You're really only limited to your ideas and to your imagination, where you can kind of just turn things into reality.
You were the three-time gold medalist of the IOI, a top programming competition in the world. Now you're running one of the top AI companies here.
When we launched Devin in March of '24, it was the first autonomous agent. One hour of human time spent managing Devin was worth like 6 to 12 hours of that human time doing the work themselves. Elon had this phrase that he really drilled into us, which is everyone is chief engineer.
Let's talk about this new era of software abundance. For us at Cognition, for example, our engineers don't type code anymore. You really can just turn your ideas into reality.
The engineer and the designer and the product manager all look at each other and say, "I don't need you guys anymore."
CEO and co-founder...
Scott Wu was a three-time global gold medalist in programming. I worked with him in the past. He's now running one of the fastest-growing AI companies in the world, helping to usher in this era of software abundance. He and Russell, co-founders, met up with us. We played some games. Not going to tell you who won. These are pretty smart guys. But it's always really interesting to hear from Scott and Russell about the cutting edge of AI, how the world's changing, and what we can create in this new era of abundance. Scott and Russell built Devin, who was the very first AI programming agent two years ago. They're already launching in all sorts of other areas. They're now transforming how governments work as well. Excited to see where they're headed next. Welcome to American Optimist. We have back Scott Wu, the CEO and founder of Cognition, and your co-founder Russell. And to remind people, Scott, you were the three-time gold medalist of the IOI, a top programming competition in the world. I think you were the one-time world champion at 17, and we worked together at Adapar after that. And now you're running one of the top AI companies here in the world. Russell, I think you started your career at Tesla as an ML engineer. You sold a company to Scale. You guys are both in your 20s still, right?
No, I'm aged out now. I'm 30.
So, you're turning 30. Okay. Well, that's okay. You're getting old like me. So, you're still right in the heart of it here. What's the average age on the team? Actually, I'm curious.
I think it's probably... So, on engineering, it's about 25. Um, and then obviously on go-to-market it's a little bit older, but yeah.
Yeah. Well, go-to-market is different. That's fair. You're running more go-to-market stuff.
Yeah, we... I think, you know, the engineering team, we have 17-year-olds, we have 18-year-olds, we have really young folks, and then... but we'll take anyone at any age as long as they're ready to grind and ready to have a big impact.
And you guys have like huge numbers of people who have won gold medals globally in programming. This is a very advanced technical team here at the cutting edge of AI.
Yeah, I mean, some of our favorite people, I would say, are people who are like, you know, 17 or 18, like finishing high school, but they had already played around with a ton of building agents themselves, like working with AI training models and so on. Um, and it's obviously... I mean, it's...
Actually, I want to ask you about this really briefly because... so you were a gold medalist in the world at 15, world champion at 17. It's obvious people can be really, really good at these things at a young age. Is there something about AI where like a young person's brain that kind of grows up and forms using it could somehow be more ahead of anyone who doesn't, or something? How do you think about that?
It's a good question. Um, yeah, you know, I mean, it's funny enough, so I went to what's called the USACO, the USA Computing Olympiad. Um, and from there, that's like the training camps and all the selection camps that choose the national team that go represent at the International Olympiad. And every year there were about 20 kids.
It was just like the top 20 kids around the US. I was from Louisiana. Most people were from like, you know, California or like New York or around like Massachusetts, like around MIT and Harvard and stuff like that. Um, but in my year actually there were a ton of others who all kind of went into AI. And so obviously Stephen and Andrew who started the company, um, you know, who started Cognition with us, um, but also a ton of others. And so Alexander Wang who started Scale, um, Demi Gu who started Pika, um, let's see who else, um, Daniel Ziegler who's one of the co-inventors of RLHF, um, Alex Wei who is like now running a lot of the reasoning efforts at OpenAI, um, Johnny Ho who started Perplexity. So we were all the same year actually out of that like group of 20 people. Um, and it was kind of an interesting one. I mean, I think there are a few things there. I think for one, obviously, I think entrepreneurship is infectious, you know, and I think, um, I mean, Alexander was, I would say, the first to really start a company and to see real success with the company. He left freshman year from college to start Scale.
And he sold Scale obviously for like 16 billion to Facebook or what, some funny structure, but huge success. But that probably inspired other people who were saying, wait, I'm that smart too, I could do this too.
