Winning the AI Race Part 3: Jensen Huang, Lisa Su, James Litinsky, Chase Lochmiller

Guys, this is one of the most amazing entrepreneurs that you're going to meet. Jim Lutinsky, this founder and CEO of MP Materials. Thanks. Good to be here. Jason,

How are you? So, let me set this up. Jim was a hedge fund guy running a pretty successful hedge fund, and he ended up investing in something called Molly Corp, which went out of business.

And you did this incredible thing: you said, 'You know what? Screw this.' You essentially shuttered the fund, took over the company, and fast forward many years later, you are the largest and only supplier and refiner of rare earth materials and maker of magnets inside the United States.

100% of the American industry. You just did two really incredible things in the last couple weeks. One was you announced an enormous public-private partnership with the DoD, $400 million, etc. And the second is you announced a really big deal with Apple.

Okay. So take a huge step back. Talk to us why rare earths matter. Tell us about the supply chain for AI. Tell us why you're doing this.

So to translate, it's not a handout from the government. They didn't gift you $400 million. They invested in your company. They have warrants. They have equity.

Why go to the government for this investment as opposed to the private markets?

How big of an industry is physical AI? We see the robots, we're told the robots are coming, there's going to be billions of them. Are they actually being deployed at the scale and pace we've been told?

Do we have talent capacity or a talent shortage? Secretary Bergam gave me a stat that we only graduate 200 people a year in the United States in mining, which is orders of magnitude different than China. What do we need to do to be competitive?

Why do you think it's been so hard to establish that idea? You find it straightforward to find good hardworking people, but the thought is that these jobs are not desirable, but they really are desirable by many people.

Tell us, you said earlier that you suspect five years from now, we'll look back and this deal with the DoD was a blueprint. Give us other areas of either physical AI or software AI or other markets where you think these public-private partnerships are really necessary to embellish US supremacy.

Was it straightforward for you to find the right person within the Trump administration that said, 'Of course, this is obvious. Let's sit down and hash this out'?

All right, we appreciate you coming, Steve.

Hi, Lisa. Nice to meet you, Lisa. It's a pleasure. Well, thanks so much for being here with us today. We don't have a lot of time, so we want to get into it. In April, it was announced that you achieved your first silicon output at the TSMC facility in Arizona on that 2nm line. This administration and the private sector have talked a lot about onshoring semiconductor manufacturing. Would love your thoughts on the on-the-ground experience in Arizona. How's it going? What's not going well? What does America need to do to get this right?

Absolutely. First of all, it's a pleasure to be here. Love the theme. I think we're all super excited about winning the US AI race. I thought if we're going to talk about chips, David, I should actually bring one. This is our latest generation AI chip, the MI355. 185 billion transistors. Takes about 9 months to build. This is 3 nanometer and 6 nanometer. But to answer your question, these AI chips are extremely complex. We're super excited about the progress in US manufacturing. Twelve months ago, people weren't sure we could do leading-edge manufacturing in the United States. We've been very early in Arizona with TSMC and got our first chips out. They're actually four nanometer. Where there's a will, there's a way. The conversation about onshoring manufacturing has been super good for the semiconductor industry. Chips are so essential to winning the AI race that we want geographic diversity and capability.

But the reports out there that TSMC couldn't get good qualified trained employees. They had to bring folks over. Is that accurate? If we're going to scale, what's the order of magnitude? Is it 10x, 100x? How are we going to build a workforce to support this new industry?

No matter when you start something new, it's going to take work. In the beginning, there were some issues. TSMC has a formula for how they build, and they had to learn how to do it well in the United States. But we've been super impressed with the progress. Yields are equivalent between Taiwan and Arizona. It's unrealistic to think the United States could compete on cost.

We're going to pay a little bit more. Give us the ballpark. 50% more, 20%?

Not 50% more. It's going to be more than 5% but less than 20%. Let's call it low double digits.

And how does that impact the business in terms of competition globally?

