The Exchanges, every show

Q Amazing. Where does physical AI show up first in a way that's really economically real? Are you seeing that already?

A We conjectured that, ah, that robotics was going to come along and decided that the first application of robotics that has both a large enough market Um, relatively standardized technology so that we could scale and get the flywheel going, um, and has real economic value was, uh, self-driving cars. And so, uh, inside Waymo, uh, our chips from NVIDIA, uh, at, at Tesla, we were in the car, uh, now we're in the data center. Um, uh, Mercedes, we're in the data center, we're in the car, we're the software stack. Uh, we, uh, worked on Alpamayo, and we open sourced it, and the reason why we open sourced the self-driving car stack is because you need it for agriculture, you need it for mail delivery, you need it for warehouse AMRs. There's so many different ways that you could apply, um, uh, autonomous navigation, uh, and none of those markets are big enough to be a self-driving car market, and we thought it was sufficiently diverse that we would create the whole stack for it. And so we're working with autonomous vehicles in all kinds of different places. Our robotics business, autonomous vehicle business, basically physical AI business is probably almost, it's like ten billion dollars, so it's really, really big already. Um, likely this will be one of the largest industries in the world, and, um, uh, it'll take longer than a couple, two, three years. It'll take less than 10, and so th…

AI assessment note: “first application of robotics that... has real economic value was, uh, self-driving cars.”

Q Is that part of the thrust behind NVIDIA being so involved?

A I think, well, first of all, I, I need to understand agents because agents is the new software, and how is this new software processed matters a lot to computer architecture. And the, the more intimate we are about, um, the nature of agents and how it's different than, than, um, uh, chatbots, which is how different than, than, um, maybe inference in the very beginning, however we think about these processing layers, the more intimate we are about the nature of the processing, the better we could design systems. We, we kind of have to live in the future five to 10 years because it takes three or so years just to build a system. It takes a couple of years to ramp it up and you're dealing and you would like them to be able to use the computer for 10 years after. And so you kind of have to live in the future for a while. And so agentic systems for us at the first principles is just what is the workload? What's the algorithm? How is it going to evolve? Where are the bottlenecks? You know, where are the MDOS laws problems? And, um, uh, how does it scale? Uh, what happens to concurrency? How do you deal with sandboxes? Um, how do you deal with MCP? How do you deal with, you know, working memory, long-term memory? How do you have all these autonomous systems, asynchronous systems working all the time? And so what kind of design architecture makes perfect sense for that? And so we have …

AI assessment note: “I need to understand agents because agents is the new software”

Q at the time, I, I think most people, even people in tech were like, What is this? Were you involved in it at all? Like, you know, because you were so connected to the researchers, to Ilya, taking that talent out of Google and Facebook, to be blunt, but reseeding the research community and opening it up, um, was such an important moment. Were you involved in it at all?

A I wasn't involved in the founding of it, but I knew a lot of the people there, and, um, uh, Elon, of course, uh, I knew, and, uh, uh, Peter Beal was there, and Ilya was there, and, Uh, we have, we have some great employees today that were there in the beginning, and I knew that they needed this amazing computer that we were building, and we're building the first version of the DGX, which, you know, today when you see a hopper, it's 70 pounds, 35,000 parts, 10,000 amps, but DGX, the first version that we built was, uh, used internally, and I delivered the first one to OpenAI, and that was a fun day, but most of our success was Aligned around, um, in the beginning, just about helping the researchers get to the next level. I knew it wasn't very useful in its current state, but I also believe that in a few clicks, it could be really remarkable, and that belief system came from the interactions with all these amazing researchers, and it came from just seeing the incremental progress. At first, the papers were coming out every three months, and then, then papers today are coming out every day, right? So you could just monitor the archive papers, and I took an interest in learning about the progress of deep learning, and, and, and to the best of my ability, read these papers, and you could just see the progress happening, you know, in real time, exponentially in real time.

AI assessment note: “I wasn't involved in the founding of it, but I knew a lot of the people”

Q we, I just really resonate with your story. I think that everyone here, I mean, would love the wisdom of, you know, your journey coming here. I mean, what should a young person learn now, given all the things that you're seeing, all the algorithms that are going to take hold in society? Um, what should a young person learn now that will still matter based on what you're seeing?