Yeah. So I think that was a big motivator, and you know, we all kind of came up together and kind of got to go through some of these things together, and I think that was a big deal. Um, I think the other thing I would say about AI particularly is I think in AI what you see is that really excelling on the technical aspect just matters much more, I think, in AI than some of these other fields. And I think there's been lots and lots of businesses in the past that have been very, I'll say, like very intense logistics businesses or very tough kind of like marketplaces to get started, or for example, um, businesses where a lot of your edge is just like how you figure out keyword distribution or how you kind of like, you know, make the right little like addicting loop and so on. Um, and AI has a ton of these too, obviously, and you know, I think all of those same skills are still necessary. However, I think in AI, in a lot of factors, in a lot of these verticals, you know, what you see is that obviously pure technical execution, it's like for every level that you push, there's still like another level to go and hit. Um, and a lot of the best companies that we see in the valley are the ones that are just able to roll out like technology pushes or breakthroughs that others have not.
And to push you guys on this though, like is a 17-year-old today who's like the Scott Wu of today, who's a world champion, who's maybe you're hiring, do they have some special edge having grown up in this world where AI is already possible, where they're using it, like is that accelerating things further or...
Yeah, I mean everyone starts with the same level of experience with AI for software engineering, right? Which is basically none. I mean, every three months you have to throw out your previous experience and build new experience because the tools get so much better.
This is probably harder for someone who's my age, I'm 43, than someone who's like 18 and still learning, or no?
Yes. Because some people say, "Oh, you know, it's going to be really hard for junior engineers now because, you know, the entry-level tasks are being done automatically by AI." But I think a lot of what we see internally, it's kind of the opposite in some way, where if you're coming in with no preconceptions about how things are supposed to be done or how things are supposed to work, then you can just go all in, just really embracing this completely new way of working. Um, but I think the AI technical depth piece is, um, it's actually not just in the sort of modern generative AI era. You know, when I was at Autopilot, I was a machine learning scientist working on the sort of the vision network, and Elon had this phrase that he really drilled into us, which is, you know, everyone is chief engineer. You know, everyone on the Autopilot team has to understand how the full stack worked. And this is actually extra important in AI because what happens is the abstraction boundaries between different teams start to break down. You know, the sort of classical way that the self-driving system worked, but you had a, you know, a perception team, you had a planning team, you had a controls team, and they had these like thin interface boundaries between them. But the nice thing about AI is you can optimize systems end to end. So, if you want to actually optimize systems end to end, you have to have an accurate mental model of how each of those pieces work. And so I think more sort of technical breadth and depth across the entire stack is becoming increasingly relevant.
That is an interesting kind of way Elon does things, which I've seen a lot of really top people, not too many but some top people do things, is in order to really be the best you have to understand everything going on. So it's really breadth and depth in a way. And so you're saying, yeah, it makes that a lot easier to do that because it can give you some of that breadth you wouldn't have otherwise.
Yeah. I mean, like the way we onboard onto our own codebase, new people, you just ask Devin all the questions of what's going on, why is this done this way?
What's the historical context? Do you tell them they're also like the equivalent of like the chief engineer where they have to learn everything, or it seems like it takes a while to learn. I mean, there's a big codebase now.
Yeah. I mean, I think in practice, so much of it is all obviously very connected. And so, I mean, a simple example of this is like we bought Windsurf, you know, seven, eight months ago at this point, but like we don't have a distinction of like, oh, this person is an engineer working on Devin or this person is an engineer working on Windsurf. Like a lot of the same people should be people who are kind of, you know, working across both of these.
So, catch us up since we last talked. Uh, like, what's the state of Cognition? Where are we now? You're probably not giving out revenue numbers, but you've grown a lot. Like, what can you tell us?
Yeah. No, it's... I mean, we've had a ton of growth over the last, I guess it's just under a year since we last talked. Um, and obviously, you know, back in July, we bought Windsurf, but I think over the last several months, I think both Devin and Windsurf have grown exponentially. One of the fun stats actually is, uh, today is March 9th, and we actually at this point have already done more Devin sessions in our customers in 2026 than we had in 2025. And so basically over the last like two months and change, we've already done more Devin usage in total than we had in all of 2025.
So it's more than like 6x or something. Yeah.