Everybody wants a GPU. The people who are going to win in AI want as much compute as possible and assurance of supply. The fact that you're not going for the lowest cost every minute is okay. Not everything needs to be in the most advanced technologies. We have a geographically diverse supply chain. Taiwan continues to be important, but the focus from this administration on getting onshore manufacturing in a big way is very good.

How much time do we have? If there was a disruption in Taiwan and we were unable to get chips from those factories, what would that look like globally?

You have to look across the supply chain. We all want to keep reserves. It's months, not years.

Lisa, there were two really interesting posts over the last couple days. One from Elon where he said in five years he projected 50 million H100 equivalents just for xAI. And Sam Altman signed a deal for a 4-gigawatt data center, $30 billion a year with Oracle. That presumes an enormous amount of chips and power. If you forecast that, how do we actually meet all of that? What needs to happen that's not happening today inside the United States?

It's a great point. We're seeing incredibly large demand for AI. Sam and Elon are leaders, but there's a lot of demand elsewhere. Nations want their own AI. The accelerator market will be over $500 billion in a couple of years. The entire ecosystem needs to scale up. We're scaling up chip design and manufacturing. The US will be a huge piece. It's not just about silicon; all the other pieces of the ecosystem have to come to the US. Today's AI action plan is an excellent blueprint.

How do you see the market evolving in the next five or six years? Is there a standard set of chips for training and inference, or do you see an explosion of different ASICs, designs, and use cases?

I believe there will be diversity of chips. There are so many use cases: science, manufacturing, design, personal AI. We'll see AI in everything. Different types of chips for different purposes. For the largest systems, you need the most compute, so GPUs are there, but lots of ASICs are in process.

You opened up a really interesting line of questioning. Mainframes were so expensive, then PCs became more powerful. You alluded to AI being run locally. When would we have a local computer that would have the power to run some of these LLM models? Do you see that as a specific market to go after?

I definitely see AI at every part of our ecosystem. You want it everywhere. In PCs today, we're putting significant AI to run local models. Maybe I don't want all my personal data all over the place.

Can you make a prediction on when the market for physical AI chips is greater than the market for chips in data centers?

I'm a big believer in physical AI. I still think it's at least five years.

You think five years? Is that that fast?

Five plus. But that is ultimately the biggest end market.

Do you think physical AI becomes the biggest end market?

It becomes a significant end market. Chips in data centers and chips at the edge are also significant.

When you look at the most cutting-edge techniques today, EUV lithography, we're only as good as what humans have invented. What happens when AI is able to invent its own method of manufacturing, different materials, different approaches we may not understand? Is any of that R&D happening at AMD or elsewhere?

AI can be extremely smart and capable. It can design future chips, but there's still a creativity of bringing it all together that humans are at the center of. I don't see AI designing our next-generation GPU, but it will help us design it much faster and more reliably.

You talked about the need to reshore more parts of the ecosystem. Obviously you guys are world-class chip design, the fabs are getting reshored. But how do you think about things like lithography? Does ASML need to start building machines in the United States, or is it okay to have that supply chain risk on an ally?

We have to accept that it's a global supply chain. Even if you reshore some components, others will be across the world. It's important to have our allies together and ensure access to the latest technologies while protecting our intellectual property.

Going to first principles, what should be done about American education? Assume there's no college, high school, nothing. You arrive in America, the situation is what it is today. What do you do? How do you build an education system to prepare the next generation for the evolving workforce?

I'm a big believer in a science and technology background. STEM is so helpful for the future workforce. The earlier we can get into the process, the better. Revitalizing the curriculum is important. Ensuring America is the best place for AI talent is key. Inspiring people when they're young to study science.

When you go to bed at night and think about the best-case scenario for this technology and trajectory, which is accelerating, what could the world look like in 10 years? We're hitting AGI, superintelligence can't be far behind. What would the world look like in the most optimistic scenario?

