BG2Pod with Brad Gerstner and Bill Gurley
NVIDIA: OpenAI, Future of Compute, and the American Dream | BG2 w/ Bill Gurley and Brad Gerstner
26 Sep 2025
Full Episode
I think that OpenAI is likely going to be the next multi-trillion dollar hyperscale company.
Jensen, great to be back, of course, with my partner, Clark Tang. You know, I can't believe it's been- Welcome to NVIDIA. Oh, and nice glasses. Those actually look really good on you. The problem is now everybody's going to want you to wear them all the time. They're going to 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 ready- It's about to go up by a billion times. Right, by a million X, by a billion X. That's right, that's right. That's the part that most people haven't completely internalized. This is that industry we were talking about, but this is the industrial revolution.
Honestly, it's felt like you and I have had a continuation of the pod every day since then. In AI time, it's been about 100 years. I was re-watching the pod recently and the many things that we talked about that stood out. The one 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 pre-training?
And people were like, oh my God. The end of pre-training. Right, the end of pre-training. We're overbuilding. This is about a year and a half ago. And you said, inference isn't going to 100x, 1,000x. It's going to 1 billion x. Mm-hmm. Which brings us to where we are today. You announced this huge deal. We ought to start there.
I underestimated. Let me just go on record. I underestimated. We now have three scaling laws. We have pre-training 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 in order to do that, you've got to 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.
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