Yeah. Yeah. And obviously, you know, we're working on making sure that that growth trend continues, and so we'll see how that goes. But no, I mean, I think the business has grown a lot. We've been working with a lot of the biggest companies in the world, you know, Citibank, Santander, um, and so on, um, on figuring out how we really, you know, transform their engineering efforts.
Yeah. One of the interesting developments since last year is, um, you know, when we launched Devin in March of '24, it was the first autonomous agent, right? It was like very early for the form factor. Um, and it was, I would describe it as kind of like just at the edge of possible then in terms of doing real useful work.
It made a lot more mistakes back then, obviously.
Yeah. Yeah. It was much less reliable. You know, our infrastructure around it, the connectivity with the rest of the codebase was a lot less mature. I mean, it took us until summer of 2024 for Devin to become the number one contributor to its own codebase, like which was like the first real milestone, and then towards the sort of the end of 2024, um, to really get deployed in production at large scale at meaningful companies. But one of the things we learned is that if you look at sort of where the technology was in 2024, it was not reliable enough to be used as the primary source of software engineering for most tasks, right? You actually still need a tighter feedback loop in that year between AI and humans for most things. And so one of the sort of first niches we found where this was actually already really useful back then was in these very large codebases that just have tons of existing technical debt or complexity, and you want to do a large-scale refactor across, you know, 10,000 services. If you're doing it this sort of normal way as a human engineer, you might have to define some new architecture and then manually go implement thousands and thousands of changes. And these changes would be complex enough that you couldn't just write a regex, but, um, you know, not so complex that AI couldn't help. And so that's kind of one of the earliest places I think we got to real strong like product-market fit is inside these like very large complex codebases. And that's one of the reasons now if you look at the state of our business, we're deployed at a lot of the largest, most complex organizations in the world. You know, like most of the top health insurers, retailers, banks, government agencies, and these places with large complex amounts of code have been actually surprisingly early adopters of Devin.
These guys just have like massive amounts of code that has been doing the same thing for 30 years on a very old architecture, and you can go in there and pretty easily accurately fix that, I guess. Huh.
Totally. It's actually... it's like to your point on is this a new skill for engineering. It's, you know, the mindset of an architect inside one of those organizations has become, you know, you're almost like a CTO of an agent fleet now. You define the problem space of what you want to go solve, and then you just spin up your army of Devins to actually go do the implementation work, and then you kind of review the results. It's like a very different way of programming. There's all these memes in San Francisco where like nerdy guys are on dates, but they're too distracted watching their fleet of agents to talk to the girl. It's like, it's a problem.
I have gotten in trouble with my wife for that.
Let's talk about this new era of software abundance, as you call it. What does software abundance mean? What's Cognition's role there?
Yeah, you know, I think the simple way to put it is, I mean, to Russell's point, all of these traditional industries, you know, it's... you in Silicon Valley, the way we think about software engineers is, you know, obviously these tech startups or these tech companies that are building. The reality is every company in the world has software, right? I mean, software is in many ways, I think, the premier knowledge work job of this century. Um, and so, you know, you're talking about like CVS or you're talking about Walmart or you're talking about UHG or you're talking about, you know, Goldman Sachs, like all of these places have so many software engineers and they have so much software that they're building. Um, because obviously so much of what we do in the world now happens, you know, over the web or, um, yeah, with computers, right? Um, and I think what we kind of think of when we think of software abundance is just getting to a point where you really can just turn your ideas into reality and build what you want to build. Um, and one way that I like to think about this is, you know, you can kind of think about software products on a scale of like, let's say a log scale based on how much reach they have or how many users they have, right? You think about the best products in the world and the products that everybody uses all the time, and this is like, you know, YouTube or Instagram or TikTok or something like that. And it's, you know, these are incredibly good products, and it makes sense. I mean, they have billions of users, and so they have, you know, a hundred thousand software engineers or tens of thousands of software engineers that are working on them, right? And you feel it in the product. You know, it's... the experience is perfect. Like there's never bugs. It doesn't go down. It's streaming you like gigabytes of data. Um, the algorithm is super addicting, right? Like they've really like perfected themselves.
I myself use two of the three you mentioned. They're too addictive, actually.
