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(0:00) James Litinsky, MP Materials
(13:32) Lisa Su, AMD
(29:45) Chase Lochmiller, Crusoe
(43:26) Jensen Huang, Nvidia
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Winning the AI Race Part 3: Jensen Huang, Lisa Su, James Litinsky, Chase Lochmiller Podcast Episode Summary
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Winning the AI Race Part 3: Jensen Huang, Lisa Su, James Litinsky, Chase Lochmiller Podcast Episode Transcript (Unedited)
Speaker: 0
00:00
Guys, this is one of the most amazing entrepreneurs that you’re sana meh, Jim Lewdinsky, the founder and CEO of MP Materials.
Speaker: 1
00:07
Thanks. Good to be here.
Speaker: 2
00:09
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Hey, Jason. Meh. How are you?
Speaker: 0
00:11
I’m sai ai. Let me 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.
Speaker: 3
00:24
Yep. Yeah.
Speaker: 0
00:25
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.
Speaker: 3
00:45
We’re a 100% of the American industry.
Speaker: 0
00:47
100% of the American industry. You Yeah. Just did two really incredible things, actually, in the last couple weeks. One was you announced an enormous public private partnership with the DOD, $400,000,000, etcetera. And then the second is you, announced a really big deal with Apple.
Speaker: 2
01:04
Yes.
Speaker: 1
01:04
Okay. So Yeah.
Speaker: 0
01:05
Take a Should I take Take a huge step back. Talk to us ai rare earths matter. Tell us about the supply chain for AI. Tell us Yeah.
Speaker: 4
01:12
Why you’re doing
Speaker: 3
01:13
this. 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.
Speaker: 3
01:30
There there was a feeling when I when I took over this site with my cofounder, and this is this goes back to 2015. There were Where
Speaker: 4
01:38
where is the site?
Speaker: 3
01:39
This oh, it’s in Mountain Pass, California. So if you you’ll be familiar if you take a forty five 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 the really the best rare earth ore body in the world.
Speaker: 3
01:53
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.
Speaker: 3
02:09
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.
Speaker: 3
02:22
Or you could have all of the magnetic capability in the world, but if you don’t have the rare earths, you’re ai on China. And so our vision from day one going back to we we originally bought these assets out of bankruptcy. Officially, it was a two year vatsal, took it out in 2017, and there was a perception that we just couldn’t compete against China.
Speaker: 3
02:41
And what we discovered actually is we could. It’s a world class site, but we had to 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.
Speaker: 3
02:58
We built out the refining capability, and then, about four years ago, we announced we were gonna build a magnetics factory in Texas. We built that factory. We have GM as a foundational customer. We’re now producing auto grade magnets, g to GM spec, and we’ll be ramping up, sales to GM at the end of this year in in magnets.
Speaker: 3
03:17
And then, Chamath, you referenced a couple it’s been a busy few months for us. We announced a 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 gonna provide, a price floor for our commodities sai 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.
Speaker: 3
03:49
And then as a result of the DOD investment, we’re gonna 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 gonna build a 10 x facility to 10 x our capacity with DOD as our a 100% offtake partner, customer and business partner because we’ll be we’ll be splitting profits fifty fifty with DOD.
Speaker: 4
04:12
So to to just translate this, it’s not a handout from the government. They didn’t
Speaker: 3
04:15
Not vatsal.
Speaker: 4
04:16
You $400,000,000. They invested in your company. They have warrants. They have equity.
Speaker: 3
04:20
Yeah. So they invested. They they both are an an owner. They also are an upside participant in our commodity to the extent that the prices, take off, and then they’re also a 100% off take customer. We have a guaranteed level of profits to wanna build out this facility, but above a certain threshold, they’re a fifty fifty economic participant.
Speaker: 0
04:39
Baby, you the American
Speaker: 3
04:41
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 gonna have enough magnets to provide, you know, real certainty in the supply chain for the physical AI revolution 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.
Speaker: 3
05:14
The taxpayer made money on doing this. And I’ll say, yeah. I actually think that that’s gonna 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.
Speaker: 3
05:32
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.
Speaker: 4
05:39
Why go to the government for this investment as opposed to the private markets?
Speaker: 3
05:43
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 two or the railroad boom where you really need government and credit. I ai, this administration, did something, you know, totally unique that Which piece ai
Speaker: 4
05:58
do you need the government?
Speaker: 3
05:59
The Mercantilism. Straight up Mercantilism. Because the Chinese will sell magnets for below the cost of raw materials.
Speaker: 0
06:05
Right.
Speaker: 3
06:06
And so every time there’s somebody who makes progress, they can put them out of business overnight. And so it’s difficult to wanna make the investment. And so, frankly, with the Department of Defense, the scale that they wanted us to build on the time frame that they wanted us to build, we there was no way we were gonna make that commitment.
Speaker: 3
06:22
We’re fiduciaries. Right? We’re shareholders. There’s There’s no way we’re gonna make that commitment without certainty that we would not be destroyed by marketallism and that we would have a customer for the magnets.
Speaker: 0
06:32
How big of an industry is physical AI? Meaning Yeah. We see the robots. We’re told the robots are coming. We we’re told there’s going to be billions of them. Are they actually Yeah. Being deployed at the scale and at the pace that we that we’ve been told?
Speaker: 3
06:48
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.
Speaker: 3
07:10
Right? Robots and drones. And I think irrespective of the scale that robotics is ultimately gonna be and and, certainly, the commercial business will be bigger than than, you know, the the defense needs. But just from a defense standpoint, this is this is an a really important supply chain that we must have. Right?
Speaker: 3
07:26
We there’s we can’t be funding cutting edge drone and robotics companies and then say, okay. But we’re gonna buy those magnets from China. That makes no sense.
Speaker: 1
07:34
Do we
Speaker: 2
07:34
have talent capacity, or do we have a talent shortage? Secretary Burgum gave me a stat, which was pretty shocking to meh, 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?
Speaker: 3
07:52
It’s a great it’s a great question. I think, Jason, I think about this question a lot.
Speaker: 2
07:56
On days. But yeah.
Speaker: 1
07:57
What’s that? Dave. Oh my god. Dave. Sorry.