This is the most transformational technology in our lifetimes. Orders of magnitude. It's not just going after one aspect. AI can make science better, medicine better, manufacturing better, every aspect of business better. In 10 years, we'd like to believe we are leveraging it to solve the world's most important problems. At AMD, we get up in the morning thinking how to use technology to solve the most important challenges.

I have a business strategy question. If we went back 20 years and wrote the tale of three companies: Nvidia, AMD, Intel, and fast-forwarded 20 years, two have thrived and one has not. If you had made the bet back then, it would have been inconclusive. There was an inherent belief that Intel had figured it out. Can you tell us the lessons learned of why you thrived and what you take away from their journey to make sure AMD doesn't play out that way?

As a CEO, we have to be paranoid every single day. The most important lesson is you have to shoot ahead of the duck. You have to think about what is the most important inflection point. Five years ago, AI was around but we wouldn't gather this audience. You had to invest many years ago to be where we are today. You'll be able to judge whether we've done a good job by how we perform five years from now. The decisions we're making will take five plus years to play out. In tech, nothing is fast, but hopefully it's lasting.

What do you think is happening in countries not in the United States? What is happening in chip design and capabilities in China and other places?

We should believe it's super competitive. The world has recognized that semiconductors are essential to national economies and security. Everyone is investing. We have a great head start because of the innovation pipeline and great companies, but we should not be confused. We need to keep up our investments. No one company can provide every solution. I love the idea of open ecosystems, collaboration across hardware, software, systems, and public-private partnerships. The countries that win bring all the smartest people and best capabilities together and let them go as fast as possible.

Right. Well, thank you for being with us. Been great. Appreciate it.

Thank you. Pleasure to meet you.

I'm Chase Lockach Miller, the co-founder and CEO of Crusoe, and I'm here to talk to you about the AI industrial revolution. I'm going to start with a quote from Warren Buffett in his 2020 shareholder letter: 'In its brief 232 years of existence, there has been no incubator for unleashing human potential like America. Despite some severe interruptions, our country's economic progress has been breathtaking. Our unwavering conclusion: never bet against America.' Buffett's words were true then, and as we enter this global race for technological dominance of artificial intelligence, they ring even truer today. American dynamism has always prevailed and will continue to do so. What made America great is that we live in the freest nation in the world, rich in land and resources and human ambition. One of the things that enabled that progress is leading investments in infrastructure over the course of his lifetime. In 2025, we stand at the start of a new era of infrastructure, the infrastructure of intelligence, driving the biggest capital investment in human history. This investment is led by the hyperscalers investing hundreds of billions of dollars per year. These are the companies with the biggest balance sheets in the history of business, going all-in. Startups like Crusoe and nation states are following suit. The opportunity is that for the first time in human history, we've been able to manufacture intelligence. Intelligence is the scarcest economic resource, and for the first time we can make it. The data centers of the future are being referred to as AI.

factories. It's a factory that takes as inputs data and algorithms and chips and energy and it outputs intelligence. This is the alchemy of intelligence. So this newly manufactured intelligence will spawn a new chapter of unprecedented productivity and development and that will serve to improve human quality of life. So the IDC estimates that AI will generate $20 trillion in economic impact by 2030. So even if you can earn a small slice of that, that hundreds of billions of dollars of investment will earn an amazing return. For each dollar invested into business related AI is expected to generate $4.60. As my friend Jensen would say, the more you buy, the more you save. Or in this case, the more you buy, the more you make. And we can grow the pie together and usher in a new era of AI-driven abundance.

So when we look at the history of American energy production and consumption, as the US industrialized, we really ramped up energy generation and also consumption. But if you look at this chart, you can see that it's kind of flatlined over the last 20 years where we're generating and consuming about 4,000 terawatt hours per year. AI is fundamentally transforming this demand picture and energy is quickly becoming the bottleneck to growth. Data centers are forecasted to account for 20% of the growth in power demand between now and 2030. And data center total power consumption is going to go from 2.5% of US power consumption to 10%. So what this means is that the technology industry that's sort of willing this infrastructure into existence fundamentally needs to bring its own power to support that growth. Which means massive investments not just in data centers but also in the energy infrastructure to support them. And this will require people, lots of people, to build, operate, maintain, and run these large scale energy investments.