Yeah. They're too addictive, right? And then you go to the next tier of like, okay, instead of billions of users, let's talk about the products that have hundreds of millions of users, right? And you're thinking about like, you know, banks or you're thinking about like, you know, apps like Uber or DoorDash or you're thinking about like, um, you know, a lot of these various other kind of services and products that we all use, right? Um, and it's similarly, it's like, you know, you feel the software and it's obviously built quite well, but it's already, you know, I think you notice a different level of like how much execution there is. And then there's a next level and a next level and next level, and it goes all the way down to, you know, your kid's website, a school website which is from like 2001 or something, and it's like an elementary school and it has like a picture that's like super outdated and has no other information about that, right? And so like maybe one way to put this is, you know, I think software abundance means making it much easier for everyone with every idea or every product or use case that they want to serve to be able to climb that ladder and build products as well as the best products in the world are built right now.
And for a lot of our listeners who are not as in the AI world, they might be CEOs of a bank or something or running other businesses, like what is AI actually changing about how engineers work, right? Like what does a great engineer's workflow look like now?
Yeah, it's a great question, and the simple way that I put this is that for us at Cognition, for example, our engineers don't type code anymore. Like that's just reality.
And this is as of the last several months. This is within the last, yeah, 3 to 6 months honestly when this shift has really happened. And there are other steps to be clear, obviously, but I think at this point, you know, you used to... maybe one way to put it is like, I mean, you used to work with punch cards, for example, and you used to work... and now in many ways the medium has shifted away from code and a lot more of it has become basically English, right? And so, you know, we obviously use a ton of Devin internally. Uh, we use, you know, Windsurf and the agents inside Windsurf internally as well. But at this point, either way, whatever tools you're using, you know, it's not really you typing out the lines of code yourself. It's you looking, understanding what it is that you want to do, thinking about, okay, how do I want to handle this case or this behavior, and you just tell the agent what you want it to do in English, right? And it goes and builds.
Yeah. And in terms of then like what the impact of that is, especially, you know, if it's like a CEO-level objective, what we're seeing across the board across the customer base is people are just getting more ambitious. You know, like it used to be the case that you have this entire software development life cycle and every step of that cycle is oriented around not wasting the super precious time of engineers writing code. And now you have suddenly this overflowing abundance of the ability to generate code from ideas, from prompts. You can iterate faster on ideas. You can just try stuff. You can try stuff a lot faster. You know, you don't necessarily need to spend three weeks on design before you then hand it off to engineering because engineering is so fast that the turn, you know, the cycle time is much tighter. Um, and so, you know, increasingly, especially at the executive level, it's like, oh, do we have to choose between A or B? Let's just try both.
I mean, could the product people themselves create some things now, or how does... I mean, we see it everywhere. There's like a joke where it's sort of, you know, the engineer and the designer and the product manager all look at each other and say, I don't need you guys anymore. Uh, because they're all just doing it all themselves, right?
It's like every person is empowered to do the other aspects of the product development life cycle. And I think it's really rewarding people who are, you know, actually personally highly agentic and thinking about, okay, what's the impact I can go have, um, and be sort of like self-reliant in that way. And I know, you know, with Devin, a lot of product managers, one of the very first things they started using Devin for was actually to not bother the engineers with questions, with silly questions. You know, how often have you... you're a new employee at a company, you don't understand what's going on somewhere, and you're a little nervous to say, hey, can you explain this to me? You know, Devin is very non-judgmental. You know, you'll ask a question, you get the answer.
I actually ask it a lot of dumb questions too. It's like, 'cause I'm the boss. I'm supposed to know something. I'm like, crap, what does that acronym mean again? But no one judges you. You just do it right away.
Totally. Yeah. You know, and it will sort of actually cite the source code alongside it too. And, you know, it kind of puts everyone actually more on the same playing field in a way where everyone has the same context.
And by the way, agency is something I'm thinking a lot about for society recently. It seems like highly agentic people is like a big advantage for everyone, right? But along those lines, like what are the habits or instincts of someone who's going to be particularly good at leveraging Devin? Like, are there certain things you're seeing?
