The Exchanges

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 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 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 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 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.”

Q Can, can you talk about physical AI versus data center AI? We talked, we talked a little bit about this today. Is there a threshold where you see physical AI accelerating and ultimately The deployment of chips outpaces the deployment of chips in data centers. Is that where the world evolves to? Or what do you think the construction of the world looks like?

A Yeah, excellent. Everything in the world that moves will be autonomous someday. And that someday is probably around the corner. So everything that moves. We already know that your lawn mower is gonna, you know, who's gonna be pushing a lawn mower around? That's craziness. Unless you want to. I mean, that's, you know. And so, so I think everything that moves will be autonomous. And every machine Every company that builds machines will have two factories. There's the machine factory, for example, cars, and then there's the AI factory to create the AI for the cars. And so maybe you're, ah, a machine factory to build human or robots. You need an AI factory to build a brain for the human or robot. And so every company in the future, in fact, the, the future of industry is really two factories. Tesla already has two factories, right? Elon has a giant AI factory. He was very early in recognizing that he needs to have an AI factory to sustain the cars that he has. Now he's got AIs in the car, but in the future, instead of, you know, I imagine that in the future, instead of a whole, whole lot of people remote, remotely monitoring air traffic control, there'll be a giant AI that's doing the remote control. And then only in the case of the, the giant AI, Um, can't handle it with a person come in to, to, uh, intercept. And so, so I think you, you see that, that these industries in the futu…

AI assessment note: “the future of industry is really two factories”

Q was, I, I would like the high value inference people to take a listen to this, and 25% of your data center space, you said, should be allocated to this Grok, LPU, GPU combo. So, can you tell us about how the industry looks at this idea of now Basically creating this next generation form of disaggregated pre-fill, decode, disag, and how people do you think will react to it?

A Yeah, and take a step back, and at the time that we added this, we went from large language model processing to agentic processing. Now, when you're running an agent, you're accessing working memory. You're accessing long-term memory. You're using tools. You're really beating up on storage really hard. You have agents working with other agents. Some of the agents are very large models. Some of them are smaller models. Some of them are diffusion models. Some of them are autoregressive models, and so there's all kinds of different types of models inside this data center. We created Vera Rubin. To be able to run this extraordinarily diverse workload. My sense is, and so we added, we used to be a one rack company, we now added four more racks.

AI assessment note: “take a step back, and at the time that we added this”