No Priors Ep. 62 | With Cognition CEO and Co-Founder Scott Wu

1. 0:00 – 1:12

   Introduction

   1. SGSarah Guo0:00

      (instrumental music plays) Hi, listeners, and welcome to No Priors. Today, we are talking to a very good human software engineer and the co-founder and CEO of Cognition, Scott Wu. The team at Cognition calls themselves an AI lab focused on reasoning, and recently, they released a demo of Devin, an AI software engineer that can increasingly handle entire tasks end-to-end, with leading results on SWE-Bench, a software engineering benchmark of real-world GitHub issues. The demo broke the internet, at least among tech Twitter, and so we're really excited to have Scott. Welcome, Scott.

   2. SWScott Wu0:35

      Hey. Great to be here. Thanks for having me.

   3. SGSarah Guo0:37

      So, you have been coding, um, and then coding competitively since you were a kid. What first got you interested?

   4. SWScott Wu0:44

      (laughs) Um, I think I always really, really liked math growing up actually. Um, my older brother, Neil, was, um, was the first to get me into programming, so I think I learned how to program when I was around nine years old. I just fell in love with it. I think the ability to, to take ideas and then make them into reality was, was always really exciting for me. I did a lot of math and programming competitions, you know, throughout school, and by the time I finished high school, I was, I was pretty set on going into tech, so...

2. 1:12 – 6:39

   IOI training and community

   1. SWScott Wu1:12

   2. SGSarah Guo1:12

      Can you explain, um, for our listeners who may, um, of some set of our listeners that are not familiar, like what IOI is?

   3. SWScott Wu1:20

      Yeah, sure. So IOI is the International Olympiad of Informatics, um, so it's the olympiad of code basically, um, and it's, uh, it works just like the, uh, the other Olympics. Um, every, every country sends in their own team of, of their best coders, and it's a competitive programming competition, um, and you know, there's gold medals, silver metals, bronze metals, and so on. And, uh, you know, the whole idea is it's very algorithmic problem-solving. And so you're given tasks to solve, and, you know, you have to figure out the, the optimal algorithms for those, and then also implement those into code.

   4. SGSarah Guo1:53

      It's like an interesting topic for somebody who, uh, was world-class at IOI. Like, what is practice like for getting better at competitive programming?

   5. SWScott Wu2:02

      Yeah, I used to, um, you know, programming in math competitions used to be my entire life when I was growing up. Um, I, uh, you know, it's what you think about in the shower, it's what you're spending all your time on, it's, you know, how, how, how v- every, every problem in life that I run into I would frame as, as a, an algorithms problem basically. Um, and a lot of it is, is, um, like a lot of other disciplines, it's just, you know, putting a lot of effort into it and being willing to, to think really analytically and, um, you know, be very brutal about your own shortcomings and, you know, focus on the things that you're not doing well and just continue to push and improve.

   6. SGSarah Guo2:39

      Is it, um, like, a known space of, like, domains and algorithms one can learn, or is it really like learning to solve problems algorithmically really well, and it, it could be completely new problems or domains in every competition?

   7. SWScott Wu2:54

      Yeah, there's definitely, um, there definitely are standard algorithms that exist, so like, you know, shortest path or something, or, you know, binary search trees, or, or things like that. And so helps a lot to learn the fundamentals, but the whole idea is that every problem in a contest is, um, is totally unique, you know. It's, it's a new problem that's never appeared before, and, um, um, you know, the, the, the beauty of the contest itself is in the creative problem-solving that you're doing to figure out the right algorithm, right? And so, you know, while the fundamentals are very helpful, a lot of it is figuring out how to use each of these pieces, and, you know, reduce the problem to a shortest path problem, or how you, you know, modify, you know, certain algorithms to make them work for, for different use cases.

   8. EGElad Gil3:35

      Yeah, I feel like there's a whole generation of people now who are, are basically people who grew up, um, with the internet being their hometown in some sense. Um, you know, where a lot of their early community or people they interact with are sort of come from that world, and I feel like that's a lot of people in the AI world today. Um, was, was that true for the IOI community as well? Was it largely, like, online interactions and trading tips with strangers who became friends and all that kind of stuff? And then if so, how has that impacted your career or your life, or, you know, working with others over time?