Speaker: 2
07:59
No. No. It’s okay.
Speaker: 5
08:01
I’m a huge fan of
Speaker: 4
08:01
the bot.
Speaker: 0
08:02
I just embarrassed myself.
Speaker: 2
08:03
All the time. Yeah. Yeah. It’s it’s
Speaker: 4
08:05
it’s it’s the only it’s the joke in white. Yeah.
Speaker: 6
08:08
And you
Speaker: 3
08:08
know I’m a fan of the pod since
Speaker: 7
08:09
day one. And I’ve totally You’re
Speaker: 2
08:10
the only there’s only one correct one. Messing with you.
Speaker: 4
08:13
I know where am I messing with you.
Speaker: 3
08:15
Was this intentional?
Speaker: 5
08:17
So
Speaker: 1
08:18
He’s a
Speaker: 4
08:18
huge fan of the pod. Yeah.
Speaker: 7
08:19
He’s a fan
Speaker: 1
08:20
of the pod. Yeah. Who are you again? Take a selfie later.
Speaker: 2
08:23
And and Ai not the Sai czar. Go ahead.
Speaker: 1
08:25
Yeah. Yeah. Go ahead.
Speaker: 3
08:26
So we have we have, 850 employees today at MP. We’re we’re gonna hire when we include what we’re building out for Apple, coupled with what we’re gonna build with DOD. We’re gonna need a couple thousand more people easily, not to mention the construction jobs. So this is, this is a key existential question for all of us as we build out this. This is where are we gonna get the tyler.
Speaker: 3
08:50
I think what we have found, you know, at Mountain Pass, and we 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 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.
Speaker: 0
09:10
Why do you think it’s been so hard to establish that that ai? Like, meaning, you find it straightforward to find good, hardworking people to get into these jobs, But the sai the thought is always that, wow, these jobs are not ai. But they really are desirable by many people.
Speaker: 3
09:26
Yeah. Absolutely. I mean, we you know, our median wage is now pushing a $100,000 a year, and there’s, you know, relative to some of the opportunity set, I mean, these are great these are great jobs and What are the ai salary? What’s that? What’s the starting salary?
Speaker: 1
09:42
Just curious.
Speaker: 3
09:42
It it really depends on the job function because Lowest You know,
Speaker: 4
09:45
there’s there’s
Speaker: 3
09:46
there’s I mean, I I think the easiest way to think about it is you can you can certainly, as an operator, make close to a $100,000 a year with us because, by the way, everybody’s an owner. We have an owner operator culture. Everyone got speak when we went public in 2020.
Speaker: 4
10:01
But somebody coming out of high school, they can make $40.50, 60 k?
Speaker: 3
10:04
Or more. Yeah. Or or it depends. Are you if we we can’t find enough electricians, we can’t find enough maintenance workers. A maintenance worker can, an electrician, they can make 6 figures today.
Speaker: 1
10:14
Tell
Speaker: 0
10:15
us, you said earlier that you suspect five years from now, we’re gonna look back, and this deal with the DOD was a blueprint. Meh. 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.
Speaker: 3
10:35
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.
Speaker: 3
10:52
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 a a good public private partnership can just solve that problem. And then there’s some other verticals and critical minerals.
Speaker: 0
11:07
Straightforward for you to find the right person within the Trump administration shah said, of course, this is obvious. Let’s sit down and hash this out. Like
Speaker: 3
11:15
Yeah. Well and I I think that’s you know, our 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 and to be clear, because Ai you know, this story’s not out there.
Speaker: 3
11:34
Our process, this was I’ve never worked so hard in my life. I mean, this was this was ai a true aggressive private equity style investment and negotiation. The transaction documents are public. You can look at that.
Speaker: 0
11:48
So Yeah. And that’s you’re saying that. They’re tough.
Speaker: 3
11:50
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 or, distressed lender type negotiation. That’s what this was. And the key thing, was they were gonna hold our feet to the fire to execute on an aggressive timeline.
Speaker: 3
12:09
They were gonna hold our feet to the fire on the costs, and so we’re exposed. If we get the costs wrong, you know, we we’re making this investment. And and sai 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 I don’t speak for them, ask them, but I think their goal was, we’re gonna take the things off the table that you can’t control.
Speaker: 3
12:32
Mercantilism, you you know, certain customer issues. We’re gonna be held to account for the things that we can control. Our ability to execute, our ability to, execute on a good ai, and our ability to control costs. So when we think about a lot of these, historically, the government 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.
Speaker: 3
12:59
Right? This is not that. This is private risk, public risk, public upside, private upside. Amazing. It’s a true shared win win win.
Speaker: 3
13:12
And, again, like I said, hold me to these words. I I hope, I I hope I’m right on this, but I think that, to the credit to the Trump administration, I think they will make money on this and have solved the national security. Alright.
Speaker: 4
13:23
We appreciate you coming, Steve.
Speaker: 3
13:24
Oh, thanks.
Speaker: 0
13:25
Thanks so much. Thanks, brother.
Speaker: 1
13:26
Yeah. It’s good.
Speaker: 4
13:26
Alright. Take care, Steve.
Speaker: 0
13:28
Thanks, Jason. Thanks,
Speaker: 2
13:30
Jacob. Thanks, Jacob. Yeah. Yeah. Okay. Ai, Lisa.
Speaker: 0
13:33
Hi. Nice to meet you.
Speaker: 4
13:35
Lisa, it’s a pleasure. Hi. Nice to meet you.
Speaker: 6
13:37
Hi. Hi. Hi. Hi.
Speaker: 2
13:37
Hi. Well, thanks so much for being here with us today. We don’t have a lot of time, so we sana get into it. In April, it was announced that you achieved your first silicon output at the TSMC facility in Arizona on that two 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?
Speaker: 2
14:00
What’s not going well? What does America need to do to get this right?
Speaker: 6
14:04
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 US, AI race. And I thought if we’re gonna talk about chips, David, I should actually bring one.
Speaker: 4
14:14
Oh. Awesome.
