Guys, this is one of the most amazing

entrepreneurs that you're going to meet.

Jim Leinsky, this founder and CEO of MP

Materials.

Thanks. Good to be here. Jason,

>> how are you?

>> So, let me let me set this up. Um,

>> Jim was a hedge fund guy running a

pretty successful hedge fund. Uh, and he

ended up basically investing in

something called Molly Corp, which went

out of business.

>> Yep.

>> Yeah.

>> 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 uh largest and

only, I think, supplier and refiner of

rare earth materials and maker of

magnets inside the United States.

>> We're 100% of the American industry.

>> 100% of the American industry. You

>> just did two really incredible things

actually in the last couple weeks. One

was you announced an enormous public

private partnership with the DoD

$400 million etc. And then the second is

you uh announced a really big deal with

Apple.

>> Yes.

>> Okay. So take us 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.

>> Rare earth magnets are really the feed

stock to physical AI. Um you know

robots, drones, everything we're talking

about today, the biggest industry in the

world to come. Um, essentially

electrified motion requires rare earth

magnets. Um, so you mentioned the

predecessor went bankrupt. Um, there

there was a feeling when I when I took

over this site with my co-founder and

this is this goes back to 2015. Uh,

there was

>> where where is the site?

>> This oh it's in Mountain Pass,

California. So if you you'll be familiar

if you take a 45minute drive from the

Las Vegas strip uh just over the border

in California uh is the site. You

actually can see it from the road. Uh,

and it's actually the really the best

rare earth or body in the world. Um, 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 uh 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. Or

you could have all of the magnetic

capability in the world, but if you

don't have the rare earths, you're

relying 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 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 we had to reorganize

the process flow and then we had to make

investments uh to move downstream. So

over the last eight years, we invested

about a billion dollars. Um Jim, as you

know, we took the company public in

2020. We built out the refining

capability. Uh and then uh about four

years ago, we announced we were going to

build a magnetics factory in Texas. We

built that factory. We have GM as a

foundational customer. We're now

producing autograde magnets uh G to GM

spec and we'll be ramping up uh sales to

GM at the end of this year in in

magnets. And then Chimath, you

referenced a couple it's been a busy few

months for us. Um we announced a a

pretty transformative public private

partnership with the Department of

Defense. DoD is um there's really three

pillars to this deal. uh DoD is becoming

our largest economic uh investor

um as well as they're going to provide

uh a price floor for our commodity so

that the Chinese sort of Chinese

mercantalism we can get into that uh

won't take the price of the commodity

below the cost of production and then as

a result of the DoD investment we're

going to accelerate the buildout 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 um uh customer and

business partner because we'll be we'll

be splitting profits 50/50 with D.

>> So to to just translate this, it's not a

handout from the government. They didn't

gift you $400 million. They invested in

your company. They have warrants. They

have equity.

>> Yeah. So they invested they they both

are an an owner. Uh they also are an

upside participant in our commodity to

the extent that the prices uh uh 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. So

there's really you, the taxpayer. Yeah.

So this is a and maybe I'll say

something wild here. This is a true

win-win. Obviously great for MP

shareholders. um 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 and

other industries. Um but it would not

surprise me if when we 5 years from now

hopefully we'll do this conference and

Chimath 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 u the

taxpayer made money on doing this and

I'll say yeah I actually think that

that's going to be the outcome um

because there's sort of an element of

mutually assured economic destruction if

the Chinese believe that America has

national champions do then there's no

point in subsidizing the rest of the

world. And so I think you can start to

see prices uh normalize for some of

these things and free up our ability to

invest and expand.

>> Why go to the government for this

investment as opposed to the private

markets?

>> Yeah. Well, because it's that issue that

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 uh did

something, you know, totally unique that

Why do you need the government?

>> Merkantalism. Straight up mercantalism.

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 time

frame that they wanted us to build. We

there was no way we were going to make

that commitment. Um 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 mercantalism and that we

would have a customer for the magnets.

>> How big of an industry is physical AI?

Meaning,

>> 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 being deployed at the

scale and at the pace that we that we've

been told?

>> 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 feed stock um I will say

that I think one of the big drivers of

our deal was the as we've seen um 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 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? We

there's we can't be funding cutting edge

drone and robotics companies and then

say, "Okay, but we're going to buy those

magnets from China." That makes no

sense.

>> 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?

It's a great It's a great question. I

think Jason, I think about this question

a lot because

>> what's that? Oh my god. Dave, sorry.

>> No, it's all good.

>> I'm a huge fan of the pod and I just

embarrassed myself all the time. It's

it's the only token whites

>> and you know I'm a fan of the pod since

day one and I'm totally embarrassed

myself.

>> That's only one correction messing with

you. Nor am I messing with you? Was this

intentional? Um, so

>> huge fan of the pod.

>> Yeah, huge fan of the pod. Who are you

again? I'll take a selfie later.

>> And I'm not the AIAR. Go ahead.

>> Um, so we have we have uh 850 employees

today at MP. We're we're going to hire

when we include what we're building out

for Apple uh 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 um this is a key

existential question for all of us as we

build out this is is where are we going

to get the talent? I think what we have

found um you know at Mountain Pass and

we we hire it all electricians um

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. 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 se the thought

is always that wow these jobs are not

desirable but they really are desirable

by many people.