   9. SWScott Wu4:03

      Yeah, definitely. I mean, I grew up in Baton Rouge, Louisiana, and so there were, there were not (laughs) a lot of other people who had the same kind of interests in math and programming that I did. And so, um, you know, a, a lot of these competitions were, was the fir, like, were, were the first time that I got to meet, um, others who, um, who had a lot of these same interests. And, um, you know, the competitions are once a year, and maybe there's training camps or things like that that are a couple times a year. But for, for the large majority of the year, we'd be talking online, and, you know, we had our, all of our own communities where we'd talk about, you know, competition problems but also kind of, um... Yeah, yeah, we, we, we became very close friends through that too.

   10. SGSarah Guo4:40

       Yeah, it's an incredibly, uh, smart community of people at least, you know, competing at the highest level. And I think you've said before that, um, CP has more in common with entrepreneurship than one might see from the outside initially, because it, it looks so, like, structured as a, as a competition. But, uh, you know, o- one of the reasons we met was, um, you, uh, knew very well somebody from high school that I had backed previously, and I found out recently that you, I think you knew, like, Alex Wang in middle school too, and I was like, "Oh, small world."

   11. SWScott Wu5:12

       Yeah. Yeah, yeah. No, it was, it was a very, very tight-knit group. Um, a lot of ... Honestly, a lot of that group (laughs) -

   12. SGSarah Guo5:18

       (laughs)

   13. SWScott Wu5:18

       ... all, uh, many, many of them became founders, um, and, um, a lot of them went into AI, and so no, it's, it's cool to see. I think there's ... You know, I, I think there's overlaps for sure. I think they're the more subtle kind of overlaps obviously, where it's, you know, you're not literally using s- shortest path or something to, you know, to, to build your company. But, uh, um, you know, a, a lot of the same constructs and a lot of the same principles are there. Um, and I think one of the really big things, uh, that I think about all the time, um, is, you know, a lot of the really great math or programming problems are kind of about, you know, what are all these assumptions that you take for granted, um, and, and which one of those might actually not be true? You know. Um, and a lot of these really tough problems and, and really interesting problems are about finding this creative idea, um...... that's, that's actually something that's, you know, you would not intuitively think of at all at first, right? And, um, I think startups are much the same way. You know, um, I think a lot of building a, a great startup is about, you know, finding something that, uh, the world doesn't believe is true yet, but actually it's, you know. And, um, kind of learning how to, how to think independently, you know, how to come at problems from a very first principles mindset. Um, and of course, you know, as with any discipline how, how to just really push yourself to grow and continue to improve over time. I think there's a lot of commonalities.

   14. EGElad Gil6:37

       It seems like a lot of what you mentioned, um,

3. 6:39 – 8:20

   Cognition’s founding team

   1. EGElad Gil6:39

      really impacted what you ended up doing at Cognition and working on Devin, because when you launched it, a lot of people saw behavior that could exist today that they didn't think could exist. Um, and a lot of people were waiting for the next sort of step up in an LLM or foundation model to get there. Um, could you tell us a little bit more about Cognition and what Devin is and how it works, and how, how you think about it in the context of rethinking what's possible today?

   2. SWScott Wu7:03

      Yeah. So Cognition got started around November. Um, and the initial group, a lot of them actually were, you know, many of my friends from this math and programming contest community. Um, and, um, you know, we, we had all, we had all, all done all of those, but since then, we've all actually had our own sort of journeys in AI. And so, you know, I started a machine learning company called Lunchclub that I ran for, um, about five years. Um, you know, others on the team, my co-founder Walden, you know, built a lot of early Cursor, for example, and my, my other co-founder Steven was one of the first engineers at Scale.AI. Um, and our whole founding team was kind of like that. You know, we, we had, uh, grown up with a love of, of math and programming and problem-solving, but we'd also spent our last, you know, five or 10 years at these different AI and AI infra companies. For one, I think code has a very special place in our hearts. But I think for two, I do think there's a lot, um, a lot that can be done with code. You know, I, I would even say like most of the progress in the world in the last 20 or 30 years has come from software. Um, and it's crazy when you think about how actually everyone everywhere needs more software engineering, not less, right? I mean, every company is hiring for more software engineers. You know, everyone has more ideas than, than time to build them. We really felt like there was a lot to do with accelerating, accelerating the pace of code.

