Scott Wu, Cognition

21:41Now you just kind of, you know, rather than any kind of pattern matching, like what really matters is just thinking about things from first principles. Like AI, you know, there's the famous like METR report, which was saying, you know, a couple years ago, AI would do about 10 to 20 seconds worth of human work without interruption. And then you'd have to, you know, guide it or direct it or it would make a mistake or something like that. 10 seconds, 20 seconds. And that's just doubled every, you know, every couple of months, basically. And now we're talking about like hours of work. So basically an AI can just take a task that would have taken humans hours of work to go do.

22:16If you go and describe that task well enough to the AI, it will just go and do the whole thing and come back to you with the result. And then you give it the next thing, the next thing. Right. And if you just ask from a first principles question, well, why can't that be days or why can't that be weeks or months of work? And then what does the world look like if everybody has an agent that can just do months of work for them at a time? Then you get to a pretty different conclusion from what we've all seen and what we've all lived for the last several years. And I think that that kind of first principles thinking is as different as it sounds and as crazy as it sounds.

22:51you know, this is one of those times where it's actually more correct than the simple pattern match, if that makes sense. So I think you've even taken this further, where you're like, well, what happens when they can work for a year unassisted? Yeah, yeah. And I think that's true, and I think we will get there. If Devin could work for a year without any human assistance, what would you have it do right now? Destroy your competitor? All sorts of things. I mean, no, I mean, I still wake up and think about this in every different, like, Like, you know, every little thing that I run, you know, dumb example.

23:23Yesterday, I was sending out, like, a bulk email. And I was, like, trying to get the, like, email formatter to work. And it's, like, kind of painful. You know, some of these things are still, like, kind of hard to use or it doesn't support a certain, like, styling of the email that you want it to go do. I think the thing, I, like, had pasted something with indents. And then it was, like, I just couldn't, like, un-indent them. because the editor, like, I don't know, there were some weird things where the editor, like, would not allow you to unindent one part but not the other or whatever. And I was just thinking, like, it's really crazy that, like, that I'm still doing this, basically, right?

23:58Like, in as much simplicity or honestly more as it would take you to explain this to another person, like, hey, here's what I want to do. I just, like, I'm just trying to make it look like this and then this and then that. Like, the rest of that execution should just be done for you, you know? And then you get to the point where you actually really just get to spend all your time thinking about, what do you want to do? What do you want to build? What do you want to create? What are the things that you want to see in the world that aren't there already? But I think what makes it interesting about what you were saying earlier is the more you increase the time, the more interesting it gets to me.

24:27So there's this guy named Edwin Land, who I won't shut up about, and he was the founder of Polaroid. Steve Jobs' hero. A lot of what we think of as Steve Jobs' ideas literally just came from Edwin Land down to the chairs and the table he would use for his presentations. It's the same thing that Edwin Land used in the 70s in Polaroid. And he thought of himself as a scientist, not as an entrepreneur, Edwin Land. He died with, I think, the third most patents. He was like Thomas Edison, some other dude, and then Edwin Land. And what he did is he couldn't figure out. He invented the industry of instant photography before you took a picture.

24:59And you're like, hey, how's it look? We'll find out two weeks from now when we get it back from Kodak. Like, I have no idea. And now he took a picture of a Polaroid. He's like, we'll find out in 60 seconds when it dries. But that was black and white forever. When I read that part where you're like, well, we're going to have agents that can work on a sister for a year. I didn't think of what I would do, which is a question I just asked. I thought of Edwin Land hiring this guy. He's like, I want you to think about how we turn this from black and white into color. And before he could begin, the guy worked there and just thought for two years.

25:26I'm like, that would be very convenient if I could have an agent attack this problem while I'm working in the background because I can't figure it out. They were just thinking about how to attack a single problem for two, in his case, this is the guy that fucking solved instant color photography. It just took them two years of thinking to do it. So this is like, I'm going to push you on this a little bit more because it's like, I don't want your year-long agent to send bulk email. Oh, I agree. I agree. Just to be clear. So, and I think at some point it's kind of, you know, you see this, right, where it's like, you know, when you're talking about seconds, you're literally talking about just like a specific command, right?

25:56When you're talking about hours, you're talking about giving it a task and having it do the task, right? And I agree with you. Obviously, bulk email is not, you know, for years, what you're talking about is like you're giving the agent a mission, basically. You know, and it's like this is way more fun. Yeah, exactly. And this this is like, what do I care about? And, you know, the answer might be, look, I want to give you a million different examples. You know, the answer might be like there's this like one societal problem, which is really important to me. And I think there's like I think it's a solvable problem.

