And I think we're all pretty AI pilled.

And if you're AI pilled, that means we

got to build a lot more compute than the

world thinks. And that these models are

going to be a lot more valuable than

people think. You combine that with

their core business, I don't know

another entrepreneur or another business

that's a better bet on the future,

right, than SpaceX. And so I think for

most institutional investors, it's a

must buy, a must own, a set it and

forget it, right, in order to have a a

real bet on both the space and the AI

future.

All right, here we go.

Early morning Silicon Valley, BG2 is

back. We're chopping it up on all things

tech and markets. To do that, I have

none other than GB in the house, Gavin

Baker from a Treaties. He's brought his

main guy, Andrew Fox. And of course, I

had to draft Clark Tang into the mix, my

partner,

um to talk to to talk about some of the

big questions of the day. You know, how

should we be thinking about the SpaceX

IPO? You know, what are the big levers?

There are big numbers out there for

what's going to happen over the course

of the next few years. So, let's break

that down a bit, help simplify it for

folks.

I Mythos launched yesterday. I want to

talk a little bit about like who's up,

who's down in the race for

superintelligence. Where are we? What

did we learn with the Mythos launch? And

Clark was in uh in in in Taiwan last

week um with Jensen at Computex and GTC.

So, what was our takeaway there? What's

going on with GPUs, memory? Where are

the bottlenecks? And where do we go from

here? To start everything off, um you

know, maybe just kick it over to you,

Gavin, talking about the SpaceX IPO. The

IPO is in 2 days. Uh you're a big

shareholder. Congratulations. We're also

a shareholder. Uh you know, we also we

expect to be buying in the IPO. The Wall

Street Journal's reporting

um you know, the Goldman Sachs are both

saying 160 billion in revenue in 2028.

Um we know that the IPO is $135 a share,

1.77 trillion. Um so

when we think about kind of what the big

levers are, there's so many moving parts

in this IPO. Um nobody's better than you

at just breaking it down, simplifying

it. What are the key levers that we

ought to be thinking about that you're

thinking about over the course of the

next few years?

>> Sure. Um so great to be here. Thank you

for having me. I thought we're going to

call it B BGG B,

but [laughter] we can stick with BG2.

I've been your house.

>> hey.

All subject to revision.

>> That's okay. That's okay. So I think

there's two

big levers or variables that I think

people should focus on. And you know,

I'm not going to comment on where I

think um those variables go.

But one is um you guys have this chart.

Um did you did you post this on X?

>> I did I did before and then we also

included a new addition with uh XAI's

new deals as well.

>> Yeah.

>> Yeah.

>> So Clark, who I've known for many years,

um

uh made a did a great analysis here.

And he shows that XAI's deal um with

Google for cloud computing

uh generates more operating profit per

gigawatt um than Anthropic, than Meta,

than Google, than OpenAI. Uh their deal

um

>> [clears throat]

>> actually with uh Anthropic

also generates probably more operating

profit than anyone but um Anthropic.

And so you know, um the your your

colleague at Altimeter, Freida, also she

calculated a 55% IRR

>> Mhm.

>> on Claude's one.

>> Mhm.

>> You know, if you can borrow money at 6,

7, 8% and invest in something with a 55%

ARR,

I'm not the most sophisticated thinker,

but that math maths.

>> Right.

>> And so I think the most important

variable, one of the two most important,

is how quickly they bring on terrestrial

data centers.

>> Mhm.

>> We do know from Jensen that uh Elon

brings data centers up faster than

anyone 122 days. Speed is literally cost

because every day you're paying

electricians and plumbers. That's cost.

And they're now monetizing them at

arguably the highest rate. And so I

think, you know, everybody should run

their own math on that, but that is a

massive variable.

>> Yes.

>> Truly massive variable. The second thing

is, you know, we have a chart and it's

wildly out of date now. It's kind of

freaking amazing. This chart is I think

is this chart from 10 days ago?