>> Yeah, absolutely. I mean we you know our

median wage is now pushing $100,000 a

year. Um and there's you know relative

to some of the opportunity set I mean uh

these are great these are great jobs.

And what are the salaries?

>> What's that?

>> What's the starting salary? Just

curious.

>> So, it really depends on the job

function because you know there's

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

$100,000 a year with us because by the

way um everybody's an owner. We have an

owner operator culture. Everyone got

stock when we went public in 2020.

>> But somebody coming out of high school

they can make 40 50 60k or more. Yeah.

Or it depends. Are you if we we can't

find enough electricians. We can't find

enough maintenance workers. A

maintenance worker can uh an electrician

they can make six figures today.

>> Tell us you said earlier that you

suspect 5 years from now we're going to

look back and this deal with the DoD was

a blueprint.

>> Yeah.

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.

>> Yeah. Um there are some major categories

obviously we've all heard about ship

building 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 um where there it's a vertical where

there might not be a market large enough

to to need five players but a a good

public private partnership can just

solve that problem and then there's some

other verticals in critical minerals

>> 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

>> Yeah. Well, and I I think that's you our

our particular deal was led by DoD and

so I have to say that the Pentagon

leadership is extraordinary. Um and uh

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 cuz I you know

this story is not out there. Our process

this was I've never worked so hard in my

life. I mean this was this was like a

true aggressive private equity style

investment and negotiation.

>> The transaction documents are public.

You can look at that.

>> So that's you saying that they're tough.

>> 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 uh distress lender type

negotiation. That's what this was. And

the key thing um 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

cost. And so we're exposed if we get the

costs wrong. You know, we we're making

this investment. And and so the key

piece of this and 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 going to take

the things off the table that you can't

control mercantalism you know certain

customer issues um we're going to be

held to account for the things that we

can control our ability to execute our

ability to um execute on a good timeline

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 right this is not

that this is private risk

public risk public upside private

upsideing it's a true shared win-winwin

and again like I said whole me to these

words I I hope uh I I I hope I'm right

on this, but I think the uh to the

credit to the Trump administration, I

think they will make money on this and

have solved the national security.

>> All right, we appreciate you coming,

Steve.

>> Yeah. Oh, thanks.

>> Thanks so much.

>> Thanks, brother.

>> Yeah, it's great.

>> All right. Take care, Steve.

>> Thanks, Jacob. I appreciate it. Yeah.

Okay.

>> Hi, Lisa.

>> Lisa, it's a pleasure.

>> Hi. Nice to meet you.

>> Hi. Hi. Hi. Hi. Hi.

>> Well, thanks so much for being here with

us today. Um, 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

2nometer line. Um, 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?

>> Well, absolutely. First of all, it's a

pleasure to be here. Um, love the theme.

I think we're all super excited about

winning the US um, AI race. And I

thought if we're going to talk about

chips, David, I should actually bring

one.

>> Oh, awesome.

>> That's okay.

>> Little bit of showand tell. So, this is

our latest generation um AI chip. It's

our MI355 chip. 185 billion transistors.

Takes about 9 months to build. Lots of

technology on it. If I just

>> nimeter,

>> this is um 3 nanometer and uh 6

nanometer. So, lots of different uh

>> I'll be putting this on eBay later.

>> I'm going to take it with me when I'm

How is that?

>> Thank you. Uh but look to answer your

question I think look these AI chips are

extremely extremely complex. Um they

have uh so much technology on it. Uh

we're super excited about the progress

in US manufacturing. I would say you

know 12 months ago people weren't sure

that we could do leading edge

manufacturing um in the United States.

Uh we've been very early in Arizona with

TSMC and we did get our first uh chips

out. um 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 uh conversation about onuring

manufacturing has been you know super

good for the semiconductor industry and

and you know frankly for you know all of

us in um semiconductors uh we're in such

an interesting place because you know

chips are so essential to ensuring that

we are able to win the AI race that um

you know we want to make sure that

there's a lot of geographic diversity

and capability there

>> but the reports out where 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

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?

>> And Lisa, you have permission to speak

freely.

>> Yeah.

>> The best way to say it is no matter when

you start something new, it it's going

to take work, right? It's it's going to

be hard. So sure in the beginning there

there were some issues of you know the

the you know TSMC has like a formula for

how they build and they just you know

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 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

>> about cost in Arizona because it's

unrealistic to think the United States

could compete on cost. Am I correct?

>> We're going to pay a little bit more.

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

>> Not not 50% more. I mean look it it it

it's going to be you know more than 5%

but you know let's call it less than

20%.

>> So low low

>> low double let's say low double digits

>> and how does that impact the business if

at all uh in terms of competition

globally? 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 the lowest cost, you

know, every minute of the day, um, is

okay. It's okay. Like obviously we're

not going to build um 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 um in that view but the the

focus um from this administration on

getting uh onfirm manufacturing in a big

way not in a small way I think is is

very good for the country. 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?

>> Yeah, you have to look across um the

supply chain, but you know, from a

structure standpoint, we all want to

keep reserves for, you know, those those

times. Uh but it's months, it's not

years.

>> Lisa, there was uh two really

interesting posts over the last couple

of days. One was from Elon where he said

in 5 years he projected 50 million H100

equivalents just for XAI and the second

was Sam Alman they signed a deal for a

four I think gawatt 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

Yeah, it's it's a great great point. I

mean, that's that's what we're seeing.

