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

Summary

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

Release Date: July 23, 2025
Podcast: All-In with Chamath, Jason, Sacks & Friedberg
Hosts: Chamath Palihapitiya, Jason Calacanis, David Sacks & David Friedberg
Guests: Jim Latinsky (CEO of MP Materials), Steve (TSMC representative), Chase Lockmiller (CEO of Crusoe), Jensen Huang (NVIDIA), Lisa Su (AMD)

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Introduction

In the third installment of the "Winning the AI Race" series, the All-In podcast delves deep into the crucial components of the artificial intelligence (AI) ecosystem. Featuring industry leaders Jim Latinsky, Steve from TSMC, Chase Lockmiller, Jensen Huang of NVIDIA, and Lisa Su of AMD, the episode explores the intertwined relationships between rare earth materials, semiconductor manufacturing, AI infrastructure, and public-private partnerships shaping the future of technology and national security.

MP Materials and the Rare Earth Supply Chain

Jim Latinsky, CEO of MP Materials, kicks off the discussion by sharing his journey from managing a successful hedge fund to leading MP Materials, which is now the sole supplier and refiner of rare earth materials in the United States. Chamath Palihapitiya commends Latinsky's transformation, highlighting MP Materials' pivotal role in the AI supply chain.

Jim Latinsky [00:24]: "We're 100% of the American industry."

Latinsky emphasizes the significance of rare earth magnets as the "feedstock to physical AI," underscoring their necessity in robotics, drones, and other AI-driven technologies. He details the complexities of mining and refining rare earths, a process dominated by China, and explains MP Materials' strategic initiatives to secure and expand the supply chain domestically.

In recent developments, Latinsky announces two major partnerships:

1. Public-Private Partnership with the Department of Defense (DoD):

   • Investment: $400 million
   • Purpose: Provide a price floor for rare earth commodities to counteract Chinese mercantilism, ensuring sustainability and growth of the U.S. rare earth industry.
   • Impact: Accelerate the magnetic supply chain and expand production capacity with the DoD as a strategic partner.

   Jim Latinsky [04:20]: "This is a true win-win... great for MP shareholders and national security."

2. Partnership with Apple:

   • Objective: Expand magnetic manufacturing capabilities to meet Apple's stringent specifications, securing foundational partnerships in the tech industry.

Latinsky also addresses workforce challenges in the rare earth sector, highlighting the need for skilled labor despite low graduation rates in mining-related fields.

Jim Latinsky [09:38]: "We're training people and there's absolutely talent out there. People are hungry to do it."

Onshoring Semiconductor Manufacturing: TSMC's Arizona Facility

Steve from Taiwan Semiconductor Manufacturing Company (TSMC) discusses the company's first silicon output at their Arizona facility's 2-nanometer line. He outlines the challenges and successes of establishing advanced semiconductor manufacturing on U.S. soil.

Steve [14:04]: "We're super excited about the progress in U.S. manufacturing. Where there's a will, there's a way."

Key Points:

• Workforce Development:
  TSMC faced initial hurdles in hiring and training a qualified workforce but achieved parity in chip yields between their Arizona and Taiwan facilities within a year.

• Cost Competitiveness:
  Manufacturing in the U.S. incurs higher costs, projected to be "low double digits" more expensive than in Taiwan. However, Steve argues that reliability and supply assurance compensate for the increased expenses.

  Jason Calacanis [16:57]: "Because it's unrealistic to think the United States could compete on cost. Am I correct?"
  Steve [16:37]: "We're going to pay a little bit more... less than 20%."

• Scaling Challenges:
  Addressing massive chip demands requires scaling the manufacturing ecosystem, with an emphasis on geographic diversity to mitigate supply chain risks.

AI Infrastructure and Data Centers: Crusoe's Ambitious Build-Out

Chase Lockmiller, CEO of Crusoe, presents a vision for the AI Industrial Revolution, focusing on the exponential growth of AI infrastructure and the accompanying energy demands.

Key Highlights:

• AI Factories:
  Crusoe builds "AI factories"—data centers designed to produce intelligence by processing vast amounts of data and algorithms.

  Chase Lockmiller [31:30]: "We're building AI factories at scale and speed... a gigawatt scale computer to drive human progress forward."

• Energy Consumption:
  AI-driven data centers are projected to consume 10% of U.S. power by 2030, necessitating massive investments in energy infrastructure.

• Modular Construction:
  Utilizing modular components, Crusoe rapidly scales its facilities, exemplified by their gigawatt-scale cluster in West Texas.

• Job Creation and Workforce:
  The AI infrastructure boom will be the largest job creation catalyst in history, requiring millions of skilled workers to build, operate, and maintain these facilities.

• Public-Private Collaboration:
  Partnerships with energy companies like Redwood Materials and GE Vernova underline the necessity of integrated efforts to support AI growth.

Chase Lockmiller [39:28]: "AI infrastructure will be the largest job creation catalyst that we've ever seen."

NVIDIA's Perspective on AI and Semiconductor Leadership

Jensen Huang, CEO of NVIDIA, provides insights into the company's role in advancing AI technologies and the broader semiconductor industry.

Key Insights:

• AI as a Productivity Booster:
  AI enhances productivity across all sectors, enabling companies to execute more ideas efficiently.

  Jensen Huang [47:00]: "AI is creating jobs. It causes us to be able to create things that other people would like to buy."

• AI as an Equalizer:
  AI democratizes capabilities, allowing even non-experts to harness powerful tools.

  Jensen Huang [47:00]: "AI is the greatest technology equalizer of all time."

• Competitive Landscape:
  While China is heavily investing in AI and semiconductor technology, Huang remains optimistic about U.S. leadership due to robust innovation pipelines and strategic collaborations.

  Jensen Huang [53:42]: "Onshoring next generation manufacturing is going to be insanely technology driven..."

• Supply Chain Resilience:
  Emphasizes the importance of an open ecosystem and collaboration across public and private sectors to maintain competitiveness against global rivals.

