NVIDIA: OpenAI, Future of Compute, and the American Dream | BG2 w/ Bill Gurley and Brad Gerstner

I think that OpenAI is likely going to be the next multitrillion dollar hyperscale company. K. Gents and great to be back, of course, with my partner Clark Tang. You know, I can't believe it's Welcome to NVIDIA. Oh, and nice glasses. Those actually look really good on you. The problem is now everybody's gonna want you to wear them all the time. They're gonna say, where are the red glasses? I can vouch for that. So it's been over a year since we did the last pod. Yeah. Over 40% of your revenue today is inference. But inference is about ready because of chain of reasoning. Yeah. Right? It's about to go up by a billion times. Right. By by by a million x by a by a billion x. That's right. That's the part that most people have, you know, haven't completely internalized. This is that industry we were talking about, but this is the industrial revolution. Honestly, it's it's felt like you and I have had a continuation of the pod every day since then. You know, in AI time, it's been about a hundred years. I was rewatching the pod recently and the many things that we talked about that stood out. The most the one that that was probably most profound for me was you pounding the table that you know, remember at the time, there was kind of a slump in terms of pretraining. Mhmm. And people were like, oh my god. Pretraining. Right. We're at the end of pretraining, we're not gonna we're overbuilding. Yeah. This is about a year and a half ago. And you said inference isn't going to a 100 x, a thousand x. It's gonna 1,000,000,000 x. Mhmm. Which brings us to where we are today. You know? You announced this huge deal. We ought to start there. I underestimated. Let me just go on record. I underestimated. We we now have three scaling laws. Right? We have pretraining scaling law. We have post training scaling law. Post training is basically like, AI practicing. Yes. Practicing a skill until it gets it right. And so it tries a whole bunch of different ways. And and, in order to do that, yeah, you you you've gotta do inference. So now training and inference are now integrated in reinforcement learning. Really complicated. And so that's called post training. And then the third is inference. The old way of doing inference was one shot. Right. But the new way of doing inference, which we appreciate, is thinking. So think before you answer. Yeah. And so now you have three scaling laws. The the longer you think, the better the quality answer you get. While you're thinking, you do research. You go check on some ground truth, and, you know, you learn some things, you think some more, you go learn some more, and then you generate an answer. Don't just generate right off the bat. Right. And so thinking, post training, pre …