00:23:52 · Elad Gil
[00:23:52] S0: You know, I think there's two types. There's three types of failure mode. I think one type of failure mode is your thing isn't working and you stick with it too long. And it's back to, like, you should eventually iterate into something really working. And if it's taking two years and nothing's working, you should you should rethink. In most cases, if not all. Again, there's counterexamples. The second thing is there's really bad advice given to things that are working that are working for people who've never had anything work. So what used to happen is you'd get advice from founders who'd seen product market fit to founders without it, and they say go hire a sales team and scale really fast and burn a lot of money into it, and that was terrible advice. But it goes the other way. If something's really working and people tell you to buy, stay as lean as possible. They'll hire executives. Don't scale. That is awful advice. And that's where a lot of companies break for a while and other people, competitors who are gonna scale come in. And it just... It's it's, a very common pattern for things that are working that founders don't actually build out their company as a product, and they don't really go for it in an aggressive enough manner. So I think that's a big failure mode. And another failure mode is spending money on stuff that just doesn't matter. You know, you start training a crazy model on something instead of just testing something sheer with an existing, like, soda lab and just seeing if people want it. You know?
00:25:09 · Aditya Agarwal, Elad Gil
[00:25:09] S1: Maybe for that last one, I'm curious. I mean, you're deploying large amounts of capital often to a pretty early stage companies. I guess, for a lot of companies that are raising 100, 200 pretty early on in their, I would say, life cycle, have you found that they actually know how to use it well? It's kind of hard. You know? Like, if you're like,. I mean, maybe you're training the models. Maybe it's all kind of, like, ultimately That's that's I [00:25:35] S0: actually have not funded many things like that. [00:25:37] S1: Okay. I mean, so I suspect that we have the same priors because I think it's quite hard to spend a 100... Like, you know, if you raise a $100,000,000 as a seed round, you can't really pay yourself a $100,000,000. You know? You know, as a founder, you have I mean, you could. Anybody who wants to [00:25:53] S0: see how he ran all his way to her face.
00:25:53 · Elad Gil, Aditya Agarwal
[00:25:53] S0: see how he ran all his way to her face. [00:25:56] S1: You gotta see it in the back. Don't see variation in the back. Some salary. Yeah. But it's it's quite hard because your range of what you can pay yourself is pretty bounded, I think, in terms of what is accepted in Silicon Valley. There's only a certain way in which you can hire and create people. It's not easy to hire well and quake. So often, if you raise, you know, a 100 at 500, I tell people that you can do it, but it requires everything to go right, like, for 12 to 18 months. And if you had any hiccups along the way, it's really hard to zig and zag the company, which is actually opposite of what you'd expect. As most people are like, boy, raised a bunch of money. Now I can, you know, afford to get it wrong. But I actually think you can... It's it's much harder to get things wrong. You might have the money, but you don't actually have room to face instead of my ding. Yeah.
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