• Future of AI Infrastructure:
  Predicts a multi-trillion-dollar investment in AI infrastructure annually, essential for sustaining AI-driven industries akin to energy and the internet.

Jensen Huang [51:46]: "We're probably a couple of hundred billion dollars, maybe a few hundred billion dollars into a multi-trillion dollar infrastructure build out per year."

AMD's Role in the AI Ecosystem

Lisa Su, CEO of AMD, engages in discussions about the semiconductor industry's capacity to meet growing AI demands and the strategic importance of onshoring.

Key Points:

• First Silicon Output at TSMC Arizona:
  Lisa Su inquires about the progress and challenges faced by TSMC's Arizona facility, touching on workforce qualifications and scalability.

  Lisa Su [14:36]: "We have to make sure that there's a lot of geographic diversity and capability."

• Competitive Edge:
  Emphasizes AMD's commitment to advancing semiconductor manufacturing to support AI advancements, ensuring that the U.S. remains a pivotal player in the global tech landscape.

• Public-Private Partnerships:
  Advocates for collaborative efforts between the government and private sector to maintain and enhance technological supremacy.

Public-Private Partnerships and National Security

Throughout the episode, the critical role of public-private partnerships emerges as a recurring theme, particularly in securing supply chains and fostering innovation essential for national security and economic growth.

Jim Latinsky [11:07]: "This administration did something totally unique... this deal was led by DoD."

These collaborations are depicted as essential in countering global competitors, particularly China, by ensuring that strategic industries like rare earths and semiconductors remain robust and independent.

Future of AI and Manufacturing

The conversation moves towards the future trajectory of AI and manufacturing, with experts predicting transformative changes across various industries:

• Physical AI vs. Data Center AI:
  Predictions indicate that physical AI, embodied in robotics and autonomous systems, will become a significant market within five years, complementing the existing data center-centric AI deployments.

  Lisa Su [21:47]: "I think it becomes a significant end market."

• AI-Driven Manufacturing:
  Envisions factories orchestrated by AI, seamlessly integrating robotics and intelligent systems to enhance productivity and innovation.

  Jensen Huang [57:38]: "Every industrial company will be an AI company or you're not going to be an industrial company."

• Energy Infrastructure:
  Emphasizes the necessity of scaling energy production to support the burgeoning AI infrastructure, highlighting the parallels with historical energy investments that fueled industrial growth.

Conclusion

The episode culminates in a robust discussion about maintaining and advancing American competitiveness in the global AI race. With insights from industry leaders like Jensen Huang and Lisa Su, the podcast underscores the importance of strategic partnerships, investment in workforce development, and sustained innovation to ensure the U.S. not only keeps pace but leads in the AI-driven future.

Chamet Palihapitiya [56:06]: "We have to continue to advance it. Onshoring next generation manufacturing is going to be insanely technology driven."

The collaborative efforts between government entities like the Department of Defense and visionary companies ensure that the United States remains at the forefront of the AI revolution, leveraging its unique advantages in innovation, resources, and strategic leadership.

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Notable Quotes:

• Jim Latinsky [00:24]: "We're 100% of the American industry."
• Jim Latinsky [04:20]: "This is a true win-win... great for MP shareholders and national security."
• Chase Lockmiller [31:30]: "We're building AI factories at scale and speed... a gigawatt scale computer to drive human progress forward."
• Steve [14:04]: "We're super excited about the progress in U.S. manufacturing. Where there's a will, there's a way."
• Jensen Huang [47:00]: "AI is the greatest technology equalizer of all time."
• Jensen Huang [54:48]: "Onshoring next generation manufacturing is going to be insanely technology driven..."
• Chamet Palihapitiya [56:06]: "We have to continue to advance it. Onshoring next generation manufacturing is going to be insanely technology driven."

This comprehensive discussion highlights the intricate nexus between material supply chains, semiconductor manufacturing, AI infrastructure, and strategic collaborations essential for securing leadership in the global AI arena.

Transcript

[Chamath Palihapitiya]

Guys, this is one of the most amazing entrepreneurs that you're going to meet. Jim Latinsky, this founder and CEO of MP Materials.

[Jason Calacanis]

Good to be here.

[Lisa Su]

How are you?

[Chamath Palihapitiya]

Let me set this up. Jim was a hedge fund guy running a pretty successful hedge fund and he ended up basically investing in something called Molly Corp, which went out of business.

[Jim Latinsky]

Yep.

[Chamath Palihapitiya]

And you did this incredible thing which is 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, I think, supplier and refiner of rare earth materials and maker of magnets inside the United States.

[Jim Latinsky]

We're 100% of the American industry.

[Chamath Palihapitiya]

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

[Jim Latinsky]

Yes.

[Chamath Palihapitiya]

Okay.

[Jim Latinsky]

So yeah, should I take a huge step back?

[Chamath Palihapitiya]

Talk to us why rare earths matter. Tell us about the supply chain for AI. Tell us why you're doing this.

[Jim Latinsky]

So rare earth magnets are really the feedstock to physical AI, you know, robots, drones, everything we're talking about today, the biggest industry in the world to come. Essentially, electrified motion requires rare earth magnets. So you mentioned the predecessor went bankrupt. There was a feeling when I took over this site with my co founder and this goes back to 2015.

[Jason Calacanis]

Where is the site?

