Latest / Cheeky Pint / Cognition CEO Scott Wu on acquiring Windsurf, AI replacing engineers, and the Moneyball-ification of everything

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Cognition CEO Scott Wu on acquiring Windsurf, AI replacing engineers, and the Moneyball-ification of everything · Cheeky Pint

• John Collison: Tell me about your upbringing and all the math stuff. I feel like you’re known for the math stuff these days. Scott Wu: Yeah, yeah. So, I grew up, I’m from Baton Rouge. 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 air emissions permitting, and things like that. And Louisiana has a lot of oil and gas, and so that’s kind of how we ended up— John Collison: A lot of the air emissions too, actually. Scott Wu: Yeah. 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 Neal, super, super close the whole way through, and Neal was about five years older than me. Neal started doing math competitions when he was in middle school, and so he would’ve been in 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, yeah. John Collison: And this is stuff like, if I ask you, what 694 is squared? 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 the frog that’s like going up, and then every night falls down the well, and how many nights… These kinds of things where you get to— John Collison: He gets on the log. Scott Wu: Yeah, yeah, yeah, yeah, where you 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 middle schoolers and high schoolers. And so I competed in the seventh grade math division, as a second grader. And I did the competition, it was my first time doing any of these, I just really liked math and stuff. And then they were calling out third place, second place, first place, and none of them were me. And I still just remember I was so upset. John Collison: That’s your supervillain origin story? Scott Wu: Yeah, 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 algebra 1 or something, and I won that year, and then I basically kept doing math competitions from there. My last year of high school, which would’ve been my junior year, I left a year early, but I did IOI, the programming algorithm. I did IOI three times, and I got golds, yeah. John Collison: Where’d you go to school? Scott Wu: So, 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— John Collison: Sorry. Obviously that’s surprising. You weren’t that good at school? Scott Wu: Well, I wasn’t that good at finishing school. I have a middle school degree, but 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. John Collison: Sure, yeah. Scott Wu: And I did that as a software engineer. That was back in 2014. John Collison: Yeah, wow. That’s a blast from the past. Scott Wu: Yeah, yeah, it was a while ago. And then after that I decided, OK, 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. John Collison: How did you end up at Addepar? And that’s very forward-thinking of them, obviously, that they took on a high school aged, high school dropout. Scott Wu: Yeah, yeah, it was a fun group. Funnily enough, there were four of us who started at the same time, as high schoolers. And it was myself, Alexandr Wang is actually another one, we started on the same day. Eugene Chen, who’s now running Phoenix DEX, and then Sreenath Are, who’s most recently at Sandbar, as the CEO. John Collison: Wait, sorry, this is a real small group theory moment. So, you and Alex were in the same group as— Scott Wu: That’s right. So, we knew each other, we met in middle school. John Collison: Alex, now of Meta. Scott Wu: Now of Meta, that’s right. John Collison: MSL, I guess. 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 MATHCOUNTS. We were both at the national competition, and then we started talking… Google Hangouts was the thing at the time, so we would always— John Collison: It turns out there’s some math in AI, yeah, this may be an— 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 a real infectiousness of being entrepreneurial too. I think Alex deserves a lot of credit for, I’d say, being the first of our group— John Collison: Alex Wang got you into the idea of starting a company? Scott Wu: Yeah. Somehow I think there’s definitely a bunch of that involved, for sure. Yeah. But also a lot of folks, Johnny Ho, who’s one of the cofounders of Perplexity, for example, Demi Guo, 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. John Collison: 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 people in their early twenties 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? Scott Wu: 28, yep. John Collison: Running Cognition. Is the presence of young people as founders of leading companies a biomarker for industry vibrancy? Where Michael Dell was young during the takeoff of the PC era, and Mark Zuckerberg was young during the takeoff of social networking, and now we’re in the takeoff of AI coding tools. Scott Wu: Yeah, I was going to say, I appreciate you calling me young. I mean, I think relative to being 18 or 19, it’s still a long ways. John Collison: The test is in your twenties. Scott Wu: So, 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 the biggest, the highest order of it. I think the reason that young founders, who were just really sharp and really determined, did very well is because, at the end of the day, being a good first principles thinker does beat experience, 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, the first principles thinking and the experience, and I think things have gotten a lot more… call it mature, as a space. And so basically it’s gotten harder, and so there are fewer that are literally coming out of college. John Collison: It feels hard to make the claim that it was easy to start a leading business in prior eras. 