Yeah. Yeah, for sure. No, I mean, one way to put it, um, is, you know, I think software engineering for people who've grown up doing programming and so on and have been through all these previous eras. You know, usually what software engineering has looked like is like, why do people who love coding love coding, right? And I think the answer is like 10% of that job is basically this really fun part of just pure problem solving, thinking about what you want to build, being creative, like understanding the different solutions, deciding, okay, what architecture makes sense here, how exactly am I going to, you know, achieve my goals here, what do I want to build, right? And then 90% of the job is once you have figured out all those parts, just doing all the dirty work of the implementation to go make that happen, right? And there's like a million bugs that your customers have reported for you, and you have to deal with this messy migration or this upgrade to make your stuff still work, or like you have to go implement all the little cases and all the little details and like write all the, you know, the front-end code that serves this thing that you just built, right? And I think what we're seeing is that the best engineers are just doing 10 times more of the first part because you don't have to do that 90%, right? You have an agent, you have Devin that's going to go and do that for you. Um, and I think to your point, that obviously means that, you know, having high agency is really, really important. Um, we think about this ourselves internally. It's one of the jokes is like, you know, we're a reasoning lab building agents. And so the things that we really value are agency and reason. Um, and I think it's to your point, like a lot of the skills that really matter are, you know, are you the kind of person that's going to think about, okay, this should be this way instead of that way, or like what is the right way to solve this problem, or what do we want to do here?
And also just like internally embracing the abundance mindset too, where, you know, a lot of times you might be in your head, oh, should we do it this way or that way? I think a lot of our best engineers, they just rip it all of the ways at the same time, and then, you know, you get a bunch of Devins come back, and then you can sort of analyze the results and try it in parallel. It reminds me again, it's kind of like the mindset of actually being a good machine learning researcher is now relevant for every type of software engineering. You know, if you look back a few years ago, what were machine learning researchers doing? So, you know, at Tesla, we had one mantra which was, uh, you know, never go to sleep while the GPUs are idling. You know, if you let your cluster idle overnight, that's just a huge waste of resources. Just kick off some experiments, you know, before you go to bed so you can wake up, you get some more data. And it's the same for all of software now. Like, why would you go to sleep while the Devins are idling, right? You could just be ripping a ton of work while you're sleeping.
Exactly. Exactly. And so I think, um, there's actually a lot of parallels in my head where it's not just about that. It's also, um, some comfort with non-determinism. You know, a lot of engineering, it's an incredibly precise craft, and it's sort of naturally uncomfortable and unnatural to not have full control over everything that's going on. But if you get a little bit more willing to embrace the non-determinism and, okay, exactly how should this be implemented, as long as we can validate system performance end to end and I can actually understand the results. Um, I think machine learning went through that lesson years ago, and now we're going through it for the rest of software.
So I mean, every once in a long while someone would probably check the machine learning... sorry, someone check like the machine assembly code actually on something if they're really trying to optimize something. Do people... are there still people checking the actual regular code on things that they really have to these days? Like how do you think about that?
Yeah, for sure. And for what it's worth, you know, I think we are still in the midst of a lot of this change. And so, you know, I think while people are producing code with just English, you know, often you do come back and you're still reviewing that code, you're making sure that it looks right, or you're looking at the code as it is to understand what's going on. Um, and I think to your point, it is kind of like in the right cases when you want to peel back the layer of abstraction. Um, and I think what we'll see over the next, you know, 12, 18 months is that we will continue to get further and further to this point where you use English for almost everything.
You don't have to do it anymore. And what does this mean? So obviously a lot's happening a lot faster. We just covered this. This is money movement. This is military stuff. This is healthcare. It's flights. It's maintenance. It's scheduling. Building new things, permitting. Like everything in the world, a lot more people realize is software. And we're going now what, five times faster, 10 times faster soon on software. It's almost like we're getting multiple years done in one year, right? So what does that mean for society? How do you think about that?
I, um, yeah, I think about this a lot. Um, have you ever seen the graph of inflation by different sectors? And you can see, you know, the sort of highly regulated, constrained sectors at the top, you know, like tuition, healthcare spending...
The cost of these sectors are broken, and everything else gets...
Right, and then the plasma screen TVs are just way down.
Japanese taught us what to do. Yeah, I kind of think that chart is going to happen for like all of society basically. And in particular, if you think about where is software, it's... we're going to enter this like hyperdeflationary cycle of software where it's so easy to build. It's so abundant. That doesn't mean that people are going to, you know, stop producing software. They're actually going to produce thousands of times more software, right? And so you're going to have this total software abundance, but basically any problem that's kind of solvable in the digital realm should just get solved in the digital realm. And so I think what you're left behind with is, um, you know, now how do we direct a lot of this new energy to going improving the physical world, going improving everything else.