[Jim Latinsky]

Oh, it's in Mountain Pass, California. So you'll be familiar if you take a 45 minute drive from the Las Vegas strip. Just over the border in California is the site. You actually can see it from the road and it's actually really the best rare earth ore body in the world. The thing about rare earths is that when you mine them, you also have to refine them. And it's really expensive and difficult to refine them. It's really a specialty chemical process and so it's really a think of it as a multi billion dollar refinery that you need to have just to separate them. And then once you separate them, you need to turn them into metal and then a magnet. And so there's multiple layers of this stream to get this supply chain. And of course you could have all the rare earths in the world, but if you don't make the magnets, you're sending it to China. Or you could have all of the magnetic capability in the world, but if you don't have the rare earths, you're reliant on China and So our vision from day one going back to we originally bought these assets out of bankruptcy. Officially it was a two year battle. Took it out in 2017. And there was a perception that we just couldn't compete against China. And what we discovered actually is we could. It's a world class site, but we had to reorganize the process flow and then we had to make investments to move downstream. So over the last eight years we invested about a billion dollars. Chamath, as you know, we took the company public in 2020. We built out the refining capability and then about four years ago we announced we were going to build a magnetics factory in Texas. We built that factory. We have GM as a foundational customer. We're now producing auto grade magnets to GM spec and we'll be ramping up sales to GM at the end of this year in magnets. And then Chamath, you referenced a couple. It's been a busy few months for us. We announced a pretty transformative public private partnership with the Department of Defense. DoD is, there's really three pillars to this deal. DoD is becoming our largest economic investor as well as they're going to provide a price floor for our commodity so that the Chinese sort of Chinese mercantilism we can get into that won't take the price of the commodity below the cost of production. And then as a result of the DoD investment, we're going to accelerate the build out of the magnetic supply chain. So we're expanding our facility in Texas for Apple. I'll talk about that in a second. But we're then going to build a 10x facility to 10x our capacity with DOD as our 100% offtake partner, customer and business partner because we'll be splitting profits 5050 with DOD.

[Jason Calacanis]

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

[Jim Latinsky]

Yeah. So they invested. They both are an owner. They also are an upside participant in our commodity to the extent that the prices take off and then they're also 100% off take customer. We have a guaranteed level of profits to want to build out this facility. But above a certain threshold there are 50, 50 economic participants. They mean there's really you, the taxpayer. Yeah. So this is a, and maybe I'll say something wild here, this is a true win win, obviously great for MP shareholders. Great from a national security and commercial national security standpoint because we're going to have enough magnets to provide, you know, real certainty in the supply chain for the physical AI revolution and other industries. But it would not surprise me if when we, five years from now, hopefully we'll do this conference and chamath, you'll say to me, Jim, you know, I remember that deal. That was the first of its kind that you did with DoD, and the government made money on you, the taxpayer made money on doing this. And I'll say, yeah, I actually think that that's going to be the outcome because there's sort of an element of mutually assured economic destruction. If the Chinese believe that America has national champions too, then there's no point in subsidizing the rest of the world. And so I think you can start to see prices normalize for some of these things and free up our ability to invest and expand.

[Jason Calacanis]

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

[Jim Latinsky]

Well, because it's that issue. This is sort of one of those, you know, obviously you have to go back to World War II or the railroad boom, where you really need government and credit. I mean, this administration did something totally unique. That.

[Jason Calacanis]

Which piece. Why do you need the government?

[Jim Latinsky]

Mercantilism. Straight up mercantilism. Because the Chinese will sell magnets for below the cost of raw materials. And so every time there's somebody who makes progress, they can put them out of business overnight. And so it's difficult to want to make the investment. And so frankly, with the Department of Defense, the scale that they wanted us to build on, the timeframe that they wanted us to build, we. There was no way we were going to make that commitment. We're fiduciaries, right? We have shareholders. There's no way we're going to make that commitment without certainty that we would not be destroyed by mercantilism and that we would have a customer for the magnets.

[Chamath Palihapitiya]

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

[Jim Latinsky]

Yeah, Well, I think that that is a question for. There's much smarter guests on this. For the rest, I'll give a plug the rest of the day. Obviously, you have the, you know, the best of the best providing that feedstock. I will say that I think one of the big drivers of our deal was the. As we've seen in Ukraine and the Middle east, the future of warfare is physical AI, right? Robots and drones. And I think irrespective of the scale that robotics is ultimately going to be, and certainly the commercial business will be bigger than, you know, the defense needs. But just from a defense standpoint, this is a really important supply chain that we must have. We can't be funding cutting edge drone and robotics companies and then say, okay, but we're going to buy those magnets from China. That makes no sense.

[Lisa Su]

Do we have talent capacity or do we have a talent shortage? Secretary Burgum gave me a stat which was pretty shocking to me 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 to build the industry here?

[Jim Latinsky]

It's a great question, I think, Jason. I think about this question a lot.

[Jensen Huang]

What's that, Dave?

[Jim Latinsky]

Oh my God. Dave. Sorry.

[Lisa Su]

No, no, talk it.

[Chase Lockmiller]

I'm a huge fan of a pot.

[Jim Latinsky]

And I just embarrass myself all the time. And you know, I'm a fan of.

[Jensen Huang]

The Potsies day one.

[Chamath Palihapitiya]

And I've totally.

[Lisa Su]

That's only one correction I'll make.

[Jim Latinsky]

Nor am I messing with you. Was this intentional?

[Jason Calacanis]

So, huge fan of the podcast.

[Jim Latinsky]

Yeah. Huge fan of the pod.

[Jensen Huang]

Who are you?

[Jason Calacanis]

Take a selfie later.

[Lisa Su]

And I'm not the Aizar. Go ahead.

[Jim Latinsky]

So we have 850 employees today at MP we're going to hire. When we include what we're building out for Apple coupled with what we're going to build with, with dod, we're gonna need a couple thousand more people easily. Not to mention the construction jobs. So this is a key existential question for all of us as we build out. This is where are we gonna get the talent? I think what we have found at Mountain Pass and we hire it all electricians, maintenance, operators, is you get people in, you train them, and then obviously you give people a career. And so we've been training a lot of people and it's a little bit more painstaking, but there's, there's absolutely talent out there. People are hungry to do it.

[Chamath Palihapitiya]

Why do you think it's been so hard to establish that that idea, like, meaning you find it straightforward to find good, hardworking people to get into these jobs, but the same, the thought is always that, wow, these jobs are not desirable, but they really are desirable by many people.

[Jim Latinsky]

Yeah, absolutely. I mean, our median wage is now pushing $100,000 a year. And there's, you know, relative to some of the opportunities that these are great. These are great jobs. And what are the salaries, what's that?

