Software engineering is being radically changed by AI. Being able to program in English by launching agents to build features and fix bugs is changing all of the techniques and the primitives and the best practices that if you're a software engineer, you've grown up with. And for a lot of people, there's a big question about whether we're going to need software engineers at all anymore.
That's why I brought Scott Wu, the co-founder and CEO of Cognition Labs, on the podcast. Scott is one of the most important experts on this topic because Cognition builds Devin, an AI software engineer. Devin came out just a couple years ago and it's already at 73 million in ARR. You can think of it like another teammate that you can talk to on Slack that has access to its own computer and can build small features or fix bugs basically autonomously. Cognition also just acquired Windsurf, a Cursor competitor. So in addition to having a fully autonomous AI software engineer, now they have a programming editor with AI built in. So they're one of the foremost players shaping the entire landscape of what programming is going to be like over the next 10 or 20 years. And that's what I talk about with Scott.
We talk about what AGI is and whether it's here or not. And we talk about what programming is going to look like now and into the future. We talk about the different bets that players like Cognition and Anthropic and OpenAI are making to win the programming and AI race. We talk about the recent wave of CLI tools that have taken off like Claude Code and Codex CLI. And we talk about how Scott sees Devin fitting into the landscape and why he acquired Windsurf. This is a great episode because Scott is that rare combination of guest that both has really practical, hands-on frontier experience because he's building, and he's also incredibly smart and incredibly articulate. If you're looking for a conversation that gets down to the actual brass tacks of what the future of software engineering is like, this is the episode for you. Enjoy.
Scott, welcome to the show.
Yeah. Yeah. Thanks for having me.
Of course. So for people who don't know, you are the co-founder and CEO of Cognition. You are the makers of Devin, the AI software engineering agent. And recently the acquirers, the lucky winners of Windsurf. It seems like you've had a really crazy couple months over there.
Yeah, it's been a fun few months for us. I was just going to say, I mean, it's been an interesting time for everyone, I think, in the AI coding space, but a crazy few months especially for us.
So the thing I want to start with first is you said something on John Collison's podcast recently that you think AGI is here. Explain.
Sure. No, I mean, so it's at least a little bit facetious, obviously, and but I think it's worth thinking about that, you know, 10 years ago, what would we have called AGI, right? And we would have said, okay, it's got to be able to pass the Turing test. Obviously, it's got to be able to converse with you and just like, you know, come off as a human and just be able to relate to you and think and, you know, in much the same way that a human does. It's got to be able to solve like, you know, tough technical problems. It's got to be able to do a lot of the same tasks. It's got to be able to interact with the real world the same way that a human would, right? And I think to first order, we've basically done all those things. I mean, passing the Turing test we've obviously done. You know, we have, you know, OpenAI and others, you know, have released work on, you know, getting a gold medal at the IMO and the IOI, you know, solving incredibly hard technical problems, building agents that can go and actually interact and reason in the world. And obviously, you know, I think there's an interesting question of, well, there's still so much more that humans do, and then there's so much more to it. And I think that's true. Maybe one way to put it is, you know, my view is that's going to be true for quite some time to come. And sometimes people ask about AGI from a perspective of like, well, AGI is when humans have nothing left to do at all, you know, or something like that. Or, you know, one of the definitions that people use is like AGI does 80% of knowledge work or things like that. You know, I think these things are really hard to define and really hard to kind of exactly clarify because humans specifically do the parts that are not automated, right? I mean, it's kind of like whether 80% of human work has been automated. You know, I claim that it already has been a long time ago, actually, because as soon as we had the tractor and as soon as we had, you know, it's like if you think about what people did a thousand years ago, we are doing way less than 20% of that work, right? And so a bit facetious for sure, but I guess my point is like, you know, I think we have a lot of levels of AI development that occur. I'm not sure there's one hard cutoff on what counts as AGI, but I think it's also very clear that, you know, we've hit a lot of the things that people would have considered insane, you know, just a few years ago.
So I think that's such a good point. Like, that's why I hate the 80% of knowledge work definitions because knowledge work changes. It's not like it's not a static thing. And I think people underestimate that once you automate one level of work, there's always another level of work above that. And I mean, we've all seen this over the last three years. A lot of the stuff that I do today is a lot of the stuff I was doing three years ago is automated now, and I'm just doing more per unit work, which is really interesting.
Yeah. Yeah. And as humans, we're always so, you know, we're human-centric in our view, I guess is a way to say it. We're very proud of ourselves and our work, which we should be. But, you know, you can imagine at some point it's like you'll just be able to just like think a couple thoughts and then have all of this come out and happen in reality. And we'll still be saying, 'Oh, you know, well, AI can't do that.' You know, the humans are still doing all the important work, right? And of course, it's like AI, you know, at that point or technology in general at that point will have made us like 10,000 times, you know, faster by virtue of doing 99.99% of the work. But it's kind of, it's very hard to define what counts as percentage of labor, right?