But it like the 10 or 12 days since this

chart since we made this chart

which shows the Pareto curves for Opus

4.7 for coding

for Codex from OpenAI. And now we've had

Opus 4.8. It was already out of date.

And now we have Fable

>> Totally.

>> and Mythos, which is freaking wild. In

10 days

>> Yes.

>> like we would have had to update the

chart twice.

>> Right.

>> But what the Pareto curve sure shows is

how much intelligence you can get for a

given amount of cost. And I do think

being all revenue will accrue to the

Pareto curve. All at least kind of

frontier model revenue will accrue to

the Pareto curve. And this is Pareto

curve for code coding. And what I think

is so impressive

is that

you can see in the chart that Composer 2

was Pareto dominant

or, you know, at the lowest level of

intelligence with very little training.

This just reflects, and I know you know

Cursor well. I think you know Cursor

a lot better than I do.

A vast amount better than I do.

[laughter]

But my understanding is that Cursor and

Anthropic have more tokens of

proprietary coding data than anyone

else. And they have more tokens of

proprietary coding data than exist on

the public internet.

And so they fed Cursor fed

um used ChemK 0.25, used their own

private data, did some RL, some

supervised fine-tuning

and they got a really good model. And

then they spent 3 weeks in the Colossus

2 cluster and they got a model that

12 days ago was Pareto dominant with

Composer 2.5. That's on their own

benchmark.

Um Cursor bench, so maybe take it with a

grain of salt. But I think this just

suggests that the Cursor data is very

valuable for coding and when it is

trained, you know, to Chinchilla optimal

or beyond Chinchilla optimal with

reinforcement learning,

you know, I think it suggests that XAI

and SpaceX AI has a shot of being a real

player in coding.

>> I mean, I think one of the interesting

things is, you know, we

you know, the way he answered the

question, right? We didn't talk about

launch.

Right? We didn't talk about Starlink or

communications. Those up until really 6

months ago were the business.

>> Yeah.

>> Right? And, you know, and then we merged

in x.ai and we merged in Cursor and then

we announced these deals where it was

very clear he was kind of building AWS

right under our nose, you know, in in in

in terms of this. But what I want to do

is go to go to Fox. Give us the

breakdown. Three big lines of business,

right? We've got the the the

communication Starlink launch business,

we've got the, you know, AI compute

business and then I want to come back to

x.ai that you were just clicking on. But

if we just go to the core business, what

do we have to assume goes right in the

core business both with launch and with

Starlink in order to achieve the numbers

that are out there?

>> Yeah, sure. So, look, I think

the thing that's foundational to

everything is the launch business.

>> Right.

>> Right? This is the kind of

crown jewel of SpaceX. Um it's something

that no one else really has, notably

reusability.

>> Right.

>> And soon rapid reusability. Right? This

is, I think, what you need to believe in

to get to the economics in AI that make

orbital compute something that's very

economically attractive.

>> Right.

>> Outside of the idea that we are in

shortage for power, shortage for chips.

Right? Um so, I think rapid reusability

is the main thing that we're watching

for and I think most people should watch

for. Um,

you know, Elon talks about it a lot, but

getting these rockets to fly at a

cadence that's comparable to an airline,

right? And and Gavin has used this

analogy before, but

um, the old rocket industry was kind of

like, imagine boarding a plane, flying

to California, getting off the plane,

the plane explodes

after. Um, so I think what SpaceX are

ultimately trying to achieve is have a

Starship fly both stages, not just the

booster. Um,

uh,

30, 40, 50 times

before you have to retrofit that ship.

Um,

and when you do that, you're amortizing

the cost of the vehicle over many

flights, right? And that's what brings

the cost down significantly.

Um,

>> But that's a really hard problem to

solve.

>> Extremely difficult and look, I think

the company, you know, have

been loud and clear, they're going to

attempt to bring back the second stage

>> Right.

>> of Starship later this year.