Speaker: 6
14:15
If that’s okay? Yep. Little bit of show and tell. So this is our latest generation, AI chip. It’s our meh I three fifty five chip. A 185,000,000,000 transistors. Takes about nine months to build. Lots of technology on it. If I just, nanometer? This is, three nanometer and, six nanometer. So lots of different, chips.
Speaker: 2
14:34
Putting this on eBay later.
Speaker: 6
14:36
I’m gonna take it with me ai I’m ready. How’s that?
Speaker: 0
14:39
Thank you.
Speaker: 6
14:40
But look, to answer your question, I ai, 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 sai, twelve months ago, people weren’t sure that we could do leading edge manufacturing in The United States.
Speaker: 6
14:58
We’ve been very early in Arizona with TSMC and we did get our first chips out. They’re actually four 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.
Speaker: 6
15:21
We are in such an interesting place because, you know, chips are so essential to ensuring that we are able to win the AI race that, you know, we sana make sure that there’s a lot of geographic diversity and capability there.
Speaker: 2
15:33
But the reports out were that TSMC couldn’t get good ai, trained employees. They had to bring folks over. Is that accurate? And, like, again, like, if we’re gonna scale like, what’s the the order of magnitude we’re going from here? Is it 10 x, a 100 x? And how are we gonna build a workforce to support this industry, which is a completely new industry for Meh?
Speaker: 4
15:52
And, Lisa, you have permission to speak freely.
Speaker: 0
15:54
Yeah.
Speaker: 6
15:55
But the best way to say it is no matter when you start something new, it it’s gonna take work. Right? It’s it’s gonna be hard. Sai, sure, in the beginning, there there were some issues of, you know, the the TSMC has, like, a formula for how they build, and they just, you know, rinse and speak.
Speaker: 6
16:11
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 the main thing that we look at is, you know, 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.
Speaker: 4
16:32
Because it’s unrealistic to think The United States could compete on cost. Am I correct?
Speaker: 6
16:37
We’re gonna pay a little bit more.
Speaker: 4
16:39
Give us the ballpark. 50% more, 20%
Speaker: 6
16:41
more? Not not 50% more. I mean, look, it it it’s gonna be, you know, more than 5%, but, you know, let’s call it less than 20%.
Speaker: 4
16:49
So low low
Speaker: 6
16:50
double Low double let’s say low double digits.
Speaker: 4
16:52
And how does that impact the business, if at all, in terms of competition globally?
Speaker: 6
16:57
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 gonna win in AI want to have as much compute in their foundation as possible, and they want assurance of supply.
Speaker: 6
17:13
We wanna 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 the lowest cost, you know, every minute of the day, is okay. It’s okay. Like, obviously, we’re not sana build, not everything needs to be in the most advanced technologies and so we have a very geographically diverse supply chain.
Speaker: 6
17:33
You know, I think Taiwan continues to be important, in that view. But the the focus, from this administration on getting, on ram manufacturing in a big way, not in a small way, I think is is very good to
Speaker: 0
17:46
be How
Speaker: 4
17:46
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?
Speaker: 6
17:59
Yeah. You have to look across, the supply chain. But, you know, from a structure standpoint, we all wanna keep reserves for, you know, those those times. But it’s months. It’s not years.
Speaker: 0
18:11
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 50,000,000 h 100 equivalents just for x sai I. And the second was Sam Altman. They signed a deal for a four, I think, gigawatt data center, 30,000,000,000 a year with Oracle. That just portends an enormous amount of chips that are necessary and power.
Speaker: 0
18:35
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?
Speaker: 6
18:45
Yeah. It’s it’s a great, great point. I mean, that’s 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, you know, the the leaders a couple of the leaders. There’s there’s a lot of demand elsewhere too. I mean, if you think about it, nations want their own AI. So there’s a very high demand.
Speaker: 6
19:05
We’re we’re imagining that just the accelerator arya, so the chips for these, you know, AI large computing systems will be, like, you know, over $500,000,000,000 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.
Speaker: 6
19:25
Certainly, what we’re doing in 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 gonna 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.
Speaker: 6
19:45
And and I think, look, I think today’s, AI action plan is actually a really, you know, excellent blueprint.
Speaker: 0
19:51
And how
Speaker: 5
19:51
do you see
Speaker: 0
19:51
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 ai Cambrian explosion of different ASICs, different designs, different use cases?
Speaker: 6
20:05
Yeah. I I I like that question because I I am a believer in there will be diversity of chips. And the reason is there’s so many use cases. Right? If you think about use cases from, you know, whether you’re talking about science ram manufacturing or design or back end or, you know, frankly, personal Ai, I think we’re gonna see AI in everything that we do, you know, certainly in your phones, in your PCs.
Speaker: 6
20:24
And so you have all these pieces. You’re gonna have different types of chips, that do that. You know, certainly, the for the largest systems, we tend to believe that, you know, you need the most compute you can meh. And so, you know, GPUs are there. But lots of ASICs are in the in the process. And, you know, we’ll see a a ai of different chips.
Speaker: 4
20:45
You opened up a 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.
Speaker: 6
20:57
Yes.
Speaker: 4
20:58
When would we have a local computer, a laptop, arya a desktop computer that would have the power we’re seeing to run some of these LLM models in in your mind? And do you see that as a specific market to go after?
Speaker: 6
21:10
Look, I definitely see the, the idea that AI will be at every part of our ecosystem, is a, 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?
Speaker: 6
21:34
It’s like, well, maybe I don’t want all ai personal data Right. You know, all over
Speaker: 2
21:38
the place. On that point, can you make a prediction on when the the market for physical AI chips is greater than the market for chips and data centers?
Speaker: 6
21:47
That’s a great question. Ai a big believer in physical AI. I still think it’s let’s call it five years.
Speaker: 2
21:53
You think five years? Is that that fast? At least five years. So you’re saying five plus?
Speaker: 6
21:58
Five plus.
Speaker: 4
21:59
Meh. Okay.
Speaker: 2
22:00
But that but that is ultimately the biggest end market, do you think? Is it do you think physical AI becomes the biggest end market?
Speaker: 6
22:06
I think it becomes a significant end market. I think you look at chips in data centers and you look at chips sai the edge, they’re also, you know, significant markets. Mhmm.
