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

(0:00) James Litinsky, MP Materials (13:32) Lisa Su, AMD (29:45) Chase Lochmiller, Crusoe (43:26) Jensen Huang, Nvidia Thanks to our partners for making this happen: NYSE : https://www.nyse.com Visa: https://usa.visa.com Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Chamath Palihapitiya?

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

• 0:08Speaker 01

  Good to be here.

• 0:09Chamath Palihapitiya?

  Hey, how are you? So, let me set this up. Jim was a hedge fund guy, running a pretty successful hedge fund.

• 0:19Chamath Palihapitiya?

  And he ended up basically investing in something called Molly Corp., which went out of business.

• 0:24Speaker 01

  Yep.

• 0:25Chamath 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.

• 0:45Speaker 01

  We're 100% of the American industry.

• 0:47Chamath Palihapitiya?

  100% of the American industry. You just did two really incredible things, actually, in the last couple of weeks. One was you announced an enormous public-private partnership with the DOD,

• 0:58Chamath Palihapitiya?

  $400 million, etc. And then the second is you announced a really big deal with Apple.

• 1:04Speaker 01

  Yes.

• 1:04Chamath Palihapitiya?

  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.

• 1:13Speaker 01

  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.

• 1:28Speaker 01

  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.

• 1:38Chamath Palihapitiya?

  Where is the site?

• 1:39Speaker 01

  Oh, it's in Mountain Pass, California. So, if you, 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 a 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.

• 2:23Speaker 01

  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.

• 3:08Speaker 01

  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 commodities 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.

• 3:49Speaker 01

  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 50/50 with DoD.

• 4:13Jason Calacanis?

  So to just translate this, it's not a handout from the government?

• 4:15Speaker 01

  Not at all.

• 4:16Jason Calacanis?

  They didn't give you 400 million dollars? They invested in your company, they have warrants, they have equity?

• 4:20Speaker 01

  Yeah. So they invested, they both are an owner, they also are an upside participant in our commodity. The extent that the prices take off and then they're also 100% offtake customer. We have a guaranteed level of profits to want to build out this facility. But above a certain threshold, they're a 50 50 economic participant.

• 4:39Chamath Palihapitiya?

  They mean the American people.

• 4:41Speaker 01

  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.

• 5:25Speaker 01

  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.

• 5:39Chamath Palihapitiya?

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

• 5:43Speaker 01

  Yeah, 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, you know, totally unique that.

• 5:58Chamath Palihapitiya?

  Which piece, why do you need the government?

• 6:00Speaker 01

  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, 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.

• 6:32Chamath 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.

• 6:43David Friedberg?

  Are they actually being deployed at the scale and at the pace that we've been told?

• 6:48Speaker 01

  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 best of the best providing that feedstock. I will say that I think one of the big drivers of our deal was, 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 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,

• 7:31Speaker 01

  but we're going to buy those magnets from China. That makes no sense.

• 7:34David Friedberg?

  Do we have talent capacity, or do we have a talent shortage? Secretary Bergam 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?

• 7:52Speaker 01

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

• 7:56David Friedberg?

  On Dave's.

• 7:56Speaker 01

  What's that?

• 7:57David Friedberg?

  Dave.

• 7:58Speaker 01

  Oh, my God, Dave, sorry.

• 7:59David Friedberg?

  No, no, talk ahead.

• 8:00Speaker 01

  I'm a huge fan of the pod, and I just embarrass myself.

• 8:03David Friedberg?

  This is used all the time.

• 8:04Speaker 01

  It's the old, it's the joke in whites. I know. And you know I'm a fan of the pod since day one, and I totally embarrass myself. There's only one correct one all day. Nor am I messing with you. Was this intentional?

• 8:16David Friedberg?

  Huge fan of the pod.

• 8:19Speaker 01

  Yeah, huge fan of the pod. Who are you again?

• 8:21David Friedberg?

  We'll take a selfie later.

• 8:22Speaker 01

  And I'm not the AI czar. Go ahead. 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 DoD, we're going to 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 going to get the talent? I think what we have found... Mountain paths and we hire it all electricians, maintenance, you know, 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

• 9:05Speaker 01

  and it's a little bit more painstaking, but there's there's absolutely talent out there. People are hungry to do it.

• 9:10Chamath 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 thought is always that wow, these jobs are not desirable, but they really are desirable by many people.