We're seeing this uh incredibly large

demand uh for AI and they're coming

from, you know, Sam and Elon are

certainly uh the you know, the the

leaders, a couple of the leaders. Uh

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 uh very high demand. We're we're

imagining that just the accelerator

market, so the chips for these um 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? Um it's the entire ecosystem

needs to scale up. So we need to scale

up um certainly what we're doing in in

chip design is trying to get chips out

as fast as possible. Uh but we're also

scaling up the entire manufacturing

ecosystem. And you know, as I said, I

don't I think the US is going to be a

huge piece of it. So, it's not just

about the silicon. There's all of the

various other pieces of the ecosystem

that have to come to the US. And and I

think look, I think today's um AI action

plan is actually a really, you know,

excellent blueprint.

>> 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 AS6,

different designs, different use cases?

>> Yeah, I I I like that question because I

I am a believer in there will be

diversity of chips. Uh 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 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 um that do that uh you

know certainly the for the largest

systems uh we tend to believe that uh

you know you need the most compute you

can get and so you know GPUs are there

but lots of AS6 are um in the in the

process and you know we'll see a variety

of different chips. You opened up a

really interesting line of questioning

there. When mainframes uh were so

expensive and then eventually wound up

having PCs that were more expensive on

their desktop, you alluded to AI being

run locally.

>> Yes.

>> When would we have a local computer, a

laptop, a desktop computer that would

have the power we're seeing to run some

of these LLM models in your mind? And do

you see that as a specific market to go

after? I look I definitely see the um

the idea that AI will be at every part

of our ecosystem um is a uh is a real

thing. I think that's one of the

advantages. If you think about the power

of AI um 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 um 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. On 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?

>> Uh that's a great question. I'm a big

believer in physical AI. I still think

it's let's call it five years.

>> You think five years? Is that that fast?

>> It's at least five years.

>> So you're saying five plus.

>> Five plus. Yes.

>> Okay.

>> But that but that is ultimately the

biggest end market. Do you think is it

phys you think physical AI becomes the

biggest end market? I think it becomes a

significant end market. Um I think you

look at chips in data centers and you

look at chips at the edge, they're also,

you know, significant markets.

>> 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? Like how are we trying

to get beyond the physical limits of

electrons shunting across a junction?

I I think this idea that the um 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 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,

>> right? Right. Uh but I do see it helping

us design the next generation GPU much

faster and more reliably. So

>> you talked about the need to like

reshore more parts of like the you know

ecosystem. Obviously you guys are

there's a 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 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 going to

we we 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 and uh you know

that that is you know something that we

protect given our intellectual property

>> and going going to first principles and

asking you the open-ended question what

should be done about American education.

I'm going to ask this a lot today.

Assume there's no college, high school,

nothing. You arrive in America, the

situation is what it is today. What do

you do? How do you build an education

system to prepare the next generation

for the evolving workforce?

>> Yeah, I'm probably a little bit biased

as maybe some of your uh guests are

today. Uh 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 um you

know sort of the process I think the

better. So some of the work that's being

done to kind of revitalize the uh

curriculum I think is is pretty

important in the um 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.

So kind of inspiring people when they're

young to uh really you know study you

know science.

>> Lisa when you um 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 super intelligence can't

be far behind that. I assume you agree

with that. um assume we hit that super

intelligence what will the world look

like in 10 years in the most optimistic

scenario if we do this right

>> well I think the exciting part about it

and you know I can say this ex very

sincerely I mean this is the most um

transformational technology sort of in

our lifetimes I mean that's the way we

should think about

>> orders of magnitude

>> 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 um, 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 um

in the world? And, you know, AI is

really our mechanism for doing that. I

have a business strategy question. If we

went back 20 years and we wrote the tale

of three companies, Nvidia, AMD, Intel,

and then you fast forwarded 20 years,

two have just absolutely thrived

and one has not. And if you had made the

bet back then, it would have been very

inconclusive that you would have picked

Nvidia and AMD. And if anything, there

was a 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

>> 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

the probably the most important lesson

that I can uh 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 5

years ago AI was around but we wouldn't

be able to gather this audience to talk

about AI because people 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 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 5 years

from now. Like the decisions we're

making will take, you know, five plus

years to play out. Uh but that's the key

thing in tech like nothing is fast but

hopefully it's quite lasting in what it

>> 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 mech capabilities in

China and other places right now?

>> We should believe that it's super super

competitive. I mean, at the end of the

day, I think the world has recognized

that um 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. Um 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 uh 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, right? I I I love

the idea of open ecosystems of uh you

know uh companies collaborating of

collaboration across the ecosystem. So

hardware, software systems um you know

collaboration across public private

partnerships because 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.

>> Great. So, thank you for being with us.

>> Wonderful afternoon. Been great.

Appreciate it.

>> Thank you.

>> Thank you.

>> Great. Pleasure to meet you. Thank you.

>> I'm Chase Locker, the uh co-founder and

CEO of Crusoe, and I'm here to talk to

you about the AI industrial revolution.

Um, 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 a artificial intelligence

uh they ring even truer today. American

dynamism has always prevailed and it

will continue to do so. So in in in sort

of the uh history of of of really what

made America great is uh you know we

live in a nation that's the freest

nation in the world and we have uh we

are just as rich in land and resources

as we are in human ambition uh 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. Uh Warren Buffett got

to witness uh investments in in power in

transportation and in uh in power in

transportation and in natural resources

to uh to enable people to go pursue

their dreams and live a better life.

