← Cheeky Pint
Cheeky Pint
Cognition CEO Scott Wu on acquiring Windsurf, AI replacing engineers, and the Moneyball-ification of everything
00:57:53
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- [00:01]
Scott Wu
Have you.
- [00:01]
Interviewer
Have you had Guinness before?
- [00:03]
Scott Wu
I have actually never had a beer in my entire life.
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Interviewer
All right, well, you're starting with the best beer. So that's it. You order your Amazon packages with Devin.
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Scott Wu
Yeah.
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Interviewer
So you're just in Slack and you ask it to buy something for you.
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Scott Wu
Yeah, yeah. Like just, Evan, can you go buy some more whiteboards for us or something like that said, I really enjoyed math competitions and going and competing and doing these things.
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Interviewer
And this is stuff like, if I ask you what 694 squared.
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Scott Wu
It is 481636.
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Interviewer
I have shuffled the cards. I am not collaborating. We give.
- [00:33]
Scott Wu
So now you have six cards and you're trying to make 163. Right. And one way that you could do that here is 2 times 8 is 16. 9 divided by 3 is 3. 3 plus 16 is 19. 12 times 12 is 144. 144 plus 19 is 163. And so almost all you're probably thinking.
- [00:48]
Interviewer
Like, I could have done that. That's too easy. Then for this scout, you can just flip it upside down like that.
- [00:57]
Scott Wu
Very good.
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Interviewer
Scott Wu is the co founder and CEO of Cognition, which makes Devin the AI coding agent. Scott is a triple I O gold medal winner and kind of famous for being a math whiz. And now he's at the cutting edge of Agentix software development. Cheers.
- [01:10]
Scott Wu
All right. Cheers.
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Interviewer
Tell me about your upbringing and all the math stuff. I feel like you're known for the math stuff these days.
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Scott Wu
Yeah, yeah. So I grew up. I'm from Baton Ridge. My parents were both chemical engineers, and so they immigrated from China for grad school. And then naturally, when they were looking for jobs, they were doing, like, air emissions permitting and things like that. And, you know, Louisiana has a lot of oil and gas, and so that's. That's kind of how a lot of.
- [01:37]
Interviewer
Air emissions too, actually.
- [01:39]
Scott Wu
Yeah, yeah, yeah. And so that's how we ended up there. I always loved math. As a kid, I had an older brother named Neil, super, super close the whole way through. And Neil was about five years older than me. Neil started doing math, like, math competitions when he was in middle school. And so he would have been in, like, sixth grade, and I was in first grade at the time. And naturally I, as a little brother, would go and just watch what he was doing and try to learn some of the same math too. And that's kind of how I first got into math. And then I found that I really enjoyed math competitions and going and competing and doing these things.
- [02:13]
Interviewer
And this is stuff like, if I ask you, what's 694 squared?
- [02:18]
Scott Wu
I think it's probably not quite things of that nature. It is 481-636. But it's things like. Yeah, like math puzzles, things like that. The frog that's like going up and then every night falls down the well and how many nights. You know, these kinds of things where you dance on the log. Yeah, yeah, yeah, yeah. Like where you kind of get to do the critical thinking and come up with interesting ideas and stuff like that. So I started doing math competitions in second grade. I remember it. There was a contest at the local college that I went to which was for like middle schoolers and high schoolers. And so I competed in the seventh grade math division as a second grader. And I, I did the competition. It was like my first time doing any of these. I just really liked math and stuff. And then they were calling out like third place, second place, first place, and none of them were me. And I still just remember I was just, I was so obsessed.
- [03:07]
Interviewer
That's your supervillain origin story.
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Scott Wu
Yeah, exactly. That's how it all began, basically. And so then I trained a bunch. The next year I was in third grade and I competed in like Algebra one or something and like I won that year. And then I basically kept doing math competitions from there. My last year of high school, which would have been my junior year, I left a year early, But I did IUI, the programming algorithm, IUI three times. And I got cold.
- [03:30]
Interviewer
Where'd you go to school?
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Scott Wu
So I went, I took a year off actually. So I left high school a year early. I wasn't that good at school, I guess I left high school a year early.
- [03:39]
Interviewer
Sorry. Obviously that's surprising. You weren't that good at school.
- [03:41]
Scott Wu
Well, I just, I wasn't that good at finishing school. You know, I have a middle school degree, but, you know, I didn't really make it through high school or college. So I left high school a year early. I spent a year actually in the bay working at a company called Addepar. Sure. And I did that as a software engineer. That was back in 2014.
- [03:56]
Interviewer
Yeah.
- [03:56]
Scott Wu
Wow, it was a blast. Yeah, yeah, it was a while ago. And then after that I decided, okay, I will go try out college after all and see what that's like. I went to Harvard for two years and then I dropped out.
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Interviewer
How did you end up at Addepar? And that's very forward thinking of them. Obviously they took on a high school aged high school dropout yeah, yeah, it.
- [04:11]
Scott Wu
Was a fun group. You know, funnily enough, there were four of us who started at the same time as high schoolers, and it was myself and Alexander Wang was actually another one. We started on the same day. Eugene Chen, who's now running Phoenix Dex, and then Srinath Ra, who's most recently at Sandbar as the cdo.
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Interviewer
Wait, sorry. This is a real small group theory moment. So you and Alex were in the same.
- [04:39]
Scott Wu
That's right. So we knew each other. We met in middle school.
- [04:42]
Interviewer
Alex now of Meta.
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Scott Wu
Now of Meta, that's right.
- [04:45]
Interviewer
Msl, I guess.
- [04:46]
Scott Wu
Yeah. And so we met in sixth grade. He was from New Mexico, I was from Louisiana. But we met in this math competition called Math Counts. We were both at the national competition and then we started talking. Google Hangouts was the. Was the thing at the time.
- [05:01]
Interviewer
It turns out there's some math in AI. Yeah, this may be an indication.
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Scott Wu
Yeah, it's a fun thing. Well, a lot of the folks, as it turns out from our vintage, ended up being. I think there's like a real infectiousness of, you know, being entrepreneurial too. I think Alex deserves a lot of credit for, I'd say being the first of our group.
- [05:17]
Interviewer
Alex Wang got you into the idea of starting a company.
- [05:19]
Scott Wu
Yeah, somehow I think there's definitely a bunch of that involved, for sure. But also a lot of folks. Johnny Ho, who's one of the co founders of Perplexity, for example, Demi Goul, who started Pika, a lot of these. Jesse Zhang, who started Decagon, a lot of us were actually competing in these math and programming competitions in the same year. And we all knew each other.
- [05:43]
Interviewer
Okay, so this gets to something. I was wondering, there's this topic that people talked about a while back of where are the young founders? There always used to be kind of people in their early 20s working on breakout companies. Michael Dell was 19 when he started Dell, 23 when he took it public. Obviously, Mark Zuckerberg was very young when he started working on Facebook. And when it was a real breakout, he was still very young. And there was a period where there was no young founders. And now there's many, many more. Like a whole bunch of the people that you mentioned. You're 28, 20. Running. Running. Cognition is the presence of young people as founders of leading companies. A biomarker for industry vibrancy. Where, you know, Michael Dell was young during the takeoff of the PC era. And you know, Mark Zuckerberg was young during the takeoff of social networking. And now we're in the Takeoff of kind of AI coding tools.