>> Right.

>> Um, and then make it reusable, you know,

re-fly the second stage next year. Um,

and from there, ramp up the cadence. But

at the end of the day, driving down the

cost of launch

is what enables all of these other

businesses and is what makes them so

attractive relative to incumbents.

>> So how many how many times are we

Starship just launched Starship 3, you

know, just launched. How many launches

are you know, do you think kind of the

consensus out there is assuming, you

know, two or three years from now? Like

what is the launch cadence? Are we

launching one of these every day or we

launching one of these every week or

every month? Like where are we in terms

of expectations?

>> Yeah, so look, I think expectations for

now, you know, we're going from, you

know, call it 160 165 launches last year

up into the high hundreds of launches in

several years and, you know, getting

into the thousands of launches probably

in the next 3 years thereafter.

>> Okay.

>> Um I think the company have aspirations.

>> Thousands of launches, you're launching,

you're doing two or three launches a

day.

>> Right.

>> Right. And then talk to us a little bit,

what is this enable? Obviously, you

know, I'm here in Silicon Valley. I

can't even I can't even keep a call on

Sand Hill Road, two decades into the

mobile revolution. I mean, it's the

craziest thing. It's like a third world.

>> It's It's a major business problem when

you're freaking out here. [laughter]

>> It's crazy. It's crazy. Right by the

Starwood dead zone. And I'm like, how

can this possibly be? So almost like

it's a joke. It's the epicenter of

technology in America and you can't

maintain a call. Okay, so we're all

going to switch to Starlink mobile when

it comes along because I don't want to

lose that call on Sand Hill Road. So

walk me through a little bit just again

high-level. Um it's a big portion of the

revenue growth expected in the business

over the course of the next two to three

years. My hunch is a lot of this is

driven uh by uh by direct to cell

connectivity. Walk me through a little

bit those economics.

>> Yeah, so look, it's actually

interesting. Um

the broadband business is still very

early stage when you think about um the

percent of households that have actually

been penetrated to date. You look at the

percent of global households with

Starlink, it's less than 1%. Uh and

that's the broadband, you know, you kind

of have a base terminal at your house,

on your car, on your boat, um and then

airlines now as well. Um

so I actually think broadband can scale

to hundreds of millions of terminals,

hundreds of millions of users. And today

the subscriber base

>> Hundreds of millions if if they get

rapid reusability of Starship, which is

really hard.

Um

you know, if if there's not competition.

Um hundreds of millions, um it's

possible.

But

maybe

>> I I always say around here, it's funny.

I love seeing PM and kind of analysts in

in this situation. It's exactly what I

do with Clark. Clark will say something,

I'll say the future is a distribution of

unknown probabilities. It's either more

likely or less likely, so give me the

distribution. Are we talking 20% 30%?

It's hilarious. It's the same

>> Well, no, 100% same thing. And like I've

watched Elon do many hard things and

this is a really hard thing. So, I think

it's reasonable to think that they're

going to succeed with rapid reusability,

but just I just think it's important to

acknowledge that like

orbital compute

you know, Starlinks, you know, Starlink

V3, Starlink direct to cell, we need we

need first reusability for Starship V3

and then rapid reusability unlocks a lot

of this.

>> Right. When I see when I see the models

that the banks are putting out there,

right? And Wall Street Journal,

everybody's reported on these. These

same things have been widely leaked.

They they they largely have the revenue

on connectivity, so let's call it

Starlink direct to cell, etc. Going

from, you know, let's call it 10 billion

to 50 billion uh by 2028. And so, I'm

not asking you guys to react to, you

know, to tell me your specific numbers,

but when I'm talking to Clark all I'm

trying to size up is order of magnitude.

Do we think we can 5x the business over

the course of the next 3 years? Is there

enough TAM both in terms of broadband

and direct to consumer? And I think the

answer to that is yes.