4. 8:20 – 9:17

   Meet Devin

   1. SWScott Wu8:20

   2. SGSarah Guo8:20

      For anyone who hasn't seen it, can you describe like what Devin does today?

   3. SWScott Wu8:23

      Sure, yeah. So Devin is an AI software engineer. What I mean by that is that Devin is, is fully able to make all of its own decisions in the same way that a human software engineer would. And so, um, you know, obviously writing and editing code is a huge piece of that. But also, you know, being able to work with the command line, being able to use the browser, being able to read documentation, being able to deploy or test or debug or all of these pieces, um, Devin does autonomously. And so, you know, you can give Devin a simple prompt, um, of what you'd like to build, uh, and Devin will just go through and, and do that all end-to-end. Uh, and you can interact with Devin as well. You know, you can see what Devin's looking at or what Devin's working on and give feedback on that too. Uh, and so it's very much meant to mirror the experience of, you know, looking over the shoulder, uh, of another engineer, to be able to see what they're doing and guide them or, or give feedback.

   4. EGElad Gil9:12

      What has been the reaction, uh, to Devin and Cognition online in the popular discourse,

5. 9:17 – 12:14

   The discourse around Devin

   1. EGElad Gil9:17

      and how do you think people are interpreting the product that you launched?

   2. SWScott Wu9:19

      Yeah, I mean, it's, it's really fun to see all the, uh, all, all the reactions, uh, to Devin. Um, we, uh, (laughs) we got a lot more views than we expected. It was, it was honestly quite something to see some of my middle school math videos, um, back on Twitter all over again. But yeah, no, I mean, it's, it's great to see the reaction. And as with anything, I mean, there's obviously the healthy mix of there are some who are skeptics who are saying, "Oh, there's no way this could possibly work." There are some who are saying, "Wow, we're all gonna lose all of our jobs."

   3. SGSarah Guo9:46

      Why do you think, like, engineering doesn't disappear as a class of work in the future?

   4. SWScott Wu9:51

      (laughs) We're obviously very excited about Devin, but, um, you know, I, I think if anything, there's gonna be more engineers, not less. Um, and I'll kind of give two reasons why. I, I think the first is, um, there's just so much demand for engineering out there, and so much demand, honestly, even in ways that we, we don't always think about, right? A lot of using Excel or a lot of, you know, working with various tools is, is in some ways, um, is there because there's... because engineering is hard, right? I think there, there are so many problems that could be solved with code, and there's so much more that could be built with code, um, that I think multiplying every single developer is going to give us more developers, not less. Um, you know, the other thing, I think, is that Devin is very much not the type to decide what to do. You know? And I think there is always this core problem of, um, how you decide what exactly to build or what problem to solve or, you know, how particular things should work. Rather than engineering going away, I think engineer actually becomes a more pure, um, you know, abstraction of those things, right? I think the average software engineers today might spend 20% of their time thinking through all these like really fundamental problem-solving questions, and 80% of the, the time writing that in code. Um, and I think they'll, they'll be able to do 5X more, and they'll be able to, to spend all their time doing this really creative problem-solving. Um, and that's what we're really excited about.

   5. SGSarah Guo11:12

      As a, as a really loose analogy, I think of like human calculators previously, right? Like, uh, you probably don't want people doing that work, and it allows you to expand to, um, much more capable human beings that would have done that work. And, uh, you, you know, one of my core optimisms around AI is that it is actually, uh, very democratizing, right? Like we, we are tapping into latent demand for lots of different activities where it was just unaffordable before, um, where like the software doesn't match the workflows that people want, or it's not at sufficient quality, or it's not as sophisticated as could be for, um, an unlimited number of things. So I, I'm very excited about that, actually.

   6. SWScott Wu11:51

      Yeah, definitely. That's largely the case I, I think in knowledge work everywhere even, right? Where, um, you know, there's, there's so much to do, um, and there's so many problems out there to solve. And I, I think, um, enabling every, every human to do more, um, is only gonna help us all accelerate faster.