26:26I think we can all be happy. But like, you know, we really need to spread awareness about it. We really need to get folks to understand the points of it. and we need to figure out how everybody should work together and coordinate on it. That's a problem that an agent can think about, right? Or even some of the kind of like sillier things too. Like, yeah, there's this video game that I really like, but I wish if I were making the game, here's exactly how I would think about all these things. And I feel like there's like this really cool idea if you could combine elements from this one game that I really like, but then incorporate some of the elements from this other game and set the agent off on that mission of like, Like, look, we're going to go and, like, make the coolest thing ever and the coolest game ever.

27:06And, like, we're going to incorporate those elements. We're going to think about how those, like, you know, nicely intertwine and work together, right? Or if it's, like, here's this, like, piece of just, like, novel science, which I'm just, like, really passionate about. You know, materials have been created this way for years and years. But, like, here's this, like, avenue of attack, which I've been wondering about and thinking about. Maybe there's a different, like, novel construction of materials. feels this way, send your agent to go work on that for years or months, right? And have it study that and explore these things and run its own experiments and trial these.

27:41I think all of these are, I think, soon going to be very possible. And to your point, it's very different. Yeah, I like the framing of we're sending them on missions. Yeah. I would have one that would pick the missions that I need to send other agents on. Yeah, yeah, yeah. Then you'll have the AI, which is like the manager AI of the missions. Yeah. And I think it's like, I think we will continue, you know, it's my example of this is like, I always joke about how, you know, if you think about our ancestors from, you know, hundreds of years ago or thousands of years ago, imagine them looking at us and what we do.

28:09And it's like, you know, you're pushing buttons, you know, and you're like sitting in a room and talking with other people and you call that a meeting. And that's like that, those things, that's work for you guys, you know, and it's like, what do you mean that's work? Like, you know, like I'm in the fields, like I'm doing this every day, you know, I'm going and farming, I'm making sure that, you know, making all of our, our, our, like, you know, clothes by hand or taking all, taking care of all these things. And that's like work, you know, but like, how, how can you guys call, you know, and my point is just, I think what we will have going forward is going to look that different from what we have today.

28:44We will look at people who, as we say, just like have these really, really interesting curiosities that they want to pursue. They have like causes that they're passionate about. They have like fun ideas or like art that they want to create. And like they're sending off their agents in pursuit of those missions. And they will think of that as work. And we will look at them and be like, wow, it's kind of crazy that that's what you get up and think about all day. I found one of my all-time favorite quotes when I was reading the book Zero to One. The quote says, the single most powerful pattern I have noticed is that successful people find value in unexpected places.

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29:54They can put your ads in front of over a billion potential customers. Other businesses have seen immediate results, have scaled to hundreds of thousands of dollars of spend per day, and increased their revenue by millions. So you want to get started quickly before all of your competitors are on Applovin. And you can do that by going to Applovin.com. That's applovin.com. When you started Cognition, did you know that you were going to try to make an automated software engineer? Was that your first idea? I'll say it was always two things. One, it was always related to code and software, which again probably has something to do with us all being programming nerds.

30:33And then two is it was always around the idea that these would be like real multi-step iterative processes, which was, I would say, was a real hot take. at the time. Like, this was 2023. Okay. So this is when I met you guys. Yeah. And I was on a walk with a mutual friend of ours. And I remember first here, this is, you guys scaled from what, like a million in revenue to like 500 million or something like that in what, like 20 months or something? Yeah. Yeah, a little more than 18 months. I remember before, I had no revenue. And I heard the idea and it was pitched as like, essentially like an automated software engineer.

31:05And I was like, holy shit. It was like a huge, you're going after labor. This is a huge market. And then you released. Yeah. I remember the demo video. Yeah. And then you got a lot of shit. Yeah, yeah, yeah. Is that a way to describe it? Well, it's kind of a mix of it, I think, is the full polar ends of the spectrum, right? So there are some people who are like, this is the coolest thing ever. And then there are some people who are like, this is the worst thing that you could buy. Similarly, in terms of the capabilities, there are some people who are like, oh, my God, everybody's going to lose all their jobs tomorrow, which is not what we've ever really believed or thought of this as.

31:45to also like, dude, there's no way this is ever going to work. And like, this is totally a scam, you know? So what was the criticism? Did you release the product earlier than you wanted to? Well, look, it wasn't even a product at the time, to be honest. I mean, it was more just like a prototype or like a demo of what was possible. And, you know, we had been working with it and playing with it for a few months at that point. And we kind of just wanted to show people some of the examples of what it was capable of. because there was a real, like, it was pretty insane for us to see as well. Like, I remember the first time that it did, like, a real task.

32:18Like, I could not sleep that night. And so it's just, like, showing that. Wait a minute, say more about that. The first task that Devin did was, like, set up MongoDB for us. And it's, like, you know, it's a standard thing. It's issues that a lot of people run into all the time when they're going and getting their kind of, like, initial, you know, DB set up or whatever. But we would just, like, run into errors. you have this whole flow where you like find an error, you Google error, find some message on Stack Overflow or whatever, or you ask ChatGP and it tells you, okay, here's what you should try.

