The Future of Software & AI | Cognition’s Scott Wu

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 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. 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 got third place here's 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.

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 game I I don't know. It's 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.

And 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 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.

So like I spent some time with Demis from Dind and what was interesting is I I I draw a lot of um like similarities between you two cuz 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, 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 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 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 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 the 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 kind of 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 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 is cuz my dad was like a really good Go player. Like he was like a seven dawn 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 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 kind of how we ended up here in the first place. Uh I was I was born after we moved to the US obviously.

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

Yeah. No, she was always uh she 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, you know, I can 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.

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

Yeah.

Always fascinated by like there's 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 [ __ ] face and yells at him for hours. Larry's adopted uh father would just tell him you're never going to amount to anything. And so that they had this like inner fire to to to disprove uh you know saying that basically no [ __ ] you dad you're wrong about this right

and then you have like the SA lers 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 Estee Lauder like category where she would tell you that like hey these people are doing amazing things you could do even better than them 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 um but no, they were always

But your mom gave you self-confidence.

Yeah, I think she she always told me that I was the best and she was always extremely proud of that, you know, it's it's the I had these like when we would go to these math competition.

Hold on. She 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 I think even when I was like tiny, she would tell me that I was extremely talented, extreme, you know, she was always a huge source of support me and she always obviously believed in whatever I wanted to do, you know, cuz you would compete in math competition, 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 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 ones that me and my brother want of course

We're going to put up pictures of other people's trophies and you better damn sure replace them with your own. you know, so like you know when when I was pretty and my brother as well, you know, we both really like these competitions and so 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 on the on like the mantel piece 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.

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What matters to you more? Like what is uh is it the the pain of losing is like losing is worse than the love like the thrill of winning? Cuz that's how you started it. You're just like I could not stand losing. Let me back up. I was 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 talked to Michael Dell about it. And he's like, "Yeah, it's it's just the fear of losing is the the pain of losing is way worse than the the the good feeling of winning."

So, I think that's definitely true in terms of how it feels in order to get anywhere. You got to lose a lot. If anything, almost like a lot of the, you know, I mean, a lot of the guys you're 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 want to stop trying if that makes sense.

Yeah. No, I I think it's impossible. I I know your personality type like there's it'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 is was a pretty big founding team is 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 hyperscaler 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 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 a coding agent or like you know like AI programming or something I I always like you know I always hear 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 Devon if we're successful is Devon is the way that humans can tell their computers what to do cuz that was the whole point of software engineering anyway right is just to be able to work with your computer computer and tell it what you want it to do I mean I doing that for the world is like a massive opportunity and and and 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 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.

Wait, say say more about that what you just said. We're all nerds. This is a big one because we can actually teach AI how to do.

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.

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

I think it is incredible. Yeah. I mean, the Well, I I would go further too and just say like at a zoomed out level, it's I honestly think that Because you know everyone talks about AGI everyone talks about well what is the future going to 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 want to make in the world. My co-founder has a slime 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 going to 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 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 the world that we're going towards and and like we want to be the ones building that.

Okay. So, talk about what Devon does today.

Yeah.

And then I I want you to flesh out that idea of like where you see in the future. It's just the the interface between humans and computers.

Sure. Yeah. So, Devon is uh today, you know, what what what folks know as as an AI software engineer. And basically what that means is that Devon 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 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 20 30 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 Devon to ship 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 might there is no like you're just the interface between 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 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 machine do the arithmetic right like the eniac was um you know when the first in some sense that the first computer out there although it was obviously very different or it would have been like you know 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 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 Tik Tok 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 want to 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 kind of see this already like at this point you don't need to know what programming languages 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 want to 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 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, the 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, and I'm giving a 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 got to go build all this out and you got to go and do that, right? You need that software to be used enough times or to be 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 what's made that that work out and made it feasible, right? But there's 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 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 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 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 know, you don't even have to look at this, but 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 a script that automates this part. I'm going to do this. I'm going to do this. Um, and that's what's going to allow it to actually go and do all these things. But but but what you start to get to, you know, 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 want to go do, right? And you wake up in the morning and it's like here 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 of the literal like, all right, put the put the pen to the paper on writing code, but that task is like, you're 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. 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 Devon 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, 10 years ago. Um but certainly um or even one year ago or 6 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 want to 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 5 years or so. In AI terms 5 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 5 years but 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.