[Jason Calacanis]

What's the starting salary?

[Jim Latinsky]

It really depends on the job function, because lowest starting job. I mean, I think the easiest way to think about it is you can certainly as an operator make close to $100,000 a year with us. Because, by the way, everybody's an owner. We have an owner operator culture. Everyone got stock when we went public in 2020, but somebody coming out of.

[Jason Calacanis]

High school, they can make 4, 40, 50, 60k or more.

[Jim Latinsky]

Yeah, it depends. If we can't find enough electricians, we can't find enough maintenance workers. A maintenance worker can. An electrician, they can make six figures today.

[Chamath Palihapitiya]

Tell us, you said earlier that you suspect five years from now we're going to 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.

[Jim Latinsky]

Yeah, there are some major categories, obviously, we've all heard about shipbuilding and advanced pharmaceutical ingredients. I mean, I think those are important ones. And then there are a number of sort of niche areas like industrial diamonds that are important for quantum computing and some of these things that you never would have thought of, where it's a vertical where there might not be a market large enough to need five players, but a good public private partnership can just solve that problem. And then there's some other verticals and critical minerals.

[Chamath Palihapitiya]

It's 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.

[Jim Latinsky]

Well, and I think that's our particular deal was led by dod. And so I have to say that the Pentagon leadership is extraordinary. And this was a mandate, though, directly from the President to solve this problem. And so again, they deserve a lot of credit for being, you know, bold here. And to be clear, because, you know, this story's not out there, our process, this was. I've never worked so hard in my life. I mean, this was. This was like a true aggressive private equity style investment and negotiation. The transaction documents are public. You can look at that.

[Chamath Palihapitiya]

So, yeah, and that's. They're tough.

[Jim Latinsky]

Yeah, they are. This was. This was as tough as it gets. Tougher than, you know, think of any, you know, blue chip private equity or distressed lender type negotiation. That's what this was. And the key thing was they were going to hold our feet to the fire to execute on an aggressive timeline. They were going to hold our feet to the fire on the costs. And so we're exposed if we get the costs wrong. We're making this investment. The key piece of this, which I think is a good model for all of us and actually will be really effective, is the goal. I don't speak for them, ask them. But I think their goal was we're going to take the things off the table that you can't control. Mercantilism, certain customer issues. We're going to be held to account for the things that we can control. Our ability to execute, our ability to execute on a good timeline, and our ability to control costs. So when we think about a lot of these, historically, the government investing in a sector and picking a winner, usually there's sort of money given to someone and it's sort of public risk, private upside. Right. This is not that. This is private risk, public risk, public upside, private upside. It's a true shared win, win, win. And again, like I said, hold me to these words. I hope I'm right on this. But I think to the credit to the Trump administration, I think they will make money on this and have solved the national security.

[Jason Calacanis]

All right, we appreciate you coming.

[Jim Latinsky]

Oh, thanks.

[Jason Calacanis]

Thanks so much.

[Chamath Palihapitiya]

Thanks, brother.

[Jim Latinsky]

Yeah, it's great.

[Jason Calacanis]

All right, take care, Steve.

[Lisa Su]

Thanks, Jacob. Hi, Lisa.

[Chamath Palihapitiya]

Hi.

[Steve]

Nice to see you.

[Jason Calacanis]

Lisa, it's a pleasure.

[Steve]

Hi, nice to meet you.

[Jason Calacanis]

Hi.

[Chamath Palihapitiya]

Hi.

[Jim Latinsky]

Hi.

[Steve]

Hi.

[Lisa Su]

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 2 nanometer line. This administration and the private sector have talked a lot about onshoring semiconductor manufacturing. Would love your thoughts of 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?

[Steve]

Well, 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. And I thought if we're going to talk about chips, David, I should actually bring one, if that's okay. A little bit of show and tell. So this is our latest generation AI chip. It's our Mi355 chip. 185 billion transistors. Takes about nine months to build. Lots of technology on it. If I just. This is 3 nanometer and 6 nanometer. So lots of different chips.

[Lisa Su]

I'll be putting this on ebay later.

[Steve]

I'm going to take it with me when I was at.

[Chamath Palihapitiya]

Thank you.

[Steve]

But look, to answer Your question? I think, look, these AI chips are extremely, extremely complex. They have so much technology on it. We're super excited about the progress in US manufacturing. I would say 12 months ago, people weren't sure that we could do leading edge manufacturing in the United States. We've been very early in Arizona with TSMC and we did get our first chips out. They're actually 4 nanometer. But what we see from it is where there's a will, there's a way. And I think all of the conversation about onshoring manufacturing has been super good for the semiconductor industry and frankly for all of us in semiconductors were in such an interesting place because chips are so essential to ensuring that we are able to win the AI race that we want to make sure that there's a lot of geographic diversity and capability there.

[Lisa Su]

But the reports out were that TSMC couldn't get good qualified, trained employees. They had to bring folks over. Is that accurate? And again, if we're going to scale, what's the order of magnitude we're going from here? Is it 10x100x? And how are we going to build a workforce to support this industry, which is a completely new industry for America?

[Jason Calacanis]

And Lisa, you have permission to speak freely.

[Steve]

The best way to say it is no matter when you start something new, it's going to take work, it's going to be hard. So sure, in the beginning there were some issues of TSMC has a formula for how they build and they just rinse and repeat and they've learned how to do that well in Taiwan. So they had to learn how to do it well in the United States. But I have to tell you, we've been super impressed with the progress. And if we look at the main thing that we look at is yields and just how many chips do we get out on a given wafer? And I would say it's equivalent between what we get in Taiwan and what we get in Arizona.

[Jason Calacanis]

Because it's unrealistic to think the United States could compete on cost. Am I correct?

[Steve]

We're going to pay a little bit more.

[Jason Calacanis]

Give us the ballpark, 50% more, 20% more.

[Steve]

Not 50% more. I mean, look, it's going to be more than 5%, but let's call it less than 20%.

