No Priors: Artificial Intelligence | Technology | Startups
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.
00:20:00 · Elad Gil, Sarah Guo
[00:20:00] S0: Yeah. I mean, the physical compute basically reinforces an oligopoly market because what it does is it creates a ceiling on the rate of progress any single lab can get if effectively assume the compute is roughly pro rata across the ecosystem to the big labs. And so in the absence of a lack of compute constraints, you're almost having an enforced oligopoly market up to a point, or at least you force closer competition between the players than what exists otherwise, which I think is an interesting artifact of this moment in time. And the question is when does that lift and what does that look like? So, yeah, it's kind of it's a very exciting time. I mean, I always wonder about second order effects of that belief of it's eighteen months away because that does suggest there could be a burnout cycle in eighteen months. Like, I know some people at one of the major labs who for a while, brought up with me, should they get married? Do people want to get married? Should they get married? Because I don't know what happens in eighteen months to the world. It's like, you should get married. You should go ahead. It'll be okay. And so I do think we're living through this very manic, very exciting, very intense work period. So, yeah, it's really fun stuff. [00:21:01] S1: I think it's kind of tragic, man. [00:21:03] S0: Really? Why?
00:21:03 · Elad Gil, Sarah Guo
[00:21:03] S0: Really? Why? [00:21:04] S1: I think the reactions of some really extraordinary research friends to it, it feels a little bit tragic. I think it like I feel like it's psychologically most similar to if people think they're gonna die. Right? Like, how would you spend the last two years of your life? Would you spend it the way you are today, or would you spend it in a very different way is a, like, a not unrelated philosophical question. And so you do have people who are like, ah, like, my contribution is a bit irrelevant given RSI in the next eighteen months. So should I get married? Should I bother to work? Should I travel? Should I only work? And, you know, I just actually think it's a much more stable and satisfying state if people act as if they have. But maybe you think that's blind.