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 >> including your own. And um 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 inherent like fight orflight response are our kind of like our ability to kind of like measure things to vastly oversimplify if 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 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 and has all, you know, it was a very different life when they grew up um in in like, you know, the '60s in China and 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 5 years from now it's going to be insane to think about all these things that we're going to have. 10 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?

Um

The reason I ask the question is cuz we have some mutual friends. I would describe our mutual friends as some of the most AGI pill 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 like somebody like you." And so I'm very curious like what do you understand about a that that most people don't and even people within the tech industry.

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

You 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? Um and in these particular periods where things that actually move and and they're real things that are different, those are the the 1% 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 MER report which was saying you know a couple years ago AI would would would do about 10 to 20 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 10 seconds 20 seconds and that's just doubled every you know every couple months basically and now we're talking about like hours of work So, 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 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 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 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 that

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

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

If Devon could work for a year without any human assistance.

Yeah.

What would you have it do right now?

Destroy your competitor.

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, 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 kind of painful. you know, 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 just couldn't like unindent them because the editor like I don't know there was some weird things where the editor like would not allow you to unindent 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 want to 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 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 the more you increase the time the more interesting it gets to me so like there's this guy named named uh Edwin Land who I won't shut up about and he was the founder of Polaroid.

Yeah.

Steve Jobs hero. A lot of Steve what we think of as Steve Jobs ideas literally just came from Edund Land down to like the chairs and the table he would use for his presentations. It's like the same thing that Hoodland used in like the 70s and Polaroid

and he thought of himself as a scientist not as an entrepreneur. Edward Land he died with I think the third most patents. He's like Thomas Edison some other dude and then Edwin Land. And what he did is he couldn't figure out he invented the the the industry of instant photography. Yeah.

Uh before you took a picture and you're like, "Hey, how's it look?" Like, "We'll find out two weeks from now when we get back in Kodak." Like, I had no idea. And he now we 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 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 Edin 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.

Sure. I'm like, that would be very convenient if I can have an agent attack this problem and while I'm working in the background cuz 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 [ __ ] 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 just to be clear. So, so I I and I I think at some point it's it's kind of, you know, you see this, right, where it's like um 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 for years, what you're talking about is like you're giving the agent a mission basically, you know, and it's it's like this

That sounds way more fun.

Yeah. Exactly. And this this is like what do I care about? And and you know, the answer might be look, I I want to I'll give you a million different examples. You know, the answer might be like there 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 um you know, we really need to to to to to spread awareness about it. We really need to get folks to understand the points of it and we we you know, we we need to figure out how how everybody should you know, work together and coordinate on. That's your point. That's a problem that an agent can can think about, right? or or even you know some 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 those elements we're going to think about how those like um you know nicely intertwine 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 for for years and years, but like like here's this like avenue of attack which I've been wondering about and thinking about of um maybe there's there's a different like novel construction of materials this way. Send your agent to go work on that for for years, you know, or months, right? and and like have it study that and explore all these things and run its own experiments and try all these. I think all of these are are I think soon going to be very possible. And to your point, it's very different.

Yeah. I like the the framing of we're sending the 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 which is like the the manager AI of the the missions. Yeah. And and I think it's like I I think we will continue, you know, it's um 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 just 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 that 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, you know? I'm going in farming. I'm making sure you know making all of our our our like you know clothes by hand or taking all all taking care of all these things and that's like work you know but like how 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 or or like art that they want to create and like they're 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 that's what you get up and think about all day.

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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 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 2023.

So this is when I met you guys

and uh I was on a walk with a mutual friend of ours and I remember first here this is you guys scaled from what like a million in revenue to like 500 million or something like that in what like 20 months or something.

Yeah. Yeah. I remember before you had no revenue and I heard the idea and it was pitched as like essentially like an automated software engineer and I was like holy [ __ ]

This 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 [ __ ]

Yeah. Yeah. Yeah.