- [06:36]
Scott Wu
Yeah. I appreciate you calling me young. I mean, I think relative to being 18 or 19, you know, still is still a long way.
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Interviewer
The test is like in your 20s, so.
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Scott Wu
So I have. I have a take on this, actually, and I'm curious to hear yours on this. I've been thinking about this question as well, and my take is actually just that overall, being a founder has just gotten harder. And that's probably like the biggest, like the highest order bit. I think the reason that young founders who were just really sharp and really determined, like, did very well is because at the end of the day, being a good first principles thinker does beat experience, you know, and just a lot of being a founder is doing something that has never existed before and coming to your own conclusions. The thing is, now there's a lot of people who have both, you know, the first principles thinking and the experience. And I think things have gotten a lot more, you know, call it mature as a space. And so it's like, you know, basically it's gotten harder, you know, and so there are fewer that are literally coming out of college. I think now they're.
- [07:30]
Interviewer
It feels hard to make the claim that, you know, it was easy to start a leading business in prior eras. You know, Facebook faced lots of competition. It's not like Dell was the only PC maker. And so I don't think they had it easy by any stretch of the imagination. However, I think you are getting at something where clearly all the large companies these days, they're very aware, they're very connected with the ecosystem. If you look at asatia or Mark Zuckerberg, they are very aware of everything that's going on, AI and they're paying a lot of attention to it. And so, yeah, maybe there aren't giant opportunities that are just being left on the ground by the big established companies.
- [08:12]
Scott Wu
Yeah. And maybe harder is not the right word. It's more just that the space is a bit more mature and there's more of a playbook and more existing knowledge. There's obviously something unique with every business, but a lot of the details of here's how you should structure equity, here's how you should figure out fundraising, here's how you should hire your initial team. Many of these things, I think, do carry over a lot with experience, where I think in previous eras, where the book wasn't written at all almost. And so it really just came down to how sharp you were and how good you were at making your own decisions. I think now there's A lot more experience to draw from. Maybe that's part of it. I also do kind of just have a theory of like, I guess I would call it like, like the money ballification of everything, you know, so. So like to give a few examples, like, one of the things that I do casually for fun is like playing poker. And poker is a very fun game. It's actually much more mathematical than a lot of people realize. It's very, you know, of course, people kind of think of it as, people.
- [09:04]
Interviewer
Know that, like the poker solvers and the odds tables and things like that. Or is it more mathematical than that?
- [09:08]
Scott Wu
No, no, I think that's right. I think that's right. Well, I think there's like a first order impression of, you know, it's all about I'm all in play, the person on the other. And it obviously is much more mathematical than that. But one thing that's kind of interesting is you see it in the evolution of the top players in space as well, that back in the day, in the 80s or 90s, the top pros, again, I don't think the idea is that it's less competitive, but the skills that made someone a really great poker player were just really great intuition. I think they understood a lot of the mathematical concepts, but just at a very system one level of just being able to think about them. And obviously they had just a good feel for the game and a good sense of how they should be able to kind of improve their own play. And now it's just all math nerds. It's basically like at some point when the space gets mature enough that. You know what I mean, where it's like, I think for a less mature space, when people don't know what the right questions to ask are or how to even kind of think about it, like what is the right frame of reference then I think there's something about just having a really sharp intuition and coming to your own conclusions. And then at some point as these things get more mature, you know, the conclusion of it kind of is math. And I feel like that's. That's been the case in a lot of different fields. And I feel like it's happening a little bit for startups as well.
- [10:22]
Interviewer
I see more spaces have kind of resolved to their underlying, like a chess engine just deciding that the position is, you know, Mason41 or something.
- [10:29]
Scott Wu
And chess is totally the same way, by the way, which is like, you know, back in the 1800s, like people.
- [10:33]
Interviewer
The romantic style of play.
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Scott Wu
Yeah, yeah, exactly. The romantic style of play. And now it's kind of like. Yeah. Like there is a right sequence of moves and, you know, just seeing how close you are to that optimum.
- [10:43]
Interviewer
Yeah. What are other domains where the monobolification of everything is shown?
- [10:47]
Scott Wu
Yeah. One of my other hobbies, which I played, at least before the advent of cognition, was a game called Super Smash Brothers. I used to play tournaments for Smash, and you saw very much the same pattern where it's a game called Melee in particular. I don't know if you've played Smash. Okay. Okay. It's for the GameCube, which came out in 2001. So it's a very old game, but, you know, people just still keep playing the same game.
- [11:09]
Interviewer
Yes.
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Scott Wu
And, you know, for the first, like, six to eight years of the game, it's like the personality was very much really wily, you know, sharp thinkers, people who are just, like, quick on their feet and coming up with these ideas. And now it's just like.
- [11:19]
Interviewer
It's just.
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Scott Wu
It's just all math. And then the people who play and do really well are.
- [11:24]
Interviewer
I think some of the RTSs are a little bit that way as well as gotten less creative as people have gotten better at them.
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Scott Wu
Yeah, yeah. And it's a funny thing where it's like, you know, there's a lot of beauty in the. In the nerd side of it, too. It's just like. It's like a difference in what skills get most selected for is maybe the way I'd describe it.
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Interviewer
Yeah. Okay. I'm getting distracted from asking about cognition. What is cognition? What does it do?
- [11:47]
Scott Wu
Yeah. So we're building the AI software engineer. We've been building Devin. We've been going for the last year and a half and most recently just acquired Windsurf. And so Devin, the agent in Windsurf, the ide, but at a high level, we really want to build the future of software engineering.
- [12:01]
Interviewer
Is it confusing for people that you have two brands, you have Cognition, the company, and then Devon, the slightly anthropomorphized instantiation of it.
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Scott Wu
We've been talking about that. I mean, now there's Windsurf as well. And so now there's a third thing, but I think some consolidation is probably good.
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Interviewer
Okay. And so people are maybe familiar with the, you know, the GitHub copilot, or the IDE style paradigm, where you're there writing code in your IDE and it helps you autocomplete it, or you can give some instructions in the IDE, I.e. nASA, the Cognition Devon paradigm instead, with Devin, you're in a Slack channel with Devin and you're prompting it to go off and build me an X or a Y. But you're talking to it as you would a coworker in Slack.
- [12:45]
Scott Wu
That's right, yeah. And so you can call it from Slack or Linear or Jira or you can call it from your IDE as well, but you don't have to. But yeah, I think that's exactly right. There's been this paradigm in the past. I would say GitHub Copilot was really the biggest kind of and most well known originator of it, of ides. And I would describe it as basically when you are typing at the keyboard as an engineer, making you a little bit faster at it and giving you the tools and the shortcuts and everything to do that faster. And Devin is a very different paradigm of what I would call an async experience where you have an agent and you delegate a task. Devin naturally operates a little bit more at a ticket level or a project level or something like that. You have some issue in GitHub or something and you tag Devin and then Devin gets to work on it.