[Jason Calacanis]

So.

[Steve]

Let'S say low double digits.

[Jason Calacanis]

And how does that impact the business, if at all in terms of competition globally?

[Steve]

Well, I think the important thing is, I mean, just think about like everybody wants a gpu, right? Like if you look across the industry, you really say you know, the people who are going to win in AI want to have as much compute in their foundation as possible, and they want assurance of supply. We want to be able to supply this no matter what happens. And so if you put that in context, the fact that you're not going for the lowest cost every minute of the day is okay. It's okay. Obviously, we're not going to build. Not everything needs to be in the most advanced technologies. And so we have a very geographically diverse supply chain. I think Taiwan continues to be important in that view. But the focus from this administration on getting onshore manufacturing in a big way, not in a small way, I think is very good for us.

[Jason Calacanis]

How much of time do we have if there was a disruption for whatever reason, we can come up with hypotheticals in Taiwan and we were unable to get chips from those factories. What would that look like globally?

[Steve]

Yeah, you have to look across the supply chain. But from a structure standpoint, we all want to keep reserves for those times, but it's months, it's not years.

[Chamath Palihapitiya]

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

[Steve]

Yeah, it's a great, great point. I mean, that's what we're seeing. We're seeing this incredibly large demand for AI, and they're coming from Sam and Elon are certainly the leaders, a couple of the leaders. There's a lot of demand elsewhere, too. I mean, if you think about it, nations want their own AI, so there's very high demand. We're imagining that just the accelerator market. So the chips for these AI large computing systems will be like over $500 billion in a couple of years. So very high growth. And when you say, what do we need to do? The entire ecosystem needs to scale up. So we need to scale up. Certainly what we're doing in chip design is trying to get chips out as fast as possible, but we're also scaling up the entire manufacturing ecosystem. And, you know, as I said, I don't. I think the US Is going to be a huge piece of it. So it's not just about the silicon. There's all of the various other pieces of the ecosystem that have to come to the US and, and I think, look, I think today's AI action plan is actually a really, you know, excellent blueprint.

[Chamath Palihapitiya]

And how do you see the market evolving in these next five or six years? Is it there's a standard set of chips for training, a standard set for inference, or do you just see an explosion, like a Cambrian explosion, of different ASICs, different designs, different use cases?

[Steve]

Yeah, I like that question because I am a believer in there will be diversity of chips. The reason is there's so many use cases. If you think about use cases from whether you're talking about science or manufacturing or design or backend or frankly, personal AI. I think we're going to see AI in everything that we do, certainly in your phones and your PCs. And so you have all these pieces, you're going to have different types of chips that do that. Certainly for the largest systems, we tend to believe that you need the most compute you can get. And so GPUs are there, but lots of ASICs are in the process and we'll see a variety of different chips.

[Jason Calacanis]

You opened up a really interesting line of questioning there when mainframes were so expensive and then eventually wound up having PCs that were more expensive on their desktop. Alluded to AI being run locally.

[Steve]

Yes.

[Jason Calacanis]

When would we have a local computer, a laptop, a desktop computer that would have the power we're seeing to run some of these LLM models in your mind? And do you see that as a specific market to go after?

[Steve]

Look, I definitely see the idea that AI will be at every part of our ecosystem is a real thing. I think that's one of the advantages. If you think about the power of AI, you want it everywhere and you want it across all different applications. And I think when you think about PCs today, we're putting significant amount of AI in them to run local models. And why would you want that? It's like, well, maybe I don't want all my personal data all over the.

[Lisa Su]

Place on that point. Can you make a prediction on when the market for physical AI chips is greater than the market for chips and data centers?

[Steve]

That's a great question. I'm a big believer in physical AI. I still think it's, let's call it five years.

[Lisa Su]

You think five years is that fast?

[Steve]

It's at least five years.

[Lisa Su]

So you're saying five plus.

[Steve]

Five plus. Yeah.

[Jason Calacanis]

Okay.

[Lisa Su]

But that is ultimately the biggest end market. Do you think physical AI becomes the Biggest end market.

[Steve]

I think it becomes a significant end market. I think you look at chips in data centers and you look at chips at the edge. There are also significant markets.

[Chamath Palihapitiya]

When you look at the most cutting edge techniques today, EV lithography, all of this whole stuff to make chips. One of the things that's observable is we're only as good as what humans have been able to invent. And I often ask the recursive question, what happens when the AI is able to invent its own method of manufacturing different materials, different material sciences, different approaches that we may not necessarily understand. Is any of that R and D happening whether in AMD or in other places? Like how are we trying to get beyond the physical limits of electrons shunting across a junction?

[Steve]

I think this idea that the AI can be extremely smart and extremely capable. Like we think about how AI can design the future chips and it will design pieces of it, but there's still a creativity of bringing it all together that I think humans are still absolutely at the center of that. So I don't necessarily see the AI designing our next generation gpu, but I do see it helping us design the next generation GPU much faster and more reliably.

[Dave]

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

[Steve]

Look, I think we're going to, we have to accept the fact that it's a global supply chain. Even if you were to reshore X number of components, you would still have Y components that are across the world. I think it's important for us to have our allies together. So that's a key piece of the conversation and ensuring that we have access to the latest generation technologies. And that is something that we protect given our intellectual property.

[Lisa Su]

Going to first principles and asking you the open ended question, what should be done about American education? I'm going to ask this a lot today. 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?

[Steve]

Yeah, I'm probably a little bit biased as maybe some of your guests are today. I'm a big believer in science and technology background as being sort of the STEM background is so helpful when we think about the future workforce. And the earlier we can get into the process, I think the better. So some of the work that's being done to kind of revitalize the curriculum, I think is pretty important in the sort of the next generation workforce. And one of the things, when I think about how we win in AI, there's so many aspects of it, but ensuring that America is the best plays for AI talent is also a key piece of that. So kind of inspiring people when they're young to really study science.