Let's see.

There we go. Now, in 2025, we stand at

a, you know, the start of a new era of

infrastructure, the infrastructure of

intelligence, and it's driving the

biggest capital investment in human

history. Uh, this investment is being

led by the hyperscalers uh who are

investing hundreds of billions of

dollars per year per year um to make

this happen. These are the companies

with the biggest balance sheets in the

history of business that are quite

literally going allin to to make this

happen. And they're not the only ones.

You know, there's also startups like

Crusoe and uh there's there's even

nation states um that are following

suit. So So what's going on there?

What's the what's the prize that that

they're going after? Um the you know the

opportunity here is that uh uh for the

first time in human history, we've

actually been able to manufacture

intelligence. um intelligence is the uh

scarcest economic resource um in the

history of the economy and and for the

first time we're actually able to make

it. Um and the opportunity here is to

actually unlock access to what has uh

historically been that scarce economic

resource. So um this is why the data

centers of the future are not being

referred to as data centers. They're

actually being referred to as AI

factories. It's a factory that takes as

inputs data and algorithms and chips and

energy and it outputs intelligence. This

is the alchemy of intelligence.

So this newly manufactured intelligence

will spawn a new chapter of

unprecedented productivity and

development and that will serve to

improve human quality of life. So the

IDC estimates that AI will generate $20

trillion in economic impact by 2030. So

even if you can earn a small slice of

that, that hundreds of billions of

dollars of investment will earn an

amazing return. For each dollar invested

into uh business related AI is expected

to generate $4.60.

Uh as my friend Jensen would say, the

more you buy, the more you save. Um or

in this case, the more you buy, the more

you make. uh and we can grow the pie

together and usher in a new era of

AIdriven abundance.

So when we look at the history of

American energy production and

consumption, uh as as as the US

industrialized, we we really ramped up

energy generation and uh and also

consumption. But if you look at this

chart, you can see that, you know, it's

it's kind of flatlined over the last 20

years uh where we're, you know,

generating and consuming about 4,000

terowatt hours per year. AI is

fundamentally transforming this demand

picture and uh and and energy is quickly

becoming the bottleneck to growth.

Uh data centers are forecasted to

account for 20% of the growth in power

demand between now and 2030. And uh data

center total power consumption is going

to go from 2.5% of US power consumption

to 10%. So what this means is that the

technology industry that's sort of

willing this infrastructure into

existence fundamentally needs to bring

its own power to support that growth. Um

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 uh and run these

these large scale energy investments.

Um so if we look at data centers you

know by the numbers I think it's

important you know as people are sort of

throwing around gigawatt scale data

centers uh of looking at the amount of

data center infrastructure that exists

today. Uh Northern Virginia is sort of

the center of the world for data centers

but it's only you know at the end of

2014 it was only 4.5 gawatt. Today we

have companies that are looking at

building single 5 gawatt facilities. And

if you look at this growth we're

building more than a uh northern

Virginia every single year in the

forecasted future.

So we need new in So if there's one

thing that you're going to take away

from this presentation, it's that we

need new infrastructure. We need lots

and we need lots of it and we need lots

of people to build, operate and maintain

it. This is what Cruso is focused on

solving. Cruso is in the business of

activating energy for intelligence of

building operating uh AI factories at

scale from from the steel to the

silicon, from the electron to the token.

And if you look at our pipeline, we have

about 40 gawatts of capacity that spans

um all sorts of energy resources from

new energy technologies like uh you know

like like uh small modular reactors to

uh renewables and and natural gas to to

power this uh innovative future.

Uh so revisiting my formula here, I

think we left off one critical component

which is the people. Uh 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. For the last year um Cruso has

been building um uh a large large scale

AI factory in Abalene, Texas. And uh you

know speed is paramount. Again this

event is winning the AI race. Uh in

order to win a race you really need

speed. Uh, and Cruso's really been

focused on on on using modular

components on on rapidly scaling

investment in in construction and

infrastructure to support this. And and

and and we've actually built a lot of

different modular components in in

factories and brought them to site. And

they're kind of like uh they're kind of

like Lego blocks that sort of fit

together to to build one of these AI

factories at at rapid scale and speed.

Um so if you look at if you look at what

this looks like today um you know this

is uh this is this is this this site

will consume over 1.2 gawatts of power

and 400,000 NVIDIA GPUs all in a single

coherent cluster. So uh this will

essentially be a gigawatt scale computer

to drive human progress forward.

Um 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.

Um, so what does this mean from a jobs

perspective? We have 4,000 people

working on site every day to, you know,

make this facility happen. Um, and you

know, it's it's it's a bunch of

different trades, electricians and

plumbers, um, and construction workers.

Um, and it's required a lot of capital,

too. We raised $15 billion to basically

put this facility and and and and bring

it into existence.

Um, 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.

Um, 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 uh behind the meter with wind

with incremental gas and grid

interconnection. Uh we did a partnership

with Redwood Materials where we built

the largest uh uh we built the the

largest micro grid with uh in the United

States with 60 60 megawatt hours of of

batteries, end of life EV batteries and

uh 20 megawatts of solar to power an AI

factory. We have a partnership with GE

Verova and engine number one uh for 4.5

gawatt of new uh gas generation capacity

to power future AI data centers. And

finally, uh, we want to announce a new

partnership that we're doing with Tall

Grass Energy in Wyoming that will

initially power 1.3 gawatts of total

compute load uh, alongside two 2 gawatts

of power generation and ultimately we

feel like this can scale to 10 gawatts

of power. Uh, so we're really thrilled

to partner with Tall Grass.