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 a Satya 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. 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 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… I guess I would call it the “Moneyballification” of everything. And to give a few examples, one of the things that I do casually, for fun, is playing poker. And poker is a very fun game, it’s actually much more mathematical than a lot of people realize. Of course, people kind of think of it as the— John Collison: I think people know that, like the poker solvers and the odds tables, and everything like that. Scott Wu: Yeah, solvers and so on. John Collison: Or is it more mathematical than that? Scott Wu: No, no, I think that’s right, I think that’s right. I think there’s a first-order impression of, it’s all about— John Collison: “I’m all in!” Yeah, yeah, yeah. Scott Wu: … just knowing what you got and… Yeah. John Collison: Exactly. Play the person on the other… Yeah. Scott Wu: Yeah, yeah, yeah. 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 the 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 1 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 improve their own play. And now it’s just all math nerds. It’s basically, 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, 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, the conclusion of it kind of is math. And I feel like 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. John Collison: I see. More spaces have kind of resolved to their underlying… Like a chess engine, just deciding that the position is mate in 41 or something. Scott Wu: And chess is totally the same way, by the way, which is back in the 1800s, people— John Collison: The Romantic style of players’ time. Scott Wu: Yeah, yeah, exactly, the Romantic style of play. And now it’s kind of like there is a right sequence of moves, and just seeing how close you are to that optimum. John Collison: What are other domains where the Moneyballification of everything is showing up? Scott Wu: One of my other hobbies, which I played at least before the advent of Cognition, it 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 played Smash? OK, OK. It’s for the GameCube, which came out in 2001. So, it’s a very old game, but people just still keep playing the same game. And for the first six to eight years of the game, the personality was very much really wily, sharp thinkers, people who are just quick on their feet and coming up with these ideas. And now it’s just all math, you know? And then the people who play and do really well are— John Collison: I think some of the RTSs are a little bit that way as well, where play has gotten less creative as people have gotten better at them. Scott Wu: Yeah, yeah. And it’s a funny thing where it’s like… There’s a lot of beauty in the nerd side of it too. It’s like a difference in what skills get most selected for, is maybe the way I’d describe it. John Collison: Yep. OK, I’m getting distracted from asking you about Cognition. What is Cognition? What does it do? 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, and Windsurf, the IDE. But at a high level, we really want to build the future of software engineering. John Collison: Is it confusing for people that you have two brands? You have Cognition, the company, and then Devin, the slightly anthropomorphized instantiation of it. Scott Wu: We’ve been talking about that. Now there’s Windsurf as well, and so now there’s a third thing, but I think some consolidation is probably good. John Collison: And so people are maybe familiar with 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. That is not the Cognition-Devin 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. 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 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, right? Where you have an agent and you delegate a task. And so 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. John Collison: And what level of task is Devin doing a good job of today? 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, especially encyclopedic knowledge and just pulling facts and things like that. There’s some things that it still makes terrible decisions on. But I think it’s the right average overall. And what we see folks typically using it for are things like bugs for example, or simple feature requests, and fixes, 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, @Devin, 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’s deploy, than it is things like building and coming up with really— John Collison: Dependency management, yeah, all that kind of stuff. Scott Wu: Yeah. John Collison: What metrics can you share on where the business is at? Scott Wu: Yeah, so Devin 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 your org. Typically in a successful org, Devin is merging something in the range of 30% to 40% of all the pull requests that come through. John Collison: And you talked about this async model, but isn’t it the case that, as I look at other... the GitHub Copilots, and the Cursors, and everything like that, they are… or Claude Code, they are not fully synchronous, because 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? 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. 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? John Collison: Yeah. Scott Wu: OK, yeah. 