[Jason Calacanis]

Lisa, when you go to bed at night and you think about the best case scenario for this technology and this trajectory we're on, which is accelerating and you're enabling, what could the world look like in 10 years? Let's say pretty obvious we're hitting artificial general intelligence at this moment. I think we'd all agree we're starting to see that, but superintelligence can't be far behind that. I assume you agree with that. Assume we hit that superintelligence. What would the world look like in 10 years in the most optimistic scenario, if we do this right?

[Steve]

Well, I think the exciting part about it, and I can say this very sincerely, I mean, this is the most transformational technology sort of in our lifetimes. I mean, that's the way we should.

[Chamath Palihapitiya]

Think about orders of magnitude.

[Steve]

Orders of magnitude. And the reason is it's not just going after one aspect. You can actually take A.I. and make science better. You can take A.I. and make medicine better, you could take A.I. and make manufacturing better. You can take AI and make every aspect of your business better. And so in my mind, 10 years from now, we'd like to believe that we are really leveraging it to solve some of the world's most important problems. I like to say Amders get up in the morning and they say, how can I use technology to solve some of the most important challenges in the world? And AI is really our mechanism for.

[Chamath Palihapitiya]

Doing that, I have a business strategy question. If we went back 20 years and we wrote the tale of three companies, Nvidia, AMD, intel, and then you fast forward it 20 years, two have just absolutely thrived and one has not. And if you had made the bet back then, it would have been very inconclusive that you would have picked Nvidia and amd. And if anything, there was an amount of inherent belief that intel had just figured it all out. Can you just tell us sort of like the lessons learned of why you thrived and maybe what you take away from Their journey that you make sure AMD doesn't play out well.

[Steve]

You know, as a CEO, we have to be paranoid every single day, right? So we don't rely on the past. But I think there are lessons of the past. And I think that probably the most important lesson that I can say for technology is you have to shoot ahead of the duck. You have to be thinking, what is the most like your question, Jason? Great question. We think about that all the time. How do we shoot ahead of the duck? And you have things that change. Technology is a beautiful place because you see big inflection points. Like five years ago, AI was around, but we wouldn't be able to gather this audience to talk about AI because people would be like, who cares? But the fact is you had to invest many, many years ago to be where we are today. And I think, I like to say that you will be able to judge whether we've done a good job or not by how we perform. Five years from now. The decisions we're making will take five plus years to play out. But that's the key thing in tech. Nothing is fast, but hopefully it's quite lasting in what it takes.

[Chamath Palihapitiya]

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

[Steve]

We should believe that it's super, super competitive. I mean, at the end of the day, I think the world has recognized that semiconductors and chips are essential. They're essential to national economies, they're essential to national security and so assume that everyone's investing. I'd like to believe that we have a great head start because of the innovation pipeline, because of the great companies that we have here. But we should not be confused that everybody's investing and we need to keep up our investments as well. I think that's why this whole idea of any one company can provide every solution that's necessary just isn't the case. I love the idea of open ecosystems, of companies collaborating, of collaboration across the ecosystem, so hardware, software, systems, collaboration across public private partnerships, because that's what it's going to take for us to win. We have to be front facing and realizing that bringing the countries that win, bring all of the smartest people and the best capabilities together and let them go as fast as they possibly can.

[Lisa Su]

So thank you for being with us.

[Steve]

Wonderful.

[Lisa Su]

It's been great. Appreciate it. Thank you.

[Steve]

Thank you.

[Chamath Palihapitiya]

Pleasure to meet you.

[Steve]

Thank you.

[Chase Lockmiller]

I'm Chase Lockmiller, 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, and it's from Warren Buffett in his 2020 shareholder letter, shareholder letter to investors. And he said, 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 it will continue to do so. So in sort of the history of really, what's made America great is, you know, we live in a nation that's the freest nation in the world, and we have. We are just as rich in land and resources as we are in human ambition to drive progress. And one of the things that's fundamentally enabled that progress to happen and that ambition to be unleashed is the leading investments that we've made in infrastructure. Over the course of his lifetime, Warren Buffett got to witness investments in power, in transportation, and in power, in transportation and in natural resources to enable people to go pursue their dreams and live a better life.

[Jensen Huang]

Let's see.

[Steve]

There we go.

[Chase Lockmiller]

Now, in 2025, we stand at a, you know, the start of a new era of infrastructure, the infrastructure of intelligence. And it's driving the biggest capital investment in human history. This investment is being led by the hyperscalers who are investing hundreds of billions of dollars per year per year to make this happen. These are the companies with the biggest balance sheets in the history of business that are quite literally going all in to make this happen. And they're not the only ones. You know, there's also startups like Crusoe, and there's even nation states that are following suit. So what's going on there? What's the prize that they're going after? The opportunity here is that for the first time in human history, we've actually been able to manufacture intelligence. Intelligence is the scarcest economic resource in the history of the economy. And for the first time, we're actually able to make it. And the opportunity here is to actually unlock access to what has historically been that scarce economic resource. So this is why the data centers of the future are not being referred to as data centers. They're actually 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, 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 1/2% 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, you know, at the end of 24 it was only four and a half gigawatts. Today we have companies that are looking at building single five 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 Crusoe is Focused on solving. Crusoe is in the business of activating energy for intelligence, of building operating AI factories at scale, from 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, 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, Crusoe's been building a large, large scale AI factory in Abilene, Texas. And you know, speed is paramount. Again, this event is winning the AI race. In order to win a race, you really need speed. And Crusoe'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 gigawatts 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 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. 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 in engine number one for four and a half 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 Tallgrass 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 Tallgrass. 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.

[Dave]

So is this stuff real? You guys started off as a bitcoin miner, and now somehow all the hyperscalers are asking you to build nonstop data centers. Why you guys?

[Chase Lockmiller]

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

[Dave]

And I know there's been like some of the biggest constraints around, you know, sort of, you know, water energy, you know, the land for this type of stuff. Like where have you seen what parts of the country, you know, 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?