So uh 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 uh and and

and and new jobs for the economy. Um

this will usher in a massive new era of

AIdriven prosperity for the United

States. And I want to leave you with,

you know, my final quote from Warren

Buffett that, you know, in this AI race,

uh never bet against America. Thank you.

So, is this stuff real? You guys, you

started off as like a, you know, sort of

Bitcoin miner and now somehow all the

hyperscalers are asking you to, you

know, build non-stop data centers. Why

you guys?

>> Um, you know, I think again it comes

back to this being a race and one of the

things that Cruso's been able to do

better than anyone is execute at speed

and scale.

>> And I know there's been like some of the

biggest constraints around, you know,

sort of, you know, water, energy, um,

you know, the land for this type of

stuff. like where have you seen what

parts of the country you 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? Um, you know,

we've been building quite a bit in

Texas. You know, Abene, Texas is this,

you know, initial facility that's gotten

a lot of coverage. Uh, you know, we we

just sort of announced another facility

in Texas. Uh, Wyoming's been a, you

know, big area of investment for us.

But, um, you know, there's a number of

other states that were sort of

evaluating, investing uh, to build large

scale AI.

>> Is it only going to be the like, you

know, sort of more rural, you know, sort

of red states or do you think that like,

you know, Oregon, Washington, etc. will

start to, you know, sort of get together

and realize they've got cheap hydropower

and, you know, cheap water and we'll try

and get you there.

>> Uh, no. You know, believe it or not,

we're actually looking at something in

California.

>> Wow. California. Gavin Newsome is gonna

bring you in.

>> I would imagine that's gonna take like

50 years with their

>> regulations. Maybe. We'll see. We'll

see.

>> And you the Do you think that the, you

know, sort of hyperscaler demand?

Obviously, we were just, you know, on

with Lisa Sue talking about the demand

for chips over the next couple years.

That's obviously correlated to the, you

know, demand with data centers. Do you

think that's actually going to play out

the way that all the public markets are,

you know, sort of projecting or 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.

>> Um I I think the important trend to

watch is sort of the capital investment

that's happening and and the term over

which that's happening. So

>> I thought like Meta backed off on it a

little bit like didn't they like for a

little bit talked about they were going

to deploy like crazy and then pulled

back although he's obviously spending a

billion dollars on chief AI scientist

now.

>> Yeah. I think you know the 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. And then in like the,

you know, data centers like Texas, like

what's the limiting factor? Like is it

like workforce to actually go build

these things? Is it like materials? Is

it the cooling towers? Is it the chips?

Is it the hyperscalers giving you the

like, you know, you know, sort of

contracts? What's the, you know, sort of

limiting reagent?

>> Um, you know, uh, labor is definitely

like a major constraint. you know, like

I I said, you know, we have about 4,000

people on site um every day. Uh we're

going to have multiple sites that are

operating with thousands of, you know,

uh folks uh basically building this

infrastructure. So, you know, uh labor

is definitely like one of the one of the

big bottlenecks and we're, you know, we

think it's really important for America

to make these massive investments in the

workforce uh to really build the

infrastructure of the future. And you

think that requires some real real

reskilling where it's like people from

oil and gas or like construction having

to go into just totally net new fields

or is it something where you guys are

actually able to pull on pre-existing

talent pools pretty quickly?

>> Um both you know there's there's a lot

of existing uh you know labor uh you

know at that facility in Abene where

we're actually pulling labor from all 50

states at this point believe it or not.

Um so

>> making like a company town importing

people in.

>> Yeah. you know we we have about 50% of

the people are are are uh you know from

Texas but um you know the uh we we are

importing a lot of labor to to make the

project happen

>> and um you know do you do you see the

company starting to go more full stack

beyond just like the operations of like

the you know sort of data centers or how

do you think about like you know you

started off with you know focus on like

you know sort of energy arbitrage now to

data centers where do you see you guys

goeselves going over time

>> yeah 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 building right now

and and one of the hardest things to do

at speed. Um but we also have you know

this managed AI cloud services layer

that um enables innovators to build uh

large scale AI applications on the

platform.

>> Makes sense. Well yeah Chase, thanks so

much for uh you know joining us on stage

and um appreciate the talk.

>> Yeah, thanks D.

>> Okay everybody, we got a real treat for

you. Jensen Wong is here.

>> Sit here.

>> All right. Sit here. Sit here.

>> The hot seat.

>> Thanks for coming.

>> Thank you. making that happen.

>> Thank you.

>> The number one podcast in the world.

>> We were saying the number one company in

the world.

>> Wow. It's

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

the pod.

>> This is This is Norman, our host.

>> Yes. Yes. And there's Steve. Uh what's

the story with the jacket? You got one

of those. You have like six.

>> I have something like 50 or 60 of them.

>> So you really?

>> Yeah.

>> Wow.

>> What is that? Tom Ford.

>> I think so. This one is I think so.

>> Yeah. That's nice. I like it. I tried I

tried that on. It was like way too much

money.

>> Well, you guys are all so fashionable.

>> Coming from you guys, it actually means

something.

>> Yeah.

>> Oh, yeah. Oops.

>> Oh, look at you. Look at you.