• 9:26Speaker 01

  Yeah, absolutely. I mean, our median wage is now pushing a hundred thousand dollars a year. And there's, you know, relative to some of the opportunity set, I mean, these are great. These are great jobs and.

• 9:39Jason Calacanis?

  What are the starting salaries?

• 9:40Speaker 01

  What's that?

• 9:41Jason Calacanis?

  What's the starting salary?

• 9:42Speaker 01

  It really depends on the job function because.

• 9:44Jason Calacanis?

  Lowest starting salary.

• 9:46Speaker 01

  There's, I mean, I think the easiest way to think about it is you can, you can certainly as an operator make close to a hundred thousand dollars 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.

• 10:01Jason Calacanis?

  But somebody coming out of high school, they can make 40, 50, 60 K.

• 10:04Speaker 01

  Or more, yeah, or it depends. Are you, 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.

• 10:15Chamath 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.

• 10:21Speaker 01

  Yeah.

• 10:23Chamath Palihapitiya?

  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.

• 10:35Speaker 01

  Yeah. There are some major categories. Obviously, we've all heard about shipbuilding and advanced pharmaceutical ingredients. I mean, I think, 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 there, it's a vertical where there might not be a market large enough to, to need five players, but a good public, private partnership can just solve that problem. And then there's some other verticals in critical minerals.

• 11:07Chamath Palihapitiya?

  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 like.

• 11:15Speaker 01

  Well, and I think that's, you know, our particular deal was led by DOD. And so I have to say that the Pentagon leadership is extraordinary. And, you know, 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 is not over. 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.

• 11:47Chamath Palihapitiya?

  I mean, you're tough, so, yeah. And that's you saying that, they're tough.

• 11:50Speaker 01

  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, you know, we're making this investment. And, and so, the key piece of this, which I think is a good model for all of us, and is 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.

• 12:32Speaker 01

  Mercantilism, you know, 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's sort of investing in a sector and quote, 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.

• 13:07Speaker 01

  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 the, to the credit to the Trump administration, I think they will make money on this and have solved the national security.

• 13:23Chamath Palihapitiya?

  All right, we appreciate you coming, Steve.

• 13:24Speaker 01

  Yeah, oh, thanks.

• 13:25Chamath Palihapitiya?

  Thanks so much.

• 13:26Speaker 01

  Thanks, brother.

• 13:27Chamath Palihapitiya?

  All right, take care, Steve.

• 13:28David Friedberg?

  Thanks, Jacob. Appreciate it. Yeah, okay. Hi, Lisa. Hi, Lisa. Nice to see you.

• 13:34Chamath Palihapitiya?

  Lisa, it's a pleasure.

• 13:35David Friedberg?

  Hi, thanks for having me. Hi, hi, hi, hi. 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?

• 14:04Speaker 04

  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 U.S. AI race. And I thought if we're going to talk about chips, David, I should actually bring one.

• 14:14David Friedberg?

  Oh, awesome.

• 14:15Speaker 04

  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.

• 14:28David Friedberg?

  That's a 2-nanometer?

• 14:30Speaker 04

  This is 3-nanometer and 6-nanometer, so lots of different chiplets.

• 14:34David Friedberg?

  I'll be putting this on eBay later.

• 14:36Speaker 04

  I'm going to take it with me when I leave. How is that?

• 14:39David Friedberg?

  Thank you.

• 14:40Speaker 04

  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 U.S. manufacturing. I would say, you know, 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, you know, super good for the semiconductor industry and, you know, frankly, for, you know, all of us in

• 15:20Speaker 04

  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.

• 15:33David Friedberg?

  But the reports out were that TSMC couldn't get good qualified, trained employees. They had to bring folks over. Is that accurate? And like, again, like if we're going to scale, like what's the order of magnitude we're going from here? Is it 10x, 100x? And how are we going to build a workforce to support this industry, which is a completely new industry for America?

• 15:52Jason Calacanis?

  And Lisa, you have permission to speak freely.

• 15:54Speaker 04

  The best way to say it is, no matter when you start something new, it's going to take work, right? It's going to be hard. So sure, in the beginning, there were some issues of, you know, TSMC has like 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, you know, 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.

• 16:31Jason Calacanis?

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

• 16:37Speaker 04

  We're going to pay a little bit more.