32:51You try that thing, you run into a different error. You just like paste that in and then, you know, and like for this one, we kind of like, at some point we're just like, okay, Devon, just try to go fix it. Just go run commands, like do whatever you need to go do it. And then it worked. And you couldn't sleep. It was truly like, because again, it's, It's like just seeing the exponential curve ahead because this was a very specific case. It was the one success that we had. It was very much like definitely a way better than average run. But no, there was this feeling of like, why shouldn't all software and all products be built this way now?

33:28Like you can just tell it what you want it to do and have it go do it. I mean, it's kind of funny because, yeah, to your point, like we did get hate in the beginning. we did not feel motivated at all from the hate to be like, oh, yeah, no, maybe you guys are right. Like, maybe we should go and, like, you know, focus on the more kind of, like, chatbot, Q &A-style product experiences. Like, maybe more than we should have. Maybe we should have done something in the middle, you know? I don't know. But, like, I think for us, like, when we had seen that and when we had done these different things, like, all of those, like, demos that we showed, you know, in our launch announcement were, like, actual runs of Devon that we had done ourselves and, like, run into and been like, holy shit, this is insane.

34:05For us, it was always kind of like, look, we can debate when or what level of effectiveness or whatever, but it's going to happen. And that's how we always felt about it. So explain the process of iteration to go from that product, you're getting a lot of hate. Actually, you know what? I called Jeremy Stern, who wrote this excellent profile of you in Colossus, which there's a lot of parts I was just laughing my ass off, by the way. And I was asking, I was like, tell me the stuff that didn't make the profile. Yeah. And he said there wasn't that much because you're kind of like an open book and a lot of people like manage their media.

34:39Yeah. And, you know, you could have to talk about certain stuff off the record or whatever. And you were just like saying everything. But you did say something about like you made the point where like when you released the first product, what was the benchmark? Sweet Bench. Yeah. It was like 13 percent. That was Devin. Yeah. And your point was that that's already better than. Yeah. At the time, the best known was like three or four percent or something like that. But obviously, yeah, 13 percent still means you fail, you know, 87 percent of the time. Was there a pronounced benefit from releasing early like that?

35:06For sure. Yeah. I mean, so for us, by the way, you know, if you kind of think about this overall AI ecosystem, I mean, a simple way to put it, it's like, dude, we were late by a lot. You know, it's like OpenAI started. Why? Why, though? Google DeepMind or Google Brain, I mean, these are obviously, you know, more than a decade old already. OpenAI started like end of 2015 or even, you know, like the Anthropic or, you know, other that folks talk about in the world, like had been around already for years. Like we were getting started in early 2024. It was like a year plus out from the ChatGPT launch.

35:35A lot of the existing players were already there. You know, the same was true in code specifically. I mean, there was, you know, GitHub Copilot, which people had already, you know, engineers had already used for years. And that was very much the like Q &A, like the autocomplete style experience of, you know, working with AI. You know, when you start a company, you kind of have nothing. Like you have no right to exist is maybe one way to put it. it's like an interesting truth about the world, which is obviously startups succeed. You know, startups succeed all the time. And then startup versus big company has been played out for, you know, for forever.

36:09But like, there's no reason, you know, in terms of resources, in terms of people, in terms of brand awareness, like you have none of the things that, you know, the big guys have. And so from that perspective, you have no right to win, you know, over what they're doing you know and the reason that you're sometimes able to anyway is if you really like um you know plant your flag on the ground and and put a stake into like what you think the future is and you run like hell towards that and and if you like turn out to be right on some of the core things and like i think we were we were wrong on a lot of things uh to be clear a lot of things we were you know so definitely we were early which is a very fair criticism i think a lot of the details and the nuances, like we learned and adjusted over time.

36:57But the idea that like you would work with AI as a coworker, you know, rather than as like a tool or a chat bot, I think was, I mean, over the last couple of years, has obviously like really grown. And I think it was like very important for us, for our brand, for recruiting, for, you know, customer work, everything for us to be, you know, the first ones that actually planted that flag in the ground and said that. So when did you have this idea where you guys are going to take a run at very, I would say ferociously to like these giant, like Fortune 500 companies or even like the U.S. Army uses you.

37:27Sure. Like where did that strategy come from? So funnily enough, so that launch was in March of 2024. And to your point, it went very viral. But again, we didn't have any customers. We didn't have a revenue. We didn't really have like... I thought your first iteration of the business model was like$500 a month or something. Wasn't it? Am I misperimenting this? So we had that. That was actually later on, actually. That was end of 2024. Okay. But, you know, initially what we started was just like, you know, a bunch of people came, asked us for the product. We were like, I don't know if this is a product that's ready for primetime, but you can try it and just let us know.