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Interviewer
Yep, yep. And what level of task is Devin doing a good job of today?
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Scott Wu
Yeah, we like to call Devin a junior engineer today. There are some things that an AI of course is way, way better than all of us at, you know, especially encyclopedic knowledge and just pulling facts and things like that. There are some things that, that it's, you know, it still makes terrible decisions on but, but I think that's the right average overall. And what we see folks typically using it for are things like bugs, for example, or like simple kind of like feature requests and fixes and, and so on where you're talking about an issue and you and your team are figuring out what you should do and you're just like, hey, Ad, definitely go do this. Or on the other hand, a lot of the more I'll call it the repetitive tedious tasks that come up often in engineering work. And so that's often migrations or modernizations or refactors or version upgrades or. It's crazy how much testing and documentation. It's crazy how much of the software engineers of the world's time is more like things like going and fixing your kubernetes deploy than it is things like building and coming up with really dependency management.
- [14:29]
Interviewer
Yeah, all that kind of stuff. What metrics can you share on where the business is at?
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Scott Wu
Yeah, so Devon is deployed in thousands of companies all over the World. We work with some of the biggest banks in the world, like Goldman and Citibank, all the way down to startups with two or three people. And in general, a lot of how we look at things is in terms of merge pull requests and getting Devin to the point where it is a significant percentage of the merge pull requests in an org. Typically in a successful org, Devin is merging something in the range of like 30 to 40% of all the pull requests that come through.
- [14:59]
Interviewer
And you talked about this async model, but isn't it the case that as I look at other, you know, the GitHub copilots and the cursors and everything like that, I mean, they are. Or cloud code, they are not, they're not fully synchronous because you now you prompt them and they go off and do something. And so are these distinctions a moment in time thing, do they kind of go away where everyone is synchronous in the cases when they can do it instantly and asynchronous in the cases where they don't? But is this a durable distinction?
- [15:27]
Scott Wu
It's a good question. I think the two experiences continue to exist for the next while, and then I actually think that figuring out the shared experience between them actually is the really interesting thing. Right. And that's a lot of recently with Windsurf and things like that. It's something that we've already been thinking about and now are pretty excited to ship some things in the near future on. Do you know the concepts of essential complexity and accidental complexity? Have you heard about this before? And I think there's a real thing where maybe one way to describe it is the ethos of a software engineer. What it means to be a software engineer, in my mind is basically just somebody who solves problems in the context of code, right? It is somebody who tells the computer what to do and makes all these decisions of, you know, it can be big decisions, like, what is the right architecture that we want to use for all this? Or it can be like a lot of these micro decisions, like, oh, like, by the way, there's like a case where this balance is less than zero. And what do we want to do here? Should we show a, you know, an error or should we, you know, request this or whatever, right? And all these decisions, you know, are what people typically call the essential complexity of, like, what is all of the actual underlying logic of the decisions of what the software is doing. Right. And the accidental complexity is basically everything else, you know, like all the things that you have to do to support things as they scale, you know, or all of your standard. For example, anytime you have a class, you probably have all the standard CRUD features along with that as well. Where, you know, everyone knows that you need to have that in your class. But there is no real decision that needs to be made in terms of going and doing that. Right. And it's an interesting thing, which is, you know, up until, you know, AI coding has come along, I feel like the meat of software engineering has been in making the decisions. And yet you spend 80 or 90% of your time doing more of the latter. You know, just going and doing the routine implementation and so on. Right. And so I think this merged experience that comes up is basically something where for anything that actually needs you in the loop, where you can go and make the decision and you're looking at the high level strategy or deciding what you want to build, you're involved and you're doing that synchronously. Then for all the parts that are hero execution, you are able to hand that off asynchronously. So the interesting thing is that obviously for an individual project there are typically long stretches that actually are one or the other and it alternates between both of them. Right. And I think what that will effectively look like is the synchronized experience is the IDE where you are looking at the code directly and you see each of these things. The asynchronous experience is the agent that will go off and do each of these things, but to be able to go back and forth between your ide.
- [17:49]
Interviewer
So you want the engineer to be interactive with the agent as it's going and working, but on the high impact moments of important choices as opposed to all the groundwork. How do you get large enterprises comfortable with giving Devin's sufficient permissions to be effective? But like you talk about the migration use case, super boring. And so you change the table and get it talking to the new table and then eventually you delete the old table. And that last step is kind of scary. I think people still have models hallucinate way less than they did, but people still have fear of the model just making something up and doing it. And so yeah, how do you get people comfortable with giving it enough power to be effective?
- [18:29]
Scott Wu
So we pretty strongly recommend that people using Devon don't give it prod database access, for example. That's one way. I don't know of any instances where it has been an issue or things like that, but obviously you'd just rather not take that chance. And the framing that I would Give honestly is we have processes for these things because humans make mistakes too. And that's why we have pull requests and review and that's why we have CI and that's why we have all these things already. Right. And so Devin naturally slots neatly into all of these things. And so typically the way that folks will work with Devin is they're doing some big code migration and they'll break up the task or Maybe they have 50,000 files that all need to go upgrade from this version of Angular to that version or something like that. And Devin will go and do each one and it'll make pull requests. And so you will go and review the code and make sure things look correct. But there's still this human.
- [19:18]
Interviewer
It's back to your point of incidental complexity where the reason a migration is time consuming is not the actual single deletion step like all the time cast comes in other places.
- [19:28]
Scott Wu
Yeah, yeah, exactly. I think in practice what we see with folks, especially in these kind of like enterprise migrations, is when folks measure internally, they see something like an 8 to 15x gain for a lot of these use cases with Devin, because yeah, as you're saying, you're just reviewing the code. You're not going and writing every single line or going through every single reference or things like that.
- [19:48]
Interviewer
So let's talk about that because I think all organizations around the world are trying to figure out the productivity impact of AI coding. And I think what everyone sees is engineers for sure want to have access to AI tools for coding. It's not totally obvious on the PRS per dev type metrics and what's happening. Generally you see some increase there, but of course it's not clear how good even a pull requests per dev metric is. And then maybe you can say that there's some ongoing maintenance cost of if you're shipping low quality code or something like that. And so I feel like everyone right now is looking for some slam dunk productivity data on what is the impact of. There's probably some CTOs looking for the slam dunk data to justify the spend to their cto. What's your view on how big is the productivity impact? Is it actually measurable?
- [20:47]
Scott Wu
Yeah, for sure. Yeah. So I think this is something where actually this gradual shift towards agents actually will help a lot, as it turns out. If anything, I think, to be honest, I think IDE productivity is often underrated because how do you state it to your point? Right? Like you look at the numbers and it's of our engineering Org on average, people took the tab completion 238 times this week. It seems quite clear that that should be worth something and it should make you faster. But how much faster does it make you? It's a bit harder to say. On the other hand, with agents, a lot of the workflow obviously is going and doing the task for you. Right. And so if it's a JIRA ticket or something, or a migration or things like that, where you typically do have a good sense of how many engineering hours are going to be needed for this and what's going on, and because it's doing the whole thing end to end, it's a lot more clear of like, yeah, you didn't have to do this migration anymore. You reviewed the PR in five minutes and that's all done.