• 16:38Jason Calacanis?

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

• 16:41Speaker 04

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

• 16:49Jason Calacanis?

  So low double digits?

• 16:50Speaker 04

  Low double, let's say low double digits.

• 16:52Jason Calacanis?

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

• 16:57Speaker 04

  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, you know, the fact that you're not going for the lowest cost, you know, every minute of the day is okay. It's okay. Like, 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. You know, I think Taiwan continues to be important.

• 17:36Speaker 04

  In that view, but the focus from this administration on getting on for manufacturing in a big way, not in a small way, I think is very good for the country.

• 17:47Jason Calacanis?

  How much 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?

• 17:58Speaker 04

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

• 18:11Chamath Palihapitiya?

  We said 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 four, I think, gigawatt data center, 30 billion a year with Oracle. That just pretends 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?

• 18:45Speaker 04

  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, you know, 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, you know, AI large computing systems will be like, you know, over $500 billion in a couple of years. So very high growth. And when you say, you know, what do we need to do? It's 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,

• 19:30Speaker 04

  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 I think, look, I think today's AI action plan is actually a really, you know, excellent blueprint.

• 19:51Chamath 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?

• 20:05Speaker 04

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

• 20:45Jason 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, you alluded to AI being run locally.

• 20:57Speaker 04

  Yes.

• 20:58Jason 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 LLL models, in your mind? And do you see that as a specific market to go after?

• 21:10Speaker 04

  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, you know, all over the place.

• 21:38David Friedberg?

  On that point, can you make a prediction on when the market for physical AI chips is greater than the market for chips in data centers?

• 21:47Speaker 04

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

• 21:53David Friedberg?

  You think five years? Is that that fast?

• 21:55Speaker 04

  At least five years.

• 21:57David Friedberg?

  So you're saying five plus?

• 21:58Speaker 04

  Five plus, yes.

• 22:00Jason Calacanis?

  Yeah, okay.

• 22:01David Friedberg?

  But that is ultimately the biggest end market, do you think? Do you think physical AI becomes the biggest end market?

• 22:06Speaker 04

  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, they're also, you know, significant markets.

• 22:14Chamath 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&D happening, whether at AMD or in other places? How are we trying to get beyond the physical limits of electrons shunting across a junction?

• 22:52Speaker 04

  I think this idea that the AI can be extremely smart and extremely capable. 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.

• 23:18Chamath Palihapitiya?

  Right.

• 23:18Speaker 04

  But I do see it helping us design the next generation GPU much faster and more reliably.

• 23:23Speaker 05

  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 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?

• 23:42Speaker 04

  Look, I think we're gonna, 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.

• 24:10David Friedberg?

  And going to first principles and asking you the open-ended question, what should be done about American education? I'm gonna 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?

• 24:31Speaker 04

  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, the STEM background is so helpful when we think about the future workforce. And the earlier we can get into The, you know, sort of 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, you know, how we win in AI, like, there's so many aspects of it, but ensuring that, you know, America is the best place for AI talent is also, you know, a key piece of that.

• 25:12Speaker 04

  So, kind of inspiring people when they're young to really, you know, study, you know, science and technology.

• 25:18Jason 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? We, let's say, it's 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. Soon we hit that superintelligence, what would the world look like in 10 years, in the most optimistic scenario, if we do this right?

• 25:48Speaker 04

  Well, I think the exciting part about it, and, you know, 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 think about AI.

• 26:00Jason Calacanis?

  Orders of magnitude.

• 26:01Speaker 04

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

• 26:39Jason Calacanis?

  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-forwarded 20 years, two have just absolutely thrived.

• 26:54Chamath Palihapitiya?

  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 is 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've thrived and maybe what you take away from their journey that you make sure AMD doesn't play out?

• 27:21Speaker 04

  Well, 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. Like 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 know, you have things that change. You know, 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, you know, I like to say that, you know, you will, you will be able to judge

• 28:06Speaker 04

  whether we've done a good job or not by how we perform five years from now. Like the decisions we're making will take, you know, five plus years to play out. But that's the key thing in tech. Like nothing is fast, but hopefully it's quite lasting in what you achieve.

• 28:20Chamath Palihapitiya?