So if we look at data centers by the numbers, I think it's important as people are sort of throwing around gigawatt scale data centers of looking at the amount of data center infrastructure that exists today. Northern Virginia is sort of the center of the world for data centers but it's only at the end of 24 it was only 4 and a half gigawatts. Today we have companies that are looking at building single 5 gigawatt facilities. And if you look at this growth, we're building more than a Northern Virginia every single year in the forecasted future. So we need new. So if there's one thing that you're going to take away from this presentation, it's that we need new infrastructure. We need lots and we need lots of it and we need lots of people to build, operate and maintain it. This is what Cruso is focused on solving. Cruso is in the business of activating energy for intelligence, of building and operating AI factories at scale from the steel to the silicon, from the electron to the token. And if you look at our pipeline, we have about 40 gigawatts of capacity that spans all sorts of energy resources from new energy technologies like small modular reactors to renewables and natural gas to power this innovative future.

So revisiting my formula here, I think we left off one critical component which is the people. AI infrastructure will be the largest job creation catalyst that we've ever seen. So I think it's important to sort of look at what this looks like in practice. For the last year, Cruso's been building a large scale AI factory in Abilene, Texas. And speed is paramount. Again, this event is winning the AI race. In order to win a race, you really need speed. And Cruso's really been focused on using modular components, on rapidly scaling investment in construction and infrastructure to support this, and we've actually built a lot of different modular components in factories and brought them to site. And they're kind of like Lego blocks that sort of fit together to build one of these AI factories at rapid scale and speed. So if you look at what this looks like today, this site will consume over 1.2 GW of power and 400,000 NVIDIA GPUs all in a single coherent cluster. So this will essentially be a gigawatt scale computer to drive human progress forward.

It's really amazing what you can kind of accomplish in a year. You see just one year ago, this is what the site looked like and this is what it looks like today. So what does this mean from a jobs perspective? We have 4,000 people working on site every day to make this facility happen. And it's a bunch of different trades, electricians and plumbers, and construction workers. And it's required a lot of capital, too. We raised $15 billion to basically put this facility and bring it into existence. And it's also required manufacturing, and a lot of the critical components have happened off-site in these controlled manufacturing environments.

But this isn't the only one. This isn't a one-of-a-kind. We also are building AI infrastructure and AI factories across America. This site in West Texas is going to be a gigawatt facility behind the meter with wind, with incremental gas and grid interconnection. We did a partnership with Redwood Materials where we built the largest micro grid in the United States with 60 megawatt hours of batteries, end of life EV batteries, and 20 megawatts of solar to power an AI factory. We have a partnership with GE Vernova and Engine Number One for 4.5 gigawatts of new gas generation capacity to power future AI data centers. And finally, we want to announce a new partnership that we're doing with Tall Grass Energy in Wyoming that will initially power 1.3 gigawatts of total compute load alongside 2 gigawatts of power generation, and ultimately we feel like this can scale to 10 gigawatts of power. So we're really thrilled to partner with Tall Grass.

So as a vertically integrated AI infrastructure company built here in America, we believe that AI factories will be the ultimate economic engine creating utility for society and new jobs for the economy. This will usher in a massive new era of AI-driven prosperity for the United States. And I want to leave you with my final quote from Warren Buffett that, in this AI race, never bet against America. Thank you.

So is this stuff real? You guys, you started off as like a, you know, sort of Bitcoin miner and now somehow all the hyperscalers are asking you to, you know, build non-stop data centers. Why you guys?

Um, you know, I think again it comes back to this being a race and one of the things that Cruso's been able to do better than anyone is execute at speed and scale.