A Well, some of the things that I saw today and some of the starters I met today was really, really quite, quite, um, encouraging. And, and, and the thing that, that, um, the big takeaway is, of course, the simple stuff is going to get automated away. And when I say simple stuff, I mean software, you know, coding. And the idea that you would, you would do a, you would solve a problem by sitting in front of a computer and you're, you're, you're actually writing, you know, writing code, that concept is obviously going to get automated away. Um, you know, in my generation, when I was, when I was growing up, we had to do long division. I mean, for God's sakes, who has to learn long division, you know? And so that got coded away. That got automated away. And so I think the simple stuff is going to get automated away, but the hard problems, the hard sciences, um, physics, chemistry, biology, uh, you know, computer science, uh, computer engineering, systems thinking, Uh, you know, all, and, and, and particularly the domains that are intersecting, uh, those hard problems will never go away. And so AI is just an incredible tool that helps us become even more ambitious, even more, um, impatient about solving these extraordinarily large and incredibly hard problems, uh, than before. And so, you know, if you, if you look at my generation, when I first graduated, A chip designer would design …

AI assessment note: “the hard problems, the hard sciences, um, physics, chemistry, biology”

Q Was there ever any thought in your mind that maybe we're too early? We, we might be the ones who actually make this revolutionary technology, but we might not be the ones to bring it to the promised land.

A Yeah, all the time, but that's, you know, then there was 900 other smart strategies and, you know, the list of new ideas that we came up with to keep the company alive and, you know, successful for just another few more days, and It was countless. And so you're solving the problem both in making sure that you stay alive long enough to proliferate this technology everywhere, looking for every possible way. You're making it easier and easier for people to use this technology. You're teaching people to do it. You're talking to software developers, and you're saying, hey, you know that imaging software that you had? Maybe Photoshop, for example, or some video imaging system for broadcast. Can we modify that so that it runs on CUDA?

AI assessment note: “Yeah, all the time, but that's, you know, then there was 900 other smart strategies”

Q kids gonna do in, in five years? What are the jobs gonna be? I mean, if law firms are using AI for discovery, if consulting firms are using AI for, you know, what, what an entry level job would be if finance firms, et cetera, et cetera. Would you argue that that's a lack of imagination, that we actually are reaching those conclusions because we can't think beyond that scenario?

A Partly. I believe the opportunities, the potential for a new college grad in computer science or computer engineering or software engineering or chip design today is far, far greater than the opportunities and potential when I came out of school. The tools they have to work with is a billion times more capable. It's super highly automated already today, and yet they're busier than ever, and the reason for that is because we have ideas of the things that we want to build that we didn't conceive of at the time without the tools that we have. AI is going to help this next generation of new college grads achieve greater things, build greater things, not take their jobs. For us to scare them into not even want to go to college, Ok, not even wanted to be a computer scientist is a disservice to society. You're not saving anybody. You're talking a whole bunch of people that out of professions that we need in the future.

AI assessment note: “Partly. I believe the opportunities, the potential for a new college grad”

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 You believed that, like, kind of a version of the, was it the Jerry Sanders real men have fabs? Like, you need to do the whole stack. Like, you got to do everything, and that LSI Logic changed you.

A What LSI Logic did was, was, uh, realized that you can express Um, transistors and logical gates and chip functionality in high-level languages. That by raising the level of abstraction in what is now called high-level design, it was coined by Harvey Jones, who's on NVIDIA's board, and I met, met him way back in the early days of Synopsys. But, but during that time, there was this belief that you can express chip design in high-level languages. And by doing so, you could take advantage of optimizing compilers and optimization logic and, and, and tools, um, and, and be a lot more productive. That logic was so sensible to me, and I was 21 years old at the time, and I, I want to pursue that vision. Now, frankly, that, that idea happened in, in, um, uh, machine learning, it happened in, you know, software programming, and I want to see it happen in digital biology so that we can, we can think about, uh, biology in a much higher level language. Uh, probably a large language model, um, would be the, the way to make it, make it representable. That transition was so revolutionary, I thought that was the best thing that ever happened to the industry, and I was, I was really happy to be part of it, and I was at ground zero, and so, so I, I saw one industry, um, change, revolutionize another industry, and if not for LSI Logic doing the work that it did, uh, synopsis shortly after, then wh…

AI assessment note: “That logic was so sensible to me, and I was 21 years old”

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 you're a publicly traded company, your stock price was terrible, you probably had a lot of pressure from investors. What made you and the people that you worked with say, we're going to keep our heads down and keep going? Because it was a long time. We're talking Six, seven, eight years on this thing that nobody understood and was, and had zero, not zero, but very little commercial success.