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

• 28:28Speaker 04

  We should believe that it's super, super competitive. I mean, at the end of the day, I think the world has recognized that, 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, you know, because of the innovation pipeline, because of, you know, the great companies that we have here, but we should not be You know, confused that everybody's investing and we need to keep our, up our investments as well. And I, I think that's why, you know, this whole idea of any one company can provide every solution that's necessary just isn't the case.

• 29:08David Friedberg?

  Right.

• 29:08Speaker 04

  Right. I, I love the idea of open ecosystems, of, of, you know, companies collaborating, of collaboration across the ecosystem. So hardware, software, systems, you know, collaboration across public, private partnerships. Cause that's what it's going to take. Like for us to win, we have to be, you know, front facing and realizing that bringing, you know, the, the countries that win bring all of the smartest people and the best, you know, capabilities together and let them go as fast as they possibly can.

• 29:39David Friedberg?

  Right. Well, Lisa, thank you for being with us.

• 29:41Speaker 04

  Wonderful.

• 29:41David Friedberg?

  This afternoon.

• 29:42Speaker 04

  Yeah.

• 29:42David Friedberg?

  It's been great. Appreciate it.

• 29:44Speaker 04

  Thank you. Thank you.

• 29:45David Friedberg?

  Pleasure to meet you. Thank you.

• 29:47Speaker 06

  I'm Chase Lochmiller, the co-founder and CEO of Crusoe, and I'm here to talk to you about the AI industrial revolution.

• 29:56Speaker 06

  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.

• 30:38Speaker 06

  So in, in sort of the history of, 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, in power, in transportation, and in, in power and transportation and in natural resources

• 31:19Speaker 06

  to, to enable people to go pursue their dreams and live a better life.

• 31:30Speaker 06

  Now in 2025, we stand at 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. 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,

• 32:15Speaker 06

  for the first time in human history, we've actually been able to manufacture intelligence. Intelligence is the scariest 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.

• 33:00Speaker 06

  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, it's 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.

• 33:46Speaker 06

  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%.

• 34:28Speaker 06

  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 at the end of '24, it was only 4.5 gigawatts. Today, we have companies that are looking at building single 5-gigawatt facilities.

• 35:13Speaker 06

  And if you look at this growth, we're building more than Northern Virginia every single year in the forecasted future. 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 of it, and we need lots of people to build, operate, and maintain it. This is what Crusoe is focused on solving. Crusoe's in the business of activating energy for intelligence, of building, 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

• 35:52Speaker 06

  all sorts of energy resources, from new energy technologies like small modular reactors to renewables and natural gas to power this innovative future.

• 36:06Speaker 06

  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 has been building a 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 has 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.

• 36:51Speaker 06

  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, you know, this is... 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. You know, 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?

• 37:32Speaker 06

  We have 4,000 people working on site every day to, you know, make this facility happen. And, you know, 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.

• 37:52Speaker 06

  And it's also required manufacturing. And a lot of the critical components have happened off-site in these controlled manufacturing environments.

• 38:01Speaker 06

  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 microgrid 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 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

• 38:44Speaker 06

  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.

• 39:25Speaker 05

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

• 39:37Speaker 06

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

• 39:47Speaker 05

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

• 40:02Speaker 06

  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 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.

• 40:20Speaker 05

  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 realize they've got cheap hydropower and, you know, cheap water and we'll try and get you there?

• 40:30Speaker 06

  Uh, you know, believe it or not, we're actually looking at something in California.

• 40:34Speaker 05

  Wow. California, Gavin Newsom's going to bring you in. I would imagine it's going to take like 50 years with their regulations there.

• 40:40Speaker 06

  Yeah, maybe, we'll see, we'll see.

• 40:42Speaker 05

  And you, do you think that the, you know, sort of hyperscaler demand, obviously, we were just, you know, on with Lisa Su, talking about the demand for chips over the next couple of years, that's obviously correlated to the, you know, sort of 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 are 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?

• 41:02Speaker 06

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

• 41:09Speaker 05

  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.

• 41:17Speaker 06

  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, on, you know, the future infrastructure that's going to power the modern economy.

• 41:38Speaker 05

  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 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?

• 41:51Speaker 06

  Um, you know, labor's definitely like a major constraint. You know, like I said, you know, we have about 4,000 people on site every day. Uh, we're going to have multiple sites that are operating with thousands of, you know, folks, uh, basically building this infrastructure. So, you know, uh, labor's 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.