And I know there's been like some of the biggest constraints around, you know, sort of, you know, water, energy, um, you know, the land for this type of stuff. like where have you seen what parts of the country you are you guys able to actually do this or have you seen any of the local regulators start to step up to you know make this stuff easier for you?

Um you know we've been building quite a bit in Texas. You know Abilene Texas is this initial facility that's gotten a lot of coverage. Uh you know we just sort of announced another facility in Texas. Uh Wyoming's been a big area of investment for us but um you know there's a number of other states that were sort of evaluating investing uh to build large scale AI.

Is it only going to be the like, you know, sort of more rural, you know, sort of red states or do you think that like, you know, Oregon, Washington, etc. will start to, you know, sort of get together and realize they've got cheap hydropower and, you know, cheap water and we'll try and get you there.

Uh, no, you know, believe it or not, we're actually looking at something in California.

Wow. California's going to bring you in. I would imagine that's going to take like 50 years with that regulation.

Maybe. We'll see. We'll see.

Yeah. And you know the do you think that the you know sort of hyperscaler demand obviously we were just you know on with Lisa Sue talking about the demand for chips over the next couple years that's obviously correlated to the you know demand with data centers. Do you think that's actually going to play out the way that all the public markets are you know sort of projecting or we like in 1999 peak you know everybody thinks that fiber is going to be deployed all over the world. Turns out all those projections were totally off.

Um I I think the important trend to watch is sort of the capital investment that's happening and and the term over which that's happening. So

I thought like Meta backed off on it a little bit like didn't they like for a little bit talked about they were going to deploy like crazy and then pulled back although he's obviously spending a billion dollars on chief AI scientist now.

Yeah. I think you know the investments they're making in people are actually rounding errors compared to the investments they're making in infrastructure and I think that's something to sort of appreciate in this moment in time like people are betting their entire balance sheets. these are the biggest, you know, and best balance sheets in the history of business and they're betting their entire balance sheet on, you know, the future infrastructure that's going to power the modern economy.

And then like the, you know, data centers like Texas, like what's the limiting factor? Like is it like workforce to actually go build these things? Is it like materials? Is it the cooling towers? Is it the chips? Is it the hyperscalers giving you the like, you know, you know, sort of contracts? What's the, you know, sort of limiting reagent?

Um, you know, uh, labor is definitely like a a major constraint. You know, like I I said, you know, we have about 4,000 people on site. um every day uh we're going to have multiple sites that are operating with thousands of you know uh folks uh basically building this infrastructure. So you know uh labor is definitely like one of the one of the big bottlenecks and we're you know we think it's really important for America to make these massive investments in the workforce uh to really build the infrastructure for the future.

anything that requires some real real reskilling where it's like people from oil and gas or like construction having to go into just totally net new fields or is it something where you guys are actually able to pull on pre-existing talent pools pretty quickly?

Um both, you know, there's there's a lot of existing uh you know, labor uh you know, at that facility in Abilene, we're we're actually pulling labor from all 50 states at this point, believe it or not. Um so

making like a company town importing people in.

Yeah. you know, we we have about 50% of the people are are are uh you know, from Texas, but um you know, the uh we we are importing a lot of labor to to make the project happen.

And um you know, do you do you see the company starting to go more full stack beyond just like the operations of like the you know, sort of data centers or how do you think about like you know, you started off with, you know, focus on like you know, sort of energy arbitrage now to data centers. Where do you see your guyselves going over time?

Yeah, I Cruso is a vertically integrated AI infrastructure business. So, you know, data centers is a key component of that and, you know, I think one of the most important pieces to be building right now and and one of the hardest things to do at speed. Um, but we also have, you know, this managed AI cloud services layer that, um, enables innovators to build, uh, large scale AI applications on the platform.

Makes sense. Well, yeah, Chase, thanks so much for, you know, joining us on stage and, um, appreciate the talk.

Thanks, Don.