- [21:38]
Interviewer
Yes.
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Scott Wu
And I think as time goes on, I think these things will become more and more and more clear.
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Interviewer
There is a view that some people have out there that coding tools are a moment in time thing that get run over by increasing model performance. GPT6 or GPT7, presumably you do not hold this view.
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Scott Wu
Yeah.
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Interviewer
How do you avoid getting run over by the labs?
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Scott Wu
Yeah, yeah, for sure. So look, I think the labs are obviously, I think they're incredible business, as best as I understand it. I would kind of describe this view as, call it the nihilist computer use take, which is just like, of course, all of these different things that we do in the world in knowledge work just involve using a computer and the AI is going to get better and better and better at using the computer until someday there is nothing left except just the AI going and using your computer and doing your work for you, to the best of my understanding, is kind of the argument there. I see the wisdom of it. This is the kind of thing that's very hard to disprove. But I think that in practice, what we've seen in the space is naturally there is a lot of contextual knowledge, there's a lot of industry details, there's a lot of. And so as we were saying, going and doing some angular migration or doing some. It's not to say that these things can't get better. In fact, I think they will continue to get much better. But I think that the way that we make models better and better at them is by giving it the right data of like, how good can you be at angular migrations? If you've never seen angular, you've never done an angular migration yourself. And there's kind of a cap on that. And obviously there are all sorts of these things of Using your datadog to go and debug errors or. I think the biggest thing I would just say here is software engineering in the real world is so messy and there's all sorts of these things that come up. And I think in practice, most disciplines look like this. And I would say the same thing about law or medicine or and so on. And so while the general intelligence will continue to get smarter and smarter, I think there is still a lot of work to do in making something both on the capability side really good for your particular use cases, but also in actually going and delivering a product experience and bringing that to customers of how that actually happens in the real world.
- [23:55]
Interviewer
So it's not a general intelligence task. It's a specific intelligence of working in the stripe code base requires some general intelligence, but requires a bunch of context, requires working within the workflows we have and everything like that. And you think that persists as an area where you need to specialize?
- [24:09]
Scott Wu
Yeah, exactly. Maybe one way to put it is I think the argument is something like a super intelligence. And I think in some sense, yes, I think we are. You could consider us short superintelligence. I think what we're getting to with RL as this thing is improving, improving, and we see more and more of the gains and people are developing the techniques. I think of RL and this paradigm of AI as basically the Platonic ideal of it is the ability to solve any benchmark, right? You have exactly a data set of here are the things that you want and here's how we measure success, and here's how we do that. And whatever that benchmark is, it can be the hardest thing ever. It can be like unsolved math problems or whatever. Someday we want to get to the point where we can just take that and train a model that will just get 100% on it. And I think, frankly, we're moving towards that idea a lot faster than most folks would have expected. I think we're really. I mean, there's been some pretty crazy developments like the IMO gold medal or, you know, the scores on suite bench or things like that. The thing is, when that happens, I don't think what we end up with is just pure asi, end of humanity, human knowledge work or whatever. I think the thing that we end up in is basically a point where the hard question is, all right, now, what is the benchmark? Right? And I think defining the benchmark in all of these spaces is kind of like a lot of the practical, real messiness of the world, right? And so for a software engineer, obviously, you know, it's like, yeah, like what are all the tools that you interact with on a day to day basis? How do you use those tools? You know, what does it mean to build a representation of the code base over time? How do you decide whether you shipping the feature was successful or not successful? All these various things and creating the right environments around them.
- [25:42]
Interviewer
And so can there be a good benchmark for a model's performance on the kinds of things that Devin wants to do, or is that just like Devin's business model and Devon's revenue? Is the benchmark intention?
- [25:53]
Scott Wu
Yeah, it's a good question. From our perspective, we have a lot of benchmarks internally. The biggest is one that we call Junior Devices, which we might need to upgrade to senior dev pretty soon. But it's basically the ability to do a variety of just random real world junior dev tasks. We've shared some of the examples. Obviously we don't publish the whole benchmark because then it would get obviated. But a lot of the tasks are things like, hey, you need to go and fix this Grafana dashboard and get this going and then pull up the results. This is a very common thing that a software engineer does. Right. And the thing that's hard about it is perhaps not some algorithmic coding thing itself, but it's like turns out on the setup, actually the server that's hosting this is running the wrong version of some package. And so you have to go through the errors and figure out what happened and then say, okay, I need to downgrade the package to this other one, which is actually the right dependency for this thing. And then I need to run it and pull this up and make sure the numbers look correct. Things like that, which are basically as close as we can make them to what real software engineers spend their time on.
- [26:51]
Interviewer
And so how have the newly released 4 Cloud, 4.1 and GPT5 done this benchmark?
- [26:56]
Scott Wu
Yeah, I mean both of them are. The two of them are better at this benchmark than any of the models that we've seen before this week.
- [27:02]
Interviewer
As you think about the AI business and industry over the next five to 10 years, you can think about all the different layers of the stack. You have the data centers, then you have labs, and then you have the application layer, such as yourself. Who benefits? Like, what gets more competitive? What gets less competitive? Are all these just classic competitive oligopolies? Yeah, lots of market structure.
- [27:27]
Scott Wu
So everyone always makes fun of me whenever I say this, but I think all the layers are going to do very well. Like, I think there's just going to be a lot of AI, and I think the prices are cheap everywhere. You know, I've been saying this at least for the last six to 12 months, and I think, you know, we've seen prices go up a decent bit across all of these, but no, at a high level. Yeah. First of all, there's going to be a lot of AI. It can't be understated in the sense that, like, I think we're kind of coming off of a decade of a lot of various B2B SaaS and so on. I think there was the Internet obviously in the 90s and early 2000s, and then there was the mobile phone and cloud, which were kind of like late 2000s, early 2010s, right. And those were some of the biggest things in the last 30 years. Over the last 10 years or so, I think there was a real time where most of the stuff that was being built was a lot more incremental, basically. Right. Like each next thing and building for a particular niche or for a small part of the workflow and making that more efficient. And AI now, I think, is the total opposite of that in the sense that now we're talking about the entirety of knowledge work and perhaps the entirety of physical work as well, depending on what happens with robotics. Right. And so first thing is, there's just going to be a lot of AI, I think. Second thing about where does the value accrue? My honest answer on that is simple thing is value accrues wherever there's meaningful differentiation in the layer. Right. You know, simple. Like if there's Nvidia and there's TSMC and there's like, for as long as Nvidia needs to work with TSMC and for as long as TSMC needs to work with Nvidia, of course there will be some rubbing up on each other's shoulders, but, like they will continue to do great. Right. And you kind of see this down the stack as well. Right. I would argue that the problems that are being solved in all these different layers are very, very different problems that have pretty meaningful differentiation. Right.
- [29:06]
Interviewer
You're saying this prevents too much vertical integration, basically where you. The layers kind of keep each doing their own thing.