37:59And so people ran. We had to go and scramble to build a system of like, oh, okay, let's do a pilot or a PSC. And we tried our best to not overpromise. And be like, guys, really, it's very early, but if you want to try it, you can try it. And we did all this. And perhaps unsurprisingly, they were all just failing, which is kind of natural. It's like you could do some pretty cool things with it. You could do some pretty interesting toy demos or projects or whatever, but it was certainly not ready to work on actual companies, like real code base and so on. And so from that point, this would have been like April or May of 2024.

38:35We were making agents work with GPT-4. This was like a very different era. So they were much more primitive agents. We kind of talked about this and got to this question of like, okay, well, what do we think it actually does look like when the agents start to get good enough for adoption? And I think what we kind of came to was, well, different tasks are different, right? And so, like, there are some tasks out there that are just really mundane and really repetitive. And you're just doing the same tedious thing over and over and over again. And there are some tasks on the other end of the spectrum that are, like, really tough, like, architecture problems or, like, deep, like, you know, deep issues that you have to, like, really understand all the context and have all the know-how to know how to fix.

39:14And, like, people were trying to use Devon for all of this and were failing at it. understandably. And so then the question was like, okay, what are the natural tasks that are like, if there's like a first task that is going to have PMF, you know, and real value from these kind of agent experiences where it can do the whole thing end to end, what is that going to be? We kind of said, okay, well, it should be some of these like really repetitive TDS-1s. It's not so cut and dry that you can just like have an automated script that does the exact thing every time. So it does take obviously some intelligence and some amount of meandering, but it is like repetitive enough and scoped enough and on a tight enough feedback loop that you can have an agent do it and it would be able to kind of go and diagnose and fix that problem.

39:56And so that's kind of what brought us to some of these, naturally, to some of these initial use cases, which were things like migrations or version upgrades or helping people upgrade from Java 7 to Java 8 or something like that, which were kind of like, as you can imagine, enterprises had these massive code bases where they would go and do all that. And it's like a 50 ,000 file code base where you have to go, it's like the same eight things that you need to change in each one. You have to be a little bit thoughtful about how you make the tradeoffs, but it's a very repetitive task. Our first success ended up being with a company called NewBank, biggest bank in Brazil by market cap at the time.

40:33And the use case was like one of these big migrations. And we had kind of a custom Devon that was like extremely, extremely optimized for doing that. And as we grew from there, you know, later on, we had kind of, as things got a little bit more mature, you know, we've had both self-serve business and enterprise business and so on. But I think from the beginning, we had always seen this value that building software in the real world and managing massive, massive products that like millions of people use every day was pretty substantially different from, you know, just building a cute website or a cute demo from scratch.

41:07Right. And I think we really, really leaned into that. And naturally, it's, you know, all the Fortune 500 or all the biggest companies in the world are software companies in 2026. You know, even the ones that folks don't necessarily think of that way, right? And like Walmart or CVS or JP Morgan or Mercedes or whatever, you know, they're all software companies, right? They have massive, massive teams of software engineers. They have tons of things that they're building and shipping and maintaining. And that kind of became like a natural thing for us to work on because like we had learned very early on that we want to work on real problems and we want to work on things that matter and that people actually care about.

41:40So wait, what percentage of your revenue is coming from enterprise then? Today, around 75%, 80%. What are people using on the self-serve? Like, what are examples of? Yeah, so we have a lot of teams who use it in self-serve. I mean, that's grown actually, that's grown quite a bit as well lately. But, you know, we have startups who, you know, we have Exa, who uses it a ton, or Open Router, or Build, you know, I ran into someone in my apartment in the elevator the other day. oh, you're the Devon guy. Like, we use Devon. Are you Devon? I've gotten that as well. Hey, are you Devon? And I was like, you know, I'm actually not, but that's okay.

42:18Why didn't we call it Scott? You know, there's kind of both self-serve and enterprise. With that said, it is entirely in both sides. It is still like actual engineering teams building real output. And so we don't focus at all on like, you know, individual hobbyists who are just like trying to make a cool thing or something like that. We focus on like real teams who are building real products that they want people to use and getting output out of that. Okay. Can you walk us through? I'm very curious. Like I'm a big enterprise. Yeah. I contact you. Yeah. Walk us through what happens to being a customer.

42:48You know, it starts with education. And everyone's gone crazy over AI and agents and so on. And those are the buzzwords of the last six months, obviously. And so they want to know more about this, but there's still a lot of detail. And like, what does it actually look like to deploy them? So, you know, we'll show them what this looks like. We'll talk them through like how we work with teams and how we partner, you know, how we direct them to the right use cases, how we give them guidance on like how, you know, to maximize their ROI or like what projects are or are not feasible with agents. And then from there, enterprises typically, obviously, have some very messy processes.