[Chase Lockmiller]

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

[Dave]

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, et cetera, will start to, you know, sort of get together and really realize they've got cheap hydropower and cheap water and we'll try and get you there.

[Chase Lockmiller]

Believe it or not, we're actually looking at something in California.

[Dave]

Wow, California. Gavin Newsom is going to bring you in? I would imagine it's going to take like 50 years with that regulation.

[Jensen Huang]

Yeah, maybe.

[Chase Lockmiller]

We'll see.

[Dave]

Do you think that the hyperscaler demand, obviously, we were just on with Lisa Su talking about the demand for chips over the next couple of years. That's obviously correlated to the demand with data centers. Do you think that's actually going to play out the way that all the public markets are projecting, or are we 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.

[Chase Lockmiller]

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

[Dave]

So I felt like Meta backed off on it a little bit. Like, did they like for a little bit talk 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.

[Chase Lockmiller]

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 and best balance sheets in the history of business. And they're betting their entire balance sheet on the future infrastructure that's going to power the modern economy.

[Dave]

And then data centers like Texas. What's the limiting factor? Is it 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 contracts? What's the limiting reagent?

[Chase Lockmiller]

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

[Dave]

Anything that requires some real reskilling, where it's like people from oil and gas or like construction having to. To just totally net new fields. Or is it something where you guys are actually able to pull on preexisting talent pools pretty quickly?

[Chase Lockmiller]

Both. There's a lot of existing labor at that facility in Abilene. We're actually pulling labor from all 50 states at this point, believe it or not.

[Dave]

Make it like a company town, importing people in.

[Chase Lockmiller]

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

[Dave]

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

[Steve]

Yeah.

[Chase Lockmiller]

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

[Dave]

Makes sense. Well, yeah, Chase, thanks so much for joining us on stage and yeah, thank you. Appreciate the talk.

[Jason Calacanis]

Yeah, thanks D. Okay, everybody, we got a real treat for you. Jensen Huang is here. Sit here, sit here, sit here.

[Lisa Su]

The hot seat. Thanks for coming.

[Jensen Huang]

Thank you.

[Lisa Su]

Great to have you.

[Jensen Huang]

Thank you. The number one podcast.

[Lisa Su]

Podcast in the world, we were saying. The number one company in the world.

[Jason Calacanis]

Wow.

[Lisa Su]

Thank you.

[Jensen Huang]

You're a fan of the Pod.

[Jason Calacanis]

You listen to the pod.

[Chamath Palihapitiya]

This is Norman, our host.

[Jason Calacanis]

Yeah. Yes. And there's Steve. What's the story with the jacket? You got one of those? You have like six.

[Jensen Huang]

I have something like 50 or 60 of them.

[Lisa Su]

You really?

[Jensen Huang]

Yeah.

[Lisa Su]

Wow.

[Jason Calacanis]

What is that? Tom Ford?

[Jensen Huang]

I think so. This one is. I think.

[Jason Calacanis]

Yeah. It's nice. I like it. I tried that on. It was like, you way too much money.

[Jensen Huang]

Well, you guys are all so fashionable.

[Jason Calacanis]

Yeah.

[Jensen Huang]

Coming from you guys, it actually means something. Yeah.

[Jason Calacanis]

Oh, yeah.

[Jensen Huang]

Oops. Oh, look at you. Look at you.

[Jason Calacanis]

Hey, we've been talking a lot about opportunity. You've talked.

[Jensen Huang]

Shaman is like a model.

[Jason Calacanis]

He is. He is.

[Lisa Su]

Okay. He's definitely in his head, he's like.

[Jason Calacanis]

Is Tom Ford your favorite? Who's your favorite?

[Jensen Huang]

My favorite is whatever my wife gets me.

[Jason Calacanis]

Ah, she dresses you as soon as she gets it.

[Jensen Huang]

For me, it's my favorite.

[Jason Calacanis]

Yes. Same with me.

[Lisa Su]

Smart man.

[Jensen Huang]

Nobody wears a suit better than Jacob. Good God.

[Jason Calacanis]

Yeah, he's a handsome man.

[Chamath Palihapitiya]

Just trying to keep up with you guys.

[Jason Calacanis]

I have two questions for you. Take them in whichever order you like. 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. What you think? What you think about job creation, transfer, displacement, et cetera. 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.

[Jensen Huang]

But he's great.

[Jason Calacanis]

He's great. I'm joking. I'm joking. How do you allocate the H1 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.

[Jensen Huang]

First of all, I wrote off $5 billion worth of hoppers. If anybody would like to have some extras, just give me a call. Jobs. We use AI across the 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 think the first idea is, the more productive you are as a company, so long as you have more ideas, you could pursue those ideas. You'll go after those ideas. And I think that AI in my case, is creating jobs. It causes us to be able to create things that other people would. Customers would like to buy. It drives more growth. It drives more jobs. All that goes together. The other thing to remember is that AI is the greatest technology equalizer of all time.

[Jason Calacanis]

Okay, explain.

[Jensen Huang]

Everybody's a programmer now.

[Jason Calacanis]

Yes.

[Jensen Huang]

You used to have to know C and then C and Python. And, you know, in the future, everybody can program a computer, Right? Just have to get up. And if you don't know how to program a computer, you don't know how to program an AI. Just go up to the 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. What's the best way to ask the question? And I'll actually write the question for you. It's incredible.

[Jason Calacanis]

Yeah.

[Jensen Huang]

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 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. 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.

[Jason Calacanis]

Yeah, you can't raw dog it.

[Jensen Huang]

No, no, not anymore. Not anymore. You can't raw dog it. I'll be sure to go home and tell people.

[Chamath Palihapitiya]

Yeah, exactly.

[Jensen Huang]

You're not going to raw dog this.

[Jason Calacanis]

Yeah, get your copilot on. Now, what about the allocation of all these?

[Jensen Huang]

Okay, so the way we allocate is this. The way we allocate is this place a po.

[Jason Calacanis]

Okay, you go to the register, you.