A Well, that's, that's when CEOs had to be CEOs. We believed on first principles. This should be quite useful. And I had to believe that it's quite useful. Now the question is, what's the strategy for creating this new architecture for computing that everybody would be able to enjoy? And the problem with computer architectures is this chicken or the egg problem. Let's say you created a brand new architecture. It's incredible. It's the most amazing thing in the world. But computers are built to run software, and if your install base is not large enough, it doesn't attract software developers, because developers want to program on large install-based computers like iPhone and PC, and, and so the problem is, even though we believed that this architecture was going to be incredible, that CUDA was going to be everywhere, Or could be everywhere. How do you get it everywhere? And if you don't get it everywhere, how do you attract the developer? And if you had no developers, who would write the killer app? And if there's no killer app, then why would people buy it? And so the answer was very simple. It was literally sitting in front of us. And it just required enormous sacrifice. The answer was, let's use GeForce, which is the GPU that is now everywhere in the world used for playing video games.

AI assessment note: “We believed on first principles. This should be quite useful.”

Q Did you ever feel like, oh man, we might have invested ahead of the demand for machine learning since we're like a decade before the whole world is realizing it?

A I guess yes and no. You know, when we saw deep learning, when we saw AlexNet, And realized its incredible effectiveness in computer vision. We had the good sense, if you will, to go back to first principles and ask, you know, what is it about this thing that made it so successful? When a new software technology, a new algorithm comes along, and somehow leapfrogs 30 years of computer vision work, you have to take a step back and ask yourself, but why? And fundamentally, is it scalable? And if it's scalable, what other problems can it solve? And there were several observations that we made. The first observation, of course, is that if you have a whole lot of example data, you could teach this function To make predictions. Well, what we've basically done is discovered a universal function approximator, because the dimensionality could be as high as you wanted to be, and because each layer is trained one layer at a time, there's no reason why you can't make very, very deep neural networks. Okay, so now you just reason your way through, right? Okay, so now I go back to, 12 years ago. You could just imagine the reasoning I'm going through in my head that we've discovered a universal function approximator. In fact, we might have discovered with a couple more technologies, a universal computer that you can teach.

AI assessment note: “I guess yes and no. You know, when we saw deep learning, when we saw AlexNet”

Q Ok, so wait, wait, first question. Was that the plan all along? Like, when, when did you realize?

A I realized, I didn't learn about it until it was too late. We should have implemented all three too, yeah. But, but we built, we built, and so we had to make the best of it. That was really an extraordinary time. Remember, Revo one 20 was mv three. NV-one and NV-two were based on forward texture mapping, no triangles but curves, and it tessellated the curves, and because we were rendering higher level objects, we essentially avoided using Z-buffers, and we thought that that was going to be a good rendering approach, and turns out to have been completely the wrong answer. And so what Revo Run-Twenty-eight was, was a reset of our company. Now remember, at the time that we started the company in 1993, We were the only consumer, three D graphics company ever created, and we, we were focused on transforming the PC into an accelerated PC because at the time, Windows was really a software rendered system. And so anyways, Riva one, 28 was a reset of our company because by the time that we realized we had gone down the wrong road, Microsoft had already rolled out DirectX. It was fundamentally incompatible with Nvidia's architecture. 30 competitors have already shown up, even though we were the first company at the time that we were founded. So the world was a completely different place. The question about what to do as a company strategy, at that point, I would have said that we made a …

AI assessment note: “I realized, I didn't learn about it until it was too late.”