Speaker: 2
22:15
When you look at
Speaker: 0
22:15
the most cutting edge techniques today, meh v 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?
Speaker: 0
22:36
Different materials, different material sciences, different approaches that we may not necessarily understand. Is any of that r and d happening, whether at AMD or in other places? Like, how are we trying to get beyond the physical limits of electrons shunting across sai junction?
Speaker: 6
22:53
I 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.
Speaker: 6
23:13
So I don’t necessarily see the AI designing our next generation GPU.
Speaker: 0
23:18
Right.
Speaker: 6
23:18
But I do see it helping us design the next generation GPU much faster and more reliably. So
Speaker: 1
23:24
You talked about the need to, like, reshore more parts of, like, the, you know, sort of ecosystem. Obviously, you guys are you guys are world class chip design. The fabs are getting reshored. But how do you think about things like lithography? Like, does that need to be, you know, sort of reshored?
Speaker: 1
23:34
Or, like, does ASML need to start building, you know, sort of machines in The United States? Or is it okay to have that type of, you know, supply chain risk on an ally? Look, I think we’re gonna we we
Speaker: 6
23:44
have to accept the fact that it’s a global supply chain. Like, even if you were to reshore, you know, 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 a key piece of the conversation and ensuring that, you know, we have access to the latest generation technologies.
Speaker: 6
24:05
And, you know, that that is, you know, something that we protect given our intellectual property.
Speaker: 2
24:10
And going 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 Meh. 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?
Speaker: 6
24:31
Yeah. I’m probably a little bit biased as maybe some of your, guests are today. I’m a big believer in, you know, science and technology background as being, you know, sort of the STEM background is, you know, 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.
Speaker: 6
24:51
So some of the work that’s being done to kind of revitalize the, curriculum, I think is 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 there’s so many aspects of it. But ensuring that, you know, America is the best place for AI talent is is also, you know, a key piece of that.
Speaker: 6
25:12
So kind of inspiring people when they’re young to, really, you know, study, you know, science technology.
Speaker: 4
25:18
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 ten years? We 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.
Speaker: 4
25:39
I assume you agree with that. Saloni we hit that superintelligence. What would the world look like in ten years in the most optimistic scenario if we do this right?
Speaker: 6
25:49
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 it.
Speaker: 0
26:00
Orders of magnitude.
Speaker: 6
26:01
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 can take AI and make manufacturing better. You can take AI and make every aspect of your business better.
Speaker: 6
26:19
And so, you know, in my mind, ten 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 I like to say, like, you know, AMDers 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?
Speaker: 6
26:36
And, you know, AI is really our mechanism ai doing that.
Speaker: 0
26:39
I have a business strategy question. If we went back twenty years, and we wrote the tale of three companies NVIDIA, AMD, Intel. And then you fast forward it twenty 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.
Speaker: 0
27:01
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 ai and maybe what you take away from their journey that you make sure AMD doesn’t play out?
Speaker: 6
27:22
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 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.
Speaker: 6
27:42
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?
Speaker: 6
27:57
But the fact is you had to invest many, many years ago to be where we are today. And I think, you know, I I like to say that, you know, you will you will be able to judge 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.
Speaker: 6
28:16
Like, nothing is fast, but hopefully, it’s quite lasting in what it is.
Speaker: 0
28:20
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?
Speaker: 6
28:28
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.
Speaker: 6
28:46
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.
Speaker: 6
29:01
And I Ai 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. Ai? I I I love the idea of open ecosystems, of, you know, companies collaborating, of collaboration across the ecosystems. So hardware, software, systems, collaboration across public private partnerships.
Speaker: 6
29:23
Because that’s what it’s going to take. Ai, 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.
Speaker: 2
29:39
Right. Really? So thank you for being with us. Wonderful. Yeah. Great. Appreciate it.
Speaker: 0
29:44
Thank you.
Speaker: 4
29:44
Thank you. Pleasure to meet you. Thank you.
Speaker: 5
29:47
I’m Chase Lochmiller, the, cofounder and CEO of Crusoe. And I’m here to talk to you about the AI industrial revolution. I’m sana 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 two hundred and thirty two years of existence, there has been no incubator for unleashing human potential like America.
Speaker: 5
30:11
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.
Speaker: 5
30:32
American dynamism has always prevailed, and it will continue to do so. So in in in sort of the, history of 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.
Speaker: 5
30:56
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 in transportation and in natural resources to, to enable people to go pursue their dreams and live a better life.
Speaker: 5
31:24
Here we go. Now in 2025, we stand at, 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.
Speaker: 5
31:50
These are the companies with the biggest balance sheets in the history of business that are quite literally going all in to to make this happen. And they’re not the only ones. You know, there’s also start ups like Crusoe, and, there’s there’s even nation states, that are following suit. So so what’s going on there?
Speaker: 5
32:07
What’s the what’s the prize that that they’re going after? The, you know, 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 and for the first time, we’re actually able to make it.
Speaker: 5
32:27
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.
Speaker: 5
32:57
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 $20,000,000,000,000 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.
Speaker: 5
33:23
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.
Speaker: 3
33:46
So when we look at
Speaker: 5
33:47
the history of air American energy production and consumption, as as as The US industrialized, we we really ramped up energy generation and, and and also consumption. But if you look at this chart, you can see that, you know, it’s it’s ai of flatlined over the last twenty years, where we’re, you know, generating and consuming about 4,000 terawatt hours per year.
Speaker: 5
34:07
AI is fundamentally transforming this demand picture, and, 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 gonna go from two and a half percent of US power consumption to 10%.
Speaker: 5
34:28
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 meh, not just in data centers, but also in the energy infrastructure to support them.
Speaker: 5
34:42
And this will require people, lots of people, to build, operate, maintain, and run these these large scale energy investments. So if we look at data centers, you know, by the numbers, I think it’s important, you know, as people are sorta throwing around gigawatt scale data centers, of looking at the amount of data center infrastructure that exists today.
Speaker: 5
35:02
Northern Virginia is the center of the world for data centers, but it’s only at the end of twenty four, 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 a Northern Virginia every single year in the forecasted future.