Yeah. So I have a particular definition of AGI that I'd love to battle around with you. Feel free to criticize, poke holes in it, but also I'm interested in what you think. So the definition of AGI that I like actually comes from child psychology. So when children are born, they are essentially totally dependent on their caregiver. You can't leave them alone for any length of time. And as they get older, you can leave them for progressively more amounts of time to be on their own. So, you know, an infant or like a toddler, you can leave for like 5 minutes or 10 minutes or something like that in their room. As they become children, you know, you can leave them alone for like hours or more. As teenagers, they go away for, you know, maybe a night at a time, and then they go to college and they're like fully autonomous. And so my definition of, and I think if you look at the development of agents and just AI in general, it has followed that same trajectory. So when GPT-3 was first on the scene, we were just at the tab-complete, you know, level of autonomy, and now we're seeing, you know, Devin or GPT-5 or Claude Code run for, you know, 10 or 15 minutes, 10 or 15, 20 minutes at a time. And you can sort of see this smooth lengthening of that leash in the same way that you see a smooth lengthening of the leash for children. And so I think a good definition of AGI is when it is economically profitable to never turn your AI off. It's always working. It's always doing something. And when enough people are doing that, I think that counts as AGI.
Yeah. Yeah. I think that's super, super fair. I would say one thing is obviously it's very dependent on dynamics in the sense that, well, if everybody has an AGI, you know, then the AGIs are competing with one another for their, you know, usefulness or something, right? Right. And so there's like some amount, but I think that's right. Yeah. Like I think there is a point when you can truly just have an always-on agent that's going and doing meaningful work and producing value off of it. Economic value is, I think, the one thing I would push back against is the idea of economic value just because as we're saying, so much of economic value is depending on, you know, how substitutable it is that you're providing, you know, or things like that. But no, that's cool.
Yeah, I feel like to your point about the doubling time of how long these agents can operate. I mean, it's like it has continued. It is insane how long that trajectory has continued. You know, it's like there's always this saying of like, you know, you can never trust an exponential curve or you can never keep predicting points on an exponential curve, and yet like they've kept coming.
I'm curious actually, one of the things that this makes me think about is because I'm thinking about growing up and I'm thinking about the process of growing up for you. I think that Devin is your first, Cognition is the first company you started, and is it so?
So I actually, I started a company before this. It was called Lunch Club. So I ran it for about 5 years.
Okay. So you're more of a veteran than I thought.
Well, it's, you know, still very much a noob.
So I am curious though, like what has that been like? You've been running Devin, you've been running Cognition for at least a few years. What were you like when it started, and what did you believe about yourself and the world, and what are you like now?
Yeah, you know, it's honestly crazy. I think especially because I had run a company before, and in the sense of like, you know, I think over the last decade or so, you know, there are a lot of different, you know, great companies that got built, but you know, the pace of what's happened in AI and the trajectory of AI has already, I think, been very vastly different from a lot of that, and it's already gone much faster. I think for me, there were a few elements for sure. There's a little bit of the chip on the shoulder of, you know, I feel like I could do better. I feel like I could do more. But honestly, I think there was also just a feeling of like I just have to try is kind of how I thought about it at the time. Like I thought about it. The way I thought about it was if you try and, you know, you build something really meaningful and it works out great, obviously that's great, you know, but you know what happens if that doesn't happen, right? And the question for me was kind of like, would you really, would you rather try and give it everything you had and just find out that, you know, it didn't work out and you weren't the one and whatever else, or would you rather not try and wonder about whether you could have done it? And I think for me the answer was pretty clear that I just wanted to give it a go. And so that's a lot of what it was like for me. In practice, obviously, it was like it was almost kind of like walking our way into building a company. You know, it's not even necessarily the case that we were saying like, oh, we're going to build a company. You know, I think at the time it was really just like exploring ideas in AI and looking into the things that we found really interesting, which naturally we're just nerds, and so AI coding is like the coolest thing, right? And so we were messing around with a bunch of these. You know, it was me and a bunch of my friends who I've known for like years and years. But as it became more clear that, hey, AI coding is going to take off and stuff like RL is going to really work, you know, over the next while, and it's going to unlock a lot of these product experiences, like I think there's a real question of like, is this the thing that we want to commit and spend our, you know, all of our focus and all of our effort on? And that was the trade-off for me.
Yeah. And how are you different now?
Yeah. And now I think I've been having a great time, to be honest. It's, and I consider myself very lucky, you know, in that. And I think there's like, there are a few things I think about it that are really nice. I think obviously the problem that we work on is great. I think people, I think of the people that you work with as the most important thing. You know, someone gave me this advice a long time ago that you're going to spend most of your time working, and so you might as well work with people that you really like. And I still, I still always thinking about that. And so like the, no, a lot of the different, you know, I think I've learned more things. Hopefully I've gotten a little bit better over the course of the last couple years. But in a lot of ways, I think it has been much the same way. You know, I think that, you know, the line of like, you know, leave it all on the field. Yeah. And I really like that mindset of like you want to go and try your best, but you also want to be able to live with the outcome. And I think that's how we very much think about it today. Of like we give it everything that we have. We do the best that we can. You know, you can control the inputs. You can't control what happens at the end. And we just want to be able to say that we gave it everything we have.