>> Uh, hey, we we've been talking a lot

about opportunity. You've talked.

>> So, mine is like a model.

>> He is. He is.

>> Okay. Good idea.

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

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

favorite, right?

>> I My favorite is whatever my wife gets

me.

>> Ah, she dresses you, right?

>> As soon as she gets it for me, it's my

favorite.

>> Yes. Same with me. Same with me.

>> Smart man. Um, how you got up.

>> Nobody wears a Nobody wears a suit

better than Jacob. Good God.

>> Yeah, Jacob. He's a handsome man.

>> Just trying to keep up with you guys.

Um, I got I have two questions for you.

Take them in whichever order you like.

Um,

we've been talking a lot about job

displacement, opportunity, short-term,

long-term. Obviously, you get to see

everybody applying the technology

because, hey, listen, you've got the

best product in town to build on.

Therefore, everybody explains to you

their hopes, their dreams. So, you have

a unique way of looking at the playing

field. You have complete information

that we don't have. So, I want to know

what you think.

Don't worry, we'll fix it. Um,

what you think that edit

>> what you think about job creation,

transfer, displacement, etc. And then

the second one, I've just always been

curious.

>> You got all these important people

knocking on your door. You got Zuck, you

got E, you got uh, uh, Sam Alman. He

seems like he's a little bit of a

headache. I'll be honest. Um, but

>> he's great.

>> He's great. I'm joking. I'm joking. How

do you allocate

the H100's and whatever else you're

selling them and still have them all

like you? Because they must ask

sometimes, hey, can I get extra? I'll

pay you extra. So, just the allocation

of a finite amount of resources and then

jobs.

>> First of all, I wrote off $5 billion

worth of hoppers. If anybody would like

to have some extras,

>> you got them.

you know, just give me a call. Uh jobs,

uh uh we use AI across a whole company.

Every single software engineer today

uses AI. Not one left behind. 100% of

our chip designers use AI. We are busier

than ever. And the reason for that is

because we have so many ideas that we

want to go pursue. AI makes it possible

for us to go pursue those ideas now that

we're not doing the mundane stuff. And

so I I think the first idea is the more

productive you are as a company um so

long as you have more ideas, you could

pursue those ideas, you'll go after

those ideas. And I I think that that AI

in my case is creating jobs. It causes

us to be able to create things that

other people would uh customers would

like to buy. Uh it drives more growth.

It drives more jobs. You know, all that

goes together. The other thing that that

to remember is that AI is the greatest

technology equalizer of all time.

>> Okay, explain.

>> Everybody's a programmer now.

>> Yes.

>> You used to have to know C and then C++

and Python and you know in the future

everybody could program a computer,

right? Just have to get up and if you

don't know how to program a computer,

you don't even know how to program an

AI, just go up to the AI and say, "How

do I program an AI?" And the AI explains

to you exactly how to program the AI.

Even when you're not sure exactly how to

ask a question, you say, "What's the

best way to ask the question?" And it'll

actually write the question for you.

It's incredible. Yeah. And so it's a

great equalizer. Everybody is going to

be augmented by AI. Everybody's an

artist now. Everybody's an author now.

Everybody's a programmer now. That is

all true. And so we know that AI is a

great equalizer. We also know that uh

it's not likely that although

everybody's job will be different as a

result of AI, everybody's jobs will be

different. Some jobs will be obsolete,

but many jobs will be created. The one

thing that we know for certain is that

if you're not using AI, you're going to

lose your job to somebody who uses AI.

That I think we know for certain. Yeah.

There's not a software programmer in the

future who's gonna be able to hold their

own. I mean, you know, typing by

themselves.

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

>> No. Not anymore. No raw dog.

>> Not anymore. You can't raw dog it. And

>> tell us.

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

>> Yeah. Exactly.

>> You're not going to raw dog this.

>> Yeah. Get your co-pilot on. Now, what

about the allocation of all the

>> Okay. So, the way we allocate is this.

>> The way we allocate is this. Uh, place a

PO.

>> Okay. That's it. That's you go to the

register, you pay, you order

>> 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 uh we we uh uh we

disclose our road map to all of our

partners uh a year in advance. Gives

everybody a chance to plan with us. They

decide how much power and how much data

center space and how much capex they

want to allocate. We plan together. We

work on transitions. It's really quite

oily these days.

>> What's the lifespan now? You, you know,

I was looking into how they're

amateurizing, you know, these units

four, five years. What happens to this

massive buildout in your six, seven, and

eight? What will be the use of those

computers if you keep building such

great products that replace them at 2,

three, four times? What do we do with

all that?

>> Concepts are happening right now. The

first thing first thing is every

generation we increase the performance

by X factors.

>> Yeah. Um if the perf per dollar perf per

watt goes up by x factors whatever your

data center

power is we just increase your revenues

by x factors right so perf per watt is

equal to revenues per per dollar equals

the cost and so we increase your perf

per dollar by x factors we reduce your

cost by x factors does 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,

>> right?

>> It's not that we're trying to make, you

know, AI so that it generates a thousand

tokens and that's it. In the future,

you're going to be generating millions

of tokens and it generates an answer as

result of that. You got to think a long

time and so you got to get that cost

down. The second idea is if you look at

the residual value of Nvidia gear right

now, Hopper for example, one year one

year later it's probably about 80% 75 to

80% of the value of the original value

and then one year later is another kind

of like 65% and then one year later is

like 50%. The reason and right now if

you try to get hoppers in the cloud it's

all sold out. The reason for that is

because CUDA is so programmable and

we're constantly the whole world not

just us the whole world is doing open

source development improving its

effectiveness.

