Cognition found its first enterprise fit in repetitive migrations

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 37:16

   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? Sure. 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 any revenue, we didn't really have like...

2. 37:38

   I thought your first iteration of the business model was like $500 a month or something, wasn't it? Am I misremembering 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, guys, I don't know if this is a product that's ready for primetime, but if you really, you know, you can try it and like just let us know.

3. 37:59

   And so, like, people ran, you know, we had to go and scramble to build a system of, like, oh, okay, let's do, like, a pilot or, like, a PSC, you know, and, like, we tried our best to, like, not overpromise and be like, guys, like, really, it's, like, very early, but if you want to try it, you know, you can try it. And we did all these, and perhaps unsurprisingly, I mean, they were all just, like, failing, which is kind of natural.

4. 38:19

   Like, it's, like, you could do some pretty cool things with it. You could do some pretty interesting, like, toy demos or projects or whatever, but it was certainly not ready to work on like an actual company's like real code base and so on, right? And so from that point, this would have been like April or May of 2024. We were making agents work with like GPT-4. You know, this was like a very different era.

5. 38:40

   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.

6. 39:02

   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. And, 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?

7. 39:34

   We kind of said, okay, well, it should be some of these like really repetitive, tedious ones. 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 like 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 like fix that problem right and so that's kind of what brought us to some of these like to some of these like, you know, initial use cases, which were things like migrations or version upgrades or, you know, helping people upgrade from like Java 7 to Java 8 or something like that, which were kind of like, you know, as you can imagine, enterprises had these massive code bases where they would go and do all that, you know, and it's like a 50,000 file code base where you have to go, you know, it's like the same eight things that you need to change in each one.

8. 40:21

   you have to be a little bit thoughtful about how you'd make the trade-offs, but it's a very repetitive task, right? Our first success ended up being with a company called NewBank, biggest bank in Brazil by market cap at the time. And the use case was one of these big migrations. And we had a custom Devon that was extremely, extremely optimized for doing that. And as we grew from there, later on, as things got a little bit more mature, 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 millions of people use every day was pretty substantially different from just building a cute website or a cute demo from scratch. And I think we really,

9. 41:08

   really leaned into that. And naturally, all the Fortune 500 or all the biggest companies in the world are software companies in 2026, 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.