No Priors: Artificial Intelligence | Technology | Startups
00:12:49 · Elad Gil, Sarah Guo
[00:12:49] S0: Yeah. It's every six months. [00:12:50] S1: Okay. So every six months. Great. Are you capturing value as costs fall and capabilities increase? Because if you're the on the wrong side of this secular change and you can't get to the other side of it, you should in fact, like, sell. You don't have good ideas about how to be on the right side of history. So I I think that's a question people should ask them ask themselves. And then if you think about our our friends at Cursor, is the way you want to compete capital compute access? And is it perhaps a maximally valuable point in time? That's an interesting question. But I think that, like, you know, more broadly, it's a it's a I feel like it's a very personal and very interesting risk management question. I do think people should ask themselves. Right? Like, the idea that there is pride around, like, never considering this as nonsense. The situational awareness situation is a good reminder that everyone has to stay alive to profit as well. Hedge funds are different than companies. They have to survive to compound. But I I think even just the premise of, like, you need to match your financing structure to your thesis horizon and then be able to, like, continually finance the company to the promised land of whatever you're trying to
00:14:04 · Elad Gil
[00:14:04] S0: do. Yeah. I think the financing part though is gonna be there because basically what's happening, because of this rapid rise of $3,000,000,000,000 plus companies in a short timeframe, an enormous amount of venture capital is starting to get returned. And that means people are raising bigger and bigger funds, and they need to put it somewhere, they're going to put it against trillion dollar companies of the future. And so I do think we're going to see an ongoing rise in valuations most likely over the next year or two, much more than we've seen today. And obviously, there'll be some great things in there and there'll be a bunch of stuff that doesn't deserve it, but I actually think financing is going to get easier, not harder. And so I'd view it less as financing and more what do you think is the true likely expected outcome of your company? Not what investors are telling you, not what the press is telling you, not what Twitter is telling you. Just sit down and run the math. And then remember that at some point, you'll probably trade at 10x or something, maybe 15x. And so then the question is, what is your thing going to be worth? And remember, eventually things slow down in terms of compounding too. And you can decide where that slowdown happens. But you kind of do that math. You do future dilution. You look at your potential outcome. You look at years of work it'll take to get there. And you can come to a conclusion because there's two types of opportunity costs that are risk management. There's risk management against the value of the thing you're doing. But the biggest opportunity cost is your time. Your most productive years of your life are on the line right now. And you can either walk away with a good amount of money to go to the next giant thing. Now having done it before, they're going to work with you again, etcetera, etcetera, or you can roll the dice. And you can decide to roll the dice. That may be the right answer. And again, for some companies, absolutely, you should do that. But for others, may be, hey, actually, now is maybe the time to go. Secondary is an intermediate option, which I actually don't think is always that great because it sells for some short term needs, but it doesn't actually create a solution. And you see a lot of people from 2020, 2021, store running companies five years later that aren't working.
00:15:59 · Elad Gil
And think of that five or six year period where they've been locked up when all the AI change happened. What is the cost of that to a great founder? So I think there's that kind of cost that people don't really talk about as much, which I think is the real cost. That's your lifetime cost. Right? And you only live once, and it's a short life. And so do you want to eventually be working on something that's going to continue to struggle, that's overcapitalized, that has runway for the next ten years or not? And that's where you end up. That's what happened with the 2020, 2021 cohort. There's tons of people running these companies that aren't working still. And we forgot about them because we're talking about AI all the time.
00:16:34 · Sarah Guo
[00:16:34] S1: That is a huge waste. My I think my point was really that even if there are lots of dollars still rotating into venture or being produced by these huge outcomes over, you know, now and over the next year or two, private markets don't have to be rational or right for long periods of time. Right? And so being smart about your ability to finance the company is the equivalent of avoiding margin calls. Right? And I I you know, some founders who are working on something that requires a, like, a technical structural point of view about how the market resolves can get very frustrated because investors will believe something that they think is wrong or stupid for a long time. And it's just the job of the founders to go navigate that narrative or that set of beliefs. And if if they think it's untenable or if they think they're, like, down some, wasteful path of their time, as you described, then they should sell the company. But it could be worse. I mean, founders, they have the concentration risk, but they could be hedge fund managers facing retail, irrational acts in the market, and redemptions next quarter. So it's just different different environment. But I don't think it's as simple as, like, financing is now free. I do think it is gonna skew, as you said, toward scale of opportunity naturally.
00:17:52 · Elad Gil, Sarah Guo
[00:17:52] S0: Perceived scale. Perceived Yeah. Perceived scale is the important angle. So the other thing a lot of people out here are working on or talking about is if you talk to people at the labs, there's this enormous manic energy right now. We're six months ish or towards the end of the year to be completely done with code, like, as a solved problem. And then we'll probably hit some form of light RSI by end of next year. And at that point, you'll have models training big chunks of the models themselves. I think it's probably more post training initially, maybe it could impact pre training over time more quickly as well. And because of that, many people believe, hey, if I have a year, year and a half left of productive work in my career, I should be working sixteen hours a day because every week is, you know, 2% of all the time I have left to be productive before I get displaced by AI. What do you think of that? [00:18:45] S1: Like, do I believe it, or what happens if it's true? [00:18:48] S0: Do you believe it? [00:18:49] S1: I think the idea that the models can improve their own training if the leading scientists working on this believe it, and it's an extension of what we are already seeing in code and math, of course, you should believe it. Right? [00:19:07] S0: Yeah. But on that timeline.
00:19:07 · Elad Gil, Sarah Guo
[00:19:07] S0: Yeah. But on that timeline. [00:19:08] S1: The data I have is that a number of very smart and even very self aware research scientists have felt that, you know, there was some knee in the curve on recursive self improvement or ASI eighteen months away, every eighteen months for the last five years. So how good of it is a predictor? It's not clear. I think the, like, the extension from code to training code to data pipeline work, is way easier to believe. The question of like how you are going to go gather that data for less verifiable, more complex domains? Or do you run into the actual constraints on the, like, physical compute accessibility side? Like, I think that's probably more of a limiter than, this being algorithmically possible.