Speaker: 5
35:18
So we need new in so if there’s one thing that you’re gonna 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.
Speaker: 5
35:35
Crusoe is in the business of activating energy for intelligence, of building, operating, AI factories at scale ram from the steel to the silicon, from the electron to the token. And if you look at our pipeline, we’ve got 40 gigawatts of capacity that spans, all sorts of energy resources, from new energy technologies ai, you know, like like, small modular reactors to, renewables and and natural gas to to to power this, innovative future.
Speaker: 5
36:03
Sai revisiting my formula here, I think we left off one critical component, which is the people. AI will be 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, you know, what this looks like in practice.
Speaker: 5
36:21
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 and Crusoe’s really been focused on on on using modular components on on rapidly scaling investment in in construction and infrastructure to support this.
Speaker: 5
36:46
And and and and we’ve actually built a lot of different modular components in in factories and brought them to ai. And they’re kinda like, they’re kinda like Lego blocks that sort of fit together to to build one of these AI factories at at ram scale and speed. So if you look at if you look at what this looks like today, you know, this is, this is this is this this site will consume over 1.2 gigawatts of power and 400,000 NVIDIA GPUs all in a single coherent cluster.
Speaker: 5
37:15
So, this will essentially be a gigawatt scale computer to drive human progress forward. You know, it’s really amazing what you can ai 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?
Speaker: 5
37:32
We have 4,000 people working on-site every day to, you know, make this facility happen. And, you know, it’s it’s it’s a bunch of different trades, electricians and plumbers, and construction workers. And it’s required a lot of capital too. We raised $15,000,000,000 to basically put this facility and and and bring it into existence.
Speaker: 5
37:50
And it’s also required manufacturing, and that’s 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.
Speaker: 5
38:08
This site in West Texas is gonna 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 the arya microgrid with in The United States with sixty sixty megawatt hours of of batteries, end of life EV batteries, and, 20 megawatts of solar to power an AI factory.
Speaker: 5
38:30
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 Wyoming that will initially power 1.3 gigawatts of total compute load, alongside two gigawatts of power generation.
Speaker: 5
38:51
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 ai, and and and and new jobs for the economy.
Speaker: 5
39:11
This will usher in a massive new era of AI driven prosperity for The United States. And I wanna leave you with, you know, my final quote from Warren Buffett that, you know, in this AI race, never bet against America. Thank you.
Speaker: 1
39:28
So is this stuff real? You guys, you know, started off as ai a, you know, sort of Bitcoin miner and now somehow all the hyperscalers are, asking you to, you know, build nonstop data centers. Why you guys?
Speaker: 5
39:39
You know, I think, again, it 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.
Speaker: 1
39:48
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?
Speaker: 5
40:02
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.
Speaker: 1
40:21
Is it only gonna 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, etcetera, 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?
Speaker: 5
40:31
No. You know, believe it or not, we’re actually looking at something in California.
Speaker: 1
40:34
Wow. Yeah. California. Gavin Newsom’s gonna bring you in. I I would imagine it’s gonna take, like, fifty years with our regular Yeah.
Speaker: 0
40:40
Maybe. We’ll see.
Speaker: 4
40:41
We’ll see.
Speaker: 1
40:41
Yeah. And, you the do you think that the you know, ai hyperscaler demand, obviously, we were just, you know, on with Lisa Hsu talking about the demand for chips over the next couple of years. That’s obviously correlated to the use of demand with data centers. Do you think that’s actually gonna play out the way that all the public markets are, you know, sort of projecting?
Speaker: 1
40:55
Or are we, like, in 1999 speak, you know, everybody thinks that fiber is gonna be deployed all over the world. Turns out all those projections were totally off.
Speaker: 5
41:03
I I think the important trend to watch is sort of the capital investment that’s happening and and the term over which that’s happening. So
Speaker: 1
41:09
I felt meh Meta backed off on it a little bit. Like, they’d be like, for a little bit talk about they were gonna deploy like crazy and then pull back, although he’s obviously spending a billion dollars on chief AI ai now.
Speaker: 5
41:18
Yeah. I think, you know, the 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.
Speaker: 5
41:30
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 gonna power the modern economy.
Speaker: 1
41:39
And then, like, the, you know, data centers like Texas, like, what’s the limiting factor? Like, is it, like, workforce to actually go build these things? Is it, like, materials? Is it the cooling towers? Is it the chips? Is it the hyperscalers giving you the, like, you know, you know, sort of contracts?
Speaker: 1
41:50
What’s the, you know, sort of limiting reagent?
Speaker: 5
41:53
You know, labor is definitely, like, a a major constraint. You know, ai I I said, you know, we have about 4,000 people on-site, every day. We’re sana have multiple sites that are operating with thousands of, you know, folks, basically building this infrastructure. So, you know, labor is definitely, like, one of the one of the big bottlenecks, and we’re you know, we think it’s really important for America to make these massive investments in the workforce, to really build the infrastructure of the future.
Speaker: 1
42:19
Anything that requires some real real reskilling where it’s, like, people from oil and gas or, like, construction having to go into just totally net new fields? Or is it something where you guys are actually able to pull on preexisting talent pools pretty quickly?
Speaker: 5
42:29
Both. You know, there’s there’s a lot of existing, you know, labor, you know, at that facility in Abilene. We’re we’re actually pulling labor from all 50 states at this point, believe it or not. So Making like
Speaker: 1
42:39
a company town importing people in?
Speaker: 5
42:41
Yeah. You know, we we have about 50% of the people are are are, you know, from Texas. But, you know, the, we we are importing a lot of labor to to make the project happen.
Speaker: 1
42:50
And, you know, do you do you see the company starting to go more full stack beyond just, like, the operations of, like, the, you know, sort of data centers? Or how do you think about, like, you know, you started off with, you know, focus on, like, you know, sort of energy arbitrage, now the data centers.
Speaker: 1
43:00
Where do you see you guys go selves going over time?
Speaker: 5
43:03
Yeah. I Khuruso 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 and one of the hardest things to do at speak, but we also have, you know, this managed AI cloud services layer that, enables innovators to build, large scale a app AI applications on the platform.
