The Exchanges

Q And this is as of the last several months?

A And this is within the last, yeah, three to six months, honestly, when this shift has really happened. And, and there are other steps to To be clear, obviously. But, but I think at this point, you know, you, you, 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, so, you know, we, we obviously use a ton of Devon internally. Uh, we use, you know, windsurf and then 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, 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?

AI assessment note: “And this is within the last, yeah, three to six months, honestly”

Q a superstar at Adapar. It's cool to see you go on to build, like, one of the giants in Silicon Valley, hopefully it looks like, but let's, let's, let's step back a bit. So about a year ago, you guys released a demo of Devon, which is what the product of Cognition is called. Did you have any idea it would go so viral? Like, what's, what's, what surprised you?

A I mean, it's, look, we, I mean, we, no, not at all. I, we, we hoped it would go, go well, you know, we were, we were, we put a lot of energy and effort into it, and And it was, ah, yeah, it was really awesome to see. I mean, I think it's, um, you know, we, we have always had the view of just skating where the puck is going, basically. And, um, around end of twenty-twenty-three, we, um, were, were working on AI, and specifically AI for coding, and we felt, um, that, that things were going to shift a lot more agentic, basically. And everyone's talking about agents now, but back then it wasn't really a term. You know, at a high level, we were kind of just, we had the view that instead of pure text interfaces, there was going to be A lot more full interaction where, you know, AI systems could, could not just read texts and answer questions, but actually just go and interact with the real world and encode. Obviously that means debugging, pulling up the logs, reading documentation, um, testing code yourself and so on.

AI assessment note: “no, not at all. I, we, we hoped it would go, go well”

Q it's like, we're creating this God and everything's gonna be totally different in 10 years. And then like other people are like, oh yeah, it's really negative. It's gonna like be centralized power and control and destroy everything. Whoever gets in charge of the top AI is gonna be negative. And then, and then there's like also the optimistic points of view, like what's your optimistic point of view here?

A Yeah, yeah, absolutely. It's, you know, the, the, in one line, this is my, my co-founder Walden says this all the time, which I love, which is, um, we've been spending our whole lives in Minecraft survival mode, and now we're gonna get to play Minecraft creative mode, you know, and, and that's like the, um, yeah, I, I think that really is like the, the, the, the really exciting future that we're heading towards, you know, I, I think the, um, the, the main thing I would just call out is like, I think human creativity, human passion, you know, desire to, to, to build things and meaning, I don't think that stops because we have some really smart AI or anything like that, right? And I think we're, if anything, we're gonna be able to spend a lot more time doing that, you know, we're gonna do really cool things, you know. One of the things that I always think about is, like, if you just imagine folks from thousands of years ago, you know, looking at us today, like, sitting here talking about this, right? And it's, it's funny to think that in the sense that, like, probably most of, of what, you know, Most of these white collar jobs out there, it's, it would be crazy for them to even think about it as work. You know, it's like you sit there, you have, you know, you're talking in the room with other people, maybe you're pushing buttons on this thing or whatever, and you call that work. …

AI assessment note: “we've been spending our whole lives in Minecraft survival mode, and now we're gonna get to play Minecraft creative mode”

Q definitely see him as a CEO and founder now. Um, but one thing you guys have done is like a significant percent of the cognition, hires are actually former founders. So not only are you hiring the best people in the world, you're hiring a ton of former founders. Like, like, why are you doing that? How are you doing that? Tell us a little bit about the talent stuff.

A Yeah, yeah, for sure. No, I mean, I, I think the, um, I, I, I think the reality is we, we just have such a massive problem that we're going after. We're just, you know, solving all of code. And I think the way we even started this company is, you know, Russell was a founder before this. I was a founder before this. All of us were, you know, I think of our, of our kind of like initial crew and the idea for us, I think was, Let's make this one the big one. You know, we're gonna go for it all. We're gonna go for the most ambitious. And even, I mean, the, the, the play of solving software engineering feels like a big enough one, you know, that, that we can all do that together. And, and, and I think that's a lot of what it comes down to, honestly, is just like, um, yeah, are, are we working on something and doing something that's, that's, that's exciting for, for folks who, to your point, are, are, are, you know, I, I think a ton of the folks at Cognition are, could, could very, very easily go off and start their own companies and get funded and build their teams and so on. And, and, and the question I think for us always has been About, um, how, how do we make this the place that, that, that makes more sense for them to do?

AI assessment note: “we just have such a massive problem that we're going after”

Q Like, are they gonna, you know, there is a question, like, people have stopped hiring quite as many engineers the last few years. Maybe they overhired in twenty-twenty-one though, like, like, what should we think about that?

A Yeah, I think there are certain economic factors, economic factors that came into there, but, but I would say it's, it is very clear if, with, with every engineering team, you know, I've never met an engineering team that's told me, all right, you know, we have this project, and then we're gonna do that project, and then we're done. No more software, you know, that's it, right? I mean, every team has a hundred things that they want to build and that they want to ship, but they have to pick You know, four of them to do this week because that's, that's all they have bandwidth for. Right. And I think that the, um, yeah, I really do just think that the demand for these things is going to grow. One of the things that I think about all the time is, you know, if you think about these, the best products in the world out there today. Right. And in my mind, that's probably, you know, YouTube, Instagram, TikTok, things like that. And obviously they have tons and tons of hours and tons of usage, but, but they're about as close to perfect as, as we get in software today, I'd say. Where it's, you know, the, the algorithm is amazing. It knows every little detail about me. You know, the, it's, it's streaming a ton of data and it's able to do that super efficiently. It never goes down. The product UX is super, super intuitive. It always, you know, it's really easy to understand what, how to do …

AI assessment note: “I really do just think that the demand for these things is going to grow.”

Q So just for intuition, again, for the listeners, why was that road problem tied to a real life thing that matters? Give us some more.

A You're obviously not doing literal shortest path problems in AI, you know, but, but, but I think that a lot of the pure technical problems that exist in AI are, yeah, are, are a lot of these things of, you know, basically, um, you know, training AI models that can solve really tough problems or understanding exactly what the right architecture Architectures, the right systems are for, for AI to be able to, to go and do these things. And I think, um, you know, with software engineering in particular, it's a, I, I'm obviously a programmer myself, and so it's a, it's, it's, it's a fun thing to, to get to spend time on, you know, teaching AI how to code as, as a programmer nerd myself. Um, and it is like, yeah, I would say a lot of it is really like thinking about thinking. You know, it's how, how do we, you know, somebody hands you a bug, and how do you fix the bug? And maybe the answer is, you know, you go and, um, you run the code locally, you, you, you reproduce it yourself, Once you've figured that out, you, you go and find the error in the logs, you look at those files, and you understand it, but, but a lot of it is teaching AI systems to do those same things, you know, and there's a lot of kind of thinking about thinking that, that goes into it.

AI assessment note: “You're obviously not doing literal shortest path problems in AI, you know, but”