Okay, everybody, we got a real treat for you. Jensen Wong is here. Sit here. Sit here. Sit here. The hot seat. Thanks for coming. Thank you. We were saying the number one company in the world. You're a fan of the pod. You listen to the pod. Yes. Yes. And there's Steve. Uh, what's the story with the jacket? You got one of those. You have like six. Do you really? Wow. Yeah. It's nice. I like it. I try I tried that on. It was like way too much money. Yeah. Oh, look at you. Look at you.

Thank you. It's great to have you. The number one podcast in the world. Wow. It's I just This is Norman, our host. I have something like 50 or 60 of them. Yeah. What is that? Tom Ford, I think. So this one is I think so. Well, you guys are all so fashionable. Coming from you guys, it actually means something. Oh, yeah. Oops.

Uh, hey, we we've been talking a lot about opportunity. You've talked is like a model. He is. He is. Okay. Good idea. He's definitely in his head. He's like, 'Is Tom Ford your favorite? Who's your favorite?' Right. Um, I got I have two questions for you. Take them in whichever order you like. Um, we've been talking a lot about job displacement, opportunity, short-term, long-term. Obviously, you get to see everybody applying the technology because, hey, listen, you've got the best product in town to build on. Therefore, everybody explains to you their hopes, their dreams. So, you have a unique way of looking at the playing field. You have complete information that we don't have. So, I want to know what you think. Don't worry, we'll fix it. Um, what you think that edit? What you think about job creation, transfer, displacement, etc. And then the second one, I've just always been curious. You got all these important people knocking on your door. You got Zuck, you got E, you got Sam Altman. 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 H100s 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.

First of all, I wrote off $5 billion worth of hoppers. If anybody would like to have some extras, you got them, you know, just give me a call. Uh Jobs, uh uh we use AI across our whole company. Every single software engineer today uses AI. Not one left behind. 100% 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'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.

Okay, explain.

Everybody's a programmer now. You used to have to know C and then C++ and Python and you know in the future everybody could program a computer, right? Just have to get up and if you don't know how to program a computer, you don't even know how to program an AI, just go up to AI and say, 'How do I program an AI?' and the AI explains to you exactly how to program the AI. Even when you're not sure exactly how to ask a question, you say, 'What's the best way to ask the question?' And it'll actually write the question for you. It's incredible. And so, it's a great equalizer. Everybody is going to be augmented by AI. Everybody's an artist now. Everybody's an author now. Everybody's a programmer now. That is all true. And so, we know that AI is a great equalizer. We also know that uh it's not likely that although everybody's job will be different as a result of AI, everybody's jobs will be different. Some jobs will be obsolete, but many jobs will be created. The one thing that we know for certain is that if you're not using AI, you're going to lose your job to somebody who uses AI. That I think we know for certain. Yeah. There's not a software programmer in the future who's going to be able to hold their own. I mean, you know, typing by themselves.

Yeah. You can't raw dog it. No.

No. Not anymore.

Not anymore. You can't raw dog it.

And tell us.

I'll be sure to go home and tell people.

Yeah. Exactly.

You're not going to raw dog this.

Yeah. Get your co-pilot on.

Now, what about the allocation of all the

Okay. So, the way we allocate is this. The way we allocate is this. Uh, place a PO. That's it. You go to the register, you pay, you get order. First you, you know, first in in the old days with Hopper, it happened so fast. It wasn't possible to keep up with the demand. But now uh we we uh uh we disclose our road map to all of our partners uh a year in advance. Gives everybody a chance to plan with us. They decide how much power and how much data center space and how much capex they want to allocate. We plan together. We work on transitions. It's really quite orderly these days.

What's the lifespan now? You, you know, I was looking into how they're amateurizing, you know, these units four, five years. What happens to this massive buildout in your six, seven, and eight? What will be the use of those computers? If you keep building such great products that replace them at 2, three, four times, what do we do with all that?

