Y Combinator Startup Podcast
00:17:01 · Garry Tan, Harj Taggar, Diana
[00:17:01] S0: Effectively. Mean, and that's... That was about Slack, I would It's like, basically, if you are a system of record, you either will be preyed upon, like, you'll release an MCP, and then maybe, like, the data... You know, you lose your moat around the data, the data goes elsewhere, like, becomes very trivial to switch. Or you kinda have to be a harness. You have to be the way people not just read and write, but actually do their work inside, you know, your system of record. [00:17:27] S3: And get the most value out of it. I mean, it's a lit... It's a little bit like the model companies. Like, was the... Was it ARC-AGI was the benchmark where... Or there was a benchmark where the... [00:17:36] S1: ARC-AGI v three? [00:17:37] S3: Yeah. Where the pre or pre Astra ChatGPT model didn't do well, but then they say, that's just because it was plugged into the wrong harness. Yeah. So it's like the model plus the harness gets you the output. [00:17:47] S0: Oh, yeah. I mean, with a custom harness, they claim Astra got to nine... North of 90% on Arc AGI three. [00:17:54] S1: Yes. I think a couple months ago, this was in the low two digits [00:17:58] S0: Right.
00:17:58 · Garry Tan, Diana, Harj Taggar
[00:17:58] S0: Right. [00:17:58] S1: Which is an impressive leap. And I think you have a very good point around software. It's not that software and SaaS is dead what people claim on the Internet. It's just that it has transformed. We're actually seeing this in the batch. The percentage of companies we accepted that do sort of full stack end to end work or a task has gone from just 10% to over 25% of the batch. This has to do with actually doing the job. The agent does the job, not just like a point solution, which old SaaS in five, eight years ago was just like a point solution, and you needed you needed someone to operate the SaaS software. Right now, it just runs by itself. And, actually, in the batch, this is where we're seeing a lot of the growth in revenue. I think I gave that stat of the median startup when it gets into YC is at zero in revenue. And it has gone from... By the end of the batch, was about 8 k in MRR. Now it's, like, about 20 in MRR, and a lot of [00:19:02] S3: these are [00:19:03] S0: huge jump. That's, like, nontrivially big jump for the median. Right? The average is even higher.
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.