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 these and then there were some people who were like you know this is like the worst you know thing that you could but similarly like in terms of the capabilities there were 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 uh or thought of this as um two 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 did you release the product earlier than you wanted to?

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 kind of just wanted to show people some of the examples of what it was capable of because it 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. Uh a and so it's just like showing that

Say more about that.

The first task that Devon did was it like set up MongoDB for us and it's like you know it's a 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 setup or whatever. U but we would just like run into errors. you you have this whole flow where you like um you find an error, you Google error, find some message on Stack Overflow or whatever or you ask Chat GB and it tells you, okay, here's here's one you should try. You try that thing, you run into a different error, you like paste that in and then you know and and like for this one, we kind of like at some point we're just like, "Okay, De just just try to go fix it. Just go run the 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 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 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, I know. 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, 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 dev that we had done ourselves and like run into and been like, holy [ __ ] this is insane for us. It was always kind of like, look, you can, you know, we can debate when or or or 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.

Well, uh, actually, you know what? I called Jeremy Stern, who wrote this excellent profile of you in Colossus, which there's a lot of parts I would just laugh my ass off, by the way.

>> And I was asking, I was like, "Tell me the stuff that didn't make uh 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 um managed their media and you know, you could have to talk about certain stuff off the record or whatever and you were just like saying everything.

>> But he did say something about like you made the point where like when you released the first product, what was the benchmark?

>> Sweet Bench. Yeah. It was like 13%.

>> That was Devon.

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

>> Yeah. At the time, the best known was like 3 or 4% 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, so, 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 were late by a lot. You know, it's like OpenAI started.

>> Why though?

>> Google Deep Mind or Google Brain. I mean, these are obviously, you know, more than a decade old already. OpenAI started like end of 2015 or even, you know, like the Anthropic or or, you know, other that 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 ChatGPT launch. A lot of the existing players were already there. You know, the same was true in code specifically. I mean, there was, you know, GitHub Copilot, um, which had people had already, you know, engineers had already used for years, and that was very much the like Q&A, like the autocomplete style experience of of you are working with AI.

You know, when you start a company, you kind of have nothing. Like, you, 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, startups succeed all the time, and startup versus big company has been played out for, you know, for for forever. Um, 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 and so from that perspective, you have no right to win, you know, over over over what they're doing, you know. And then the reason that you're sometimes able to anyway is if you really like, um, you know, plant your flag in the ground and and and put a stake into like what you think the future is and you run like hell towards that and and if you like turn out to be right on some of the core things. And like, I think we were, we were wrong on a lot of things. Uh, to be clear, a lot of things. We, you know, so 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 co-worker, you know, rather than as like a tool or a chatbot, I think was, I mean, over the last couple years has 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, you know, the first ones that actually planted that flag in the ground and said.

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? 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 any revenue. We didn't really have like we had.

>> I thought your first iteration of the business model was like $500 a month or something, wasn't it? Am I miserable?

>> So, we had that, that was actually later on, actually. That was 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 product. We were like, "Guys, I, 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 a system of like, okay, let's do like a pilot or like a POC, you know, and like we tried our best to like not overpromise and be like, "Guys, like really, it's like very early, but if you want to try it, you know, you can try." And we did all these, and perhaps unsurprisingly, I mean, they were all just like failing, 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 actual companies, like real codebase 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 is like a very different era. 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 kind of 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 how to 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 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, 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 7 to Java 8 or something like that, which were kind of like, you know, as you can imagine, enterprise had enterprises had these massive codebases where they would go and do all that, you know, and it's like a 50,000 file codebase 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 make the trade-offs, but like, it's a very repetitive task, right?

Our first success ended up being with a company called New Bank, 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 Devon that was like extremely, extremely optimized, um, 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 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 500 or all the biggest companies in the world are software companies in 2026, you know, even the ones that that folks don't necessarily think of that way, right? And like Walmart or CVS or JP Morgan or, um, or or Mercedes or what, 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 think we had had learned very early on that we want to work on real problems and we want to work on things that that that matter and that people actually care about.

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

>> 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, 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 OpenRouter, or built, you know, I, I ran into someone in in my apartment in the elevator the other day, like, "Oh, you're the Dev guy?" Like, "We use Dev." Uh.