Concepts are happening right now. The first thing first thing is every generation we increase the performance by X factors. Yeah. Um if the perf per dollar perf per per watt goes up by x factors whatever your data center power is we just increase your revenues by x factors right so perf per watt is equal to revenues perf per per dollar equals cost and so when we increase your perf per dollar by x factors we reduce your cost by x factors does that make sense that's the the first idea and so every single the reason why we're moving so fast is we're trying to increase everybody's revenues we're trying to decrease everybody's cost so that we have the benefit of driving AI cost down as far as possible so that we can have thinking AI, right? It's not that we're trying to make, you know, AI so that it generates a thousand tokens and that's it. In the future, you're going to be generating millions of tokens and then generates an answer as result of that. You got to think a long time and so you got to get that cost down. The second idea is if you look at the residual value of Nvidia gear right now hopper for example one year one year later it's probably about 80% 75 to 80% of the value of the original value and then one year later is another kind of like 65% and then one year later is like 50%. The reason and right now if you try to get hoppers in the cloud it's all sold out. The reason for that is because CUDA is so programmable and we're constantly the whole world, not just us, the whole world is doing open-source development, improving its effectiveness. And so what's amazing is the performance of Hopper increases over time because we're improving the software stack. It Hopper improved in performance by us and others by a factor of four, right, in the time that we shipped it. Now, you can't get that out of a CPU, right?

Jensen, can you explain to us um Elon's tweet and the impact to to your industry? He said, 'We're going to have 50 million H100 equivalents by in 5 years from now.' And everybody started to feverishly do the math because if he has 50 million H100 equivalents, then OpenAI will have that much or more. Meta will have that much or more, Google, etc., etc., etc. Can you just explain to us layman what that means? what he just said and how it impacts your business.

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 would will be revolutionized. New applications will be created. so on and so forth that all the things that we know agentic AI reasoning AI robotics AI so on so forth we we know all those things now every industry healthcare education transportation you name 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 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 right 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-trillion dollar infrastructure buildout per year.

Yeah. What about manufacturing?

And the reason for that is because you want the new infrastructure which increases revenue driving your cost down. Right. That's right.

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?

First of all, you guys know you're talking about the United States. uh the the uh I know that there's there 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 have the 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 going to be insanely technology driven. Uh robotics technology, AI technology. You're going to have factories that are going to be orchestrated by AI orchestrating a whole bunch of robots that are AI building products that are effectively AIS, right? So, you're going to have this layers of inception and the amount of technology necessary to create that is is really insane. We've I I love President Trump's vision, bold vision of reindustrializing the United States. That entire band of industry that's missing, we out we outsource too much of it. Frankly, we don't need to insource all of it, but we ought to bring onshore the most advanced, the most economy sustaining, driving, national security enhancing parts of the industry. You know, people always degrade down to tennis shoes. We don't have to go there. We just manufacture chips and AI supercomputers. In Arizona and Texas, we will in the next four years probably produce about half a trillion dollars worth of AI supercomputers. that half a trillion dollars worth of AI supercomputers will probably drive a few trillion dollars worth of AI industry, right? And so that's only in the next several years and and uh they're doing great. Arizona is doing great.

And so there's um there's a lot of talk about American competitiveness today and the White House rolled out its AI action plan and Nvidia is making very big bets on the United States. And so as a CEO of a global company, what do you see are America's unique advantages that other countries don't have?

America's unique advantage that no country possibly have is President Trump and let me let me explain why. one uh on the first day of his administration he realized the importance of AI and he realized the importance of energy for the last I don't know how many years energy production was was vilified if you guys remember we can't create new industries without energy you can't reshore manufacturing without energy you can't sustain a brand new industry like artificial intelligence without energy if we decide as a country the only thing we want is IP to be an IP only a services only country then we don't need much energy but if we want to produce things something as vital as artificial intelligence then we need energy and so I'm just delighted to see pro to accelerate AI innovation to accelerate the growth of energy so that we can sustain this this new industry and um you know go after the the new industrial revolution Big big deal.

Can you talk about physical AI versus data center AI? We tal 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 construction of the world looks like?

