The Exchanges

Q NVIDIA has moved into larger and larger, let's say, like, unit of support for customers. I think about it going from single chip to, you know, server to rack, and VL-CNII. How do you think about that progression? Like, what, what's next? Like, should NVIDIA do full data center?

A In fact, we built full data centers. The way that we build everything, unless you're building If you're developing software, you need the computer in its full manifestation. Um, we don't, we don't build PowerPoint slides and ship the chips, and we build a whole data center. And until we get the whole data center built up, how do you know the software works? Until you get the whole data center built up, how do you know your, you know, Your fabric works and all the things that you expected the efficiencies to be, how do you know it's going to really work at the scale? And, and that's the reason why, that's the reason why it's not unusual to see somebody's actual performance be dramatically lower than their peak performance as shown in PowerPoint slides. And, and, and it's, computing is just not used to, it's not what it used to be. You know, I say that the new unit of computing is the data center. That's to us.

AI assessment note: “In fact, we built full data centers.”

Q tech world. And you're really pushing forward what is potentially one of the most important waves of all time in technology, which is AI. Um, 10 years from now, 20 years from now, looking back, are there any specific things that you want to accomplish, either through the context of the company or more broadly, or other things that you, looking back 20 years from now, you really hope happen?

A That's, that's a good question, and in fact, that's a good way to think. The best way to think about what to do today is to go out into the distance, stand in the future and look back. You guys probably do the same. And so I'll, I'll go out 10 years and look back at what did I wish I had done then? Then do it now. That's the answer. And so there, there are a couple of industries we really believe we can make a contribution to. One of them is healthcare and drug discovery. This is a problem that is computationally, numerically, insanely complex. The number of combinations is beyond the number of atoms in the universe. It's a very large problem space. And we finally have the necessary tools to maybe, you know, Chip away at that. And at the very minimum, we now have the ability to understand the language of And now potentially the meaning of amino acids and sequences and right proteins and chemicals and such. And so if you can understand, uh, the structure, you can understand the language, you can understand the meaning of the problem space, you might have a chance of solving it. And, and so I, I think one, we're very excited about that. I'm really, really hoping that, that, um, uh, we, Uh, go create a foundation model for, for, uh, multi-physics for climate science. And, um, so that we can ask a questions, you know, if, if these human factors and these human drivers, and we make …

AI assessment note: “there are a couple of industries we really believe we can make a contribution to”

Q And you were a star at LSI with your co-founders. At what point did you know, I have to start a company?

A Uh, it wasn't my idea. It was theirs. Uh, Chris and Curtis wanted to, wanted to leave Sun. Uh, they had their own reasons, and, uh, I was doing really well at LSI Logic, and, and I enjoyed my job, and we have two kids, Lori and I, and, um, I, and, you know, just like, just like you, they, they wouldn't stop hounding me, and, and they said, hey, you know, we, we want to start this company, and we really need you to come along, and, And, uh, and I told him that I really needed to have a job and, and, um, and so anyways, uh, they need to figure out what to do. Uh, at the time, the, the, the Valley was, was, uh, um, the way of designing computers was rather, rather split between, uh, general purpose computing, uh, versus using accelerators. And, um, about 99% of, of, uh, of, uh, the Valley was, uh, believed in general purpose computing. And about one percent believed in acceleration. And, um, and, and for 25 years, 99% was right. So we, we, uh, we decided to start a company on, on accelerated computing. Um, and, you know, at the time, the only thing you could really do with accelerated computing is, is, uh, find applications or find problems that were barely solvable or unsolvable by general purpose computing. And that's kind of what we dedicated our company to do. To solve problems that normal computers can't. And if you, if you follow that mission to its limit, um, it led us to, …

AI assessment note: “it wasn't my idea. It was theirs.”

Q in the past that NVIDIA has done things like trained models, and you've done some really interesting things there. Is that going to be an increasing part of what you do in the future, or are you mainly focused on the chip side, or how do you think about that mix of helping to push forward some research as well as, you know, being the underlying platform for the industry?

A Well, we're a computing platform company, and we have to go, go up the stack as far as we need to. So that developers can use it. And so the question is, what is a developer? And in the beginning, of course, a developer is somebody who, uh, controls their own operating system. And, and so in those days, we might only have to go as far up as device drivers or the layers slightly underneath that somehow, um, that, that, um, uh, to, to enable developers. But, but for scientific computing and all these different domains, the developer is actually using maybe a solver. And they need the algorithms of that domain to be somehow expressed in a way that could be accelerated, which is the reason why when we moved into these multi-domain physics problems, we realized that we have to develop the algorithms themselves. Because the algorithms of, of, of, um, solving a problem relates to the computer architecture that's underneath. And if the architecture is CPUs, Connected through MPI and, you know, ethernet or whatever it is. Um, that algorithm is surely very different than thousands of processors that's connected by fabric inside one GPU and thousands of GPUs inside a data center. So obviously the, the algorithm has to be reframed and refactored. And so our company got very good at designing computer algorithms. It could be for particle physics or fluid dynamics or, and then of course, one…

AI assessment note: “we're a computing platform company, and we have to go up the stack”

Q Do you think that's what happens, like, earliest on? Because in, in markets that are this immature, it seems one of the fastest paths to market could be full verticalization, right? Because you just have control of iteration speed.

A The different, the difficulty, difficulty of verticalization for technology that is, that is general purpose is that you don't have the R&D scale to build a general purpose technology. Now, of course, open source helps that tremendously. Which is the reason why you're going to see a, you know, a big surge of vertical opportunities in AI in the next several years. My, my prediction would be over the course of the next five years, the excitement is going to be verticalization. Notice we were excited about open evidence. We're excited about Harvey. We're excited about cursor. Cursor is, is a horizontal, but it's kind of a horizontal vertical, you know? And so, I'm, I'm, I'm super excited about all the verticals. You know, a lot of people said, yeah, AI is going to get so, God AI is going to get so good that all these rapper companies are going to be obsolete. It's just, it misses the big point. You know, the, the reason why you could talk about, the reason why somebody can talk, talk about, somebody who's creating technology could talk about the life of a surgeon is because they've never been a surgeon. The reason why somebody who builds an AI and talks about the life of an accountant and a tax, You know, a tax expert because they've never been a tax expert, you know? And so, so I, I think these, you know, the reason why somebody could talk about being a busboy without being a bus…

AI assessment note: “over the course of the next five years, the excitement is going to be verticalization.”

Q Could you talk about how you think about employment and jobs and sort of what people are saying and what you think the real narrative is there?

A Maybe what I'll do is I'll, I'll ground it on, uh, three points in space, three points in time. Now, Uh, maybe, uh, uh, very near future, and then some, some point out, out in the distance, and, and maybe, maybe some counter narratives, um, something else to think about with respect to jobs in the near term. Uh, one of the most important things is that, that AI is not just, AI is software, but it's not pre-recorded software, as you know. For example, Excel was written by several hundred engineers. They compiled it. It's pre And then they distribute it as is for several years. In the case of AI, because it takes into the context, what you asked of it, what's happening in the world, right? Contextual information. It generates every single token for the first time, every time. Which means every time you use the software and, and everything that we do, AI is being generated for the first time ever. Just like intelligence. Our conversation today relies on some You know, ground truth and some knowledge, but it's every single word is being generated for the first time here. The thing that's really, really quite unique about AI is that it needs these computers to generate these tokens every single time. I call them AI factories because it's producing tokens that will be, you know, used all over the world. Now, some people would say it's also part of infrastructure. The reason why it's …

AI assessment note: “three new industries have emerged. Number one, well, three new type of”