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 All right, so I want to jump ahead a little, because in the late nineties, NVIDIA developed a new technology called parallel computing, and this basically gave your chips the ability to perform multiple calculations at the same time, multiple tasks, right? But this was another gamble, because I think a lot of other companies had tried and failed to produce parallel computing chips, right?

A Um, uh, during that time, there were all kinds of different processors being created, and, um, people were trying to come up with new ways of doing computation, and then we realized that computer graphics, if it was just beautiful, but the world was static, it was hard to create beautiful and immersive worlds, and so you really need to find a way to bring physics into that virtual world so that, you know, waters would flow and Yeah. You know, leaves would blow in the wind, and explosions would look like explosions, and, and so we would, we would try to use the processor, which was incredibly parallel, to express, you know, the types of algorithms that represent real-time physics today, and so that was really the beginning of our journey down that world of general purpose programmability. Meanwhile, uh, scientists around the world noticed That NVIDIA's processors were super powerful.

AI assessment note: “we would try to use the processor, which was incredibly parallel, to express”

Q So what, what do you think about the end state of open source? Do you see this decentralization of architecture as well and decentralization of compute to support open weights and a totally open source approach to making sure AI is broadly available to everybody?

A I believe we fundamentally need Models as a first class product, proprietary product, as well as models as open source. These two things are not A or B. It's A and B. There's no question about it. And the reason for that is because models is a technology, not a product. Models is a technology, not a service. For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love Not to go fine tune my own. I would really love to keep using ChatGPT. I love to use Claude. I love to use Gemini. I love to use X. And they all have their own personalities, as you know, which is kind of depends on my mood and depends on what problem I'm trying to solve. You know, I might, you know, do it on X or I might do it on ChatGPT. And so that, that segment of the, of the industry is thriving. It's going to be great. However, There, all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models. The open model industry we're contributing tremendously to, it is near the frontier, and quite frankly, even if it reaches the frontier, I think that products as a service, world-class products as, as models as a product is going to continue to thrive.

AI assessment note: “I believe we fundamentally need Models as a first class product, proprietary product, as well as models as open source.”

Q Do you think what happens is these guys try and they realize, oh my god, it's too much, and then they come back. Is that why the share grows?

A Well, we're gaining share for several reasons. One, Um, our velocity has gone, we help people realize it's not about building the chip, it's about building the system. And that system's really hard to build. Uh, and, and so their, their, their business with us is increasing. In the case of AWS, I think they just announced, I think it was yesterday, that they're gonna buy a, a million chips, uh, in the next couple years. I mean, that's a lot of chips from, from AWS, and that's on top of all the chips they've already bought. And so we're delighted to do that. But, number one, We're gaining share this last couple of years because we now have Anthropic coming to NVIDIA, Meta SL is coming to NVIDIA, and the growth of open models is incredible, and that's all on NVIDIA. And so we're growing in share because of the number of models. We're also growing in share because outs, All of these companies are outside the cloud, and they're growing regionally in enterprise and industries at the edge, and that entire segment of growth is, you know, really hard to do if it's just building an ASIC.

AI assessment note: “we help people realize it's not about building the chip, it's about building the system.”

Q be an Android Type open source platform that you're going to play a major part in with dozens of, uh, car providers. And then maybe on the other side, there could be an iOS with Tesla or Waymo. What's your strategy thinking there and how that chessboard emerges? Because it feels like you have a pretty deep stack and in some ways you're competing and in other places you're collaborative.

A Yeah. Um, It's taking a step back. We believe that everything that moves will be autonomous, completely or partly. Someday. Number one. Number two, we don't want to build self-driving cars, but we want to enable every car company in the world to build self-driving cars. And so we built all three computers, the training computer, the simulation computer, the evaluation, evaluation computer, as well as the car computer. We developed the world's safest driving operating system. Uh, we also created the world's first reasoning autonomous vehicle. So that it could decompose complicated scenarios into simpler scenarios that it knows how to navigate through, just like us, reasoning systems. And so that reasoning system, called Alpamayo, has enabled us to achieve incredible results. We, Open this, we, we vertical optimization, we horizontally innovate, and we let everybody decide, do you want to buy one computer from us? In the case of Elon and Tesla, they buy our training computers. Um, do they want to buy our training computer and our simulation computers? Or do you want to let us, uh, work with us to do all three and even put the car computer in your car? So we, you know, our attitude is we want to solve the problem. We're not the solution provider. And we're delighted however you work with us.