• 42:19Speaker 05

  And you think that it 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?

• 42:29Speaker 06

  Both. You know, 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.

• 42:39Speaker 05

  Making like a company town, importing people in.

• 42:41Speaker 06

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

• 42:50Speaker 05

  And do you see the company start to go more full stack beyond just like the operations of like the 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 yourselves going over time?

• 43:03Speaker 06

  Yeah, Crusoe is a vertically integrated AI infrastructure business. So, you know, data centers is a key component to that. And, you know, 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.

• 43:22Speaker 05

  Makes sense. Well, yeah, Chase, thanks so much for joining us on stage.

• 43:25Speaker 06

  Yeah, thank you.

• 43:26Speaker 05

  We appreciate the talk.

• 43:27Speaker 06

  Yeah, thanks, Dylan.

• 43:29Jason Calacanis?

  Okay, everybody, we got a real treat for you. Jensen Huang is here.

• 43:32Jensen Huang?

  Sit here.

• 43:33Jason Calacanis?

  Right there. Sit here, sit here.

• 43:34Speaker 05

  The hot seat.

• 43:37Speaker 05

  Thanks for coming.

• 43:37Jensen Huang?

  Thank you.

• 43:38Speaker 05

  Great to have you.

• 43:39Jensen Huang?

  Thank you. The number one podcast in the world.

• 43:43Speaker 05

  We were saying the number one company in the world.

• 43:45Jason Calacanis?

  Wow.

• 43:45Jensen Huang?

  Thank you.

• 43:46Jason Calacanis?

  You're a fan of the pod. You listen to the pod.

• 43:50Chamath Palihapitiya?

  This is Norman, our host.

• 43:52Jason Calacanis?

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

• 43:57Jensen Huang?

  I have something like 50 or 60 of them.

• 44:01Speaker 05

  Do you really?

• 44:01Jensen Huang?

  Yeah.

• 44:02Jason Calacanis?

  Wow. What is that, Tom Ford?

• 44:04Jensen Huang?

  I think so. This one is, I think so.

• 44:06Jason Calacanis?

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

• 44:09Jensen Huang?

  Well, you guys are all so fashionable. Coming from you guys, it actually means something.

• 44:13Jason Calacanis?

  Yeah. Oh, yeah. Oops.

• 44:14Jensen Huang?

  Oh, look at you. Look at you.

• 44:17Jason Calacanis?

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

• 44:20Jensen Huang?

  Vermont is like a model.

• 44:23Jason Calacanis?

  He is, he is.

• 44:24Speaker 05

  He's definitely in his head. He's like.

• 44:27Jason Calacanis?

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

• 44:29Jensen Huang?

  My favorite is whatever my wife gets me.

• 44:31Jason Calacanis?

  Ah, she dresses you right.

• 44:32Jensen Huang?

  As soon as she gets it from me, it's my favorite.

• 44:34Jason Calacanis?

  Yes, same with me.

• 44:35Speaker 05

  Smart man.

• 44:37Jensen Huang?

  Nobody wears a suit better than Jacob. Good God.

• 44:40Jason Calacanis?

  Yeah, Jacob is a handsome man.

• 44:42Speaker 05

  Just trying to keep up with you guys.

• 44:44Jason Calacanis?

  I have two questions for you. Take them whichever order you like.

• 44:49Jason Calacanis?

  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 about job creation, transfer, displacement, etc. And then the second one, I've just always been curious. You've got all these important people knocking on your door. You've got Zuck, you've got E, you've got Sam Altman.

• 45:33Jason Calacanis?

  He seems like he's a little bit of a headache, I'll be honest, but...

• 45:36Jensen Huang?

  He's great.

• 45:37Jason Calacanis?

  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.

• 45:56Jensen Huang?

  First of all, I wrote off $5 billion worth of Hoppers. If anybody would like to have some extras...

• 46:03Jason Calacanis?

  You got them.

• 46:04Jensen Huang?

  You know, 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.

• 46:49Jensen Huang?

  It drives more jobs. You know, all that goes together. The other thing that to remember is that AI is the greatest technology equalizer of all time.

• 46:57Jason Calacanis?

  Okay, explain.

• 46:58Jensen Huang?

  Everybody's a programmer now.

• 47:00Jason Calacanis?

  Yes.