   7. EGElad Gil12:08

      I think this is one of the really interesting things about the product was, um...You know, I feel like there are more companies showing

6. 12:14 – 14:28

   Building Devin’s UI

   1. EGElad Gil12:14

      up now, kind of showing me Devin-like UIs. And before, a lot of people built agents and you'd start the agent and it would go off, and then 30 minutes later, it would come back with something that you didn't like. And in the case of Devin (laughs) , um, uh, you know, because i- it feels like a lot of agents today are almost, like, really, um, eager interns, right? There's an intent to build what you want it to, but then sometimes it goes off the beaten path. And the ability to steer it back onto what you want it to do matters a lot. And in the context of Devin, I think it's really interesting that you had those four tabs of, you know, planning, and here's what we're gonna do, and sort of check that off, and then the shell, and the code, and then the browser, and showing, like, you know, where, uh, where it's interacting with the world. And I think that's a really powerful paradigm. And so I'm a little bit curious about what was the inspiration for that. Was it literally you thought of it as, "Hey, I'm peering over somebody's shoulder," or was it something else that really inspired that UI, which I think is starting to catch on in other areas now?

   2. SWScott Wu13:05

      Yeah. Yeah, I mean, I think one of the nice things about the space, obviously, is that, you know, we get to build it for ourselves, in a way, and we're all obviously software engineers. And we ourselves use Devin all the time, um, in our own work, even when we're building Devin, working through each of the pieces basically and figuring out what it took from us to, um, you know, to, to, to work well with Devin. Um, as you kinda mentioned, I mean, if an agent is, you know, you give it the task and then it goes off and does it, then you, uh, you know, it basically only has one try to do it all and to do everything perfectly. Whereas if you have a human intern or a junior engineer or whatever it is, you know, um, if they're working on a project, um, over the course of the day and, you know, you have to check in four or five times and just give a quick, you know, 30 seconds of feedback on what's going on, um, you know, for one, it, it helps them learn a lot. And for two, um, you know, it means that, uh, um, they can still, you know, give you a lot of, uh, really valuable work over that time. Um, and obviously, it's, it's, it's much, much faster to be doing that than, you know, to do all of those pieces yourself. I, I think we worked backwards from, you know, what made sense and, and how we would wanna interact with an AI coding teammate, um, and, um, thought through technically how we would be able to get those pieces to work.

   3. SGSarah Guo14:20

      What is Devin today better and worse at than human software engineers? How does that change how you guys use it internally?

7. 14:28 – 18:44

   Devin’s strengths and weakness

   1. SGSarah Guo14:28

   2. SWScott Wu14:28

      The encyclopedic knowledge is obviously really, really useful. Uh, I think, honestly, with DevOps and dev setup, um, I, I think there's a lot that it's very good at. You know, I, I think DevOps is just kinda hard for humans. Actually, one of the first, um, the first really exciting moments with Devin was, um, we were trying to set up, um, you know, get a database spinning, um, and get Kubernetes up, and, um, whatever else, you know, for our own purposes, and we were stuck after, like, an hour or something, and, you know, going down the rabbit hole of debugging errors and stack traces and whatever, and we just asked Devin, "Hey, can you set this up please and then let us know how you did 'cause we can't do this right now?" (laughs) Um, and Devin actually did, and that was, that was one of the first, like, really exciting moments, um, for us. I, I think the, um, you know, the, the, the step-by-step flow of, you know, editing and working with things, you know, running commands live, looking at the errors that come up, you know, all those pieces of, hey, like, do I have this port open, or, like, maybe I need to install this package, or, or whatever, you know, worked very, very well in an agent workflow. Um, so that's a big one. I think, um, I think data analysis is another big one, um, which we've seen a lot as, as a good use case. Um, I think Elade, you had a few use cases in that bucket actually in particular. But, uh, that's another very, um, end-to-end flip, right? Often, you're, you're trying to find the right datasets from the internet or, you know, pull out a CSV file, then you're sanitizing the data, then you're running, you know, whatever your exact analysis is, and then you're building a visualization of that analysis. And, you know, being able to just do that all, um, from start to finish, um, with an agent is, is another really good use case. Um, and yeah, I think as far as things that it's worse at, you know, um, Devin is very much not the one who is going to be deciding what to do, if that makes sense. Um, and so, you know, we very much think of Devin as y- you provide the, the precise formulation of, of what you want built or what you want done, and Devin is the one that is doing the thoughtful execution of that, right? And so, you know, you wouldn't be able to just go to Devin and say, "Hey, you know, build me a great business or something," or, "Build me..." you know (laughs) , um, something like that, um, because, um, yeah. I mean, I think a lot of Devin's focus is, um, understanding how to take precise ideas and really formulate them into code and do that entire, you know, testing process and debugging process and installing packages and deploying and, and whatever else that are involved with that.