- [29:12]
Scott Wu
Exactly, yeah. And I think there's a real diff where. Yeah. You know, as soon as you go from hardware to obviously foundation model training is its whole own can of worms. And very much like the DNA of the company is finding exceptionally strong researchers giving them as many GPUs as you can afford to give them and setting up a culture that kind of like orients around that. Right. And then the application layer I would say is really focused. I would say obviously it has a lot of the elements of research as well, but I think in particular is really, really focused on just figuring out how to make one use case work. For us, for example, the only thing that we care about is building the future of software engineering. And maybe one thing I would call out is people often talk about AI code abstractly in a vacuum. I think there are a lot of companies that think about code in the foundation model layer or things like that. I think we uniquely really think about software engineering and all of the messiness that that comes with and all the product interface and all of the delivery and the usage model and of course a lot of these particular capabilities that come with that. So I think there's like a real, you know, everyone has their own DNA and everyone has their own things that they do best.
- [30:18]
Interviewer
Yeah, that makes sense. We at Stripe have been thinking a lot about building the economic infrastructure for AI and what is required. You can have an agent acting on behalf of a person and you want to be able to just be prompting or doing stuff in your app. And part of the tool we use that your AI can engage in is going off and conducting commerce in the real world. And so we're building infrastructure for that. And then we noticed that because of the economics of AI, everyone has usage based models, right? Per token, per what have you. And so we're building out usage based billing infrastructure. And again we find the billing systems people are building on Stripe, they're very different from the classic SaaS is per seat pricing. Whereas again, everything in AI is per unit consumed. And you can get into how the agents engage in commerce with each other where there's no human in the loop. So there are all these ways in which our product roadmap is being formed. But I'm curious what you think the economic infrastructure for AI needs to look like. Are there things that we should be keeping in mind?
- [31:23]
Scott Wu
Yeah, yeah, for sure, yeah. Seat based to usage based. Big one for sure. I think on both sides, right? From the perspective of one, seats don't really make sense when it is like the AI themselves are arguably seats as well. They're doing a lot of the labor too. And then on the other side, I think usage obviously just goes so naturally with the cogs themselves because a lot of this is effectively GPU spend on how much you're spinning the models basically. And so I Think that makes a ton of sense. The other big one which comes to mind, obviously, is just for there to be an entire agent economy as well. Right. And so I think today, I would say is still probably more of a talking point than a reality. But I think as things are pretty rapidly changing and, and getting to the point where your agents are. Funnily enough, we use Devin. Devin is obviously entirely focused towards software engineering. But we order our doordash on Devin, we order our Amazon packages with Devin, and it's like there are pieces of that that turn out to work nicely anyway.
- [32:17]
Interviewer
You order your Amazon packages with Devin?
- [32:19]
Scott Wu
Yeah.
- [32:20]
Interviewer
So you're just in Slack and you ask him to buy something for you.
- [32:22]
Scott Wu
Yeah, yeah. Like just, Evan, can you go buy some more whiteboards for us or something like that? Yeah.
- [32:26]
Interviewer
At a certain point, do the real world things you asked Devin to do run into just blockers with sites trying to block bot activity?
- [32:37]
Scott Wu
A lot of Devin working really well obviously relies on Devin being able to do these things, get through a bit. But some of these things I think are quite natural with the model, which is you often have API keys or secrets or things like that that you want Devin to be able to hold onto. So that works for credit card numbers as well. Obviously there's a lot of work of real world software engineering doesn't involve a lot of just going and browsing the web and finding different sites and, and clicking around on them, even if you're just testing your own front end or putting up documentation or something. And so good browser use, I think is an important piece of that as well. And I think it's just kind of something that's.
- [33:09]
Interviewer
So shouldn't you build a consumer app? Doesn't everyone want this magic wand app where you can just have your virtual assistants? Like there's a million virtual assistant startups. It seems like none of them have really gotten to any scale.
- [33:20]
Scott Wu
Yeah, it's a fun question, I think, from our perspective. I think on the one hand it's fun seeing Devin go and do these doordash things at the same time. We also just know that, you know, our team is so small, we just don't have the kind of, you know, focus to be able to do that. In addition to doing software engineering, you're pulling up Devin and you're seeing this and then on the other side there's like the ide there. But like, you know, Devin's just going on doordash or something. You know, it's a very like fish out of water experience. And I think it's fine for us.
- [33:47]
Interviewer
To keep, you know, the way a lot of product development follows from people noticing how a product is being emerges. Exactly. And these emergent patterns like Twitter especially, people started linking to photos off site, so they built in native image support or the hashtag was invented by the community. So similarly, you're checking the Devon logs and you notice people are buying a lot of doordash. Maybe that's a suggestion on the product side.
- [34:11]
Scott Wu
Yeah, it's funny. Well, to be fair, it's most suggestion ourselves. I know.
- [34:16]
Interviewer
It's still emerging product usage.
- [34:17]
Scott Wu
I agree, I agree. It's a fun one. Yeah, that's funny.
- [34:20]
Interviewer
I love that.
- [34:21]
Scott Wu
Yeah, we had a fun one where Devin was. Walden had a flight that got canceled and was trying to use Devin to go and negotiate with the airline to get the refund for it. And Devin went to the site and naturally the site forwards you to their agent to have the conversation. And then Devin was kind of like explaining these things and wasn't making progress. And then at some point, Devin said, this is not working. I need to speak to a human right now.
- [34:43]
Interviewer
And did it?
- [34:44]
Scott Wu
It did. It did, yeah. So it got to the human and then the human got on the line and then it sent the link to the airline contract of like, oh, section 22 says this, this and that. And then Walton actually did get.
- [34:54]
Interviewer
But sorry, Devin was speaking.
- [34:55]
Scott Wu
Devin was chatting with the human, basically made the robot agent.
- [34:59]
Interviewer
That's funny.
- [35:00]
Scott Wu
Equivalent. And then got you a human.
- [35:02]
Interviewer
And did it successfully get the flight refund?
- [35:04]
Scott Wu
It got the refund, yeah.
- [35:04]
Interviewer
Okay. Again, the people want this. And going back to the economic infrastructure for AI, the other thing that we think about is it feels like trust is going to become a bigger deal online. And I don't quite know what form that takes because obviously it's been a big bad Internet for a long time. A lot of scams out there. It's a lot of hacking. But I don't know, the hacking attempts become more sophisticated, the deepfakes and everything. And so having a good sense of who is a trusted individual, who is a trusted business just seems to become much more important in this world.
- [35:41]
Scott Wu
Yeah, yeah, like related to that too. I also think one of these things, I think the cloudflare with agents and everything is a hot topic. And explain the cloudflare issue for me. Oh, yeah, of course. So there's a lot more agents browsing the web these days, and there's been certain things, you know, protections set up to not give agents access to websites. And I think the paradigm, you know, up until now, the paradigm for a lot of this, I mean there's robots, txt and all these things has often been basically almost like, you know, there are tons of things which you are not allowed to do.
- [36:11]
Interviewer
Yes.
