But we have startups who, you know, with Exa, who uses it a ton or open router or built. I ran into someone in my apartment in the elevator the other day. Oh, you're the Devon guy. Like, we used Devon. Are you Devin? I've gotten that as well. Hey, are you Devin?

And I was like, you know, I'm actually not, but that's, that's okay. Why didn't we call it Scott? You know, there's kind of both self-servant enterprise. With that said, it is entirely, in both sides, it is still like actual engineering teams building real output. And so we don't focus at all on like, 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 getting output out of that.

Okay. Can you walk us through? I'm very curious. Like, I'm a big enterprise. Yeah. I contact you. Walk us through what happens to being a customer. You know, it starts with education. 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 and like, what does it actually look like to deploy them? What is like? So, you know, we'll show them. them what this looks like. We'll talk them through like how we work 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, 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 want to 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, their repos, getting through security or whatever, that's like it, usually for typical companies, it's like a 12 to 18 month cycle. The thing that we do naturally is we just work with them to figure out how we go and do that as fast as is humanly possible.

Did you send employees down to South America for New Bank? Well, so New Bank, I mean, the first one, honestly, our entire team was the Ford deployed team. Like, we all flew to Brazil. Like, I mean, it's like the first case, you know, it's like, let's be real here, okay? Agents did not work generally, okay? And so there was a lot of like, how do you make it work for very specific?

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, 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. Hold on. We'll get to where it is now. But the idea of, like, no, we did deploy a team. We deployed the whole company. Oh, yeah. Yeah, getting the first customer, obviously,

was like a real, you know, it was like, I mean, I wonder what they thought of then. But like, yeah, it was like 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 it in the right kind of like, you know, 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 one company. And yeah, it was fun. So what do you do today? So today, it's obviously much more, it's much more self-starred and, you know,

agents are so much more capable, obviously. And we've figured out a lot more things with the unverting experience. But a lot of it is, you know, we're saying these cycles typically take 12 to 18 months. We try to get deployed with folks, you know, than like three months. And a lot of that requires folks to, obviously, first of all, requires them to really appreciate that it's a priority. I mean, if you have 25,000 software engineers that you're, you know, an org that's, that's, that's running, that, that, that, that, that, that, that, that, that, that, that, that, that cost, you know, 10 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 have all the, you know, we can deploy in their private cloud. We have very strict data agreements. We have, like, tight airwalls on, on, on, on, on, on all these things, obviously, um, you know, do you know, do a place physically have to go? Are they, are they, are they working with, like, like,

Are these deals so big that they're working with a specific company and only that company for a period of time or no? Typically, no. We have a, you know, we have a full, like, forward-deployed motion. But a lot of what that looks like, as we're saying, is a little bit more like user education and guidance. And so we will fly, you know, we still do this, but 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 get success, help them with their, like, set up and their playbooks, like all these things for how to use dev. but it's much more a kind of like, look, we're here to assist you guys,

we're giving you a lot of this kind of direction and so on, but like you are yourself, you know, in your team is the one that's using Devon and running all 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 10 times faster on. And we'll tell you for the ones that it's not.

And here's what workflows you should use instead or here's how you should get to value instead. But it looks more like that than like a literal like, you know, we are using Devin on your on your behalf or something. Deal 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, 11 Labs, Uber and DoorDash all used deal. Many of the top founders I know have personally invested in Deal after using their product. And what they discovered

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to deal.com forward slash Senra. That is, deal.com. forward slash Senra. Wait, so tell more about how you align the incentives between your company and then your customers. Yeah, so a couple of things here, which I think are really important. One, obviously, is just like really working with them on the very clearly defining the ROI, which I think is a very important. I mean, it's like 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 want to know that that's actually doing real value for you and you want to be able to identify where you are. Do you think all this stuff is a little crazy? I think it is directly correct, but I think 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 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 obviously I think the like, the math works out in terms of like, you know, the GPs 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 want to 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 the projects and the initiatives? And like this project, which was scoped out for 18 months and was going to be handed off to, you know, an outsourced contractor and was going to cost you 15 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 at three months, you know, stuff like that, which I think is super important. The second thing, which I'll call out, especially is just being neutral. A lot of discussion, obviously. What do you mean by that? Being neutral with respect to all the labs, the models themselves. I think you said you like being Switzerland. Yeah, we like being Switzerland.

Exactly. And so I think it's like an important thing of, you know, we are just as incentivized as they are to figure out how to make their token spend efficient, right? And so Devon is purposely meant to be, 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. Describe what a compound model system is. Yeah, yeah.

So for each different part of your use case, I mean, imagine, let's say you tell Devin, hey, customer just reported this bug. You know, there's a juror 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 any engineer should, like,

it's going to go and try to reproduce the bug itself. Right. So it'll say, okay, let me spin up the process. product locally, we click around, try to follow the same steps that they follow 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 know, pinpointing what are 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 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 want to actually use Max thinking and you want to use the very best models you can get your hands on in the world. 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 immediate answer, 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 going to serve you your GPD for this, or we're only going to serve you OEFIS for this. Devin can use any of the different models that has in its arsenal, which include all of these models from Anthropic, Open AI, Google, et cetera,

but also our own models, right, or open source models out there, and it will dynamically go and choose these models for these tasks. How do you think about this? Like, you're a customer of them, but also competitor with. Yeah.