>> Are you Devon?

>> I've gotten that as well. "Hey, are you Devon?" And I was like, "You know, I'm actually not, but that's that's that's okay." Uh.

>> Why don't we call it Scott?

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.

>> Okay. Can you walk us through? I'm very curious. Like, I'm a big enterprise. Yeah.

>> I contact you. Yeah.

>> Walk us through what happens to being a customer. 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. 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?

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, 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, um, the repos, getting through security or whatever, that's like a usually, um, uh, for for typical companies is like a 12 to 18 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 as 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 deployed team. Like, we all flew to Brazil. Like, I mean, it was this 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 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, this is what you do in that case, and this is what Devon needs to know, and Devon 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.

Hold on. We'll get to where it is now. But the idea of like, no, we didn't deploy a a team. We deployed the whole company.

>> Oh, yeah. Yeah. It was, I mean, it was getting the first customer obviously was like a real, you know, it's like, uh, I mean, I wonder what they thought of that.

>> But, but, but like, yeah, it was like a 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 Devon to do this correctly and build in the right kind of like, um, you know, the the right orchestration for Devon 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.

>> So what do you do today?

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, agents are are so much more capable, obviously, and we've, we've figured out a lot more things with the onboarding experience. But, 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 3 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 25,000 software engineers that you're, you know, in an org that's that's that's running that that that that that costs, 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, 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 all these things. Obviously, um, you know.

>> Do employees physically have to go? Are they are they working with like, 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?

>> Typically, no. Um, we have, uh, you know, we have a full like deployed motion, but a lot of what that looks like as we're saying is is 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 success, help them with their like setup and their playbooks, like all these things for how to use Devon. 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 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 Devon right now can make you 10 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 Devon on your on your on your behalf or something.

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>> Wait, so tell more about how you align the incentives between your company and then your customers.

>> 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 everyone's talking about this right 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.

>> You think all this stuff is a little crazy.

>> I think it is directionally correct, but I think there's some, you know, the, 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 that 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 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 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, um, and 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 $1 million and you get it all done in 3 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, what do you mean by that?

>> Being 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 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 Devon 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 subtask of a task or something like that.

>> Describe what a compound model system is.

>> Yeah. Yeah. So, so for each different part of your use case, I mean, imagine, so let's say you tell Devon, 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 Devon actually go do, right? First of all, Devon'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, 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? And 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 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 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 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 Devon, rather than being pegged to one model and saying, "Oh, we're only going to serve you GPT4 for this, or we're only going to serve you for this." Devon can use any of the different models it has in its arsenal, which include all of these models from Anthropic, OpenAI, Google, etc. 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.

>> How do you think about this? Like, you're a customer of them, but also competitor with.

>> 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 that we care about, obviously. Um, and there's a lot of value in focus and in building products and building our whole home, whole motions specifically around that.

>> Say more about how you think about focus and the and 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 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 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 in Spotify, right? People are saying, so like, you know, 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 going to care way more about music than they are. And I think for us, it's true. It's like, we, 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 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, 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 codebase 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 just 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. I love that you use the example of the war between Spotify and the rest of the like Apple Music. For example, Jimmy I, who also came on the show and now is actually a friend of mine. He actually called me yesterday about this, cuz anything anytime anything happens with AI music and Spotify, he calls me. But we talked about this because he's like, "You don't understand." Like he had like 40 years of experience in the music industry, had all the relationships. He's like, "Apple bought me for $3 billion. 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 the one of the biggest companies in the world. And then he talked about, he's like, that wind up being a huge, he thought it was an asset. It was a huge liability. Yeah.

>> Because they're like, we don't give a [ __ ] about getting a couple million more, tens of millions or more of subscribers. We invented the most successful consumer product of all time. And he said something like they just clipped his wings.

>> Like he tried to do something 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 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. It was like, but like Microsoft already has GitHub Copilot. Like, isn't that everybody's just going to use that, right? And it's like, it was 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 the 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, 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 of like, oh, like yeah, like startups versus, you know, like, why should you can give all the rational.