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. It is somebody who tells the computer what to do, and makes all these decisions of… It can be big decisions like, “What is the right architecture that we want to use for all of this?” Or it can be, like a lot of these microdecisions, like, “Oh, by the way, there’s a case where this balance is less than zero, and what do we want to do here? Should we show an error, or should we request this?” Or whatever. And all these decisions are what people typically call the essential complexity of what is all of the actual underlying logic of the decisions, of what the software is doing? And the accidental complexity is basically everything else. All the things that you have to do to support things as they scale, 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 everyone knows that you need to have that in your class, but there’s no real decision that needs to be made in terms of going and doing that. And it’s an interesting thing, which is, up until 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, of just going and doing the routine implementation and so on. And so I think this merged experience that comes up is basically something where, for anything that actually needs you in 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 pure 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 agent. John Collison: 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 grunt work? How do you get large enterprises comfortable with giving Devin’s sufficient permissions to be effective? But 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. And I think people still have a… Models hallucinate way less than they did, but people still have fear of the model just making something up and— Scott Wu: For sure. John Collison: … doing it. And so yeah, how do you get people comfortable with giving it enough power to be effective? Scott Wu: So, we pretty strongly recommend that people using Devin don’t give it prod database access, for example. John Collison: That’s one way. Scott Wu: 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. 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. 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— John Collison: It’s back to your point of incidental complexity, where the reason a migration is time-consuming is not the actual single deletion step, all the time cost comes in other places. Scott Wu: Yeah, yeah, exactly. I think in practice what we see with folks, especially in these kind of 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 were 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. John Collison: 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, 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 at 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? 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? 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. 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. And I think as time goes on, I think these things will become more and more and more clear. John Collison: 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. GPT-6 or GPT-7. Presumably, you do not hold this view. Scott Wu: Yeah. John Collison: How do you avoid getting run over by the labs? Scott Wu: Yeah, yeah, for sure. So, look, I think the labs are obviously… I think they’re incredible businesses. 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. This, 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… How good can you be at Angular migrations if you’ve never seen Angular, and you’ve never done an Angular migration yourself? And there’s this 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 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. John Collison: So, it’s not a general intelligence task, it’s a specific intelligence of, working in the Stripe codebase 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? Scott Wu: Yeah, exactly. Maybe one way to put it is, I think the argument is something like a superintelligence. And I think in some sense, yes, I think you could consider us short superintelligence. I think what we’re getting to with RL, as this thing is improving and improving, and we see more and more of the gains, and people are developing the techniques, I think of RL in this paradigm of AI as basically, the platonic ideal of it is the ability to solve any benchmark. You have exactly a dataset 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 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 ideal a lot faster than most folks would’ve expected. I think we’re really… I mean, there’s been some pretty crazy developments, like the IMO gold medal or like the scores on Sweetmatch 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?” 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, it’s like, yeah, what are all the tools that you interact with on a day-to-day basis? How do you use those tools? What does it mean to build a representation of the codebase over time? How do you decide whether shipping the feature was successful or not successful? All of these various things, and creating the right environments around them. John Collison: 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… Is Devin’s business model and… Devin’s revenue is the benchmark, essentially. Scott Wu: Yeah, yeah, yeah. Yeah, it’s a good question. From our perspective, we have a lot of benchmarks internally. The biggest is one that we call junior dev, 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. And so 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. And the thing that’s hard about it is perhaps not some algorithmic coding thing itself, but it 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, “OK, 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. John Collison: And so have the newly released Claude 4.1 and GPT-5 done this benchmark? Scott Wu: Yeah. 