43:20And so for most software or most just generally vendors that they'd want to work with, it's often, as you can imagine, for a massive bank, adopting software, giving it access to all of their repos, getting through security or whatever, that's usually, for typical companies, it's like a 12 to 18-month cycle. The thing that we do naturally is we just work with them to figure out how we go and do that as fast as is humanly possible. Did you send employees down to South America for NewBank? Well, so NewBank, I mean, the first one, honestly, our entire team was the foreign deploy team. Like we all flew to Brazil.

43:54Like, I mean, it's like the first case, you know, it's like, it's like, let's be real here. Okay. Agents did not work generally. Okay. And so there was a lot of like, how do you make it work? Very specific. Like we all flew to Brazil. I was there. The whole team was there. We were sitting there with their engineers, understanding, okay, so this is what you do in that case, this is what you do in that case, and this is what Devin needs to know, and Devin needs to be able to read these things, and going and debugging their exact problems. Now, obviously, it's not like that at all. Hold on. We'll get to where it is now.

44:24But the idea of, no, we didn't deploy a team. We deployed the whole company. Oh, yeah. That's hilarious. Getting the first customer, obviously, was like a real, I mean, I wonder what they thought of that. But yeah, it was literally like, okay, let's go through all these different things that you guys think could make sense. Let's go through each one. Let's try some of it manually ourselves to understand what the task looks like. Let's see if we can teach Devin to do this correctly and build in the right orchestration for Devin to be able to do this. And let's just like basically building the product was almost like building for one company.

44:59And yeah, it was fun. So what do you do today? So today, it's obviously much more self-started, and agents are so much more capable, obviously. And we've figured out a lot more things with the onboarding experience. But a lot of it is, we're saying these cycles typically take 12 to 18 months. We try to get deployed with folks in like three months. And a lot of that requires folks to, obviously, first of all, it requires them to really appreciate that it's a priority. I mean, if you have 25 ,000 software engineers and an org that's running that costs$10 billion a year or something that you think can move three times faster, usually it is a priority.

45:40But then it's like figuring out how we get through the security reviews. We can deploy in their private cloud. We have very strict data agreements. We have tight air walls on all these things, obviously. Do employees physically have to go? Are they working with, like, are these deals so big that they're working with a specific company and only that company for a period of time or no? Typically, no. We have, you know, we have a full, like, forward deployed motion. But a lot of what that looks like, as we're saying, is a little bit more like user education and guidance. And so we will fly, you know, we still do this, but we'll fly out and kind of like go and see customers and work with them, point them to the right use cases, teach them how to get success, help them with their like setup and their playbooks, like all these things for how to use Devon.

46:29But it's much more a kind of like, look, we're here to assist you guys. We're giving you a lot of this kind of like direction and so on. But like you are yourself, you know, and your team is the one that's using Devon and running all that. Right. So that's a lot of what that looks like. We're set up to be as incentive aligned with our customers as possible. And so a lot of it is like, is not just like, oh, here's this tool, like hand it over to your engineers and like find out what they say about it. It's like, okay, well, let's figure out like, what are the initiatives that you guys really care about?

46:56And like, we will point you to for those initiatives. Here are each of the projects that we think Devin right now can make you 10 times faster on. And we'll tell you for the ones that it's not. And here's, here's what workflows you should use instead, or here's how you should get to value instead. But it looks more like that than like a literal, like, you know, we are using Devin on your behalf or something. Deal is how the best founders turn the world into their talent pool. I've been studying how history's greatest founders operate for a decade. And one thing they all have in common is they understand that recruiting and hiring the very best talent is your most important priority.

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48:04He said, we built Eleven Labs to break down language and communication barriers. With Deal enabling us to hire and support exceptional talent anywhere, we can accelerate our innovation and bring more voices, stories, and ideas to every corner of the world. Deal is trusted by over 40 ,000 businesses. Learn how they can help your business today by going to deal.com forward slash Senra. That is deal.com forward slash Senra. Wait, so tell more about how you align the incentives between your company and then your customers. Yeah, so a couple things here, which I think are really important. One, obviously, is just like really working with them on the very clearly defining the ROI, which I think is a very important, I mean, it's like everyone's talking about this, right?

48:48Like over the last few months, like people are going crazy on their token budgets and, you know, one engineer can spend so much. And, you know, you want to know that that's actually doing real value for you and you want to be able to identify where you are. Do you think all this stuff is a little crazy? I think it is directionally correct but I think there's some you know there are definitely some places where people have gotten carried away you know people talk about oh like yeah like we rank our engineers by how many tokens they're spending well let's try and rank people by how much output they're actually producing or how much good work you know is actually getting done you know but obviously I think that like the math works out in terms of like you know the GPUs are expensive but like if your engineers are actually able to ship three times more you know then it's very clearly worth it you just want to make sure you're doing it the right way right obviously a lot there on just like tying to specific outcomes, tying to, okay, what are the actual tickets that are getting done?