>> And so what's amazing is the performance

of Hopper increases over time because

we're improving the software stack it

Hopper improved in performance by us and

others by a factor of four

>> right

>> in the time that we shipped it. Now, you

can't get that out of a CPU, right?

>> Jensen, can you explain to us um Elon's

tweet and the impact to to your

industry? He said, "We're going to have

50 million H100 equivalents by in 5

years from now." And everybody started

to feverishly do the math because if he

has 50 million H100 equivalents, then

OpenAI will have that much or more. Meta

will have that much or more, Google,

etc., etc., etc. Can you just explain to

us layman what that means what he just

said and how it impacts your business?

>> Um,

one of the biggest observations about AI

is that there's there's the industry of

applications that AI has created. It's a

revolutionary technology. Every industry

would will be revolutionized. New

applications will be created. so on so

forth that all the things that we know

agentic AI reasoning AI robotics AI so

on so forth we we know all those things

now every industry healthcare education

transportation you name manufacturing

all revolutionized the one part that

that that we observed and and made a

great contribution to is that in order

to

sustain those applications you need

factories of AI you have to produce

produce AI unlike unlike software you

write the software and that's it in the

case of AI you have to continuously

produce it generate the tokens

>> right

>> in a lot of the same ways that energy

production was a large part of the

economy a couple two 300 years ago I

think it actually peaked out at 30%.

Yep.

>> There's a whole there's going to be a

whole industry of just producing tokens

and this is going to be the new

infrastructure just as we have the

energy production infrastructure, we

have the internet infrastructure and we

got to build out that plumbing and now

we got to we have to build out the AI

infrastructure. My sense is that we're

probably, you know, a couple of hundred

billion dollars, maybe a few hundred

billion dollars into a multi-t trillion

dollar infrastructure buildout

>> per year.

>> Yeah. What about manufacturing?

>> And the reason for that is because you

want the new infrastructure which

increases revenue driving your cost

down.

>> Right.

>> That's right.

>> What about manufacturing in the US? So

where are we? We um you know we've seen

stories of TSMC in Arizona. We asked

this question earlier about how it's

going. Is the US equipped? Uh what is it

going to take for us to get there to

have onshore fabs?

>> First of all, you guys know you're

talking about the United States.

uh the the uh I know that there's there

lots of concerns and and everybody's you

know worried about competition and

things like that but we are talking

about America here. This is this is

unquestionably the most technology rich

country in the world

>> and this is the most innovative

countries in the world and the computer

industry I have the I have the honor to

serve is the single greatest industry

our country has ever produced. I think

we could acknowledge that.

>> Yep.

>> The the level of leadership of the

computer industry, the technology

industry is just unimaginable worldwide.

And so this is our national treasure.

This is one of our country's assets. We

have to make sure that we continue to to

to advance it. Um onshoring

next generation manufacturing is going

to be insanely technologydriven. Uh

robotics technology, AI technology.

You're going to have factories that are

going to be orchestrated by AI

orchestrating a whole bunch of robots

that are AI building products that are

effectively AIS, right? So, you're going

to have this layers of inception. And

the amount of technology necessary to

create that is is really insane. We've I

I

love President Trump's vision, bold

vision of reindustrializing the United

States. That entire band of industry

that's missing, we out we outsource too

much of it. Frankly, we don't need to

insource all of it, but we ought to

bring onshore the most advanced, the

most

economy sustaining, driving, national

security enhancing parts of the

industry. You know, people always

degrade down to tennis shoes. We don't

have to go there. We just manufacture

chips and AI supercomputers. In Arizona

and Texas, we will in the next four

years probably produce about half a

trillion dollars worth of AI

supercomputers. that half a trillion

dollars worth of AI supercomputers will

probably drive a few trillion dollars

worth of AI industry, right?

>> And so that's only in the next several

years and and uh they're doing great.

Arizona is doing great.

>> And so there's um there's a lot of talk

about American competitiveness today and

the White House 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?

America's unique advantage that no

country possibly have is President

Trump.

And

let let me let me explain why. One uh on

the first day of his administration, he

realized the importance of AI and he

realized the importance of energy.

For the last

I don't know how many years

energy production was was vilified if

you guys remember.

>> 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 serviceson

country then we don't need much energy.

But if we want to produce things,

something as vital as artificial

intelligence and we need energy. And so

I'm just delighted to see pro to

accelerate AI innovation to accelerate

the growth of energy so that we can

sustain this this new industry and um

you know go after the the new industrial

revolution. Big big deal.

>> Can can you talk about physical AI

versus data center AI? We tal we talked

a little bit about this today. Is there

a threshold where you see physical AI

accelerating and ultimately the

deployment of chips outpaces the

deployment of chips in data centers? Is

that where the world evolves to or what

do you think

of the world looks like?

>> Yeah. Excellent. Everything in the world

that moves will be autonomous someday

>> and that someday is probably around the

corner. So everything that moves, we

already know that your lawn mower is

going to, you know, who's going to be

pushing a lawnmower around? That's

craziness. Unless you want to. I mean

it's you know and so so I think

everything that moves will be autonomous

and every machine

every company that builds machines will

have two factories.