I feel that. I mean, I definitely feel like inside of every, I just love the people that I work with, and that makes it so much better to run a company when you're just, everyone's having a great time together, you know? And I think another way to frame leave it on the field that I've felt myself is I would do this even if it failed. And even if it didn't make a lot of money, and I think that's actually somewhat rare today. And it sounds like that is a big part of your journey too, because these are things that you're just interested in and playing around with yourself anyway, regardless of whether it was going to be a gigantic company.
Yeah. It's like, you know, everything could blow up today. You know, who knows what happens, you know, all that we have a huge AI winter or, you know, something happens with the hardware or whatever, and then, you know, AI collapses, and I would still, you know, and our company collapses and whatever, you know, God forbid, and I would still be like, that was an amazing two years. Like I had a great time. I'm really happy that I got to do this for sure.
But how does it, how has it changed your relationship to something that you used to love and just play around with, having the amount of responsibility that you currently do to do that thing?
Yeah. Yeah, that's a good question. I mean, it's, I grew up doing a lot of coding, obviously. I mean, funnily enough, you know, despite all of these, arguably, you know, because of my day-to-day work, I get to do less coding now than I did before. And so I don't know exactly what that implies, you know, and I obviously like to do more coding whenever I can. But I certainly have not gotten to the point of saturating my desire to write code, if that makes sense. I do think there's something really satisfying about just building the, like, you know, it's, it's funny. I think one of the things that comes to mind here for me is like, is the otter logo of Devin. I don't know if you've seen the otter like on Twitter or on things like that. It has always been an informal logo of ours. There's a question of whether to make it the official logo, but like, I know random company stuff, right? But like the reason I say it is because it actually is really how we think about it internally, which is Devin is just like our little buddy, you know, like a little, you know, cute little otter with his own computer, and he's just like typing away and doing tasks for you. And that's kind of how we've always thought about it. Like I think if we were, I mean, we are all programmers ourselves, obviously, you know, if we really felt like this was going to be the end of programming, I think we would be way, way, way less excited about this problem. And I think instead it is kind of just like teaching your own buddy how to code and then starting on that journey in a way that's been really fun for us.
I think that's a really good segue into the thing, one of the things I wanted to talk about is just how you see the discipline of programming changing. I think I've seen on my end, and I'm curious if this is similar to what you're seeing. I've seen on my end there's this, obviously there's people who don't want to use AI at all, but then there's this sort of bifurcation between I think more traditional engineers who are adding AI into their existing processes, and then there's like kind of AI-first engineers who maybe only learn to code with AI, or maybe they are senior engineers from the past but are just like going full-on into AI, and they're AI-first, and they're only touching the code if they absolutely have to, which is a very different mindset. And I think that group of engineers has, there's a whole different set of ways of thinking and different primitives for how to do good engineering if you're only orchestrating agents. I'm curious if that's what you're seeing or how you see the landscape evolving.
No, I think that's definitely the case. And it reminds me of like, you know, one of my favorite fun facts actually that I like to share is, do you know that teachers actually used to picket and protest against the idea of calculators?
So when calculators first came out, there were a lot of protests of like, you know, we can't have this. This threatens math education and all that good stuff. And, you know, obviously, I mean, we did okay as a society despite having calculators in our lives. And I guess the point that I want to make with it is like, sure, I think there are some things that go away, and you know, maybe people are, for example, maybe people have their multiplication tables memorized a little bit less, you know, in the post-calculator era, but obviously, you know, if you just look at the combination of humans with the tools and what they can do, you know, the answer is much, much more today. And so I think what is going to happen, or you know, what's kind of actively happening already, is I think there's going to be a somewhat different education path for, you know, how to be a really great engineer in the post-AI age. The thing that's interesting is like there's so many levels of AI improvement that are like actively happening right now that kind of change that answer. But from what we can see, I think we can kind of imagine that a lot of what that looks like is more about really deeply understanding, you know, logical fundamentals, being able to break down problems and articulate the answers to them, you know, being able to think about different strategic trade-offs, thinking about architectures and so on, right? And less about, you know, just going and debugging your Kubernetes or, you know, knowing all of these kind of obscure libraries or understanding, you know, some very particular like esoteric syntax or something like that, right? And I think that trend is already happening, you know. And so I think some people say that that means that like computer science has no value. Some people say that computer science has way more value. I tend to think it's more of the latter. And the reason for that is because obviously you are still the one at the helm making the decisions, right? And a lot of how you make decisions and how you decide what to build and how you think about, you know, the trade-offs that you're making is, you know, it all goes back to computer science fundamentals.
Yeah, I agree with that. I think if you want to make the analogy, and I think it is a good analogy to make, to say, well, you're becoming a manager instead of an IC when you use these agents. The best managers typically have, if you're an engineering manager, typically have technical backgrounds, or the best CEOs too for software products, for example, tend to be able to like go deep into how everything works to help resolve issues and also to have good expectations set for what can be done.