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. John Collison: As you think about the AI business and industry over the next 5 to 10 years, you think about all the different layers of the stack. You have the data centers, and then you have labs, and then you have the application layers such as yourself. Who benefits? What gets more competitive? What gets less competitive? Are all these just classic competitive oligopolies? What’s the market structure? 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. I think all the— John Collison: There’s just going to be a lot of AI? Scott Wu: I think the prices are cheap everywhere. I’ve been saying this at least for the last 6 to 12 months, and I think we’ve seen prices go up a decent bit across all of these. But no, at a high level, first of all, there’s going to be a lot of AI. It can’t be understated, in the sense that I think we’re kind of coming off of a decade of a lot of various B2B SaaS. And so 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 late 2000s, early 2010s, 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, 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. And so, first thing is, there’s just going to be a lot of AI. I think the second thing, about where does the value accrue? And my honest answer on that is, the simple thing is value accrues wherever there’s meaningful differentiation in the layer. If there’s NVIDIA, and there’s TSMC, for as long as NVIDIA needs to work with TSMC, and for as long as TSMC needs to work with NVIDIA, of course there’ll be some rubbing up on each other’s shoulders, but they will continue to do great. And you kind of see this down the stack as well. I would argue that the problems that are being solved in all of these different layers are very, very different problems that have pretty meaningful differentiation. John Collison: You’re saying this prevents too much vertical integration, basically? Where you get the layers kind of keep each doing their own thing? Scott Wu: Exactly, yeah, yeah. And I think there’s a real diff where, as soon as you go from hardware to… Obviously foundation model training is its whole own can of worms, and very much 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 orients around that. 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 a real… Everyone has their own DNA and everyone has their own things that they do best. John Collison: That makes sense. And 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 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, 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’s 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 informed, 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? Scott Wu: Yeah, yeah, for sure. Yeah, seat-based to usage-based, big, big, big, big one, for sure. I think on both sides, right? From the perspective of one, seats don’t really make sense when it is… 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 it is effectively GPU spend on how much you’re spending on 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. And so I think today I would say is, still probably more of a talking point than a reality, but I think things are pretty rapidly changing, 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— John Collison: Sorry— Scott Wu: … of— John Collison: You order your Amazon packages with Devin? Scott Wu: Yeah. John Collison: So, you’re just in Slack, and you ask it to buy something for you? Scott Wu: Yeah, yeah. Just, “@Devin, can you go buy some more whiteboards for us?” Or something like that, yeah. John Collison: At a certain point, do the real-world things you ask Devin to do run into just blockers with sites trying to block bot activity? Scott Wu: A lot of Devin working really well, obviously it relies on Devin being able to do these things and get through it, 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. And so that works for credit card numbers as well. And 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 clicking around on them, even if you’re just testing your own frontend 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 emerged. John Collison: So, shouldn’t you build a consumer app? Doesn’t everyone want this magic wand app, where you can just have your virtual assistant? There’s a million virtual assistant startups, it seems like none of them have really gotten to any scale. 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 our team is so small, we just don’t have the kind of 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 the IDE there, but Devin’s just going on DoorDash or something. It’s a very fish out of water experience, and I think it’s fine for us to keep it. John Collison: But you know the way a lot of product development follows from people noticing how a product is being used— Scott Wu: Like emerging patterns, yeah. John Collison: Exactly, in these emerging 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 Devin logs and you notice people are buying a lot of DoorDash. Maybe that’s a suggestion on the product side of things? Scott Wu: Yeah, it’s funny. To be fair, it’s mostly just ourselves. John Collison: I know, it’s still emerging product usage. Scott Wu: I agree, I agree. It’s a fun one, yeah. John Collison: That’s funny. I love that. 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 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.” John Collison: And did it? 