>> Yeah, here's what I just say very simply

is I have um

I I travel with Starlink. Um

I'm I'm a big video gamer and very

consistently wherever I am in the world,

Starlink is the best connection.

>> Yes.

>> It's the fastest, it's lowest latency

and I do think once they get to rapid

reusability, it's also going to be

they're going to have the cheapest cost

per gigabyte or megabyte delivered um

and better faster, cheaper has been a

winning formula.

And so, 50 billion, that's, you know,

0.3%

penetration of the global telecom

market. Now, maybe there's some

deflation with Starlink pricing.

Um but that's the way I'd frame it up.

>> Yeah. I like betting on better, faster,

cheaper.

>> Um, Clark, I would say probably the

biggest surprise of the last six six

weeks

is that Elon, you know, we talked about

it on all-in podcast, we called it EWS,

Elon web services, right? That that he

struck these huge deals with Anthropic

and Google.

I don't even think people were thinking

about SpaceX in the AI compute game,

right? We If you looked at the models as

of a few months ago, it was

connectivity, so Starlink, and then it

was x.ai, the model.

But this whole category of taking all of

this compute, which he's uniquely good

at standing up, right? And then

reselling it in a way that's highly

profitable was not in a lot of people's

forecast. Now it's a major component of

the forecast. You know, you and I did

this podcast with Jensen, where Jensen

said Elon is an N of 1.

>> What they achieved is is singular. Never

been done before. Just to put in

perspective, 100,000 GPUs, that's

you know, easily the fastest

supercomputer on the planet as one

cluster.

Um, a supercomputer uh,

that you would build would take normally

3 years to plan.

>> Right.

>> And then they deliver the equipment, and

it takes 1 year

to get it all working.

Yes. We're talking about 19 days.

>> Wow.

>> N of 1 is right. Elon is an N of 1.

>> And his ability to secure supply, stand

up the supply, you know, deploy it in a

way that's uh, you know, coherent and

effective for both himself and I guess

now for others. So, walk us through kind

of that. It looks to me again like this

is a major component of the revenue

story.

>> Totally. I mean, so we we were all at

the macro hard data center, and it was

just very evident the amount of

engineering that was that had gone into

building these sites.

Um,

you people always talk about Google and

their ability to to build a TPU and sell

the TPU to Anthropic to generate

revenues for AI.

I think it's a pretty similar dynamic

here with Elon able to secure power, uh

build these sites faster than anyone

else and also be able now to monetize it

to um to the this massive AI market

that's ahead of us.

Um

if you look at the the relationships

that he's forged with a lot of his

suppliers, you know, be it Jensen, be

it, you know, all of these different um

different sites that actually want xAI

as a tenant. Um his his ability to

finance these deals at at very

attractive uh financing rates relative

to a lot of the other players in in the

space, you know, these are advantages

that compound over time. And when you've

built the credibility to stand up these

sites and monetize at these levels, um

you know, it's very it's actually a very

attractive uh

very attractive proposition for for a

lot of folks involved. Um and actually,

you know,

if you look at the these deals in

particular,

Gavin, you you pointed out, but, you

know, they're they're actually

monetizing, you know, perhaps better

than

other players in the space by selling

this infrastruc-

>> a lot higher.

>> Um Google is obviously paying SpaceX a

huge premium for this compute. Fox, you

said something that I thought was really

important, which is, you know, it may

very well be that in order to get, you

know, first in line on space compute,

which Google certainly wants to do, that

they're willing to pay a premium for

their terrestrial compute. And so, to

me, that's how you kind of square the

circle as to why the premium. Any

thoughts?

>> Yeah, look, I think there's some of that

embedded there, but um look, at the end

of the day, SpaceX can stand up compute

quickly.

They can stand it up coherently. And

they can stand up a lot of it in one

place and have it readily available. So,

look, I think that's most of the

premium, but outside of that certainly

people are going to space over time.

>> I have to pay a little call option to

get first in line for space.