49:33What are the projects and the initiatives? And like this project, which was scooped out for 18 months and was going to be handed off to, you know, an outsourced contractor and was going to cost you 15 million. Like, let's just talk about how you do this all internally with your own team and you do it for 1 million and you get it all done in three months, you know, stuff like that, which I think is super important. The second thing which I'll call out, especially is just being neutral. A lot of discussion, obviously. What do you mean by that? Being neutral with respect to all the labs, the models themselves.

50:05Yeah, I think you said you like being Switzerland. Yeah, we like being Switzerland, exactly. And so I think it's like an important thing of, you know, we are just as incentivized as they are to figure out how to make their token spend efficient, right? And so Devon is purposely meant to be, you know, a compound model system. And so all of these different things, you know, for the right task task or even the right subtask of a task or something like that. Describe what a compound model system is. Yeah, yeah. So for each different part of your use case, I mean, imagine, let's say you tell Devin, hey, customer just reported this bug.

50:38You know, there's a Jira ticket. Can you take a look at the ticket and like go and solve the whole thing, right? What does Devin actually go do, right? First of all, Devin's probably going and like investigating, understanding like what does the report say? What's going on? Second of all, probably, you know, as any engineer should, like it's going to go and try to reproduce the bug itself, right? So it'll say, okay, let me spin up the product locally. We click around, try to follow the same steps that they followed and see if I can make the bug happen as well, right? Then if you did that, then like, okay, now you're looking at the logs.

51:05You're trying to figure out what went wrong. You're pinpointing what are the particular files or what was the flow of, you know, what was the command flow that led to this. Then you do the debugging. Then you go and test it again. Make sure, you know, all these steps, right? Then you put it up for review. And it turns out that there are different models that are good for different parts of these tasks, right? And even across different tasks, obviously, also, it's like some tasks are these really crazy hard ones where you want to actually use max thinking and you want to use the very best models you can get your hands on in the world, right?

51:34And then many other tasks are, you know, boilerplate enough or repetitive enough that what you care about is just getting it done really fast, getting an immediate answer, having the ability to verify that it was correct, but then beyond that, making sure that it was like as cheap and fast and efficient as possible, right? And so Devin, rather than being pegged to one model and saying, oh, we're only going to serve you GPT for this or we're only going to serve you Oakfish for this, Devin can use any of the different models it has in its arsenal, which include all of these models from Anthropic, OpenAI, Google, et cetera, but also our own models, right, or open source models out there.

52:08And it will dynamically go and choose these models for these tasks. How do you think about this? Like you're a customer of them, but also competitor with. Yeah. Yeah. No, I mean, look, I think in practice, there's a lot of positive, some work for us to do together. And so, like, you know, the way that we think about ourselves, a couple things. One, software is the only thing that we care about, obviously. And there's a lot of value and focus and in building products and building our home home motions specifically around that. Say more about how you think about focus and the value in it. You know, back to what we were saying about startups, right?

52:42Like, why do startups ever win at all? And if you do everything, you will lose to Microsoft or Google who does everything but also has like trillions of dollars, more resources, and 100 ,000 more people than you, right? And infinitely more brand name, right? And like the way that you build, you know, like a real kind of like, you know, a real like lasting business or lasting product is by really, really focusing and narrowing on one specific thing, making a very concentrated bet. And then obviously, you know, your bet has to end up being right. But from there, it's a lot of really tight execution.

53:16And so in software, I love, you and I have talked about this. I love the quote from Daniel Ek in Spotify. People were saying, YouTube's trying to go and do this, and Apple has Apple Music. And why should there be? He was like, I can give you all the other reasons, but the truth is we're just going to care way more about music than they are. And I think for us, it's true. It's like we are just going to care so much more about like what does it look like to build software end to end at Goldman Sachs or at like Mercedes-Benz or something like that, you know. And there's a lot of nuance in that. And there's like a lot of messiness in that, right?

53:50It's not as simple as like, oh, here's a sandbox algorithms problem. Go and code me the correct, you know, 30 line program that solves this problem or something. It's like how do you work with all the messiness of the real world? How do you understand the code base as it exists today? How do you like collaborate with all the humans on it? How do you plug into their ticketing system? How do you give the agent the ability to test its own code and run everything locally, right? Like all of these are obviously super hairy, messy problems. And that's what we care a lot about, right? And so from that perspective, it's a very kind of nice, like, you know, the labs have their own products.

54:21I'm sure they'll continue to do more. But in practice, like there are a lot of nice ways for us to cooperate. Obviously, like on top of that, like being the Switzerland means that, you know, folks can work with us and kind of like trust in us that we will kind of route them to the right models. that we will optimize the price performance for them, that we will kind of like direct them to the right use cases and so on, because, you know, we're not incentivized for them to spend more on the models either. You know, we're incentivized for them to get value and to get output out of it. I love that you use the example of the war between Spotify and the rest of the Apple Music, for example.