- [36:12]
Scott Wu
As a non human and I think what we will probably need to see a lot more of over time is basically like delegating access, if that makes sense. Like making it more clear that an agent can do something on your behalf. And in some sense you are attaching some of your reputation to it too. There's a monetary question of how this works out, but there's also just actions that the agent takes are attributable to you and on your behalf.
- [36:37]
Interviewer
That's a great point. Right now we have bots versus no bots, clankers versus clankers not allowed. Whereas instead it needs to be bots allowed if you sign for them.
- [36:48]
Scott Wu
Yeah. As I was just saying, simple version is just if you're signed into your Google Chrome email account and you have a verified address, then you can have an agent run in that browser window and do things. But you're responsible for the work that it does.
- [36:59]
Interviewer
Yes. Yeah, it's sort of like API key permissions, but at a mass consumer scale across everything and all websites and everything. I like that. How does the existence of Devon affect your own hiring of engineers?
- [37:13]
Scott Wu
Yeah, I mean, from our perspective, we've always loved keeping the core engineering team very tight and very elite.
- [37:20]
Interviewer
What's tight? Like 30 people.
- [37:22]
Scott Wu
Yeah. So up until a few weeks ago, our whole team is about 35 people, of whom across all worlds, across all roles. Yeah. Of whom, I mean, almost everyone actually is an engineer by background, funnily enough. But what we call core engineering was about 19 with windsurf. Obviously the team count has grown a lot, but actually with core engineering itself, it hasn't actually gotten all that much bigger. It's gone from 19 to something in the range of like 30 to 35.
- [37:46]
Interviewer
Okay, so you keep the engineering team smaller. And how are the engineers themselves different versus a company being built 20 years ago?
- [37:54]
Scott Wu
Yeah, so it's a pretty different profile of the work that we have to do in the sense that there is a lot of execution and implementation that has to be done, but Devin does that so that humans don't need to. And so what we typically look for, our whole interview process, for example, for a lotties, is basically just having people build their own Devin in eight hours and seeing how far they get with it.
- [38:16]
Interviewer
Sorry, build their own version of Devin or build stuff with Devin Build their.
- [38:19]
Scott Wu
Own agent, own full end to end agent in like eight hours or six hours or whatever. Yeah, I think what we find is, and I think we'll see this trend generally in software engineering which is knowing all the little, memorizing all the facts or knowing all the little details or being really good at syntax of some language or things like that are going to be less important and what's going to be more important are a lot of the high level decision makings or understanding the technical concepts really well, having a good sense of products and just having a good intuitive sense of like what to build and what to do and being like a self owner that way too. And so yeah, a lot of our team actually are specifically former founders, which is kind of a fun one. Like of our initial kind of 35, I think, I think 21 of us have founded a company before. And so it's been a very high density of that.
- [39:05]
Interviewer
Wow, when will you hire your last engineer?
- [39:08]
Scott Wu
It's a good question. I'll make a distinction here, which is I think that there will come a point and my guess on this point is probably in the neighborhood of, let's say two, three, four years from now where we stop using code as the main interface. And basically being a software engineer really is just instructing your computer and telling your computer what to do and saying, oh, you're looking at your own product and you're saying hey, you think two.
- [39:33]
Interviewer
To four years from now software engineers are not really looking at code in their day to day just like they don't look at assembly today.
- [39:39]
Scott Wu
Exactly, yeah. And so that's going and looking at your own product and deciding oh yeah, we need to make a new page here. By the way, all this data, let's save this this way and let's index this according to X, Y and Z, because here are the things that lookups that we need to do or whatever. Making a lot of these architectural decisions but not looking at the code them, at least in the majority of circumstances. I think at that point obviously the jobs change a lot. Funnily enough, I think if anything we will have way more software engineers, not fewer. And I think just because the interface is not code anymore doesn't mean that the core skills of software.
- [40:14]
Interviewer
Yes.
- [40:15]
Scott Wu
People often ask us like my son or daughter is in high school or is just starting, should they even be studying computer science? And my answer is always absolutely yes. If anything, funnily enough, I feel like university computer science always had the opposite sin of teaching you the concepts. What programming was about and what computer science was about and not enough of like, all right, here's the syntax that you need to use and here's what it means to get a react app set up and whatever. I think we'll get to a point where those theoretical concepts and that high level understanding of maybe in one line like the model of a computer and how to make decisions, problem solve with the computer as a tool, that is what programming will be. And if anything there's going to be a lot more software engineers. I think one of the nice things is everyone talks about Jevons Paradox and how it relates to AI. I think there's nowhere that it's more true than software because we really never seem to run out of demand for more code.
- [41:08]
Interviewer
You can just write a lot of software. Yeah.
- [41:10]
Scott Wu
The half joking way to say this is despite how many software engineers in the world, we all know this. There are so many products out there that are still so bad. You're logging into your bank or you're dealing with your like, you know, check out at retail or whatever. And then there's all these things that are still like super outdated, super buggy. You know, you're logging into your healthcare platform or whatever and you're trying to click around and find your thing and.
- [41:31]
Interviewer
It'S like we haven't finished writing all this after yet. Yeah. Isn't it shocking that the UIs haven't changed at all? So we still, we talked to Siri which is the same, I mean button placement and the same brand on the iPhone as pre transformer models. You prompt Devin via Slack.
- [41:47]
Scott Wu
Yeah.
- [41:47]
Interviewer
You know we use our AI tools in, you know, in a web browser and we enter them into a text box like you know, we're playing Zork in the 1980s or whenever that came out and so 70s maybe. I don't know how old Zork is. Do you know what Zork is?
- [42:03]
Scott Wu
I don't.
- [42:03]
Interviewer
Oh, you're too young. It was like the original text based adventure game.
- [42:07]
Scott Wu
Oh, I see, I see.
- [42:08]
Interviewer
Yeah, yeah, yeah. But yeah, when are we going to see AI UIs? Because it's very retro right now.
- [42:15]
Scott Wu
Yeah. My high level thought on this is you always see this with new waves of technology. I think mobile phone is a great example where the initial apps kind of just look like basically websites but in a spin box. And over time you can still get a lot of value out of those. The core value prop of the phone was already there. But of course over time we built a lot of cool touch interfaces or we developed A lot of the science of what makes a good app us.
- [42:42]
Interviewer
Yeah. But we have no multi touch, we have no rubber banding.
- [42:45]
Scott Wu
Yeah. I think we are entering that phase now where for a few years it was just kind of like replacing existing flows and just using AI to do that better. And now we're starting to think about bit more of these kind of various generative flows. I mean maybe the simplest example that comes to mind is a lot more products now have the little chat box at the bottom where rather than having to click through all the menus yourself, you can just kind of ask the chat box and find that. Which is one very, very simple version of that. But I think there's way more innovation to do. Yeah.
- [43:15]
Interviewer
One framing I was thinking about with this is it became clear shortly after the invention of the transistor and the microchip that everything would have a microchip in this. Right. You know, everything could benefit from having a small computer in it and you know, your car would have a small computer in it and your dishwasher would have a small computer in it and you know, everything and there's some equivalent where everything will pass through a transformer model before it's consumed.
