Y Combinator Startup Podcast
00:07:02 · Vivian Shen, Paul Graham
[00:07:02] S0: That's the way we want it. And I'm curious, can you tell me a story about a founder where when they started YC, they were maybe not ambitious enough, and then they became very ambitious during the batch? [00:07:12] S1: Those stories are rare because most of this quality is inborn. And so like Sam Altman famously was extremely formidable the first time I met him before we even accepted him. And that's more the rule than the exception. So the one case where you see founders it's not that they lack ambition, it's more like they've been trained not to show it. Because when you're young, you're often in situations where you're supposed to be obedient. You're not supposed to like go off and do what you want. You're supposed to do what people tell you. Maybe you have pushy parents who tell you to do what they want, right? And so you've never let yourself go. That's the one case. But even then, would argue that the person was ambitious. They'd just been trained not to show it. [00:08:01] S0: Yeah. Actually, that's a good point because I think nowadays a lot of people think about startups like another badge that they should collect along
00:08:01 · Vivian Shen, Paul Graham
[00:08:01] S0: Yeah. Actually, that's a good point because I think nowadays a lot of people think about startups like another badge that they should collect along [00:08:08] S1: Only the if it fails. If it works, it's like your life's work. You don't have any more badges, right? And so what kind of badge is a failed startup? Not much of a badge. I think if people think that they're not thinking it through, right? Like honestly, we would get it was quite early on in YC's history when people started applying to YC for the prestige. Right? Like they would apply to Harvard hoping they would get in. But the thing is, if you apply to Harvard and get in, you should do it because no matter how stupid you are, there's easy majors you can choose and sort of slide through. You'll get a Harvard degree and everybody will be impressed with you. With startups, there's no easy major. So Right? It's like applying to Harvard and being forced to study theoretical physics. It's really rough if you're not very hardworking and clever and determined. And so these people trying to do YC as a credential, they had no idea what they were asking for. Didn't realize, like, this is not this is a really, really unsuitable credential. [00:09:13] S0: There are many easier ways to get credentials. [00:09:15] S1: Yeah. To do that. Yeah. If you want to seem cool, like starting a startup is just about the least efficient way to do it. Yeah. It's brutally hard. [00:09:23] S0: Yep. [00:09:24] S1: And you won't seem cool for years of years of brutal hardness before anyone will think you're cool.
00:09:24 · Paul Graham, Vivian Shen
[00:09:24] S1: And you won't seem cool for years of years of brutal hardness before anyone will think you're cool. [00:09:29] S0: 100%. When you were describing some of the founders before you used the word formidable, can you tell us a little bit more about how that word came to be, what it means to you as well? [00:09:38] S1: Well, a lot of the language of Y Combinator is Maya and Jessica's private language, right? And so we already had this word before we started Y C, like for a certain kind of person. And I described it in an essay. I said I think that it's someone who gets what they want. That's the test. Do you get what you want? Because how formidable are you if you don't get what you want? So it's someone who gets what they want in any situation. And so if you invest in them and you have stock in their company and they have stock in this company, your interests are aligned. If they get what they want, you get what you want. So that's why investors want people who are formidable. Pretty good. Yeah, because they get a part of what the founder gets. [00:10:23] S0: So changing tracks a little bit. At startup school, Patrick Collison recently said that lean startups may be dead. [00:10:30] S1: Did he? [00:10:31] S0: And part of that is because he says, you know, it's really easy to raise money these days. AI lets you work on many things in parallel. You could spin out a bunch of agents and work on a bunch of things. Do you agree with him, or would you have a rebuttal?
00:10:31 · Vivian Shen, Paul Graham
[00:10:31] S0: And part of that is because he says, you know, it's really easy to raise money these days. AI lets you work on many things in parallel. You could spin out a bunch of agents and work on a bunch of things. Do you agree with him, or would you have a rebuttal? [00:10:45] S1: I've never really been clear what a lean startup is. I mean, we already had a kind of startup that we funded in YC, and maybe it has a lot of overlap with what was described in this book, The Lean Startup, but I haven't actually read it. And so, I mean, why would I, right? Tolkien didn't read all those other fantasy books people wrote. And so I don't exactly know what a lean startup is. But if you tell me what qualities you might think might be obsolete, maybe I could answer that. [00:11:13] S0: Let's say starting with just a little bit of money on a very focused area. [00:11:18] S1: You mean it might be dead starting with a little bit of I don't think so. I think there's a lot of things you can do for cheap. Mean, tokens are very expensive at the moment. But that's just because there's a shortage of GPUs. Token prices will certainly, inference prices at any given level of inference go down dramatically, like 30x a year or something like that. So even token prices are somewhat misleading because you're getting higher quality tokens over time. So I think, no, technology always gets cheaper. I think you can still start a startup on not much money. [00:11:53] S0: Even if it's a rocket startup or any of
00:11:53 · Vivian Shen, Paul Graham
[00:11:53] S0: Even if it's a rocket startup or any of [00:11:56] S1: the It's harder to start a rocket startup on no money. But even a rocket startup, can start on not much money because what you do is you adjust for how much money you've got. Like you can't build an actual rocket. What you can build is you can build a design for a rocket and show that to and you can simulate it, right, or show it to experts. If it's convincing enough, you can go to investors and get a bit of money to do the next stage. You're always able to raise another round, right? So you just do what you can on the money you've got. As long as you can get to some kind of milestone, then you can convince investors to give you more. [00:12:31] S0: Yeah. I think Philip from Starcloud was saying that they basically just wrote a white paper and booked a launch, and they raised the money for Starcloud. [00:12:39] S1: Yeah. Nothing as convincing as booking a launch, right? It's got to happen if you booked a launch. But he's a huge famous expert in the stuff he works on. Right? So he comes with automatic cred. It's easier for him to do that than some kid who just graduated from college. [00:12:54] S0: So let's transition to talk a little bit about AI more broadly. I'm curious what surprised you about AI.
00:12:54 · Vivian Shen, Paul Graham
[00:12:54] S0: So let's transition to talk a little bit about AI more broadly. I'm curious what surprised you about AI. [00:13:01] S1: Well, I was studying AI back in the 1980s, a very different kind of AI that would never have worked. I mean, was a joke. So I've been thinking about this a lot. And I had a lot. I spent a lot of time thinking about what AI would eventually be like. And the way we thought it was all going to play out was you start out with like a perfect fly. Right? You know what? It doesn't do much. It's like a fly. Like a fly's brain. Right? It can only do what a fly does, but it does it as well as a fly. Right? And then you work your way up from flies to mice, you know, to cats and eventually you get monkeys and then humans. Right? But at every point, you're you're perfect. You're doing what they do well. And instead what we got was basically a full on human but full of shit. Like the first AIs, the first versions of chat GPT were like an undergrad trying to bullshit his way through a paper. Something plausible sounding. Full human stuff, like not even this fly stuff. Full human, but like terrible. Right? And so you start... And so instead of starting with perfect and then working your way up to human, you start with human and then work your way towards perfect. So it's sort of the opposite quality ended up being the one that got optimized. That, nobody expected that. Nobody expected when the first plausible AI turned up, it would be like a bullshitting undergraduate. Right? [00:14:26] S0: Just the slot machine.