AI assessment note: “we horizontally innovate, and we let everybody decide, do you want to buy one computer”

Q way that I haven't seen in a long time. You've been around a long time. You're very close with President Trump at this stage. Help us understand, like, what is the nature of industry-government relationships? We saw that dinner last week with all the CEOs. You know, you spent a lot of time. Is it unique? Have you seen anything like this in your career over the last 30 years?

A It was, it was hard to go to D.C. in the past, as you know. Getting an appointment is almost impossible. President Trump has a open door to leaders who wants to come in and help them understand the future. This is an administration that believes in growth. Fundamentally, President Trump wants America to grow. If we can grow economically, we will be strong militarily. If we could be, if we could grow economically, we will be secure. I've never met somebody who is secure who's poor. Being, being rich as a nation is an essential part of national security, and he knows that. He also wants America to win the AI, the AI race. This is going to be a very long-term race, and, um, and he understands that this is a pivotal time. He wants the technology industry to run. He wants everybody in the world to be built on American technology. These are sensible, logical things. You know, the opposite is strange to me. If I take everything and I just reversed it, we want our country not to grow. And because we don't want our country to grow, we don't need any energy because we know we need energy to grow. And so let's not have any energy. And in fact, we don't want our technology industry to lead. He understands that our technology industry is our national treasure.

AI assessment note: “President Trump has a open door to leaders who wants to come in”

Q have the fortitude, the resolve to then go embrace these, you know, technologies? We're, we're going to see a hundred percent of driving go away by humans. That's just, it's, that's a beautiful thing in the lives saved, but we have to recognize that's fifteen million people in the United States, 10 to fifteen million, who are employed in that way, and, and so that is going to happen, yes?

A I think, I think that jobs will change. For example, um, there are many chauffeurs today, uh, who drives the car. I believe that many of those chauffeurs will actually be in the car, sitting behind the drive, the steering wheel, while the car is driving by itself. And the reason for that is because, remember what a chauffeur does. In the end, these chauffeurs, they're helping you, they're your assistants, they're helping you with your luggage, they're helping you, I mean, they're helping you with a lot of things. And, and so, I wouldn't be surprised, actually, if the chauffeurs of the future become your mobility assistant, and they are helping you do on a whole bunch of other stuff, and the car is driving by itself.

AI assessment note: “I think that jobs will change. For example, um, there are many chauffeurs today”

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 that, I mean, I know how meaningful that is in any company, but for you, given that, I feel like the NVIDIA journey is, um, particularly amplified on these dimensions, right? And like, you know, you went through two, two, if not three, like 80% plus drawdowns in the public markets to have investors who've stuck with you from day one through that must be just like, So much support.

A Yeah, yeah, it is incredible, and you hate that any of that stuff happened, and, and most of you, you know, most of it is, is out of your control, but, you know, 80% fall, it, it, it's an extraordinary thing, no matter how you look at it, and I forget exactly, but I mean, we, we traded down at about a couple of Two, three billion dollars in market value for a while because of the decision we made and going into CUDA and all that work, and your belief system has to be really, really strong. You know, you have to really, really believe it and really, really want it. Otherwise, it's just too much to endure. I mean, because, you know, everybody's questioning you and employees aren't questioning you, but employees have questions.

AI assessment note: “Yeah, yeah, it is incredible, and you hate that any of that stuff happened”

Q How should the layman think about what that business is versus when you hear about these big data center build-outs that's happening in, in on the ground?

A Well, we should definitely work on the ground first because we're already here. And number one. Number two, we should prepare to be out in space, and obviously there's a lot of energy in space. The challenge, of course, is that cooling, you can't take advantage of conduction and convection, and so you can only use radiation, and radiation requires very large surfaces, and so now that's not an impossible thing to solve, and there's a lot of, a lot of space in space, um, but nonetheless, The expense is still quite there, is, is there. Uh, we're gonna go explore it. We're already there. We're already radiation-hardened. Uh, we have, we have, uh, uh, CUDA in satellites around the world. Um, they're doing imaging, image processing, AI imaging, and, um, and that kind of stuff ought to be done in space instead of sending all the data back here and do imaging down here. We ought to just do imaging out in space, and so there's a lot of things that we ought to do in space, and in the meantime, Uh, we're going to explore what is the architecture of data centers look like, uh, in space, and it'll take, it'll take years. It's okay. We got, I got plenty of time.