• 47:01Jensen 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 even know how to program an AI, just go up to the AI and say, "How do I program an AI?" The AI explains to you exactly how to program the AI. Even when you're not sure exactly how to ask a question, say, "What's the best way to ask the question?" And it'll actually write the question for you. It's incredible.

• 47:24Jason Calacanis?

  Yeah.

• 47:25Jensen 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.

• 48:02Jason Calacanis?

  Yeah.

• 48:03Jensen Huang?

  There's not a software programmer in the future who's gonna be able to hold their own, I mean, you know, typing by themselves.

• 48:10Jason Calacanis?

  Yeah, you can't raw dog it, no.

• 48:12Jensen Huang?

  No, not anymore.

• 48:13Jason Calacanis?

  No raw dog.

• 48:14Jensen Huang?

  Not anymore. You can't raw dog it. I'll be sure to go home and tell people.

• 48:19Jason Calacanis?

  Yeah, exactly.

• 48:20Jensen Huang?

  You're not gonna raw dog this.

• 48:21Jason Calacanis?

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

• 48:24Jensen Huang?

  Okay, so the way we allocate is this. The way we allocate is this. Place a PO.

• 48:29Jason Calacanis?

  Okay. So you go to the register. You pay, you get it.

• 48:35Jensen Huang?

  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.

• 49:02Jason Calacanis?

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

• 49:19Jensen Huang?

  Two concepts are happening right now. The first thing, first thing is every generation, we increase the performance by X factors.

• 49:24Jason Calacanis?

  Yeah.

• 49:25Jensen Huang?

  If the perf per doll, perf per watt goes up by X factors, whatever your data center power is, we just increased your revenues by X factors.

• 49:36Chamath Palihapitiya?

  Right.

• 49:37Jensen Huang?

  So perf per watt is equal to revenues. Perf per dollar equals the 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 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, you know, AI so that 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've got to think a long time. And so you've got to get that cost down. The second idea is, if you look at

• 50:19Jensen Huang?

  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

• 51:04Jensen Huang?

  by a factor of four in the time that we shipped it. Now, you can't get that out of a CPU.

• 51:10Chamath Palihapitiya?

  Right. 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 by, 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?

• 51:46Jensen Huang?

  One of the biggest observations about AI is that there's the industry of applications that AI has created. It's a revolutionary technology, every industry will be revolutionized, new applications will be created, so on and so forth. All the things that we know. Agentic AI, reasoning AI, robotics AI, so on and so forth. We know all those things now. Every industry, healthcare, education, transportation, you name it, manufacturing, all revolutionized. The one part that we observed and made a great contribution to is that in order to sustain those applications, you need factories

• 52:28Jensen Huang?

  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, three hundred years ago, I think it actually peaked out at 30%. 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 have to build out the AI infrastructure. My sense is that we're probably, you know, a couple of hundred billion dollars, maybe

• 53:10Jensen Huang?

  a few hundred billion dollars, into a multi-trillion dollar infrastructure build-out.

• 53:16Speaker 01

  Per year?

• 53:17Jensen Huang?

  Yeah.

• 53:18David Friedberg?

  What about manufacturing?

• 53:20Jensen Huang?

  And the reason for that is because you want the new infrastructure, which increases revenue, driving your costs down.

• 53:26Speaker 01

  Right.

• 53:27Jensen Huang?

  That's right.

• 53:28David Friedberg?

  What about manufacturing in the US? So where are we? We, you know, 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?

• 53:41Jensen 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.

• 54:16Speaker 08

  Yep.

• 54:17Jensen Huang?

  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 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. Right? So you're going to have this layers of inception. And the amount of technology necessary to create that is really insane. We've...

• 54:56Jensen Huang?

  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 in-source 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

• 55:41Jensen Huang?

  worth of AI industry. And so that's only in the next several years. And they're doing great. Arizona's doing great.

• 55:47Speaker 08

  And so there's a lot of talk about American competitiveness today, and the White House ruled 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?

• 56:06Jensen Huang?

  America's unique advantage that no country possibly have is President Trump.

• 56:15Jensen Huang?

  And let me, 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, was vilified, if you guys remember. Yeah. 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.

• 56:58Jensen Huang?

  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, you know, go after the, the new industrial revolution. Big, big deal.

• 57:20David Friedberg?