- [43:39]
Scott Wu
Yeah. One of my thoughts on this too is I think, I think AI is, I'd say uniquely different from some of these previous ways in an important way, which is personal computer or Internet or mobile phone. All of these had one of two things, or often both. One was a big hardware component of like. Yeah, you had to just go ship modems to everybody and you'd have to get people on the Internet and you had to give everyone a phone first. Right. And then two was like a very core critical mass effect or like empty room effect or whatever, network effect, whatever you want to call it, where the Internet was great and all obviously, but it doesn't really get that useful until all your friends are on the Internet too. And the restaurant that you're looking up is on the Internet too, and various other things as well. AI actually has neither of those problems. And as a result, what you kind of see is as soon as the tech works for somebody, it's pure software, it can work single player and give you a ton of value directly. It works for everyone. I think there's been a few things that we've seen as a result of that. One is, you know, there's a new person posting that they're the fastest company from 1 million to 100 million every couple of weeks because AI is just so much faster as soon as it Works. It works for everyone.
- [44:47]
Interviewer
Yes.
- [44:47]
Scott Wu
But I think the other part of that is, I think, to your point, I think there's actually a bit of lag with product, I would say, where I think you could freeze all the capabilities today and have no new models and no new research come out, and there would still be a whole decade of product progress to make. Whereas I think before the product progress kind of tracked alongside the distribution itself. Now it's been much more sudden where it's like two years total, where everyone's been thinking about it. And honestly, if we factor in a lot of the more recent capabilities, agentic capabilities, things like that, it's arguably less than one year for a lot of these. And we are all kind of grappling with that all of a sudden and trying to figure out what the right new product experiences are. Right. And so it's just taking a bit more time.
- [45:26]
Interviewer
What are your AGI timelines?
- [45:28]
Scott Wu
Yeah, I think we have AGI.
- [45:31]
Interviewer
Okay. Now.
- [45:32]
Scott Wu
Well, so I was gonna say there's this joke that people talk about, which is back in 2017, if you ask, do we have AGI, the answer is no. And today, obviously, if you ask if we have AGI, the first thing everyone always says, well, you have to go define AGI.
- [45:46]
Interviewer
This hemming and hawing.
- [45:47]
Scott Wu
Yeah. And I think it's kind of true in some sense, of devin will order.
- [45:51]
Interviewer
Your doordash for you. Sounds like AGI to me.
- [45:53]
Scott Wu
Yeah. Yeah. So obviously a bit of a facetious answer, but my honest opinion is I think there is some rapid singularity, superintelligence thing that people kind of talk about. I would guess it's pretty hard to say nothing's impossible, but I would guess that that's not something that happens in the immediate, immediate future. Especially because, as we said, a lot of the work to do is going and collecting all the real world. What are the problems that you want to solve? How do you define success for all these things? With that said, I think we're going to just keep. I think it's not so binary, basically. I think we're just going to keep rolling out more and more improvements and these things are going to be more and more capable, but I don't know that we have some sudden shift, at least for the next few years.
- [46:36]
Interviewer
No, that makes a lot of sense. We got to talk about Windsurf. It played out so quickly, so give us the play by play.
- [46:43]
Scott Wu
So we heard the news that it was going to be Google buying Windsurf, or I guess not technically buying this whole deal. That was happening that Friday the same time everyone else did.
- [46:54]
Interviewer
Okay, so this is not something that played out in advance. The Friday when the news came out.
- [46:57]
Scott Wu
It was basically just as sudden for us. We heard some r rumors. Maybe the night before.
- [47:02]
Interviewer
Devin was scrolling Twitter for you.
- [47:03]
Scott Wu
Yeah, yeah, exactly. Yeah. Devin came back and said, hey, you guys should check this out. We probably should look at this. And so we heard the news that. And naturally that afternoon we were kind of talking about it, thinking about, like, is there something that we should do off of this? It's not uncommon that there's some crazy news that happens in AI, but this is especially, I think, in our space. We talked about this idea. We reached out to them cold that evening and got to meet the new Windsurf leadership, you know, Jeff and Graham and Kevin that evening. And as we were kind of both talking about it, I think we kind of came to this conclusion together, which is, if there is something to do here at all, then it has to be ready to go by Monday morning, you know, because everyone, all the customers were realigned. The whole team was like, do I have a job? Do I not have a job?
- [47:50]
Interviewer
It was a melting ice cube.
- [47:51]
Scott Wu
Exactly. And so it's like, if it even waited until Thursday instead of Monday, like it was. People were going to cancel their contracts. People were going to get, you know, be interviewing at other places. And so. And so we said, okay, what this means is, like, if we want to explore this, we have to go just spend the entire weekend on this nonstop. A lot of fun moments there. I mean, we got to kind of the handshake agreement that Saturday, and then obviously there's all the legal and everything to figure out. You know, we all pulled an all nighter that Sunday night. We had a very optimistic plan that we were going to get some sleep.
- [48:23]
Interviewer
You also filmed all night of the Saturday night, or did you get some sleep?
- [48:26]
Scott Wu
We got a couple hours of sleep on Saturday. It was especially, I mean, a huge shout out to Jeff and Graham and Kevin because they had had a pretty rough few days before as well, actually. And so they were already pretty deprived coming into it. We were going through it. We had this optimistic view that we were going to get it signed on Sunday night. And so then we could go and focus on filming and figuring out how we address the team and everything. Obviously that did not happen, and we got it signed on Monday at 9am Even so, US and the lawyers were up all night basically just sorting out all these things. We luckily filmed the kind of Windsurf video In the Windsurf studio, we said, okay, we should just film it anyway.
- [49:00]
Interviewer
You realize you're gonna announce acquisitions without a video.
- [49:02]
Scott Wu
Yeah, yeah, yeah. It's always nice to have one. Yeah. And then as soon as we got things signed, we were up in front of the whole team and giving them the update and then sharing that publicly pretty soon after. It was a lot of. It was fun. I live for these moments, honestly.
- [49:18]
Interviewer
So you read the news on Friday and you signed the deal. Signed and announced the deal on Monday. But that means that you decided more or less instantaneously that you wanted to buy the remaining part of Windsurf.
- [49:30]
Scott Wu
Yeah. So I think we talked it through on Friday evening, and I think from our perspective, there are a few things that were nice about this. First of all, obviously, we know the space very well, so in that sense, we didn't really have to diligence the product or the customers, because we knew that. Right. But as we were kind of understanding the pieces of what happened exactly with the team, how many of the folks are still there and who last, we found that there was a very nice synergy in the sense that there was a core kind of research and product engineering team that went to Google and all of the other functions were entirely intact, which includes enterprise engineering, infra deployed engineering, go to market, marketing, finance, operations, all these various things. And funnily enough, I think with cognition, for better or for worse, I think we had done a good job of building out this core research and product engineering team, but we're, I think, a little bit behind on growing all the functions. And so we found a very natural fit there as well. And as we were kind of just talking, it's like they had JP Morgan and we had Goldman Sachs, and they had. There were all of these kind of just like very natural ways to fit in. And so I think from our perspective, yeah, we knew there was something really interesting there and we wanted to do it. And a lot of the rest was just figuring out the details.