AI assessment note: “we should definitely work on the ground first because we're already here.”

Q What about manufacturing in the US? So where are we? We, um, you know, we've seen stories of TSMC in Arizona. We asked this question earlier about how it's going. Is the US equipped? Uh, what is it going to take for us to get there to have onshore fabs?

A First of all, you guys know you're talking about the United States. Uh, the, the, ah, I know that there's, there's lots of concerns, and, and everybody's, you know, worried about competition and things like that, but we are talking about America here. This is, this is unquestionably the most technology rich country in the world. And this is the most innovative countries in the world. And the computer industry I don't have to, I have the honor to serve is the single greatest industry our country has ever produced. I think we could acknowledge that. Yep. The, the level of leadership of the computer industry, the technology industry, is just unimaginable worldwide. And so, this is our national treasure. This is one of our country's assets. We have to make sure that we continue to, to, to advance it. Um, onshoring. Next generation manufacturing is gonna be insanely technology driven. Uh, robotics technology, AI technology. You're gonna have factories that are gonna be orchestrated by AI. Orchestrating a whole bunch of robots that are AI, building products that are effectively AI's, right? So you're gonna have this in layers of inception, and the amount of technology necessary to create that is really insane. We've, I I love President Trump's vision, bold vision, of re-industrializing the United States. That entire band of industry that's missing, we outsource too much of it, frankl…

AI assessment note: “In Arizona and Texas, we will, in the next four years, probably produce”

Q He seems like he's a little bit of a headache. I'll be honest. Um, but he's great. He's great. I'm joking. I'm joking. How do you allocate The H-one hundreds and whatever else you're selling them, and still have them all like you, because they must ask sometimes, hey, can I get extra? I'll pay you extra. So, just the allocation of a finite amount of resources and then jobs.

A First of all, I wrote off five billion dollars worth of hoppers. If anybody would like to have some extras. You know, just give me a call. Uh, jobs. Uh, we use AI across the whole company. Every single software engineer today uses AI, not one left behind. A hundred percent of our chip designers use AI. We are busier than ever. And the reason for that is because we have so many ideas that we want to go pursue. AI makes it possible for us to go pursue those ideas now that we're not doing the mundane stuff. And so I, I think the first idea is the more productive you are as a company, um, so long as you have more ideas, you could pursue those ideas. You, you'll go after those ideas. And I, I think that, that AI, in my case, is creating jobs. It causes us to be able to create things that other people would, uh, customers would like to buy. Uh, it drives more growth. It drives more jobs. You know, all that goes together. The other thing that, that to remember is that AI is the greatest technology equalizer of all time.

AI assessment note: “Uh, jobs. Uh, we use AI across the whole company.”

Q know, sometimes it ruffles feathers. Like, do you have any advice for people about an organization and how you. Navigate that really? Like how do you build an org that allows you to think in first principles? Cause if the fortune 500 did that, like the fortune 500 will probably look a lot more like Nvidia than not. And it doesn't like you, you have built a very unique company.

A My state of mind when I'm, my state of mind is always, uh, starts with curiosity. I have a whole bunch of questions myself. And, and, um, of course, like anybody else, I'll seek the shortest path to the answer, um, but oftentimes the, the answers from the people that are near me, uh, might not be satisfying, and, and I might have other questions, and maybe they're, they're busy doing something, and they're pursuing something, and so my first, my first inclination is to go discover the answers to my own curiosity. Um, my second is if I find that the information is and that the domain of information or, you know, particular field, uh, could be really important to somebody and could be important to our company, then my next inclination is how can I learn as much as possible so that I could be of service to the company and share with everybody else? You know, this is no different than, than you when you're, you're sharing knowledge. I mean, I watch your podcasts and I watch your, your videos and I really enjoy them. You're sharing ideas with everybody else. In a lot of ways, I think a, a CEO is in service of the company, in service of all the people that are working there, and you want to empower them with some insight. And so that's really where it's coming from. It's not so much a management technique, but a personality technique. You know, I, I want to empower you, and this is s…

AI assessment note: “It's not so much a management technique, but a personality technique.”