Speaker: 1
43:22
Makes sense. Well, yeah, Chase, thanks so much for, you know, so joining us on stage. And,
Speaker: 4
43:26
Ai appreciate the talk.
Speaker: 1
43:27
Yeah. Thanks, Ai.
Speaker: 4
43:29
Okay, everybody. We got a real treat for you. Jensen Huang is here.
Speaker: 7
43:32
Sai here.
Speaker: 2
43:33
No. We’re ai there. Sit
Speaker: 4
43:34
here. Sit here.
Speaker: 2
43:34
The hot seat. Thanks for coming. Thank you. Making ai happen.
Speaker: 7
43:39
Thank you. The number one podcast in the world.
Speaker: 2
43:43
We were saying the number one company in the world.
Speaker: 4
43:45
Oh, wow. Thank you. Yeah. You’re a fan of the pod. You listen to the pod.
Speaker: 7
43:49
Ai just listen to the pod.
Speaker: 0
43:50
This is Norman, our host.
Speaker: 4
43:52
Yeah. Yes. And there’s Steve. What’s the story with the jacket? You got one of those? You have ai six?
Speaker: 7
43:57
I have something ai 50 or 60 of them.
Speaker: 0
44:00
So you
Speaker: 2
44:01
do you really? Yeah.
Speaker: 7
44:02
Wow. What
Speaker: 4
44:02
is that? Tom Ford?
Speaker: 7
44:04
I think so. This one is. Sai think so. Yeah.
Speaker: 4
44:06
It’s nice. I like it. I tried I tried that out. It was, like, way too much money.
Speaker: 7
44:09
But you guys are all so fashionable.
Speaker: 2
44:11
Yeah. Right. Coming from
Speaker: 7
44:11
you guys, it actually means something. Yeah. Oh, yeah. Oops. Oh, look at you. Look
Speaker: 0
44:16
at you.
Speaker: 4
44:17
Hey. We we’ve been talking a lot about opportunity. You’ve talked Shimon
Speaker: 7
44:21
is like a model.
Speaker: 4
44:23
He is. He is.
Speaker: 2
44:24
Okay. Good. Ai can’t definitely in his head. He’s like
Speaker: 4
44:27
Is Tom Ford your favorite? Who’s your favorite?
Speaker: 7
44:29
Ai my favorite is whatever my wife gets me.
Speaker: 4
44:31
Ah, she dresses you. Right?
Speaker: 5
44:32
Sai soon
Speaker: 7
44:33
as she gets it for me, it’s my favorite.
Speaker: 4
44:34
Yes. Same with me. Same with me.
Speaker: 2
44:35
Smart man. Now you got a
Speaker: 1
44:37
Nobody wears a
Speaker: 7
44:38
nobody wears a suit better than Jacob. Good god. Yeah. Jacob is
Speaker: 4
44:41
a handsome man.
Speaker: 0
44:42
Ai trying to keep up with you guys.
Speaker: 4
44:44
Ai got I have two questions for you. Take them 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.
Speaker: 4
45:02
Therefore, everybody explains to you their hopes, their ram, so you have a unique way of looking at the playing field. You have complete information that we don’t have. So I sana know what you think. Don’t worry. We’ll fix it.
Speaker: 4
45:16
What you think Can
Speaker: 7
45:18
I fix that and edit?
Speaker: 4
45:19
What you think about job creation, transfer, displacement, etcetera. 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, Ram Altman. He seems like he’s a little bit of a headache. I’ll be honest.
Speaker: 4
45:36
But
Speaker: 7
45:36
He’s great.
Speaker: 4
45:38
He’s great. I’m joking. I’m joking. How do you allocate the h one hundreds and 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. Sai just the allocation of a finite amount of resources and then jobs.
Speaker: 7
45:56
First of all, I wrote off $5,000,000,000 worth of hoppers. If anybody would like to have some extras 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. A 100% of our chip designers use AI.
Speaker: 7
46:16
We are busier than ever. And the reason for that is because we have so many ideas that we sana go pursue. AI makes it possible for us to go pursue those ideas now that we’re not doing the mundane stuff. And so I I think the first idea is the more productive you are as a company, so long as you have more ideas, you could pursue those ideas.
Speaker: 7
46:37
You’ll you’ll go after those ideas. And I I think that that AI, in my case, is creating jobs. It causes us to be able to create things that other people would, customers would like to buy. It drives more growth. It drives more jobs.
Speaker: 7
46:49
You know, all that goes together. The other thing that that to remember is that AI is the greatest technology equalizer of all time.
Speaker: 4
46:57
Okay. Explain.
Speaker: 7
46:58
Everybody’s a programmer now.
Speaker: 4
47:00
Yes.
Speaker: 7
47:01
You used to have to know c and then c plus plus and Ai. And, you know, in the future, everybody could program a computer. Right? Just have to get up. And if you don’t know how to program a computer, you don’t even know how to program an AI, just go up to the AI and say, how do I program an AI?
Speaker: 7
47:14
And the AI explains to you exactly how to program the AI. Even when you’re not sure exactly how to ask questions, what’s the best way to ask the question? And ai actually write the question for you. It’s incredible.
Speaker: 1
47:24
Yeah.
Speaker: 7
47:24
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.
Speaker: 7
47:46
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 gonna lose your job to somebody who uses AI. That I think we know for certain. Yeah. There’s not a software programmer in the future who’s gonna be able to hold their own, I mean, you know, typing by themselves.
Speaker: 2
48:10
Yeah. You can’t
Speaker: 4
48:11
raw dog it. No.
Speaker: 7
48:12
No. Not anymore.
Speaker: 4
48:13
No raw dog.
Speaker: 7
48:13
Not anymore. You can’t raw dog it.
Speaker: 0
48:17
Tell us I’ll
Speaker: 7
48:17
be sure to go home and tell people.
Speaker: 0
48:19
Yeah. Exactly. That’s true.
Speaker: 7
48:20
Sana raw dog this.
Speaker: 4
48:21
Yeah. Get your co pilot on. Now what about the allocation of all the
Speaker: 7
48:24
Okay. So the way we allocate is this. The way we allocate is this, place a PO.
