Y Combinator Startup Podcast
00:19:03 · Garry Tan, Diana
[00:19:03] S0: huge jump. That's, like, nontrivially big jump for the median. Right? The average is even higher. [00:19:08] S1: And it has to do with doing the full end to end job with, for example, doing insurance broker, actually doing the clinical intake, doing the full end to end workflow of, I don't know, medical billing, etcetera. And these are the ones that are growing a lot. And I think there's another factor where that's happened. I think we talked about this in couple episode ago. We, right now, are about almost a year since agentic coding started to work since Opus 4.5 that we're seeing these workflows fully blossom. And the result are basically people want their job just be done and are willing to buy software that just gets the job done.
00:19:50 · Jared Friedman, Harj Taggar, Garry Tan, Diana
[00:19:50] S2: I think when people hear these revenue numbers growing so fast, an easy knock on it is like, maybe it's just AI hype, and these companies are just like shelling out money for AI products because it's like the cool thing to do. And to be fair, that's probably some of that. But I think, like, the bull case is actually something that we said in an episode, like, two years ago when agents were really just beginning to be a thing where we were like, actually, the products are just gonna be more valuable. Like, if they automate the whole job, they will actually just be more valuable than some, like, system of record that tracks the job but doesn't do the job. And therefore, companies will just, like, pay more money for the... For for further product. I definitely see that in companies that I work with where, yeah, they just go to a company, and like the value proposition is so great that like large enterprises are willing to write big checks very, very early. [00:20:37] S3: Yeah. A company that I'm seeing having this effect is Juicebox, an AI recruiting tool. [00:20:42] S0: Oh, yeah. [00:20:43] S3: It's been on incredible rate for like the last couple of years now. But they'd started out as, I mean I would say it was essentially sort of LLM powered people search. Like the thing that they did was you could type in sort of the spec of the type of person you wanted to hire and it did a really good job [00:20:55] S1: of
00:20:55 · Diana, Harj Taggar
[00:20:55] S1: of [00:20:55] S3: pulling the good profiles of people that you may want to contact. But then you still have to go and contact the people. And recently they've launched an agent product which is really taking off and the agent like doesn't just search for the people and like contacts the people, eventually they'll be able to schedule the interview, do a bunch of things. That sounds awesome. Yeah and they're seeing that that's gonna just on a like per account basis I think is gonna double or triple like the revenue they make from a single customer because customers want more and more of these agents. I don't think it's fair to say that it's like it's not like it's like automating the job of the recruiter at all. It's just like it's just changing it. Like it that like the recruiters didn't necessarily want to be doing like that sort of rote reach out to like 500 people anyway. Like the thing that makes the recruiter job, I would say like more skilled and interesting is like there's like culture fit. That's just gonna be really hard for like an AI to do a phone screen that assesses like how well someone's gonna be like a culture fit and the human element of it. And so I think they're finding that the recruiters themselves are actually really excited to use the agents because it frees them up to do the work that they feel is like unique and interesting.
00:22:00 · Diana
[00:22:00] S1: The other shocking stat is that the companies that really accelerate during the batch, they really start taking off. One of the things that we started experiencing this year that we never experienced in the past is we have companies breaking from 0 to 7 figures in revenue during the batch, and that is in a span of three months. And that's shocking. In the past, that would have taken, for a company to get to that, about eighteen months or more, and they're doing it in that amount of time. Part of it is they're solving real problems. And because of agent decoding, they're actually building products that are a lot more mature as well. And they're able... These founders that are super AI build run, I don't know, 20 coding agent sessions to get to that product maturity.
00:22:51 · Jared Friedman, Garry Tan
[00:22:51] S2: And there's another category of companies that's also been growing super fast recently, which is companies that sell data or RL environments to the labs. This one might be interesting to talk about, because a a lot of these companies are pretty stealthy. They tend to have a disincentive to talk about how well they're doing instead... You know, compared to most companies that like to talk about how well they're doing. And so I think people out there might not realize how big a category this has become. When YC funded scale back in 2016, this was like a tiny little niche thing. It wasn't even a category. There was... Initially, it was basically just scale who was doing, and then Mercor began to do it, and then like a couple other companies. But in the last couple of years, it's become a big category. We pulled the data recently, and just in the last two years, YC has funded more than a dozen companies that are each making more than $10,000,000 a year selling data or oral environments to the labs. [00:23:41] S0: And in many cases, hundreds of millions of dollars. [00:23:43] S2: It may make us 100... Hundreds of millions of dollars. And these are companies that were just just a couple of years old. [00:23:48] S0: That's pretty fast to revenue, honestly. [00:23:50] S2: Yeah. It's like pretty bananas. I... Do you wanna talk about anything, Garry?
00:23:50 · Jared Friedman, Garry Tan
[00:23:50] S2: Yeah. It's like pretty bananas. I... Do you wanna talk about anything, Garry? [00:23:53] S0: I mean, the big ones, I mean, I think AfterQuery and DataCurve, both really really great. I mean, there are probably too many to name that are honestly, like, maybe don't even wanna be mentioned because Yeah. What, you know, once you have something that's working, you almost don't want people to know. I think that it's kind of natural to understand this, though. I mean, data is one of the legs of the scaling law, and, you know, much has been made of compute. But without the data, how are you gonna make these models that much better? The RL environment thing is interesting. I mean, there's a lot there. I mean, there's a lot of, like, pure customization that's happening for specific use cases. Like, you'll you'll have, like, RRL environments for finance, for instance, and someone can go very, very infinitely deep with that. And it's like a little bit of expertise, it's a bunch of computer science, it's some systems work. But RL seems to be, I mean, one of the big engines for how... I mean, people are maybe bench... Benchmark maxing a little bit more than they should, but it costs money to do it, and it's seemingly here to stay in terms of how big model companies are gonna approach it.