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 Walden actually did get— John Collison: But sorry, Devin was speaking? Scott Wu: Devin was chatting with the human. John Collison: I see, yeah. Scott Wu: Basically they made it past the robot agent— John Collison: That’s funny. Scott Wu: … equivalent, and then got to a human. John Collison: And did it successfully get the flight refund? Scott Wu: It got the refund, yeah. John Collison: OK. 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. I don’t quite know what form that takes, because obviously it’s been a big bad internet for a long time. There’s a lot of scams out there, there’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. Scott Wu: Yeah. Related to that too, I also think one of these things, I feel like the Cloudflare with agents and everything is a hot topic, and— John Collison: Explain the Cloudflare issue for people not familiar. Scott Wu: Oh yeah, of course. So, there’s a lot more agents browsing the web these days, and there’s been certain things, protections set up to not give agents access to websites. And I think the paradigm… Up until now, the paradigm for a lot of this stuff, I mean, there’s robots.txt and all these things, has often been basically almost like, there are tons of things which you are not allowed to do as a nonhuman. And I think what we will probably need to see a lot more of over time is basically delegating access, if that makes sense? 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. John Collison: It’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. Scott Wu: Yeah, as I was just going to say, simple version is just like, 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 all of it, you’re responsible for the work that it does. John Collison: Yes. Yeah, it’s sort of like API key permissions, but at a mass consumer scale across everything, and all the websites and everything. I like that. And how does the existence of Devin affect your own hiring of engineers? Scott Wu: Yeah, from our perspective, we’ve always loved keeping the core engineering team very tight and very elite. John Collison: What’s tight? Like, 30 people? Scott Wu: So, up until a few weeks ago, our whole team was about 35 people, of whom— John Collison: Across all roles? Scott Wu: Across all roles, yeah. Of whom, 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 30 to 35. John Collison: OK, so you keep the engineering team smaller, and are the engineers… How are the engineers themselves different, versus a company being built 20 years ago? Scott Wu: 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 lot of these, is basically just having people build their own Devin in eight hours, and seeing how far they get with it. And I think— John Collison: Sorry, build their own version of Devin, or build stuff with Devin? Scott Wu: Build their own version, their own agent— John Collison: Ah, OK. Scott Wu: … their own full end-to-end agent in eight hours, or six hours, or whatever. I think what we find is, and I think we will 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 then just having a good, intuitive sense of what to build and what to do, and being 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. Of our initial kind of 35, I think 21 of us have founded a company before, and so it’s been a very high density of that. John Collison: Wow. Scott Wu: Oh, did I just lose? OK. John Collison: Very good. Scott Wu: Yeah. Maybe I should have just come out earlier and then— John Collison: I was wondering that. Yeah, I think that could have been— Scott Wu: Interesting. John Collison: Good game. Scott Wu: It was a nice game. Yeah. John Collison: When will you hire your last engineer? 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— John Collison: You think two 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? 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. And 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 themselves, 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… People often ask us, “My son or daughter is in high school, or has just started… 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 doing too much— John Collison: It’s too theoretical. Scott Wu: … of teaching you the concepts of what programming was about, and what computer science was about, and not enough of like, “All right, here’s 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, the model of a computer and how to make decisions and 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 and more software. John Collison: You can just write a lot of software. Scott Wu: Yeah. The half joking way to say this is, despite how many software engineers in the world, you know, we all know this, there are so many products out there that are still so bad. John Collison: Yeah. Scott Wu: You know, you’re logging in to your bank, or you’re dealing with your checkout at retail, or whatever, and there’s all these things that are still super outdated, super buggy. You know, you’re logging in to it, to your healthcare platform or whatever, and you’re trying to click around and find your thing. John Collison: We haven’t finished writing all the software yet. But isn’t it shocking that the UIs haven’t changed at all? So, we talk to Siri, which is the same button placement and the same brand on the iPhone as pretransformer models. You prompt Devin via Slack. We use our AI tools in a web browser, and we enter them into a text box, like 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? Scott Wu: I don’t. John Collison: Oh, you’re too young. It was like the original text-based adventure game. Scott Wu: Oh, I see, I see. John Collison: Yeah, yeah, yeah. But yeah, when are we going to see AI UIs? Because it’s very retro right now. 