Speaker: 0
48:31
Okay. Because that’s it. Just that’s it. You go to the register. You pay. You order.
Speaker: 7
48:35
First, you, you know, first in in the old days with hopper, it happened so fast. It was impossible to keep up with the demand. But now, we we, dis we disclose our road map 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 wanna allocate. We plan together. We work on transitions.
Speaker: 7
49:00
It’s really quite orally these days.
Speaker: 4
49:02
What’s the lifespan now? You you know, I I was looking into how they’re amortizing, you know, these units four or five years. What happens to this massive build out in year six, seven, and ‘8? 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?
Speaker: 7
49:19
Concepts are happening right now. The first thing first thing is every generation, we increase the performance by x factors. Yeah. If the perf per doll perf per watt goes up by x factors, whatever your data center power is, we just increase your revenues by x factors. Right.
Speaker: 7
49:36
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 Yeah. The first idea.
Speaker: 7
49:48
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 sai so that we have the benefit of driving AI cost down as far as possible so that we can have thinking AI.
Speaker: 0
50:02
Right.
Speaker: 7
50:02
It’s not that we’re trying to make, you know, AI so that it generates a thousand tokens and that’s it. In the future, you’re gonna be generating millions of tokens, and that generates an an answer as a result of that. You gotta think a long ai. And so you gotta get that cost down.
Speaker: 7
50:16
The second idea is if you look at the residual value of NVIDIA gear right now, Hopper, for example, one year one year later, it’s probably about 80%, 75 to 80% of the value of the original value. And then one year later is another ai like 65%. And then one year later is like 50%.
Speaker: 7
50:37
The reason and 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. Mhmm. And so what’s amazing is the performance of Hopper increases over time because we’re improving the software stack.
Speaker: 7
51:01
It Hopper improved in performance by us and others by a factor of
Speaker: 4
51:06
four Right.
Speaker: 7
51:07
In the time that we shipped it. Now you can’t get that out of a CPU.
Speaker: 0
51:10
Right. Jensen, can you explain to us, Elon’s tweet and the impact to you to your industry? He said, we’re gonna have 50,000,000 h 100 equivalents by in five years from now. And everybody started to feverishly do the math Because if he has 50,000,000 h 100 equivalents, then OpenAI will have that much or more.
Speaker: 0
51:33
Meta will have that much or more. Google, etcetera, etcetera, etcetera. Can you just explain to us, Laymon, what that means, what he just said, and how it impacts your business?
Speaker: 7
51:46
One of the biggest observations about AI is that there’s there’s the industry of applications that AI has created. It’s a revolutionary technology. Every industry would will be revolutionized. New applications will be created. So on and so forth. That all the things that we know. Agentic AI, reasoning AI, robotics AI, so on and so forth.
Speaker: 7
52:07
We we know all those things now. Every industry, health care, education, transportation, you name it, manufacturing, all revolutionized. The one part that that that we observed and and made a great contribution to is that in order to sustain those applications, you need factories of AI.
Speaker: 7
52:29
You have to produce AI. Unlike unlike software, you write the software and that’s it. In the case of AI, you have to continuously produce it, generate the tokens. Right. In a lot of the same ways that energy production was a large part of the economy a couple of two, three hundred years ago, I think it actually peaked out at 30%.
Speaker: 7
52:48
Yep. There’s a whole, there’s sana be a whole industry of just producing tokens. And this is gonna be the new infrastructure, just as we have the energy production infrastructure, we have the Internet infrastructure, and we gotta build out that plumbing. And now we gotta be we have to build out the AI infrastructure.
Speaker: 7
53:06
My sense is that we’re probably, you know, a couple of $100,000,000,000, maybe a few $100,000,000,000 into a multi trillion dollar infrastructure build out. Per year.
Speaker: 2
53:17
Yeah. What about manufacturing?
Speaker: 7
53:21
And the reason for that is because you want the new infrastructure, which increases revenue, ai your cost down.
Speaker: 0
53:26
Right. Mhmm.
Speaker: 5
53:26
That’s right.
Speaker: 2
53:27
What about manufacturing in The US? So where are we? We, you know, we’ve seen stories of TFMC in Arizona. We asked this question earlier about how it’s going. Is The US equipped? What is it gonna take for us to get there to have onshore vatsal?
Speaker: 7
53:41
First of all, you guys know you’re talking about The United States. The the, I know that there’s there’s lots of concerns and and everybody’s, you know, worried about competition and things like ai. But we are talking about America here. This is this is unquestionably the most technology rich country in the world. Mhmm. And this is the most innovative countries in the world.
Speaker: 7
54:05
And the computer industry Ai don’t have the I have the honor to serve is the single greatest industry our country has ever produced. I think we could acknowledge that.
Speaker: 4
54:16
Yep.
Speaker: 7
54:16
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 gonna be insanely technology driven. Robotics technology, AI technology.
Speaker: 7
54:39
You’re gonna have factories that are gonna be orchestrated by AI, orchestrating a whole bunch of robots that are AI, building products that are effectively Ai. Right? So you’re gonna have this in layers of inception, And the amount of technology necessary to create that isn’t is really insane.
Speaker: 7
54:54
We’ve I I love president Trump’s vision, bold vision of reindustrializing The United States. That entire band of industry that’s missing, we out we outsource too much of it, frankly. We don’t need to in source all of it, but we ought to bring onshore the most advanced, the most economy sustaining, ai, national security enhancing parts of the industry.
Speaker: 7
55:22
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 super supercomputers will probably drive a few trillion dollars worth of AI industry.
Speaker: 0
55:42
Right.
Speaker: 7
55:42
And so that’s only in the next several years And and, they’re doing great. Arizona’s doing great.
Speaker: 0
55:47
And so there’s, there’s a lot of talk about American competitiveness today, and the White House rolled out its AI action plan. And NVIDIA is making very big bets on The United States. And so as a CEO of a global company, what do you see are America’s unique advantages that other countries don’t have?
Speaker: 7
56:06
America’s unique advantage that no country possibly have is president Trump. And let 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 ai, if you guys remember.