54:54Jimmy Iovine, who also came on this show and now is actually a friend of mine, he actually called me yesterday about this because anytime anything happens with AI music in Spotify, he calls me. But we talked about this because he's like, you don't understand. Like, he had, like, 40 years of experience in the music industry. He had all the relationships. He's like, Apple bought me for$3 billion. Spotify at the time, he's like, we're going to go head-to-head with them. Spotify only had 3 million paid subscribers at the time Jimmy's going to fight the war. And he's like, I have one of the biggest companies in the world.

55:20And then he talked about it. He's like, that wound up being a huge—he thought it was an asset. It was a huge liability. Because they're like, we don't give a shit about getting a couple million more, tens of millions or more of subscribers. We invented the most successful consumer product of all time. And he said something like they just clipped his wings. and just like, he was trying to do something like, oh, this isn't important to us. Where Daniel was going to die if he was not successful. He just cared about it a lot more. Dude, even in the two years that we've been around, you know, I've heard so many different versions of the same argument.

55:48Because when we started, as you can imagine, this was true for us. It was true for everybody in the space, building the space. The number one pushback that you would always hear from investors, from other people, it was like, but like Microsoft already has GitHub Copilot. Like, isn't that, everybody's just going to use that, right? And it's like, it's a very reasonable thing to say in some sense because, you know, they did own all of GitHub and they did have a partnership with OpenAI and they did have like VS Code and so on. I think in practice, the reality is there's so much more innovation and so much more to build that there was a lot of like positive, you know, Microsoft's like a great partner of ours and we do a lot of things together and we build even more together, right?

56:22And I think the reality is like people have said this forever of like, oh, like, yeah, like startups versus, you know, like why should it? You can give all the rational arguments. Ten years ago, it's like Google will do this or Facebook will do this. It's like, oh, why should Datadog exist? Or why should Snowflake or Databricks exist? You know, the clouds have all of it. You know, the clouds care about observability too. The clouds care. And the reality is like, it's in some sense a bit of an uncreative way to think about things, I think. Like if there was like one thing that, okay, here's what we all know is going to be the end state future.

56:53And everybody's just working toward it. And whoever has the most resources toward it wins. And of course, yeah, you know, it's like we know who has the most resources to it, right? But if there are millions of problems out there to solve, there's lots of different things. The world is dynamic. Things change all the time. There's lots of new ideas or opportunities or ways to build new products. Then the reality is like, of course, there's lots of different niches to own. There's different things to really bet on. There's different focuses to spend your time on. And I think that will continue. Yeah, I mean, I think it's like for better or for worse, after the last couple months of news, you know, cognition, I think, has been a bit more of the like.

57:29we've become a bit more known as the folks betting on independence in some sense, because obviously there have been some high-profile acquisitions. It's funny you said that, because one of the questions I'm going to ask you, which you probably won't answer, is how many different acquisition offers have you had? I will not answer that question. Dozens? More? Dozens is a lot, dude. I don't know about dozens. The time. It's probably only a handful that could actually afford to buy you, if you would sell. But the amount of times they keep coming back. Oh, I see. Well, yeah, it's definitely. Anyway.

58:01This independence part is really interesting to me. We were on the phone, me, you, and a mutual friend on three-way, like, I don't know. I think it was actually last summer. And we're not going to say the company. But I was like, Scott, what do you think about, you know, an acquisition with X, not X the platform, X the blank company? And you're like, I don't know how we'd be able to afford them. Just assume I meant you buying them. And I just fucking cracked up laughing. It's hilarious. No, so I think like, yeah, yeah. And it's like folks, there are folks taking acquisitions and doing things. There's some big high-profile ones.

58:41I think those are great, to be clear. And I think it's a very reasonable path for exit. As I mentioned, for us, it's like we've all come into this, like we want to build a generational business. That's what we're really excited about. And I think people, I think today sometimes, I've seen some more of this nihilism where they say, oh, like, yeah, maybe it's just, maybe it's too late and maybe it's not possible anymore. And like. What does that mean? What's not possible? Like maybe it's not possible to build a new independent business because everything else, you know, it's like. Because the labs are going to do everything.

59:06Because all the opportunities take it, you know. And it's like, guys. Those people aren't founders. Founders are rationally optimistic. Yeah. They're just not founders. Yeah. They believe even there's no evidence that they should succeed. Yeah. That they will succeed. Yeah. So people like that just need to get a job. Dude, I'd say this even with Devin. But if you think that going onto the Devon web app or any other coding product out there today and giving the instructions the way that you do right now and then you get the pull request out and then that's how you review it and that's how you build software, if you think that that's going to be the way that software is built forever, then yeah, then nothing will change.

59:39But I think we have 10 more generations of these different product experiences to come, right? And building those and doing those, it's like that is what innovation is. And that's what's going to happen. The advantage I have of reading for the last decade of all these biographies of physicists and entrepreneurs. Like, you're studying somebody's life, but in many cases, if you were so successful, they wrote a book about your life. You usually, it's almost like you get a minor in, like, new industry creation. Yeah. Every single time, it's just like, we're too late. It's over. There's no more opportunity.