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 smaller box. And over time, you could still get a lot of value out of those, your 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 UX. John Collison: Yeah, but we’ve no multitouch, we’ve no rubber banding. Scott Wu: Yeah. Yeah. I think we’re entering that phase now, where for a few years, it was just kind of replacing existing flows and just using AI to do that better. And now we’re starting to think about a bit more of these kind of various generative flows. 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 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. John Collison: 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 it, right? Everything could benefit from having a small computer in it, and your car would have a small computer in it, and your dishwasher would have a small computer in it, and everything. And there’s some equivalent where everything will pass through a transformer model before it’s consumed. Scott Wu: Yeah. One of my thoughts on this too is, 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 had to get people on the internet and you had to give everyone a phone first. And then two was a very core critical mass effect, or an empty room effect or whatever, network effect or 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 kind of works for everyone. I think there’s been a few things that we’ve seen as a result of that. One is, 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. 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. And so it’s just taking a bit more time. John Collison: What are your AGI timelines? Scott Wu: Yeah, I think we have AGI. John Collison: OK. Now. Scott Wu: So, I was going to say, there’s this joke that people talk about, which is, back in 2017, if you ask, “Do we have AGI?” The answer’s no. And today, obviously, if you ask if we have AGI, the first thing everyone always says, “Well, you have to go define AGI.” John Collison: Yeah, this hemming and hawing. Scott Wu: Yeah, yeah, yeah. And I think it’s kind of true in some sense of— John Collison: Devin will order your DoorDash for you. Sounds like AGI to me. 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 very 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, that 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. John Collison: No, that makes a lot of sense. We got to talk about Windsurf. Scott Wu: Oh, yeah! John Collison: It played out so quickly, so give us the play-by-play. 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 at the same time everyone else did. John Collison: OK. So, this was not something that played out in advance—the Friday where the news came out. Scott Wu: Yeah. It was basically just as sudden for us. We heard some rumors maybe the night before— John Collison: Devin was scrolling Twitter for you. 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 then, and naturally that afternoon we were talking about it, thinking about it, 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. And we talked about this idea. We reached out to them cold that evening, and got to meet the new Windsurf leadership, 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. Because everyone, all the customers were reeling, the whole team was like, “Do I have a job? Do I not have a job?” John Collison: It was a melting ice cube. Scott Wu: Exactly. And so it’s like if it even waited until Thursday instead of Monday, people were going to cancel their contracts, people were going to be interviewing at other places. And so we said, “OK, what this means is, if we want to explore this, we had to just spend the entire weekend on this nonstop.” A lot of fun moments there. I mean, we got to the handshake agreement that Saturday, and then obviously there’s all the legal and everything to figure out. We all pulled an all-nighter that Sunday night. We had a very optimistic plan that we were going to get it signed by— John Collison: Did you also pull an all-nighter the Saturday night, or did you get some sleep? 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— John Collison: Totally, it was very essential. Scott Wu: … before as well actually, and so they were already pretty sleep-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 9:00 a.m., because us and the lawyers were up all night, basically just sorting out all of these things. We luckily filmed the Windsurf video in the Windsurf studio. We said, “OK, we should just film it anyway.” John Collison: You realize you could announce that position without a video? Scott Wu: Yeah, yeah, yeah. It’s always nice to have one. 