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

**Jensen Huang** (0:00)
I think that OpenAI is likely going to be the next multi-trillion dollar hyperscale company.

**Brad Gerstner** (0:22)
Jensen, great to be back, of course, with my partner, Clark Tang. I can't believe it's been-

**Jensen Huang** (0:26)
Welcome to Nvidia.

**Brad Gerstner** (0:28)
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, wear the red glasses. I can vouch for that. So it's been over a year since we did the last pod. Over 40% of your revenue today is inference. But inference is about ready because of chain of reasoning.

**Jensen Huang** (0:46)
It's about to go up by a billion times.

**Brad Gerstner** (0:47)
Right, by a million X, by a billion X.

**Jensen Huang** (0:50)
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.

**Brad Gerstner** (0:59)
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 rewatching 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.
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. The end of pre-training. We're overbuilding. It's just about a year and a half ago. And you said, inference isn't going to 100x, 1000x. It's going to 1 billion x. Which brings us to where we are today. You announced this huge deal. We ought to start there.

**Jensen Huang** (1:43)
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, 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. But the new way of doing inference, which we appreciate, is thinking. So think before you answer. And so now you have three scaling laws. 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 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. And so thinking, post-training, pre-training, we now have three scaling laws, not one.

**Brad Gerstner** (2:47)
You knew that last year, but is your level of confidence this year in the inference is going to 1 billion X, and where that will take the levels of intelligence, is it higher? Are you more confident this year than you were a year ago?

**Jensen Huang** (2:58)
I'm more confident this year, and the reason for that is because look at the agentic systems now. And AI is no longer a language model, and AI is a system of language models. And they're all running concurrently, maybe using tools, some of us are using tools, some of us are doing research, and there's a whole bunch of stuff. And it's all multimodality, and look at all the video that's been generated. I mean, it's just crazy stuff.

**Brad Gerstner** (3:23)
It really brings us to kind of the seminal moment this week that everybody's talking about the massive deal you announced a couple of days ago with OpenAI Stargate, where you're going to be a preferred partner, invest a hundred billion dollars in the company over a period of time. They're going to build 10 gigs. And if they used Nvidia for those 10 gigs, that could be upwards of 400 billion in revenue to Nvidia. So help us understand, just tell us a little bit about that partnership, what it means to you, right, and why that investment makes so much sense for Nvidia.

**Jensen Huang** (3:53)
So, first of all, I'll answer that last question first. And then I'll come back and present it my way. I think that OpenAI is likely going to be the next multi-trillion dollar hyperscale company.

**Brad Gerstner** (4:11)
Why do you call it a hyperscale company?

**Jensen Huang** (4:13)
Hyperscale, like Meta is a hyperscale. Google is a hyperscale. They're going to have consumer and enterprise services, and they are very likely going to be the world's next multi-trillion dollar hyperscale company. And I think you would agree with that. If that's the case, the opportunity to invest before they get there, this is some of the smartest investments we can possibly imagine. And you have to invest in things you know. And it turns out we happen to know this space. And so the opportunity to invest in that, the return on that money is going to be fantastic. So we love the opportunity to invest. We don't have to invest, and it's not required for us to invest, but they're giving us the opportunity to invest. Fantastic thing. Now let me start from the beginning. So we're partnering with OpenAI in several projects. The first project is the buildout of Microsoft Azure. We're going to continue to do that. And that partnership is going fantastically. We have several years of buildout to do, hundreds of billions of dollars of work just to do there. The second is the OCI buildout. And I think there's some 5, 6, 7 gigawatts that are about to be built out. And so, working with OCI and OpenAI and SoftBank to build that out. Those projects are contracted, we're working on it, lots of work to do. And then the third is CoreWeave. And so, all of CoreWeave 4, I'm talking about OpenAI still. Everything in the context of OpenAI. And so, the question is, what is this new partnership? This new partnership is about helping OpenAI, working, partnering with OpenAI to build their own self-build AI infrastructure for the first time. And so, this is us working directly with OpenAI at the chip level, at the software level, at the systems level, at the AI factory level to help them become a fully operated hyperscale company. I mean, this is going to go on for some time. It's going to supplement the amount of, you know, they're going through two exponentials, as you know. The first exponential is the number of customers is growing exponentially. And the reason for that is the AI is getting better, the use case is getting better, just about every application is connected to OpenAI now. And so, they're going through the usage exponential. The second exponential is the computational exponential of every use, right? Instead of just a one-shot inference is now thinking before it answers. And so, these two exponentials compounding their compute requirements.