>> 10 years ago it was like Google do this or Facebook. It's like, oh, why should DataDog exist or why should Snowflake or Databricks exist or whatever, you know, the clouds, the clouds have all, you know, the clouds care about observability too. The clouds care. And the reality, it's like, 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 going to be the end state future and everybody's just working toward it and whoever has the most resources toward it wins. 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 ways to to to build new products. Um, then the reality is like, 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 think it's like a, 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. It's funny that you said that because I was, I want, I wanted one of the questions I'm going to ask you, which you probably won't answer, is 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 only a handful that could actually afford to buy you if you would sell. But the amount of times they keep coming back.

>> Oh, I see. Well, yeah. Anyway.

>> No, this 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, um, we're not going to say the company, uh, 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 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 [ __ ] cracked up laughing. I was like, "It's hilarious."

>> No. So, so I, I think like, um, yeah. Yeah. And it's, it's like folks, like there, there 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 for exit. As mentioned, for us, it's like, we, we've, we've all come into this like, we want to build a generational business. That'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.

>> 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.

>> The labs are going to do everything.

>> All the opportunity is taken, you know, and it's like, guys.

>> Those people aren't founders. Founders are rationally optimistic.

>> Yeah.

>> They're just not founders.

>> Yeah.

>> They they believe even there's no evidence that they should succeed, that they will succeed.

>> So people like that just need to get a job. Dude, I'd say this even with Devon, like, if, if you think that going onto the Devon web app and like giving the or any other coding product out there today, you know, and and like giving the instructions 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 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 is like, that is what innovation is. Like, that's that 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 entrepreneurs, like you're studying somebody's life, but in many cases, if they were so successful, uh, 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. 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 business 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 business is. So this idea that's like, we got to the end of history is just [ __ ]

>> Yeah.

>> Uh, there. And, you know, I don't mean to push you on this. 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've 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. I mean, it's like the, you know, people have asked me sometimes before, they've asked me like, um, 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, um, is like, we would sell if we thought it was the most ambitious thing to do. It's kind of an oxymoron because obviously, but, but, you know, it's like my genuine answer in the sense that like, that's like what we care. We care about, you know, it's like the, I mean, it's funny you talk about money, like, I mean, I, I don't even, dude, I don't have like, I don't have a car.

>> You live in an apartment. I just just realized.

>> I have rent apartments. Yeah. It's like, I don't know. I think I like eating sushi. That's fun. As soon as she doesn't cost that much, you can do that off of an engineer salary as well.

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

>> Yeah.

>> Right. I asked Kareem from Ramp, like, that's before we did this like deep partnership.

>> And it's just like.

>> It's 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 own 25% of the company, you would have made $250 million. You're 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. Like, I just want to build [ __ ] 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, Yeah.

>> Know what I mean? But like, you know, like, there's a usually a mixture of obviously the nerds and some other people, and like, you kind of built this like almost like your social network is like.

>> I'm not 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. No. So my friends are all also enduring way.

>> I love that. Yeah. But what, what, what the people I'm most interested in, it is just like, there's no price, right? 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 [ __ ] 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're currently the second wealthiest entrepreneur from Baton Rouge, Louisiana.

>> I don't know if you remember, we've talked about this.

>> We talked about this. Yeah. Yeah.

>> Yeah.

>> The first one.

>> Chicken Figure is great business, but I'm a customer of that that business as well.

>> The first one is Todd Graves. Yeah.

>> Owns Raising Cane's.

>> Yeah.

>> I, I talked to him. He was on the show. Yeah.

>> You tell him you'll give a hundred billion dollars for your chicken finger empire.

>> Yeah. He, no, he, there's no. He's turned down, and I know this for facts. I've talked about it. He's turned down crazy acquisition 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, I, this is not rational, but, but like, the way that I would describe it is 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, you know? It wouldn't be fine. But, but like, 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, we could have gone for it all. We could have pushed harder and we didn't. 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 kind of circular. I don't know. But, but it's like, why, 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 and build what we were meant to build, you know, and and maybe that's something, maybe that's nothing, but like, you'd rather find out and see. Yeah.

>> I think this idea of you have one life, go.

>> Yeah.

>> Is a perfect place to end. 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.