>> There you go. Good one.

>> We've all been investing in the neo

cloud space. So, like there's a

fundamental belief around this table I

that that we lack the compute needed to

continue to push the frontier on

intelligence. So, we have to build a lot

of compute, okay? Now there's a there's

competition going on. On one end you

have the hyper scalers who are building

out that capability. Then we have AI

dedicated clouds that are building out

that capability. And now literally in a

matter of weeks, right, we have a a you

know a giant that's emerged in this

category which is SpaceX.

The question to you Gavin is can they

consolidate this market, right? Because

if I think about a marketplace, Elon has

a unique ability to get the supply. He

has a unique ability to cut deals on the

other side and nobody can stand it up

like he can stand it up. So, I think

there might be a real consolidation in

the AI compute market where you have the

hyper scalers on the one hand and on the

other hand, you know, he may emerge as

the largest, strongest player in the AI

compute market.

>> Yeah, so I think they're are they the

number four number five hyper scaler

today after the Google deal?

>> Um it will be number four.

>> Kind of wild.

>> Yeah.

>> In 30 days

we went from not being an AI hyper

scaler to being number four. And we

passed a lot of companies including

Oracle.

>> Coreweave is a huge business, right?

That we're we're investors in, you know,

and have been investors in, right? But

there are a lot of other players, the

Nebius's of the world, the Iron's of the

world. And I would say that they're

probably 50 neo labs being funded in

Silicon Valley right now as we speak

because of the shortage in compute.

>> Absolutely.

So, that's

kind of crazy in 30 days. That's just

extraordinary.

What I would say is there I think there

is a belief that these data centers are

commodities.

>> Mhm.

>> And I do not share that belief.

Um I don't think anybody around this

table shares that belief.

And in the same way that Elon was able

to re-engineer a rocket from first

principles and make it reusable,

he engineered an electric car from first

principles. You know, everyone else was

trying to, you know, make an electric

car like an internal combustion engine

car and he thought about it differently.

And um

I think he looked at data center design

from first principles and he designed

something fundamentally different. And I

did actually ask the team. I said, "Hey

guys, maybe I'd be a little less public

about things that are very obvious to

you [laughter]

>> Right.

>> about how to design a data center, but

are revelations to other people

because I think what you're doing is

maybe um

more differentiated than you perhaps

realize cuz what you're doing is so

logical to you,

but maybe lot not logical to everyone

else. And that's how he was able to do

it in 122 days.

>> Yeah, to I mean to that point, Brad,

yesterday we were meeting one of our

portfolio companies and we were talking

about behind the meter and we're, you

know, really thinking about it. There's

only maybe two or three two or three

players now that can actually reliably

engineer behind the meter data center.

And you know, there's real engineering

work that goes into all of this. So, if

you think about this,

if you're a gas combustion if you're

Vernova and you say we only have a

certain number of gas combustion

engines. Now, we can sell them to x.ai

or we can sell them to one of these

startup neo clouds. Who are you going to

sell them to?

>> Well, and there's another dynamic.

Everyone starts making more money when

the GPUs get energized and sold faster.

So, literally speed is money for all of

the suppliers. Power, land, turbines.

So,

I think it's we'll we'll see.

>> Right.

>> Hey Brad, man.

>> And but but this is just we're just

talking terrestrial. I do I do want to

hit on and then you can flip it back on

me. Talk to me, okay, So, let's let's

assume, right, that they continue to

build out the terrestrial landscape.

They continue to find buyers for that.

Um, walk us through, you know, what this

unlocks, you know, and how this is

related to space data centers because I

think, you know, once you start talking

terrafab capacity and beyond. So, we're

talking 1,000 gigs, right? And this year

what what we're doing, 25 or 30 gigs

just to put it all in perspective.

>> 20, yeah.

>> Right?

>> 20, 25 gigs.