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. John Collison: 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? Scott Wu: Yeah, so I think we talked it through on Friday evening, and I think from our perspective, there were 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 left, 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 funny 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 were, I think, a little bit behind on growing all the other functions. And so we found a very natural fit there as well. And as we were just talking… They had J.P. Morgan and we had Goldman Sachs, and there were all of these just 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. John Collison: 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 Devin, 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? Scott Wu: 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 Devin 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 of players in town already. But as it turns out, having the IDE, there actually were a lot of natural synergies with a lot 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 a lot of these basic things, like being able to access your DeepWiki and your IDE, or being able to use all of the Devin 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 complements, and so from there, kind of felt like if there was a right person to work with and do this with, it would be— John Collison: So, in six months, do I buy Devin’s and I get Windsurf bundled? Scott Wu: Yeah. John Collison: Do I separately buy Windsurf and I can buy Devin? Scott Wu: Yeah. John Collison: How will it work? Scott Wu: Yeah, lots to figure out still. We certainly want to keep each of the product philosophies the same. Like I mentioned, I think there will still continue to be both sync and async products, but I think making the integration between them much stronger and much easier, I think, is 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 use one of the two, I’d imagine that they would still be able to do that. John Collison: It’s obviously been an interesting aspect of the AI space, that there has 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… It’s a funny moment in time thing, right? Scott Wu: Yeah. I certainly don’t feel like I’m the expert on this one. The thing that I find funny, there’s one new bell or whistle each time, I feel like there’s— John Collison: Unlike all the legal and contractual stuff. Scott Wu: … Adept. Inflection. Characters. Scale. You see there’s one, and now we do this licensing deal and now… And so I think the meta game around that is certainly developing. There is some amount of polarity at the top level of AI as a space, in the sense that there is a point at which you want to just have… 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. John Collison: You’re saying you would expect more M&A, whether it be like classical M&A or this new model of M&A, because there are scale benefits in this game? 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, you become a hyperscaler or bust. And so for some companies that feel like that is the trajectory and the moon shot that they want to go for, then that’s one thing, but for others, working with someone is something that people do. John Collison: And so now as you’re bringing the Windsurf team on board, Cognition has this very intense culture. You guys work, you work on the weekends, you all work out of this house, and so you’re doing this buyout offer. Scott Wu: Yeah, yeah, yeah. I think for us, 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 situation for everyone, because, let’s just be honest, it isn’t for everyone, and I think it is a very intentional thing there— John Collison: Was it the intensity, you wanted people to… What did you want people to opt in to? Scott Wu: Opt in to the intensity and the new culture and yeah, we’re going to be going after some very ambitious goals. I think by revenue standards, or by whatever you want to call it, folks might call us a mid- or later-stage company, but from our perspective, we’re still very much early stage, in terms of 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, 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. John Collison: Yeah. Every day, Cognition is the largest company you’ve ever run. You’re speedrunning coming up to… It was true of me with Stripe as well, to be clear, but you’re speedrunning 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? Scott Wu: Yeah, yeah. No, I’ve got a lot to learn still, for sure. I think many of these functions are, if anything, like I mentioned, we have underinvested 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. So, having a lot of close friends who are wanting— John Collison: People you went to math camp with, apparently. 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 that 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 Karim from Ramp, for example, or all these various folks from math competitions, or my previous cofounder, Vlad from Lunchclub, a lot of different folks that I talk to for advice, and I think it really does help a lot. John Collison: Last question, I’m curious, what is your information diet, in terms of how you learn about the world? Scott Wu: Yeah, I feel like Twitter is really… For tech news, is really the place to be. We share a lot of things— John Collison: Do you not find there’s too much video in the algorithm these days? Scott Wu: I think there— John Collison: It’s kind of become TikTok. 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 make sure you can convey your point with no sound and with the first three seconds, as much as you can do that. I think there are still another 5x of users you reach that are in that camp. The Twitter algorithm is the extent of how AI affects my information diet. Maybe I should be more— John Collison: But that’s being you on the receiving end of AI, as opposed to you using AI as a tool. Scott Wu: It’s a good point, it’s a good point. I should have Devin, just a GitHub action— John Collison: The morning report, like Zazu. Scott Wu: Have a common job, basically, where Devin just goes and does the morning report, and gets that. There is a lot of optimization to do, still. John Collison: The president’s daily briefing. Yeah. Well, Scott, thank you. This was awesome. Scott Wu: Yeah, thank you so much for having me.