   3. SGSarah Guo16:54

      What can you tell us about how it works and, you know, what work happens in the foundation model layer and the system around it and, like, how you guys invest in improvement?

   4. SWScott Wu17:02

      Yeah. Um, yeah, I mean, I (laughs) ... unfortunately, I can't get too much into the details of how it works. I mean, I think it's, uh, um, you know, it's, it's obviously our, our, um, o- o- one of the, one of the areas that we, we spend the most effort on. Um, but, um, you know, I think a lot of it comes down to understanding the, the interface of the problem and understanding how exactly to optimize the problem that we're solving for, right? And I think one way that I frame it, for example, is, you know, suppose you were given, um, an issue, you know, th- you were given a GitHub issue and you needed to solve it. Maybe there was some bug that you needed to fix, right? Um, you know, there certainly is a view where you could take the entirety of the code base and you could, um, figure out from the bug exactly what the diff was that needed to be done, um, and write out that diff in clean text, and you could do that, right? And so, you know, some, some perfect intelligence, um, you know, human or AI, in theory would be able to do that, right, because it would be able to figure out exactly what's going on and understand exactly what needs to be fixed, right? But I think practically for humans today and, you know, we think also for AIs today, um, the, the cleaner path, um, to do that, um, involves, you know, running the code yourself, right, reproducing the bug or, you know, adding debugging statements and print statements and then rerunning the code, or taking a look at the logs or, you know, asking Stack Overflow or, or all of these things, right? And a lot of the, the problem that we solve is figuring out how to, um-... yeah, h- how to, how to get Devin to think in that way and to make decisions in that way. So a lot of this planning and, and evaluation is, is what we spend our time on.

   5. EGElad Gil18:39

      How do you think this all evolves over time? So for example, say we extrapolate out one or two years,

8. 18:44 – 22:43

   The evolution of coding agents

   1. EGElad Gil18:44

      and then maybe five years. Like, what, what proportion of code do you think is written by agents like Devin, or, uh, what do you think changes in terms of how software engineers work in both the shorter term as well as longer term?

   2. SWScott Wu18:55

      Yeah, so I mean, I think in the longer term, like I think in the five to 10 year horizon, I mean, I think what we call software, um, I think, um, changes a lot, you know. And, and I think, um, we think of Devin or, or the steps that we're building with Devin as kind of the next generation of human computer interfaces, right? Where, um, you know, I think 10 years from now, people will look back and think, wow, isn't it crazy that, you know, you had to learn all these esoteric languages, and you had to, you know, work through all these stack traces or whatever just to be able to communicate with your computer, right? I, I mean, at the end of the day, software engineering today is a discipline about being able to work with your computer and, and to, to have the computer do what you want it to do. Um, and, um, I think that, you know, what happens with software engineering is, I, I think, um, software engineers everywhere get to spend all their time on, you know, the really fun part of the problem, which is, um, taking any problem that you're given and figuring out exactly what the form of the solution should be, right? And so all of this work of, you know, what are the cases and the, the edge cases and the details? What are all the flows? What are the architectures to build to solve each exact problem? Um, and you know, taking it from that to that code implementation of exactly that, um, you know, is something that the Devins of the world will solve, right? I think the really exciting thing is, is there's so much, so much demand for software, right? (laughs) And so, um, you know, it's, it's, it's crazy to think about how, you know, there are 30 million software engineers in the world, but, um, you know, in aggregate, it's still only 0.4% of people, right? Giving those people the ability to do much more and also, um, enabling so many other people to be able to work with software, um, is something that we're really excited about. Um, in the, in the next one to two years, I think, um, you know, I think obviously there's, there's a lot of kind of broader, um, changes that will happen. Um, I do think, um, software engineers, I think very quickly, you know, who, who work with AIs and are kind of very AI native on this trend are, um, are going to be able to, to multiply themselves and do more. It's a really interesting time to be in AI, you know. I think there's a lot of rising tides and, you know, the hardware's getting better all the time. You know, the foundation models are getting better all the time. Um, obviously all of the agent work that we do is, is, is getting better and better all the time. And so, um, I think, um, yeah, I, I think things will move pretty quickly here.