1:00:06Yeah. And the way I thought about this was, like, the best definition of a business I ever heard actually came from Richard Branson, where he says, all business is an idea that makes somebody else's life better. And if that's true, which I believe it is, then that means there's infinite opportunity in the future, now and in the future, because there's infinite ways to make somebody else's life better. And that's all a business is. So this idea that's like, we got to the end of history is just bullshit. And I don't mean to push on this, but I am personally curious though, because I know you're already rich.

1:00:33I don't think money is your North Star based on the conversations we've had in the past. But there's gotta be some crazy number somebody can throw at you where you're just like, fuck, I have to take this. Not really. I mean, it's like the, you know, people have asked me sometimes before, they've asked me like, okay, but like really though, would you guys like, this is what I'm doing right now. And the way that I say it sometimes is like, we would sell if we thought it was the most ambitious thing to do. It's kind of an oxymoron because obviously, but, but, but, but, you know, it's like my genuine answer in the sense that like, that's like what we care, we care about, you know, it's, it's like the, I mean, it's funny you talk about money.

1:01:14I don't even, dude, I don't have a car. You live in an apartment, I just realized. I have a merchant apartment. Yeah, it's like, I don't know. I think, I like eating sushi. That's fun. Sushi doesn't cost that much. You can do that off of an engineer's salary as well. Just so you know, if this is true, then you are the type of entrepreneur that I find the most fascinating in the world. Because when startup founders come to talk to me, it's just like, I don't give a shit about your startup. I ask the same question. It's like, is this your last business? I asked Kareem from Ramp, that's before we did this deep partnership.

1:01:49And it's just like, okay, you could sell it. Like the Zuck example, where they're like, why didn't you take a billion dollars? I think you own 25 % of the company. You would have made$250 million. You're like 22. He's like, well, what would I do with the money? I would just start another social network. I kind of like the one I have. I just want to build shit anyways. So what do I do? And then the other element of this, which I think is almost tied into you, where it's like, I feel like you're just having a lot of fun. like even being around here it's just like there's your company's weirder in the composition of the people because it's literally just all nerds like and I think you like you know and I mean well like you know like there's usually a mixture of obviously the nerds and some of the other people and like you kind of built this like almost like your social network is like you're not allowed here if you're not a nerd yeah yeah it's like a physical manifestation of your social network but like the way I would describe this is like okay cool so my friends are also nerds yeah I agree I mean that in obviously an enduring way I love that yeah But the people I'm most interested in is just like there's no price, right?

1:02:48Like the example I use is go ask Steve Jobs. I'll give you$2 trillion, but you can't work on Apple. Yeah. What the fuck do you think he would have said? Yeah, I agree. No. He's like, well, I want to work on Apple. That's what I want to do. There's a funny thing that I wanted to tie together, which I didn't find the opportunity until now, is you are currently the second wealthiest entrepreneur from Baton Rouge, Louisiana. I don't know if you remember, we've talked about this. We've talked about this, yeah, yeah. The first one. Chicken finger is a great business, but I'm a customer of that business as well.

1:03:19The first one is Todd Graves. Yeah. Owns Raising Cane's. Yeah. I talked to him. He was on the show. Yeah. You tell him you'll give$100 billion for your chicken finger empire. Yeah. No. He's turned down, and I know this for a fact because I've talked about it, he's turned down crazy acquisitions offers. He's like, I don't care about, it's not the money. Yeah. No, I mean, for us, it's very like, again, this is not rational, but the way that I would describe it, it's like, I feel, and I think all of us do, that it'd be one thing if we tried and we gave it our all and we just weren't good enough, that'd be fine.

1:04:00It wouldn't be that. Dude, I'd be salty as hell. It wouldn't be fine. But it would be an outcome. it would be like an outcome that I could live with, you know? But if we felt like, you know, we could have gone for it all, we could have pushed harder, and we did it. Like that, I think it's like, I just, I don't think we would like live with ourselves in that outcome. And that's like the, if you have me explain it, it's kind of circular, I don't know, but it's like, why are we so excited to do this? Why do we do, you know, it's like, we want to achieve our potential and build what we were meant to build, you know?

1:04:35And maybe that's something, maybe that's nothing, but like you'd rather find out than see it. Yeah. I think this idea of you have one life, go. Yeah. Is a perfect place to end. Thanks for the time, Scott. Yeah. Thanks for having me. I hope you enjoyed this episode. Please remember to subscribe wherever you're listening and leave a review and make sure you listen to my other podcast founders. For almost a decade, I've obsessively read over 400 biographies of history's greatest entrepreneurs searching for ideas that you can use in your work. Most of the guests you hear on this show first found me through founders.

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