[Jensen Huang]

Pay, you order first. You know, first. In the old days with Hopper, it happened so fast it was impossible to keep up with the demand. But now we disclose our roadmap to all of our partners 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.

[Jason Calacanis]

What's the lifespan now? I was looking into how they're amortizing these units four or five years. What happens to this massive build out in year 6, 7 and 8? What will be the use of those computers if you keep building such great products that replace them at 2, 3, 4 times? What do we do with all that.

[Jensen Huang]

Concepts are happening right now? The first thing is every generation we increase the performance by X factors. If the perf per watt goes up by X factors, whatever your data center power is, we just increase your revenues by X factors.

[Chamath Palihapitiya]

Right.

[Jensen Huang]

So perf per watt is equal to revenues. Perf per dollar equals the cost. And so when we increase our perf per dollar by X factors, we reduce your cost by X factors. Does that make sense? That's 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. It's not that we're trying to make 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 it generates an answer. As a 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 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 it's 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 in the time that we shipped it. Now, you can't get that out of a cpu, right?

[Chamath Palihapitiya]

Jensen, can you explain to us Elon's tweet and the impact to your industry? He said, we're going to have 50 million H100 equivalents in five 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, et cetera, et cetera, et cetera. Can you just explain to us Layman what that means, what he just said, and how it impacts your business?

[Jensen Huang]

One of the biggest observations about AI is that there's the industry of applications that AI has created as a revolutionary technology. Every industry 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 and so forth, we know all those things now. Every industry, healthcare, education, transportation, you name manufacturing, all revolutionized. The one part 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 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% 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 build out per year. Yeah.

[Lisa Su]

What about manufacturing?

[Jensen Huang]

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

[Lisa Su]

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

[Jensen Huang]

First of all, you guys know you're talking about the United States. I know that there's lots of concerns and everybody's worried about competition and things like that. But we are talking about America here. 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 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. Onshoring next generation manufacturing is going to be insanely technology driven. 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.

[Chamath Palihapitiya]

Right.

[Jensen Huang]

So you're going to have this layers of inception and the amount of technology necessary to create that is really insane. 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, 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 with AI supercomputers will probably drive a few trillion dollars worth of AI industry. And so that's only in the next several years. And, and they're doing great, Arizona is doing great.

[Chase Lockmiller]

And so there's, there's a lot of.

[Chamath Palihapitiya]

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.

[Chase Lockmiller]

And so as a CEO of a global company, what do you see are America's unique advantages that other countries don't have?

[Jensen Huang]

America's unique advantage that no country possibly have is President Trump. And let me explain why. One, 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 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, and 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 new industry and you know, go after the, the new industrial revolution. Big, big deal.

[Lisa Su]

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

[Jensen Huang]

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 lawnmower 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 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 a machine factory to build human 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 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 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 can't handle it with a person come in to intercept. And so I think you see that these industries in the future, every industrial company will be an AI company or you're not going to be an industrial company.

[Chamath Palihapitiya]

There was 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 Sea moment. Then maybe this week, last week there was this Kimmy model moment. But then it kind of fizzled out. Can you just 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.

[Jensen Huang]

Yeah, excellent question. 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 Seq. When it came out, it was a great win for the United States. It was an incredible win. What people did and two reasons. First, imagine if Deep Sea came out and only ran on Huawei. I just want us to pretend use that thought experiment totally right.

[Jason Calacanis]

You got two parallel universe.

[Jensen Huang]

Exactly. Could you imagine if QN came out and only worked on non emerg Tech Stack? Could you imagine if Kimi 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.

[Chamath Palihapitiya]

Yeah.

[Jensen Huang]

And half of the world's developers are in China.

[Chamath Palihapitiya]

So speaking of developers, the second, the.

[Jensen Huang]

Second, I'm sorry, the second thing is really 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 training is basically memorization and generalization, two concepts. Post training is teaching you how to think. And so now with deep seq R1 Kimi Kimi K2Q1 3 you now have reasoning models that can allow to 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. Yeah.

[Chamath Palihapitiya]

The last question for me is that we see 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 dollar contracts. Now it's, you know, model researchers. And then there was a, there was a post this weekend 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?

[Jensen Huang]

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 they're doing, don't feel sad for anybody at my layer.

[Jason Calacanis]

Everybody's doing okay.

[Jensen Huang]

Yeah, my layer is doing just fine. I tell. 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 create with enough funding behind them. Create an open AI.

[Chamath Palihapitiya]

It's a, it's not a 150 people.

[Jensen Huang]

Yeah, it's not a, it's not. Well, deep seeks 150 people. Moonshots. 150 people.

[Chamath Palihapitiya]

Right, right.

[Jensen Huang]

And so I mean look at the original OpenAI was about 150 people, DeepMind, you know, and they're all about that size I think, I think, you know, there's something about the elegance of small teams. And that's not a small team, that's a good, good sized 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?

[Lisa Su]

Right, yeah.

[Jason Calacanis]

Speaking of options, by the way, somebody.

[Lisa Su]

Told me we need to wrap because.

[Jason Calacanis]

I'm going to do this one question. Somebody who was inside Your organization told me with the options, that you have a secret pool of options and that you will randomly, just, if somebody does a great job, drop a bunch of RSUs on top of them, and that you have this, like, little bag of options you carry around and that you give them out.

[Jensen Huang]

That's nuts.

[Jason Calacanis]

Is that true?

[Jensen Huang]

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.

[Jason Calacanis]

Yeah.

[Jensen Huang]

At the end of every cycle when they present it. And they said, 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 takes care of.

[Jason Calacanis]

All right. Well done.

[Lisa Su]

Thank you. Thank you.

[Jason Calacanis]

Joseph.

[Chamath Palihapitiya]

Great to see you.

[Jensen Huang]

It's great to see you.

[Lisa Su]

We have an event in la. We'd love to continue the conversation.

[Jensen Huang]

So we'll send you the world's number one podcast.

[Lisa Su]

There you go. Thank you.