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

• 57:38Jensen 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 gonna, you know, who's gonna 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. 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. Right. And so every company in the future, in fact,

• 58:21Jensen Huang?

  the, the future of industry is really two factories. Tesla already has two factories, right? Elon has a giant AI factory. He was very early in recognizing that he needs to have an AI factory to sustain the cars that he has. Now he's got AIs in the car, but in the future, instead of, you know, I imagine that in the future, instead of a whole lot of people remotely monitoring air traffic control, it'll be a giant AI that's doing the remote control. And then only in the case that the giant AI can 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.

• 59:05Chamath 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 DeepSeek moment, then maybe this week, last week, there was this Kimi 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 their first, whether it's AGI or, you know, superintelligence?

• 59:33Jensen Huang?

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

• 1:00:18Chamath Palihapitiya?

  Totally.

• 1:00:19Jensen Huang?

  Now you got two parallel universe. Exactly. Could you imagine if QN came out and it only worked on non-American 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.

• 1:00:52Chamath Palihapitiya?

  Yeah.

• 1:00:53Jensen Huang?

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

• 1:00:55Chamath Palihapitiya?

  So, speaking of developers.

• 1:00:56Jensen Huang?

  The second, the second, I'm sorry.

• 1:00:58Chamath Palihapitiya?

  Go ahead.

• 1:00:59Jensen Huang?

  The second thing is really a big deal. When DeepSeek 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. You 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 DeepSeek R1, Kimi, Kimi K2, Q1, 3, you now have reasoning models that can allow, that help you to 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.

• 1:01:42Chamath Palihapitiya?

  Right.

• 1:01:42Jensen Huang?

  Yeah.

• 1:01:43Chamath 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 contracts. Now it's, you know, 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?

• 1:02:17Jensen Huang?

  First of all, I've created more billionaires on my management team than any CEO in the world. They're doing just fine.

• 1:02:27Jensen Huang?

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

• 1:02:34Speaker 05

  Yeah, everybody's doing okay.

• 1:02:35Jensen Huang?

  Yeah, my layer is doing just fine. I tell, I, but, but the important, the big idea, though, is that you're highlighting is that the impact of a, a hundred and fifty or so AI researchers can probably create with enough funding behind them, create an OpenAI. It's a, it's not a-

• 1:02:54Speaker 05

  A hundred and fifty people?

• 1:02:55Jensen Huang?

  Yeah, it's not a, it's not, well, DeepSeek's a hundred and fifty people, Boomshots a hundred and fifty people.

• 1:03:00Speaker 05

  Right.

• 1:03:00Jensen Huang?

  Right?

• 1:03:01Speaker 05

  Right.

• 1:03:01Jensen Huang?

  And so, I mean, look at the original, uh, OpenAI was about a hundred and fifty people. Uh, DeepMind, you know, 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 sized team with the right infrastructure. And so that kind of tells you something. A hundred and fifty people, if you're willing to pay, say, twenty billion dollars, thirty billion dollars to buy a startup with a hundred and fifty AI researchers, why wouldn't you pay one?

• 1:03:28Speaker 05

  Right.

• 1:03:28Jason Calacanis?

  Hey, uh, speaking of options-

• 1:03:30Jensen Huang?

  By the way, we, we-

• 1:03:30Jason Calacanis?

  Somebody told me-

• 1:03:31Jensen Huang?

  We need to wrap, because-

• 1:03:32Jason Calacanis?

  Yeah, I don't know, but I'm gonna 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.

• 1:03:52Jensen Huang?

  That's nuts.

• 1:03:53Jason Calacanis?

  Is that true?

• 1:03:54Jensen Huang?

  Uh, 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-

• 1:04:03Speaker 05

  Yeah.

• 1:04:03Jensen Huang?

  At the end of every cycle, of when they present it, and they say, 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 forty-two thousand employees, and a hundred percent 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 itself.

• 1:04:27Speaker 05

  Exactly.

• 1:04:27Jensen Huang?

  All right, well done. Thank you.

• 1:04:28Jason Calacanis?

  Thank you, Justin.

• 1:04:29Speaker 05

  Great to see you.

• 1:04:30Jensen Huang?

  Nice to see you. We have an event in LA, we'd love to continue the conversation, so we'll send you a note. The world's number one podcast.

• 1:04:36Jason Calacanis?

  There you go.

• 1:04:37Jensen Huang?

  Thank you.