   3. EGElad Gil21:13

      What do you think are the things that will improve agents the most going forward? Is it the underlying foundation models? Is it reasoning, forms of memory, self-play, something else? I'm just sort of curious at a high level, even putting aside what Devin is doing, but like, agents in general, like what, what, what is needed in the field to push these things forward?

   4. SWScott Wu21:30

      Yeah, I honestly, my honest answer is yes to all of the above. You know, I think all of those things will be great. You know, I think, um, you know, even like inference speed ups are going to be very useful for, for the end experience. Um, I think, um, as you said, I mean, I think better reasoning, for example, in the base models is gonna be really, really great. Um, I think, you know, figuring out some of this planning and, you know, better tool use and so on. Um, the way I'd kind of describe it is, um, you know, um, I think agents today are, are a combination of all of these factors. Um, and in some sense, I think there's, (laughs) there's a, there's a bit of a race between all of them where a, a perfect intelligence AI, um, in theory wouldn't need anything else, right? It would be able to just look at the code and tell you exactly what you need to change, right? At the same time, you know, um, you know, some perfect tool user, for example, perhaps might, might also be, you know, a great solution. And I think what we'll see is, you know, all of these are going to be a rising tide, and it'll be a matter of, um, you know, which, which of these sp- spaces see the most progress, um, as far as how much impact they have on agents.

   5. SGSarah Guo22:35

      What do you think is gonna be important from a human software engineer or just, like, human technology person five years from now? I realize that's a really long

9. 22:43 – 26:48

   Tips for human engineers

   1. SGSarah Guo22:43

      time scale in AI, um, but it's certainly not, like, encyclopedic knowledge anymore, right?

   2. SWScott Wu22:49

      Yeah, yeah. And I mean, I think there's, there's, there's a meme that, you know, the hottest new programming language is English, right? And, uh, I mean, I think there's a lot of truth to that. Um, but with that said, I think that, you know, the software engineering fundamentals are obviously still super, super valuable, right? Um, people, you know, um, for example, like, I think, you know, the internet today is, is something that we all kind of are able to use and kind of take for granted. But people who work with these networks, um, it's certainly very helpful for them to understand the details o- of TCP, right? And I think similarly, I think, um, you know, I, I think we'll be able to communicate our ideas in English and work with all these things, but, you know, understanding the internals of, um, of how computers work and understanding logic gates and, you know, a lot of these core kind of pieces, like these core foundations I think will still be very useful, right? And so, you know, um, whether that's, um, you know, algorithms or technologies or, um, logical reasoning or, or things like that. Like I, I think the, you know, I think the role of a software engineer, um, five or 10 years from now looks something like a mix between a technical architect and a product manager today. You know, where, where a lot of what you do is, you know, you take problems that you're facing or that your business is facing or whatever, and you're really thinking about and breaking down what exactly the solution should be.

   3. EGElad Gil24:06

      How do you think about it, uh, on an even farther timeframe? Because when I... If, if it was five years ago, I would have told either my kids or people who have kids, um, you know, "You should study computer science and math." 20 years from now, I'm not as certain, so I'm sort of curious how you think about the future of, uh, of this field, if much or all the work, including a lot of the planning is actually done by machines at some point.

