Y Combinator Startup Podcast
00:14:26 · Vivian Shen, Paul Graham
[00:14:26] S0: Just the slot machine. [00:14:28] S1: Yeah, and still has a lot of that flavor, doesn't it? [00:14:31] S0: Yes. [00:14:32] S1: I hear all these stories about how AI is solving famous open problems in math and I can't even get it to answer questions about when restaurants are open. [00:14:40] S0: That's what's called the jagged frontierness. [00:14:42] S1: Yeah. They must know things I don't know. Or they have research versions. Maybe they can find out when restaurants are open. [00:14:49] S0: The other thing, are famously very good at taking complex topics and making them sound very simple. So what is the official PG definition of AGI?
00:14:49 · Vivian Shen, Paul Graham
[00:14:49] S0: The other thing, are famously very good at taking complex topics and making them sound very simple. So what is the official PG definition of AGI? [00:15:01] S1: Oh. Well, honestly, I mean, could you do better than the Turing test? I think Turing was pretty good, right? One of the one of the ways I knew we were getting close to AGI was I actually had to go and look up the definition of the Turing test. That in itself is a piece of evidence. Like, what was the Turing test again? Are we there already? I think what's happened now is we thought back in the day that AI would be sort of like this finish line and AGI would be this finish line, we'd cross it, it would be very clear. But when you're standing on the finish line, you realize it has width. From back in the 1980s, it looks like it's just a line going across the horizon. But here you realize, okay, some bits of AI are way across the finish line. Some bits are like maybe in the middle of it. Who knew the line had dimension? The finish line is actually the sort of smear. And I think the best answer you can give is like, we're on the smear. It turns out to be a smear and we're on it. [00:15:57] S0: And that's why you can't get your restaurant times. [00:15:59] S1: Yeah. Because they're in a bad part of the smear. Whereas the Riemann hypothesis, maybe we're already there.
00:15:59 · Paul Graham, Vivian Shen
[00:15:59] S1: Yeah. Because they're in a bad part of the smear. Whereas the Riemann hypothesis, maybe we're already there. [00:16:04] S0: Okay. Changing back to talk about startups. So the other thing you've said is that the best predictor of success for a startup is the pace that they ship new stuff. Yeah. With AI, does that change? Do you think there's a new metric? [00:16:18] S1: No. No. There's... I mean, we have all these powerful AI tools, there's still a lot of startups in this batch that are not shipping fast enough. And so, obviously, there's variation in shipping speed. It's not just the rate at which you can produce things. You have to think of these ideas first. Right? Everyone always asks me, like, with all this AI, is anything still the same? And the answer is so far, almost everything is exactly the same. The only weird the only weird new thing is the companies have these giant AI bills. It used to be start ups' big bill... Big cost was salaries. That was all that mattered, salaries. Right? Everything else was cheap by comparison because it was just laptops and people. And now suddenly you got to pay for GPUs. Giant, giant. I mean, tens of thousands of dollars a day in tokens. [00:17:05] S0: I'm also curious, how does being in a batch like YC give founders an advantage?
00:17:05 · Vivian Shen, Paul Graham
[00:17:05] S0: I'm also curious, how does being in a batch like YC give founders an advantage? [00:17:11] S1: There's a bunch of reasons. One is starting a startup is normally a very lonely business. You have all this upside. You have complete control over what you're doing. The downside is you don't have any colleagues. And so this is like working in an office with all these colleagues, except you're all working on your own startups. So there's that, you have colleagues. You can also learn from what the other startups are doing at the same time you are. So you have some technical problem, probably somebody in the batch had that problem. You can just ask them how they solved it, right? And another advantage is what we call the YCGDP, which is you have some sort of product, you can sell it to the startups in your batch. Almost no matter what it is, you can find some. And they're the exact kind of users you want. Early adopters who decide quickly, right? And they sort of have to at least listen to your pitch. [00:17:58] S0: How did you come up with the idea for YC?
00:17:58 · Vivian Shen, Paul Graham
[00:17:58] S0: How did you come up with the idea for YC? [00:18:02] S1: Gradually. Initially, the idea was just going to be we didn't have the batch idea at first. I... Part we had at first was to do... Was to be an angel firm because at that point, there were VC firms who would do these giant late stage round and there were angel investors who were individual people investing their own money, but there was no... There were no angel firms. That was the original idea, to be an angel firm like a VC firm, but invest small amounts of money earlier and use standardized paperwork. And then having decided that, we thought, all right, we need to learn how to be investors since we don't know how to be investors. We'll just fund a whole bunch of startups all at once. And so we thought to ourselves, well, all these college students get summer jobs working at Microsoft. Wouldn't they like to start startups instead? We'll create this alternative to getting a summer job. That's why the batch takes place during the summer. It turns out to be the perfect length, but that was just an accident. And so we thought, we'll fund a whole bunch of startups. We'll replace summer jobs. It'll be for current undergrads. They won't mind if don't... If we're not real investors because they're not real founders, right? Just a bunch of college students. And it turned out in the course of that, we became real investors and they became real founders very quickly. And this batch thing was so great. This thing that we just discovered by accident, we thought, all right, we're gonna always do investing that way.
00:19:25 · Vivian Shen, Paul Graham
[00:19:25] S0: How has YC changed or not changed since it's grown? [00:19:29] S1: It's changed very little. It's basically like the same thing, but more of it. You know, like the problems the startups have are pretty much the same. There's a lot more of them. But they, you know, they live in small groups that are just like individual bits of YC. So one of the things people saying YC... When people say YC is jump the shark, one of the things they always say is, oh, it's so big now. Well, people used to say that back when we had 40 startups in the batch. Right? They would say, oh, come on. The old days are gone. There's 40 startups in the batch. Right? People are always saying that. And if you if you divide YC up into little bits where each startup is part of a pod of 70 startups, then it's just like they were back in 2012 when there were 70 startups. [00:20:15] S0: Where do you think the next trillion dollar company comes from? What are the founders of it like? [00:20:20] S1: The founders of it are formidable. In fact, that is the answer to your question. The answer to your question of where the next giant company comes from is it comes from the right founders. Right? It's not some particular idea. I mean, it probably is some idea rather than others. It's probably not dog walking. Right? Maybe. [00:20:39] S0: Who knows? Maybe. [00:20:40] S1: Maybe. I mean, startups ideas are very immutable, but it it comes from the next trillion dollar founders is where it comes from. And they probably have good ideas. Right? Whatever they're working on is probably promising.