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 going to, you know, who's going to be pushing a lawnmower around? That's craziness, unless you want to. I mean, it'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. M and so maybe you're uh 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. Yeah. Uh Tesla already has two factories, right? Elon has a giant AI factory. He's he was very early in recognizing that he needs to have an AI factory to sustain the 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, it'll be a giant AI that's doing the remote control and then only in the case of the giant AI um can handle it with a person come in to to uh intercept. And so so I think you see that that these industries in the future, every industrial company will be an AI company or you're not going to be an industrial company. There was uh a couple of moments throughout the course of this year where people 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 Kimmy 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, super intelligence? 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 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 it only ran on Huawei. I just want us to pretend. Use that thought experiment. Totally. Now you got two parallel universe. Exactly. Could you imagine if Q came out and only worked on non-American tech stack? Could you imagine if Kim came out and it only worked on non-American tech stack and these are the top three open models in the world today? It is downloaded hundreds of millions of times. So the fact of the matter is American tech stack all over the world being the world's standard is vital to the future of winning the AI race. You can't do it any other way. We've got to be you know as you know any computing platform wins because of developers. Yeah. And half of the world's developers are in China. So speaking of developers, the second the second I'm sorry, please go ahead. The second thing and really a big deal when Deep Sea came out, we were thrilled for the second reason which is we now have a super efficient reasoning model. And the reason for that is because the old models are one shot. Give it a question. Everything was memorized. You know the pre pre-training is basically memorization and generalization. Two concepts. post training is teaching you how to think. And so now with DeepSeek R1, Kimmy Kimmy K2, um, uh, Q13, you now have reasoning models that can allow that help you think. And so the reason why I was so excited is if each pass of a thought is energy efficient, then you can think for a long time, right?

Yeah. The last question from for me is that we see uh this capital being applied to human capital in a way that we never thought was possible. It used to be NBA players signing $300 million contracts. Now it's, you know, uh model researchers and then there was a there was a post this weekend that that said that there was a person that was offered a billion dollars over four years by Meta. Now, if that's happening at this layer, why hasn't it happened at your layer? because you are the enabler of all of that. And how do you think all of this human capital is going to actually play out?

First of all, I've created more billionaires on my management team than any CEO in the world. They're doing just fine. Okay. And so, and and they're doing don't feel sad for anybody at my layer. Yeah. Everybody's doing okay. Yeah, my layer is doing just fine. I I tell I but but the important the big idea though is that you're highlighting is that the impact of a 150 or so AI researchers can probably with enough funding behind them create an open AI it's a not a 150 people yeah it's not a it's not well deepseek 150 people moonshots 150 people right right and so I mean look at the original uh open AI was about 150 people uh deep mind you And they're all about that size. I think I think um you know there's something about the elegance of small teams and that's not a small team. That's a good good size team with the right infrastructure. And so that kind of tells you something. 150 people if you're willing to pay say $20 billion $30 billion to buy a startup with 150 AI researchers. Why wouldn't you pay one? Right.

Uh speaking of options, by the way, somebody told me we need to wrap because I'm going to do this one question. Somebody who was inside your organization told me with the options that uh you have a secret pool of options and that you will randomly just if somebody does a great job dropped a bunch of RSUs on top of them and that you have this like little bag of options you carry around and that you nuts. Is that true?

I Yeah, I'm carrying in my pocket right now. So listen, so this is what happens. I review I review everybody's compensation up to this day. Yeah. at the end of every cycle of when they present it and they send they send me everybody's everybody's recommended comp. I go through the whole company, I've got my methods of doing that and I use machine learning, I do all kinds of technology and I sort through all 42,000 employees and 100% of the time I increase the company's spend on opex and the reason for that is because you take care of people everything else take care takes care of itself.

All right, well done. Thank you.

Thank you, Jess. Great to see you.

Great to see you.

We have an event in LA. We'd love to continue the conversation. So, we'll send you a note.

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