00:15:29 · Sam Altman, John Collison
[00:15:29] S1: Correct. We try never to reward people for using a lot of compute. Sure. Like, no token leaderboards. It's actually slightly bad to be high on the token leaderboard, certainly not a promotion. But but it's like... It's a skeptical default, if you can show that you're doing something important, you will have nothing but appreciation. [00:15:46] S0: But at a certain point, this will need to change as... Because you're describing the increasing orders of magnitude, and right now you have a researcher who does some very interesting math for $10,000,000, and you're like, oh, a little pricey, but cool math, and then a $100,000,000. You know, when they're doing the billion dollar math, you're like, look, I'm really happy that you're making so much math progress, but I really need you to cool the jets with the math. [00:16:09] S1: Look, we did let it get out of control for, like, one week on the math, intentionally so, because you have [00:16:13] S0: to be like... And it worked? [00:16:14] S1: Well, you don't know how... You don't know how far it's gonna go until you try. [00:16:18] S0: Yeah yeah. [00:16:19] S1: And I think it's important to have... When there's a new paradigm, I think it's important to let people be fairly unconstrained. So when we did the Navier-Stokes proof, we understood absolutely nothing about the scaling laws of teams, of agents. You know, are you supposed to, like... [00:16:35] S0: The very recent Navier Stokes Yeah.
00:16:35 · John Collison, Sam Altman
[00:16:35] S0: The very recent Navier Stokes Yeah. [00:16:37] S1: Are you supposed to give them, like, the smartest model? Are you supposed to give them, like, many copies, fewer copies, longer rollouts, shorter rollouts? We did not understand that at all. And now, we understand it a little bit. I think we can still understand it, like, much better. But like has happened in every other new paradigm we've discovered, I am confident that we will find our, you know, 10x improvements per year at how we allocate dollars or computer flops, whatever you want measure, into useful things. So I think this is a thing that our company is particularly good at, but also, like, fits an important property of the field, which is let people explore at the limit of what is possible, and then trust that the incredible optimization pressure will figure out how to do this in an efficient way. I... And, you know, directionally speaking, I would rather us be more willing to let people use crazy amounts of compute, and let users have extremely high end plans to figure out the edge of the possible and then figure out how to make that efficient at mass scale. [00:17:43] S0: But when does the optimization function come in? [00:17:45] S1: Well, think we're already doing it. I mean, like, I bet we could redo Navier Stokes at one one hundredth of the cost a year from when it was done. So we did it in, like... [00:17:57] S0: Not just knowing how the proof works, but, like, just with better approaches. [00:18:00] S1: Yes. Smarter models and better approaches. [00:18:02] S0: How much shall the math cost?
00:18:02 · John Collison, Sam Altman
[00:18:02] S0: How much shall the math cost? [00:18:04] S1: Order 10,000,000 per, like... For for, like, Navier Stokes, I think. [00:18:08] S0: Okay. Maybe a little more. 10,000,000 ish. That that order yeah. Yeah. But but for one proof. And so... 10,000,000? [00:18:16] S1: Well, no, no. Because then we kept getting better so fast. Like, we understood more. It. And we spent way less than that on the average one of those. [00:18:22] S0: But, like, hundreds and millions of dollars of math. [00:18:24] S1: Yeah. Yeah. Yeah. That kind [00:18:25] S0: of work. Okay. Interesting. [00:18:26] S1: But that include, like, that includes, like, much more other discovery than we... What we have published. Yes. So it was, like, maybe we spend We that [00:18:33] S0: more than the 700? We do. Of course. When did get those? Second album?
00:18:33 · John Collison, Sam Altman
[00:18:33] S0: more than the 700? We do. Of course. When did get those? Second album? [00:18:38] S1: We are trying to learn how to... This is like a real tension. I think on one hand, very clearly, and I feel unequivocal on this point, humanity deserves fundamental knowledge, and we should not be sitting on a major fundamental science discovery. Like, the world deserves that and gets to know it. And I think that outweighs another very important equity, which is if you are a scientist, your career and, you know, a significant amount of your enjoyment of fulfillment in life comes from discovering science.: Yeah.: And so if we're going to continue to do this for math, or begin to do this for other fields, then I think we more than owe it to the people that work in those fields to figure out, you know, what is the solution space where the world gets benefit from the knowledge, and they also get the best version of the future that they can have. So we are working through that right now. I did not... I don't know. [00:19:40] S0: I think you may be too contrite. I did not interpret that moment as particularly bad for AI, where you had... I think everyone saw the... Mathematicians.... The union up in arms, and I think everyone else says, yeah. I mean... I don't... As I said... Have you paid attention to 2026?
00:19:40 · John Collison, Sam Altman
[00:19:40] S0: I think you may be too contrite. I did not interpret that moment as particularly bad for AI, where you had... I think everyone saw the... Mathematicians.... The union up in arms, and I think everyone else says, yeah. I mean... I don't... As I said... Have you paid attention to 2026? [00:19:56] S1: As I said, I don't think it was bad for AI, and I think I think on net, the world deserves to understand Yeah. How the universe works. But I do have empathy for the math... Like, I think we need to do this. I think we should do this. I think, like, if we can go understand how to, you know, a theory of all of physics or how to cure a bunch of diseases Mhmm. Like, clearly, that is knowledge the world should have. But I would like to figure out a way to do this that is as sensitive to the communities that have driven these fields forward for centuries as we can. And it's just a... It's a hard thing. [00:20:31] S0: I wasn't that worried about the math. I thought it was [00:20:33] S1: great. But you agree that mathematicians are not that happy? [00:20:35] S0: Yeah. But it's fine. Like, the longshoremen union weren't happy about the poor automation. Why not? The old crew member. [00:20:44] S1: No. I I do. I... Why do I feel differently about this? There was one weekend when we got a new model, and in... I'm actually probably not supposed to say this, but I will since we're drinking. [00:20:53] S0: Exactly. We're half pint in. Yeah. Perfect time for...
00:20:53 · John Collison, Sam Altman
[00:20:53] S0: Exactly. We're half pint in. Yeah. Perfect time for... [00:20:55] S1: The name of the channel was model name Dash Hold My Beer. And people were just throwing that at the hardest problems they could. And people just, like, didn't think the model was gonna do these things, and and we had this collective experience of people's, like, hardest research problems from their academic career. The model was just knocking down one after the other in many different fields. Was incredible to watch. Kind of two nights in a row, was up most of the night because I was so excited that I couldn't go to bed. It was like... Many of those problems that we published, many other things. And I was tremendously excited and happy, but I also had, like, much more sadness and melancholy and, like, a feeling of, like, something ending than I was expecting. So, again, I agree this is the right thing on behalf of the world. And and also, I agree that this is just, like, the shape of reality and nothing that anyone can stop or should stop. But I do have, like, real... [00:21:57] S0: I mean, so wistfulness and nostalgia you can feel. [00:22:00] S1: It's more than nostalgia. Wistfulness.