There's the machine factory for example

cars

>> and then there's the AI factory to

create the AI for the cars.

>> And so maybe you're uh a machine factory

to build human or robots.

>> You need an AI factory to build a brain

for the human or robot. Right? And so

every company in the future, in fact,

the the future of industry is really two

factories.

>> Yeah.

>> Uh Tesla already has two factories,

right? Elon has a giant AI factory. He's

he was very early in recognizing that he

needs to have an AI factory to sustain

the the cars that he has now. Now he's

got AIs in the car, but in the future

instead of, you know, I imagine that in

the future instead of a whole whole lot

of people remote remotely monitoring air

traffic control, it'll be a giant AI

that's doing the remote

>> control and then only in the case of the

the giant AI um can handle it with a

person come in to to uh intercept. And

so so I think you see that that these

industries in the future, every

industrial company will be an AI company

or you're not going to be an industrial

company. There was a couple of moments

throughout the course of this year where

people almost threw in the towel and

said, "Oh, we lost to China." Right.

There was the Deep Seek moment, then

maybe this week, last week, there was

this Kimmy model moment. Um, but then it

kind of fizzled out. Can you just uh

explain to us how big of a threat they

really are in terms of getting to

supremacy, getting there first, whether

it's AGI or, you know, super

intelligence? Yeah, excellent question.

Um, the Chinese AI labs are the world

world's leading open

open model companies. They they offer

the most advanced open models. Open

source is fantastic. If not for open

source, we know startups won't exist.

And to the extent that we believe that

the future is going to be the future

industry is going to be today's

startups, they're going to need open

open source models. And Deep Seek when

it came out, it was a great win for the

United States. It was an incredible win.

What people didn't and two two reasons.

First, imagine if Deep Seek came out and

it only ran on Huawei. I just want us to

pretend. Use that thought experiment.

>> Totally. You got two parallel universes.

>> Exactly. Could you imagine if Q came out

and it only worked on non-American tech

stack? Could you imagine if KI came out

and it only worked on non-American tech

and these are the top three open models

in the world today? It is downloaded

hundreds of millions of times. So the

fact of the matter is American tech

stack all over the world being the

world's standard is vital to the future

of winning the AI race. You can't do it

any other way. We've got to be you know

as you know any computing platform wins

because of developers.

>> Yeah. And half of the world's developers

are in China.

>> So speaking of developers,

>> the second the second I'm sorry, please

go ahead. The second thing and it's

really a big deal. When DeepC came out,

we were thrilled for the second reason

which is we now have a super efficient

reasoning model. And the reason for that

is because the old models are one shot.

>> Give it a question. Everything was

memorized. You know the pre pre-training

is basically memorization and

generalization. Two concepts. post

training is teaching you how to think.

And so now with Deep Seek R1, Kimmy

Kimmy K2, um, uh, Q13, you now have

reasoning models that can allow that

help you think. And so the reason why I

was so excited is if each pass of a

thought is energy efficient, then you

can think for a long time,

>> right?

>> Yeah.

The last question from for me is that we

see uh this capital being applied to

human capital in a way that we never

thought was possible. It used to be NBA

players signing $300 million contracts.

Now it's you know uh model researchers

and then there was a there was a post

this weekend that that said that there

was a person that was offered a billion

dollars over four years by Meta. Now if

that's happening at this layer,

why hasn't it happened at your layer?

because you are the enabler of all of

that. And how do you think all of this

human capital is going to actually play

out?

>> First of all, I've created more

billionaires on my management team than

any CEO in the world. They're doing just

fine.

Okay. And so, and and they're doing

don't feel sad for anybody at my layer.

>> Yeah, everybody's doing okay.

>> Yeah, my layer is doing just fine. I I

tell I but but the important the big

idea though is that you're highlighting

is that the impact of a

150 or so AI researchers can probably

create with enough funding behind them

create an open AI

>> it's a it's not a

>> 150 people

>> yeah it's not a it's not well deepseek

150 people moonshots 150 people right

>> right

>> and so I mean look at the original uh

open AI was about 150 people uh deep

mind you know 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 goodiz team with the right

infrastructure. And so that kind of

tells you something. 150 people if

you're willing to pay say $20 billion

$30 billion to buy a startup with 150 AI

researchers. Why wouldn't you pay one?

>> Right. Uh speaking of By the way, we

told we need to wrap because I'm going

to do this one question. Somebody who

was inside your organization told me

with the options that uh you have a

secret pool of options and that you will

randomly just if somebody does a great

job dropped a bunch of RSUs on top of

them and that you have this like little

bag of options you carry around and that

you leave them out.

>> That's nuts.

>> Is that true?

>> Yeah, I'm carrying in my pocket right

now. So listen, so this is what happens.

I review I review everybody's

compensation up to this day.

>> Yeah. at the end of every cycle of when

they present it and they send they send

me everybody's everybody's recommended

comp. I go through the whole company.

I've got my methods of doing that and I

use machine learning. I do all kinds of

technology and I sort through all 42,000

employees and 100% of the time I

increase the company's spend on opex and

the reason for that is because you take

care of people. Everything else take

care takes care of itself.

>> All right. Well done. Thank you.

>> Thank you Jess. Great to see you.

>> Great to see you.

>> We have an event in LA. We'd love to

continue the conversation. So, we'll

send you a note.

>> The world's number one podcast.

>> There you go. Thank you.