- [50:36]
Interviewer
So you got to acquire a bunch of people who have lots of familiarity with the space. They have a product offering that is in an adjacent but not identical place to Devon. And so you get to accelerate it sounds like the go to market efforts and broaden out the product portfolio. That's how you think about it?
- [50:54]
Scott Wu
Yeah, yeah, yeah, absolutely. And then, of course, the products themselves, I think, are, funnily enough, we were thinking about what does the interaction of an Async product like Devon look like with a more sync product. And we had some ideas for Certain synchronous things that we wanted to build. We weren't going to build an IDE entirely because it felt like there were a couple players in town already. But as it turns out, having the IDE there actually were a lot of natural synergies with a lot of of the synchronous stuff that we thought about. And very simple thing, we shipped wave 11 a few days later after we closed that deal. And there are a lot of these basic things like yeah, being able to access your deep wiki in your IDE or being able to use all of the dev and code based representation in search or spinning up the agent there and all of these things. I think we just felt a lot of natural compliments and so from there felt like if there was a right person to work with and do this with, it would be.
- [51:42]
Interviewer
So in six months, do I buy Devon's and I get Windsurf bundle? Do I separately buy Windsurf and I can buy Devon?
- [51:48]
Scott Wu
Yeah.
- [51:49]
Interviewer
How will it work?
- [51:50]
Scott Wu
Yeah, a lot to figure out still. We certainly want to keep each of the product philosophies the same. Like I mentioned, like, I think there will still continue to be both Sync and Async products, but I think making the integrations between them much stronger and much easier. And I think it's going to be really nice and so certainly a lot that'll be much easier from the customer perspective. But if for some reason they really wanted to but use one of the two, I'd imagine that they would still be able to do that.
- [52:11]
Interviewer
It's obviously been an interesting aspect of the AI space that there's been a number of these 49% licensing type deals. To avoid the risk of an acquisition being blocked. Companies buy a license to the IP and then the talent that they want to be able to be sure comes with the company. Do you think that stays as a thing in the AI space? It's a funny moment in time thing, right?
- [52:36]
Scott Wu
Yeah. I certainly don't feel like I'm the expert on this one. It's the thing that I find funny. There's one new bell or whistle each time. You know, I feel like there's that unlike all the collection character scale, you know, it's like, you see like there's one. Oh like and now we do, you know, this licensing deal and now we. And so I think the metagame around that is certainly developing. There is some amount of polarity at the top level of AI as space in the sense that like there is a point at which you want to Just have like, you know, these things do scale with resources and they scale. And so I think basically the games get bigger, I guess is one way to put it. And I think for most companies the question is basically whether they think they will get there themselves or whether they want to work with another company.
- [53:21]
Interviewer
You're saying you would expect more M and A, whether it be like classical M and A or this new model of M and A. Because there are scale benefits in this game.
- [53:28]
Scott Wu
Yeah, maybe one of my hot takes is I think for a lot of the big. Of course there will be many medium sized outcomes in AI, but I think in this space a little bit more so than previous ones. It's a little bit more polarized towards like you become a hyperscaler or bust. And so for some companies that feel like that is the trajectory and the moonshot that they want to go for, and that's one thing. For others, working with someone is something that people do.
- [53:55]
Interviewer
And so now as you're bringing the Windsurf team on board, Cognition has this very intense culture. You know, you guys work, you work on the weekends, you all work out of this house. And so you're doing this buyout offer.
- [54:10]
Scott Wu
Yeah, yeah, yeah. I think for us it's, you know, and most folks have been really excited to come in and do it and only a small fraction have taken the buyout. But I think from our perspective, we just want to make sure it's an opt in, you know, situation for everyone. Because, you know, let's be honest, it isn't for everyone. And I think it is a very kind of intentional thing.
- [54:31]
Interviewer
There wasn't the intensity. You wanted people to, what did you want people to opt into?
- [54:35]
Scott Wu
Opt into the intensity and the new culture. And yeah, we're going to be going after some, some very ambitious goals. You know, I think, you know, by, by revenue standards or by, you know, whatever you want to call it, you know, there, there are, you know, folks might call us a mid or later stage company, but from our perspective, you know, we are still very much early stage in terms of the, the profile of what happens next and how much more there is to build and how much more there is to do. And obviously at an early stage, yeah, we do all have to be signing up for the uncertainty and the willingness to just go and take on a different challenge every week and to put in a lot of hours and to have that culture. That was a big piece of it. Obviously, regardless of what happens, we wanted to make sure people were well taken care of.
- [55:22]
Interviewer
Every day. Cognition is the largest company you've ever run. You're speed running. Coming up to it was true of me with Stripe as well, to be clear. Because you're speed running, learning how to run a company. I'm curious, how do you learn this stuff? How do you use AI, but how do you learn more broadly?
- [55:40]
Scott Wu
Yeah, I've got a lot to learn still, for sure. I think many of these functions are, if anything, like I mentioned, we have under invested in a lot of functions maybe because they're not as top of mind for us as they should be. And now that's something that we're pretty actively working to do more of. I don't believe in professional coach or career coach in the literal sense, but I think obviously you learn a lot from your peers and your friends who are doing similar things. Having a lot of close friends who.
- [56:10]
Interviewer
Are people you went to math camp with apparently.
- [56:13]
Scott Wu
Yeah. And learning from all these different folks. And I do think as an entrepreneur it helps a lot to have a close group of friends. Then you can just be very honest and say this thing is totally messed up and I have no idea what we're going to do. And please tell me if you have done anything like this before or things like that, which has been really helpful. I think Eric and Kareem from Ramp for example, or all these various folks from math competitions or my previous co founder, Vlad from Lunch club. A lot of different folks that I talk to for advice and I think it really does help a lot.
- [56:46]
Interviewer
Last question. I'm curious, what is your information diet in terms of how you learn about the world?
- [56:53]
Scott Wu
Yeah, a lot of. I feel like Twitter is really for tech news, I think is really the place to be. We share a lot of things.
- [57:01]
Interviewer
Do you think there's too much video in the algorithm these days? I think they're like it's kind of become TikTok.
- [57:07]
Scott Wu
There's a lot of video, but then I just don't watch the videos for the most part. Or you see the first few seconds, which is an interesting thing to think about as people who are making videos too of like make sure you can convey your point with no sound and with the first three seconds like as much as you can do that. I think there are still like another like 5x of users you reach that are in that camp. The Twitter algorithm is the extent of how AI affects my information.
- [57:28]
Interviewer
But that's you on the receiving end of AI as opposed to you using AI as a tool.
- [57:32]
Scott Wu
It's a good point. It's a good point. I mean, I should have Devin, you know, just GitHub action. The morning report, Like Zaza, basically, where Devin just goes and does the morning report and gets that. There's a lot of optimization to do still.
- [57:44]
Interviewer
The President's daily briefing.
- [57:46]
Scott Wu
Yeah.
- [57:46]
Interviewer
Well, Scott, thank you.
- [57:47]
Scott Wu
This is awesome. Thank you so much for having me.