>> Okay, so so once we start scaling up,

walk us through,

do we have to have space data centers in

order to get excited about buying the

IPO, right? And then there's obviously

this this debate in the world. I I heard

Jeff Bezos say, you know, I think it's

more like 6 years, but Elon's going to

say three because if if he says six,

then it will take even longer. So, say

three and we may get it in four or five.

But are space data centers integral and

essential to, you know, the IPO? And

what do you think the timeline is, Erin?

There are you guys.

>> So, I don't think I think if you think

about those variables around what

Crusher could mean for XAI.

And we do have an existence proof that

once you really get on that Pareto

frontier,

revenue can scale rapidly and it's

called Entropic. And there does seem to

be an exhaust There seems to be a lot of

demand for coding. And I do think I'm

John Massad posted something very

interesting.

>> The The founder of Replit.

>> The founder of Replit. It he called it

bitter lesson adjacent that coding may

be the fastest path to AGI and ASI

because if you really go to coding, you

can write code if a model's good at

coding to do anything. So, I think

that's a profound point and I think

coding is going to continue to be very

important. So, I think if you think

about that variable,

if you think about Starlink direct to

cell enabled by Starlink V3, and you

think about how quickly they can or

cannot bring on terrestrial compute, I I

think orbital compute is is

is necessary for the IPO valuation,

but it's certainly important and it's

>> Well, maybe another way to say it is you

think you

you may think we're going to get to ASI

faster than we're going to get to

orbital compute. That may take us from

300 IQ to 400 IQ, 500 IQ, and beyond.

Um and the ability to scale it up to

consume 10% of, you know, global GDP,

but maybe maybe that's where we should

move next.

>> No, no, I think on the orbital compute,

I think Foxy would be great our Clark to

lay out the math from first principles

on, you know, Clark has this great chart

on, you know, the gigawatts it costs,

you know, the dollars per gigawatt.

>> Right.

>> Walk us through the economic case.

>> Yeah. Yeah, so I mean, on this point of

is orbital key to investing here? I

don't think it is and I'll first point

I'll make is what are the implied

monetization rates

based on expectations today for the AI

business? And you know, I think you

threw out the $160 billion number that's

been leaked out there that people are

talking about.

The implied monetization rate on that

number is something like $14 billion per

gigawatt per year for the AI business.

They just signed Anthropic at 22 to 23.

They just signed Google at 50.

>> Right.

>> Right. So, I I think you can invest

behind the AI business terrestrially and

still be excited about it. But with

orbital

>> an important point. Excited about it if

they can get the land and the power.

>> Right. But but but I mean I I think for

most investors, right? They get They

have an easier time getting their head

around how SpaceX wins terrestrially.

Like can they go get land, power, and

chips? The answer to that is high

probability yes, okay? And what we're

saying is at the rate they're monetizing

that, that gets you to the numbers that

are being leaked out there before you

even have to take the leap of faith that

they're going to extend the lead with

orbital data centers. But take us there

on that, too.

>> Sure. Yeah, so so look, with orbital, I

think the key thing is um two-stage

reusability.

>> Yeah.

>> And beyond that, rapid two-stage

reusability.

>> Yeah.

>> So, today with Starship, they've shown

that they can successfully reland the

booster.

The second stage, we'll see what happens

later this year. I think they're

attempting to bring that back and then

make it reusable by next year.

Um

but the thing that's important about

two-stage reusability when it comes to

the economics for orbital compute,

right, is the cost per kg comes down

significantly. You know, we're talking

about going from $1,500 per kg on

Falcon, somewhere in that range, to 250

per kg, something lower. Um and the more

that you can reuse the rocket,

the more that price comes down.

>> Right.

>> Right, cuz you're just depreciating the

cost of the launch.

And eventually, you asymptote to the

cost of the fuel.

>> Right.

>> Right.

Assuming you can use a rocket for

forever.

>> Yes.

>> Right, which will take a very long time

for us to to really achieve that. But,

um and at that point, we're talking

about something well south of 250 per

kg.