Q What about embedded applications? So, you know, my daughter's teddy bear at home wants to talk to her, What goes in there? Is it a custom ASIC? Or does there end up becoming much more, kind of, a broader set of TAM with developing tools that are maybe different for different use cases at the edge and in an embedded application set?

A We think that there's three computers in the problem at the largest, at the largest scale when you take a step back. There's one computer that's really about training the AI model, developing, creating the AI. Another computer for evaluating it. Depending on the type of problem you're having, like, for example, you look around, there's all kinds of robots and cars and things like that. You have to evaluate these robots inside a virtual gym that represents the physical world. So it has to be software that obeys the laws of physics. And that's a second computer. We call that Omniverse. The third computer is the computer at the edge, the robotics computer. That robotic computer, one of them could be self-driving car, another one's a robot, another one could be a teddy bear, little tiny one for a teddy bear. One of the most important ones is one that we're working on that basically turns the telecommunications base stations Into part of the AI infrastructure. So now, all of the, it's a two trillion dollar industry. All of that in time will be transformed into an extension of the AI infrastructure. And so radios, radios will become edge devices. Factories, warehouses, you name it. And so, so there are three, these three basic computers All of them, you know, aren't going to be necessary.

AI assessment note: “another one could be a teddy bear, little tiny one for a teddy bear.”

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”

Q almost threw in the towel and said, oh, we lost to China, right? There was the deep seek moment, then maybe this week, last week there was this Kimi model moment. Um, but then it kind of fizzled out. Can you just, uh, explain to us how big of a threat they really are in terms of getting to supremacy, getting there first, whether it's AGI or, you know, superintelligence?

A Yeah, excellent question. Um, the Chinese AI labs are the world, world's leading open, open model companies. They, they offer the most advanced open models. Open source is fantastic. If not for open source, We know startups won't exist. And to the extent that we believe that the future is going to be, the future industry is going to be today's startups, they're going to need open, open source models. And Deep Seek, when it came out, it was a great win for the United States. It was an incredible win. What people didn't, and two, two reasons. First, imagine if Deep Seek came out and only ran on Huawei. I just want us to pretend. Use that thought experiment. Totally.

AI assessment note: “Deep Seek, when it came out, it was a great win for the United States.”

Q fifty million H-one hundred equivalents by, in five years from now. And Everybody started to feverishly do the math, because if he has fifty million H 100 equivalents, then OpenAI will have that much or more, Meta will have that much or more, Google, et cetera, et cetera, et cetera. Can you just explain to us, Lehman, what that means, what he just said, and how it impacts your business?

A Um, One of the biggest observations about AI is that there's, there's the industry of applications that AI has created. It's a revolutionary technology, every industry will be revolutionized, new applications will be created, so on and so forth, all the things that we know. Agentic AI, reasoning AI, robotics AI, so on and so forth, we know all those things now. Every industry, healthcare, education, transportation, you name it, manufacturing, all revolutionized. The one part that, that, that we observed, and, and made a great contribution to, is that in order to Sustain those applications. You need factories of AI. You have to produce AI. Unlike, unlike software, you write the software and that's it. In the case of AI, you have to continuously produce it, generate the tokens. In a lot of the same ways that energy production was a large part of the economy, A couple, two, 300 years ago, I think it actually peaked out at 30%. Yep. There's a whole, there's going to be a whole industry of just producing tokens, and this is going to be the new infrastructure, just as we have the energy production infrastructure, we have the internet infrastructure, and we got to build out that plumbing, and now we got to, we have to build out the AI infrastructure. My sense is that we're probably, you know, a couple of hundred billion dollars, maybe a few hundred billion dollars into a Multi-trillio…

AI assessment note: “we're probably... a few hundred billion dollars into a Multi-trillion dollar infrastructure buildout.”