No, I mean, look, I think in practice, there's a lot of positive some work for us to do together. And so, like, you know, the way that we think about ourselves, a couple things. One, software is the only thing that we care about, obviously. And there's a lot of value in focus and in building products and building our whole motions specifically around that. Say more about how you think about focus in the value in it. 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 100,000 more people than you, right? And an infinitely more brand name, right? And like the way that you build, you know, like a real kind of like, you know, 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 like, you know, from there, it's like a lot of just like really tight execution. And so in software, you know, I love, you know, I have talked about this. I love the quote from Daniel Eck in Spotify, right?

People were saying, so like, you know, there's like YouTube's trying to go and do this and Apple has Apple music and like, 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 going to care way more about music than they are. And I think for us, it's true. It's like we are just going to 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. And there's a lot of nuance in that. And there's like a lot of messiness in that. It's not as simple as like, oh, here's a sandbox algorithms problem. Go and code me the correct 30-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? All of these are obviously super hairy, messy problems. And that's what we care a lot about, right? And so from that perspective, 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 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, you know, folks can work with us and kind of like trust in us 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. I love that you use the example of the war between Spotify and the rest of the Apple Music, for example. Jimmy Iveen, who also came on this show, and now as actually a friend of mine, he actually called me yesterday about this, because anytime anything happens with AI music and Spotify, he calls me. But we talked about this because he's like, you don't understand.

He had like 40 years of experience in the music industry. He had all the relationships. He's like, Apple bought me for $3 billion. Spotify at the time, he's like, we're going to go head to head with them. Spotify only had 3 million paid subscribers. At the time, Jimmy's going to fight the war. And he's like, I have one of the biggest companies in the world. 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. Because they're like, we don't give a shit about getting a couple million more, tens of millions and more of subscribers. We invented the most successful consumer product of all time. And he said something like they just clipped his wings. And he was like, he was trying to do something. They're like, oh, this isn't important to us.

Where Daniel was going to die if he was not successful. He just cared about a lot more. Dude, even in the two years that we've been around, you know, I've heard so many different versions of the same argument. Because when we started, as you can imagine, this was true for us. It's true for everybody in the space, building the space. The number one pushback that you would always hear from investors, from other people,

people, it was like, but like Microsoft already has GitHub co-pilot. Like, isn't that, everybody's just going to use that, right? And it's like, it's a very reasonable thing to say in some sense because, you know, they did have all, you know, they did own all of GitHub, and they did have a partnership with opening app, And they did have, like, BS 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. 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 I think the reality is, like, people have said this forever of like, oh, like, yeah, like startups versus, you know, like, why should it? You can give all the rational arguments. Ten years ago was like Google will do this or Facebook will do this. Yeah, yeah, for sure. It's like, oh, why should Datadog exist or whatever. You know, the clouds have all. You know, the clouds care about observability too. the cloud's care. And the reality is like, it's in some sense a bit of an uncreative way to think

about things, I think. Like if there was like one thing that, okay, here's what we all know is going to 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 to it, right? But if there are millions of problems out there and solve, there's lots of different things, the world is dynamic, things change all the time. There's lots of new ideas or opportunities or ways to build new products, then the reality is like, of course, there's lots of different niches to own, there's different things to really bet on, there's different focuses to spend your time on. And I think that will continue. Yeah,

I mean, I think it's like for better or for worse after the last couple months of news, cognition, I think, has been a bit more of the like, we've become a bit more known as the, you know, the folks betting on independence in some sense because obviously there have been some high profile acquisitions. It's funny you said that because I was, I want to, one of the questions I'm going to ask you, which is probably won't answer. It's like, how many different acquisition offers have you had? I will not answer that question. Dozens? More? Dozens is a lot, dude. I don't know about dozens, but. Okay. The time. It's probably a only handful that could actually have a Ford to buy you if you would sell, but the amount of times

they keep coming back. Oh, I see. Well, yeah, it's definitely, anyway. This independence part is really interesting to me. We were on the phone, me, you and a mutual friend, on three-way, like, I don't know. I think it was actually last summer. And we're not going to say the company. But I was like, Scott, what do you, what do you think about, you know, an acquisition with X, not X, the platform, X the blank company? You're like, I don't know how we'd be able to afford them.

You just assumed I meant you buying them. And I just fucking cracked up laughing. It was like, it's hilarious. No, so I think like Yeah, yeah And it's like folks Like they're folks taking acquisitions And doing things

There's some big high profile ones I think those are great to be clear And I think it's a very reasonable path to exit As I mentioned for us, it's like we've We've all come into this like we want to build a generational business And so I'm really excited about And I think people I think today sometimes

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 What does that mean? What's not possible? Like, maybe it's not possible to build a new independent business because everything else, you know, it's like all the labs are going to do everything. All the opportunity is take it, you know.