So,

then you look at the specs of these AI

satellites. You know, Elon did a great

>> Yeah, that pod that that pod was

incredible that he laid out the other

day, the specs on the satellites.

>> It was really great because I think they

are finally showing people, here's how

you could viably design one of these

satellites.

And how heavy is the satellite? How many

could you fit into a Starship launch?

And when you back into the numbers, you

get to something like 5 MW of capacity

per Starship launch.

>> Right.

>> There's 100 metric tons in one of those

Starships. So, you can back into the

math of how much will it cost per

gigawatt

to launch these satellites into space.

>> Right.

>> Launch this compute into space. Um

and the math that you get to before you

account for things like

bad GPUs, bad satellites, right, these

will all be things that happen.

But the math you get to is it's about $5

billion per gigawatt of CapEx to put

these in space.

>> Right.

>> For comparison, terrestrially,

talk about the switch gears, the

generators, the transformers, the shell,

getting the power, that today is about

25 20 to 25 billion per gigawatt.

So we're talking about a 5x reduction

in cost

on half of your bill of materials

>> Right.

>> for the data center.

>> Right.

>> Which is a huge number.

>> Yeah, just just very simply, I mean just

to say that put it cost $60 to put a

gigawatt

on the ground today. And we'll call it

35 of that is are the GPUs and the

silicon that's doing the training and

the inference.

And 25 billion is the land, the shell,

the power, and the cooling.

I would hypothesize that those elements

are probably going to be inflationary,

so that 25 billion may not go down.

And because space, power, cooling are

effectively free in space, and when I

say space, I mean land. You know,

there's no land in space, but there is

space.

Um

you're you're talking about putting a

gigawatt into space for 30 billion and

having lower operating costs. Now the

dynamic versus 60 billion that's

inflationary, and that third and that 30

billion, that five may be deflationary

over time.

But what we need to consider

is you know, the reliability and the

maintenance. And so as long as you know,

everybody can do the math,

but as long as these satellites in space

aren't failing at an at an astronomical

rate, the math maths. As you can see,

and by the way, we know GPUs melt and

lasers fail. We know this happens in

data centers,

particularly during big training runs.

And yeah, I mean GPUs melt.

Um so as long as the reliability and

maintenance is not dramatically lower,

the math is there once we have

reusability and then rapid reusability

for Starship V3.

>> I when you

when we look at this, okay, so we we

went through Starlink and we said,

"Okay, like it it just stands to reason

we're going to have direct to cell on

Starlink." Like the assumptions there

are, you know, again, seem like you can

get your head around. Then when it comes

to building terrestrial data centers,

again, not a hard one to think that

based on these couple deals that Elon's

going to build a much bigger Starlink's

going to build or SpaceX is going to

build a much bigger business there. And

then you have this call option on space

that would drop the price even further.

The one thing we haven't talked about is

their model, right? And I find this

surprising, right? Six Six months ago,

x.ai was competing, they were doing

pretty well, but they've done something

dramatic over the course of the past

couple

couple months, which is they bought

Cursor, right? Cursor is 700 800 people

was already doing incredibly well from a

revenue perspective. Our own

projections were that they could exit

this year at up to $10 billion

of revenue, so they were growing very

fast

one of the leading coding agents, but

they also had this incredible team with

the potential, right, to really build a

frontier level model, but they were

compute constrained. So all of a sudden,

they get bought by X. X has massive

compute that they can now train on.

And when I think about the revenue in AI

that like if I look at that line item in

the models having it go from $10 billion

to $150 billion, yes, a lot of that will

be the core weave type business that

they have, but the question is how much

of that is going to be the core x.ai

business that's really powered by the

new team from Cursor. So any thoughts on

that, Kevin?

>> Right now, so Composer 2.5 was Pareto

dominant 12 days ago. It was trained on

the Kimmy K2.5 base model.