Speaker: 7
56:37
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.
Speaker: 7
56:57
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 this new industry and, you know, go after the the new industrial revolution.
Speaker: 7
57:19
Big, big deal.
Speaker: 2
57:20
Can can you talk about physical AI versus data center AI? We talk 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?
Speaker: 2
57:35
Or what do you think
Speaker: 7
57:36
Everything in the world
Speaker: 2
57:37
of the world looks like?
Speaker: 7
57:38
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.
Speaker: 7
57:51
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 robot. Ai.
Speaker: 7
58:18
And so every company in the future, in fact, the future of industry is really two factories. Yeah. Tesla already has two factories. Right? Elon has a giant AI factory.
Speaker: 7
58:29
He’s he was very early in recognizing that he needs to have an AI factory to sustain the the cars that he has. Now he’s got Ai in the car, but in the future, instead of, you know, I imagine that in the future, instead of a whole whole lot of people remote remotely monitoring air traffic control, it’ll be a giant AI that’s doing the remote Mhmm.
Speaker: 7
58:49
Control. And then only in the case of the the giant AI, can handle it with a person come in to to, intercept. And so so I think you you see that that these industries in the future, every industrial company will be an AI company. Or you’re not gonna be an industrial company.
Speaker: 0
59:05
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 Ai. Right? There was the deep speak moment, then maybe this week, last week, there was this Kimi model moment. But then it ai 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, to whether it’s AGI or, you know, superintelligence?
Speaker: 7
59:33
Yeah. Excellent question. The Chinese AI labs are the world’s 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 gonna be the future industry is gonna be today startups, they’re gonna need open open source models.
Speaker: 7
01:00:01
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 two reasons. First, imagine if Deepsea came out and only ran on Huawei. I just want us to pretend. Use that thought experiment.
Speaker: 2
01:00:18
Totally. Right.
Speaker: 4
01:00:19
Ai you got two parallel universe. Exactly.
Speaker: 7
01:00:21
Could you imagine if QN came out and only worked on non American tech stack? Yeah. Could you imagine if Kiwi 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 Meh tech stack all over the world being the world’s standard is vital to the future of winning the AI race.
Speaker: 7
01:00:45
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.
Speaker: 0
01:00:52
Yeah.
Speaker: 7
01:00:53
And half of the world’s developers are in China.
Speaker: 0
01:00:55
So speaking of developers
Speaker: 7
01:00:56
The second the second I’m sorry.
Speaker: 0
01:00:58
Okay. Go ahead.
Speaker: 7
01:00:59
The the second thing is really a big deal. When Deepsea 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.
Speaker: 0
01:01:13
Yeah.
Speaker: 7
01:01:13
If you have a question, everything was memorized. You know, the pre pretraining is basic memorization and generalization, two concepts. Post training is teaching you how to think. And so now with deep speak r one, kimmy, kimmy k two, q one three, you now have reasoning models that can allow that help you think.
Speaker: 7
01:01:34
And so the reason why I was so sai is if each pass of a thought is energy efficient, then you can think for a long time.
Speaker: 1
01:01:42
Right.
Speaker: 3
01:01:42
Yeah.
Speaker: 0
01:01:43
The last question ram from 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 $300,000,000 contracts. Now, it’s, you know, model researchers. And then, there was a there was a post this weekend that that sai that there was a person that was offered a billion dollars over four years by Meta.
Speaker: 0
01:02:06
Now, if that’s happening at this layer, why hasn’t it happened at your layer? Because you are the enabler of all of vatsal. And how do you think all of this human capital is going to actually play out?
Speaker: 7
01:02:16
First of all, I’ve created more billionaires on my management team than any CEO in the world. They’re doing just fine. Okay? And so and and, they’re doing don’t don’t feel sad for anybody at my layer.
Speaker: 2
01:02:34
Yeah. Everybody’s doing okay.
Speaker: 7
01:02:35
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 150 or so AI researchers can probably create with enough funding behind them, create an OpenAI. Mhmm. It’s a it’s not a
Speaker: 0
01:02:54
150 people.
Speaker: 7
01:02:56
Yeah. It’s not a it’s not well, DeepSeek’s a 150 people. Right. One shah a 150 people. Right. Right?
Speaker: 0
01:03:01
Crazy.
Speaker: 7
01:03:01
And sai, I mean, look at the original, OpenAI was about a 150 people. Yep. Ai, 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 ai team with the right infrastructure.
Speaker: 7
01:03:16
And so that kinda tells you something. A 150 people if you’re willing to pay, say, $20,000,000,000, $30,000,000,000 to buy a arya with a 150 AI researchers, why wouldn’t you pay
Speaker: 1
01:03:27
one? Right.
Speaker: 4
01:03:28
Yeah. Hey, speaking of options By the
Speaker: 2
01:03:30
way, we Somebody told me We need to wrap because Yeah. I know. But We we have this
Speaker: 4
01:03:33
I’m gonna do this one question. Somebody who is 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 put
Speaker: 7
01:03:51
them out. That’s nuts. That’s
Speaker: 4
01:03:53
Is that true?
Speaker: 7
01:03:54
Ai yeah. I’m carrying it in my pocket right now. So listen. So this is what happens. I review I review everybody’s compensation up to this day
Speaker: 4
01:04:03
Yeah.
Speaker: 7
01:04:03
At the end of every cycle when they bryden it. And they sent they sana me everybody’s everybody’s recommended account. 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.
Speaker: 7
01:04:18
And a 100% of the time Sai increase the company’s spend on OPEX. And the reason for that is because you take care of people, everything else take care takes care of.
Speaker: 2
01:04:27
Sai Yeah.
Speaker: 4
01:04:27
Alright. Well done. Thank you. Thank you, Justin.
Speaker: 0
01:04:29
It’s great to see you.
Speaker: 2
01:04:30
Alright. Nice to see you. We haven’t bryden in LA. We’d love to continue the conversation.
Speaker: 7
01:04:34
Yeah. So we’ll send you a note. Yeah.
Speaker: 2
01:04:36
The world’s number one podcast. There you go.
Speaker: 0
01:04:38
Thank you.