00:26:43 · Elad Gil
[00:26:43] S0: Yeah. I think you're you're setting different expectations for yourself. I do think there are circumstances where you raise a 100 of my dollars and you bill, you know, like what Pi is doing, you know, on the robotics side or building the foundation models for robotics. Like, that needed a lot of money because it needed a lot of computing, did a lot of training to next. You know? Great. There's a rational... A rationale behind it. I I just think it's tough depending on what you're doing, and so it really has to be tailored against what are you actually trying to accomplish and what proportion of it is GPU versus headcount? And often you see people raises the grounds, and, you know, 80% of it is supposed to go to GPU or something, and the other 20% is headcount. So you you see these differential ratios versus I'm raising a 100 just to have people, which I think is a a tougher thing for a very early company.
00:27:28 · Aditya Agarwal
[00:27:28] S1: And, Elad, when you came here last, we're gonna switch topics a little bit. It's a lot of AI, so we're gonna switch topics a little bit. We'll come back to the AI. Don't worry. I think when you were here last, crypto and Web three was still pre the sadness, pre the winter. And I think it's actually kind of, you know, we're starting to see some interesting companies come about again. So I guess trying to... Where are you in terms of, would say, whether it be crypto rails, distributed fan of fi transactions, in terms of are you doing any investments there? And there's obviously a lot of talk around how agents might end up being one of the key catalysts to essentially allowing you know, kind of like the transaction rails that only... Utilizing the transaction rails that only exist kind of at Cripple and Web three. Are you a bull there? Like, do you agree?
00:28:18 · Elad Gil
[00:28:18] S0: You know, I think ultimately, there's a lot of APIs for payments. And so, you know, anything that's programmatic, an agent can interact with. Sure. And that could be a stablecoin or it could be a traditional payment system, but it's more just like what sort of access you provided things. And Stripe, I think, has done both, right, effectively. They've been thinking about agented APIs for payments, and they've also been doing a lot of really interesting work. And stablecoins... I kind of use Stripe as, like, the hidden crypto company that nobody's talking about that's doing pretty crazy stuff between their acquisitions and Oh, yeah. You know, it's a acquisition and a... Yeah. They're doing some other interesting stuff. You know, I'm I'm a long term crypto bull, but it has cycles, and we're obviously in a down cycle right now in my anticipation. This is not investment advice. My anticipation is that it's gonna get worse before it gets better. And if you look in historical cycles, you know, Bitcoin should probably drop to somewhere between, say, 35 and the forties, and, you know, maybe a dozen. It does better than that this cycle and some of the fifties or sixties or whatever, but... And it's already in the sixties or it has been. If I do just kinda look at it, you you have a very standard crypto cycle, and you have them halving, and it runs again. You know, it's the same stuff, and maybe it not... But people always say I wanna apply this cycle, and then it applies again, and at some point, it won't apply anymore. But that kinda drives a lot of the ancillary behavior in crypto because so much wealth in the crypto world will pile up in that. But you see people trading sequentially across, the different crypto assets with Bitcoin as sort of one of the more stable pools, and then have a bunch of other tokens on top of that, and then you have other companies. And so I think we have these boot bust cycles that also draw founders in and out. One thing that I think is really excite... Interesting is there's a certain type of technical founder that depending on when they graduated from school, they either went into crypto or they went into AI, and it's like a six month difference. Literally. It really is. They've actually done analysis of this in the context of
00:30:13 · Aditya Agarwal, Elad Gil
[00:30:13] S1: It's like pre SPF, post SPF. You know? [00:30:15] S0: Yeah. You know, seriously, it really it really is... Or pre ChatGPT, post ChatGPT, right, or whatever it is. And it really swings people's outcomes in careers, and there's actually really interesting data in general that I remember seeing years ago where if you graduate into a recession versus a boom cycle, you make dramatically less over your entire career. And the reason is you have fewer opportunities. You have your jobs. You never manage to get bored early. Like, all the things that come with a boot cycle, miss. And all the things and the hardness of the bear cycle you hit. [00:30:49] S1: You're just also less optimistic. Like, I I read that same study, which is that you expect the world to wish me and ruin my life. I think you're doing okay. [00:30:58] S0: So Never mind. [00:31:00] S1: You know, I I think you just expect that, like, hey. Your your expectation out of the world is that growth is low and that there are larger systemic forces which are actually preventing you from flourishing as opposed to kind of like YOLO. It's just like, you know, you go up and up. Right?
00:31:15 · Elad Gil
[00:31:15] S0: Yeah. And so I think that same thing is... I'm not saying financially. I just mean in general, there's... There was that crypto AI divide. Yeah. And you had founders who started crypto companies, and they stuck with them in many cases. And then you had other people who started AI companies, and it was a few month of difference. It's the exact same type of person. And so I think that's kind of fascinating. And, you know, there's all these weird cocoa defects in the medic of... Like, there's all this stuff we don't talk about that's kinda happening in the background. I think another interesting question, which we can skip if you don't wanna talk about it, but I think, you also see Silicon Valley kinda moving these five to seven year cohorts of people. And so there's this interesting question of, like, how do people maintain relevance across multiple cycles or portazitabine? Or why do two people seem equally good have very different outcomes? Is it them? Is it probabilistic? Is it... You know, if you run a Monte Carlo simulation in a person's life, you rerun their life a billion times as the expected outcome of that person. And how do these circumstances impact that? So there's all these really interesting things that you can think about in terms of tell. I'll give you another talent question. Jensen Huang, amazing CEO, brilliant, amazing strategist, like, so good at, like, running the company and contouring way. Biggest market cap in the world, like, high EQ, very technical, like, amazing. For years, he was running a $6,000,000,000 company. Right? For thirty years. For thirty years, he ran a company, and he was this hidden gem of a billion CEO who eventually steered things into AI and, you know, game over. How many other Jensen Huangs are there out there running public companies where it's just in a market that right now isn't boomy, but they're exceptionally talented? Is it zero? Is this uniquely good and that may be trivial? Is it a 100? Is it... Like, how do you identify these people, and how do you unlock them? And so I think there's these really interesting sort of talent questions. Like, what is the aggregate available talent on the planet and how you harness it in different ways that I think is kinda never discussed really.
00:33:08 · Aditya Agarwal
[00:33:08] S1: I mean, curious to get your... You kind of mentioned one of the central questions is, like, a value chain. Are there not enough good opportunities, or are there not enough, you know, good founders? Where do you kind of, like, you know, almost fall at?