And it's like, guys. Those people aren't founders. Founders are rationally optimistic. Yeah. They're just not founders. Yeah. They believe even there's no evidence that they should succeed, that they will succeed. Yeah.

So people like that just need to get a job. Do you, I'd say this even with Devon. Like, if you think that going on to the Devon web app and like giving the or any other coding product out there today, you know, and like giving the instruction. the way that you do right now, and then you get the pull request out, and then that's how you review it, and that's how you build software. If you think that that's going to be the way

that software is built forever, then yeah, then nothing will change. But, like, I think we have 10 more generations of these different product experiences to come, right? And, like, building those and doing those, it's like, that is what innovation is. And, like, that's what's going to happen. The advantage I have of reading, you know,

for the last decade of all, like, all these biographies of physical entrepreneurs, like, you're studying somebody's life, but in many cases, if they were so successful, they wrote a book about your life, you usually, it's almost, like you get a minor in like new industry creation. Every single time, it's just like we're too late, it's over, there's no more opportunity.

Yeah. And the way I thought about this was like the best definition of a business I ever heard actually came from Richard Branson, where he says all businesses is an idea that makes somebody else's life better. And if that's true, which I believe it is, then that means there's infinite opportunity in the future now and in the future because there's infinite ways to make somebody else's life better. And that's all the business is. Yeah.

So this idea that's like we got to the end of history is just bullshit. Yeah. There, and, you know, I don't mean to push it on this. Like I am personally cured. though because I know you're already rich. I don't think money is your North Star based on the conversation we had in the past, but like

there's got to be some crazy number somebody can throw at you where you're just like, fuck, I have to take this. Not really on it. I mean, it's like the you know, people have asked me sometimes before. They've asked me like okay, but like really that would you guys

like this is what I'm doing right now. And the way that I say it sometimes is like we would sell if we thought it was the most ambitious thing to do. It's kind of an oxymoron because obviously it is. But it's like my genuine answer in the sense that like, that's like, well, we care about it. You know, it's like the, I mean, it's funny you talk about money. Like, I don't even, dude, I don't have, like, I don't have a car.

You live in an apartment, I just realized. I have an merchant apartment. Yeah, it's like, I don't know. I think, I like eating sushi. That's fun. The sushi doesn't cost that much. You can do that. off of an engineer's sour as well.

Just so you know, if this is true, then you are the type of entrepreneur that I find the most fascinating in the world. Because when a startup founders come to talk to me, it's just like, I don't give a shit about your startup. I ask the same question. It's like, is this your last business? Yeah.

Right? I asked Kareem from Ramp like this before we did this like deep partnership. Yeah. And it's just like, okay, you could sell it. Like the Zuck example where they're like, why didn't you take a billion dollars? I think you owned 25% of the company. You would have made $250 million or like 22.

He's like, well, what would I do with the money? I would just start another social network. I kind of like the one I have. I just want to build shit anyways. So, like, what do I do? 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 because it's literally just all nerds. Like, and I think you like, you know, like there's usually a mixture of obviously the nerds and some of other people. Like, you kind of built this, like, almost like your social network is like now... You're not allowed here if you're not a nerd, yeah. Yeah, it's like a physical manifestation of your social network. But like the way I would describe this is like... Okay, cool, cool.

So my friends are all in Westerners. Yeah, I agree. And I mean that in obviously. No, no, that's great. I love that, yeah. But what I, the people are most interested in it is just like there's no price, right? Yeah. Like, the example I use is, go ask Steve Jobs.

I'll give you $2 trillion, but you can't work on Apple. Yeah. What the fuck do you think he would have said? Yeah. I agree. No. No. He's like, well, I want to work on Apple. That's what I want to do. There's a funny thing that I wanted to tie together, which I didn't find the opportunity till now, is you were currently the second wealthiest entrepreneur from Baton Rouge, Louisiana.

I don't know if you remember we've talked about this. We've talked about this. Yeah, yeah. Yeah. The first one. So you can figure it's great business, but I'm a customer of that business as well. The first one is Todd Graves. Yeah. Owns Raising Cain's. Yeah. I talked to him. He was on the show. Yeah. You tell him, you'll give $100 billion. dollars for your chicken finger empire. Yeah.

No. He's turned down, and I know this for facts, I've talked about it. He's turned down crazy acquisitions offers. He's like, I don't care about it. It's not the money. Yeah. No, I mean, for us, it's very like, like, again, it's not rational.

But 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 at all and we just weren't good enough, then be fine. Like, okay, it would.

It wouldn't be that. I'd be, I'd be, I'd be, I'd be, I'd be, I wouldn't be fine. But, but, but, like, it would be, like, 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 could have gone for it all, we could have pushed harder, and we did it. Like, that, I think it's like, I just, 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 almost, it's kind of circular, I don't know, but, but it's like, why are we so excited to do this? why do we do, you know, spend all, it's like, we want to achieve our potential and build what we were meant to build, you know, and maybe that's something and maybe that's nothing, but like, you'd rather find out and see, you know.

I think this idea of you have one life, go. Yeah. It's a perfect place then. Thanks for the time, Scott. Yeah. Thanks for having me. 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.