   4. SWScott Wu24:31

      Yeah. Um, I mean, I love math, so I have to say, it's, uh, (laughs) it's a worthwhile experience even if it doesn't, uh, (laughs) if it doesn't end up being practically useful. But no, I- I mean, I think these, I- I think a lot of these fundamentals will, will stay useful for a long time. There's obviously a lot of questions that come up about, um, you know, super intelligence and the singularity a- and all of this. Um, and you know, it's very hard to predict. Uh, I- I think everyone in AI, it's, you know, we've, we've all made our, our own predictions and, you know, tried to make our guesses. But I- I think it's, it's hard to be very high confidence. Um, but with that said, I do think that, um, you know, we'll, we're gonna see AI's concrete impacts on, on work and economy and, and people's lives, I think, a lot sooner than that. You know, I- I think, um, the, the way that, that we think about the problem is that, uh, you know, even with the tools that are available today and the technologies that exist today, there's so much that's possible to, to really impact people's lives, right? Um, and, um, you know, we're still very, very early in, in this whole AI revolution. I mean, even ChatGPT was, um, about a year and a half ago at this point. And there's a lot more to do and, and a lot more to build, you know, both on, on the research side and on the product side, so...

   5. SGSarah Guo25:44

      That's the best guess I have. I, um, uh, I had lunch, uh, Scott, as you know, with one of your new hires today, and he's like, "It just makes sense to me that, like, Scott Wu is gonna be, like, the last human software engineer/labeler, right?" And I'm like, "All right, like without a better plan, I'm gonna have my kids, like, be the best labeler they can of that data." (laughs)

   6. SWScott Wu26:06

      Yeah, yeah. Yeah, no, I mean, I- I think, uh, I- I would guess that, you know, I- I think good reasoning and good fundamentals and, you know, good understanding of technology, you know, are, are gonna be useful for quite some time to come. Um, I mean, maybe eventually there will be a point where, you know, you just upload all this knowledge into your brain or whatever and, you know, you just become a perfect rational decision-maker, and, um, I'm sure that'll be (laughs) a very exciting future. But I- I think, you know, with, with what we have today, I, I, I imagine that, you know, a lot of these logical fundamentals will, will stay relevant. Yeah.

   7. SGSarah Guo26:37

      Maybe on that note, um, I think one of the, um, things that I, um, really admire is just the, like, uh, broad strength of the, um, the founding

10. 26:48 – 29:27

    Hiring at Cognition

    1. SGSarah Guo26:48

       team and the early team at Cognition. Can you just talk a little bit about, like, your approach to hiring?

    2. SWScott Wu26:54

       Yeah. No, thank you so much. I mean, I think the, uh, you know, I- I- I obviously think our, our, our team is really exceptional. I think we, um... Yeah, I- I think a lot of the last, um, you know, five or six months has been really thinking about, um, just the size of the problem (laughs) that we're going after, you know. Uh, and software, software is, is massive, you know. It's, it's a, it's a huge driver for change and it's also a very deep and interesting problem. Um, and, you know, a lot of our team today... In fact, I think almost everyone on our team today, um, I think was, um, was either a founder themselves before or were explicitly thinking of starting their own company, if not for Cognition. Um, and I do think that, um, you know, working with people who are, um, very high ownership, you know, very interested to drive and, you know, much more focused on, on outcomes than they are on, you know, politics or anything like that, um, is, is very important. Um, on top of that, I of course think, you know, I think creativity, you know, work ethic, you know, good communication. I mean, a- all of these are, are, are super useful. Um, I think, um, we've, um, you know, thus far been mainly kind of go through a very close network of, of people that we've known well and worked well with and, and kind of grown our team that way.

    3. SGSarah Guo28:16

       Are there other specific types of people that you're looking to hire now?

    4. SWScott Wu28:19

       Yeah. I mean, I- we're, we're always looking for, for really great engineers and researchers, you know. I think, um, we're still very, very early, uh, and it's a very big mission of what we want to do. And so, um, I think, uh, yeah, I mean, uh, great, great technical thinkers who are, are excited to, you know, to work on multiple different parts of, of the business, um, I think are, are what we're largely looking for, you know. I think between, um, between customers and general strategy, research, engineering and product and, um, and all of this, you know, it's, uh, we're kind of all doing everything, (laughs) which is how it often is in an early startup. And so, um, yeah, I- I mean, I think we, uh, yeah, we- we'd love to, to bring on more, more people with those archetypes.

    5. SGSarah Guo29:04

       Awesome. Thanks for being here, Scott.

    6. EGElad Gil29:05

       Yeah, thanks for doing it.

    7. SWScott Wu29:06

       Yeah, thanks for having me.

    8. SGSarah Guo29:09

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Episode duration: 29:28