>> Right.

>> Now, what's happening is the Grok 4.3

1.5 trillion parameter model is

training.

One would hypothesize based on scaling

laws that that will might be a better

base model. And then the cursor data is

being injected into the pre-training

process, not just reinforcement

learning.

And we'll see, and I think that is going

to be a very important data point when

that comes out. And I just think

everyone should keep in mind that once

you are at multiple places on that

Pareto curve, if you have compute, you

can scale really rapidly.

>> You know, that that to me is if I had to

say what the one piece that's being lost

in the story,

right? Like it's easy for everybody to

get excited about the deals with

Anthropic because you can put your hands

around that. You know how much revenue

it is. I see debate about, you know, the

90-day termination and how long they

last and what multiple do you put on

those revenues. But I think the thing

that's getting lost is I think they've

dramatically advanced their capability

when it comes to building a frontier

model. People outside Silicon Valley may

not know, you know, Michael and the team

at Cursor as well. This is an

extraordinary team that he just

downloaded, right, into SpaceX. SpaceX

was already building good models. And

what they have is

they have this way to monetize compute

that gives you this call option that you

can pull all that compute in-house,

right, to train a model and then to run

the model. I suspect if there's an

upside surprise, if we went around the

table, I'd say this is the place that's

getting the least amount of attention

and could have the biggest upside

surprise. Any any thoughts, Clark, on

what you think is being overlooked or

areas that you think are misunderstood

about the business today?

>> I I would say I would say

what the last few weeks have proven is

that Elon, um,

their team can stand up all this

compute. Actually, if you just, you

know, went back 1 and 1/2 years, you

know, they were behind in the race to

stand up compute. They were you know

they they didn't have that many H100s.

They brought in Colossus. Then they

brought in Colossus 2 at a scale much

larger than anyone else. And now you

know as we gear for Vera Rubin

you know

from you know a lot of my conversations

it looks like they've you know secured

maybe up to 20% of Vera Rubin capacity

especially in the early days of you know

when you know these these chips are very

scarce that that they're going to have a

a

a lead on all of this because you know

people think that they can stand up this

compute better. So I think they'll all

you know what what the last few weeks

have actually shown is that Elon you

know Elon will

take you know take a shot at hitting the

frontier but if it you know if for

whatever reason

um they

they have over procured some capacity

this is a very scarce asset that they've

shown that they can monetize at actually

you know best in class margins and

payback periods.

>> The irony is like you know

you and I've been doing this long enough

to know I mean that's why Bezos built

AWS. Right? He had to build capacity for

Black Friday.

>> Yeah.

>> Right? But then the rest of the year he

sat on all this capacity they had to

build and he figured out a really

incredible way to monetize this. And by

the way investors at the time 2009 2010

when he was building out the capability

around AWS hated it.

>> Of course.

>> Because he was consuming all that free

cash flow. My meanwhile he was digging

the biggest gold mine in the history of

the world. One of the biggest.

>> One of the biggest.

>> Among them among them at the time was

probably the biggest.

>> Yeah Google search might want to have a

we'll have a we'll have a discussion.

>> [clears throat]

>> By the way I do think it is important.

Grok 4.3 I think the cursor if they

acquire it that may end up being very

important. But Grok 4.3 was on the

Pareto frontier and has of 10 or 12 days

ago and this these things move fast. But

most intelligent 500 billion parameter

model in the world. And they were on the

frontier and there are four companies on

the frontier.

xAI, SpaceX AI, Google one with Gemini

3.1 Pro, and then the rest of it was

dominated by Anthropic and OpenAI. But

they were on the Pareto frontier and now

we'll see what they do with Cursor.

>> Yeah. Um I want to come back to that in

a second.

>> way, man, I want to ask you some

questions.

>> go go go. What do you think? So you

think the biggest source of potential

upside is the model?

>> Yes.

>> What do you think?

>> I think that's the I think that's the

thing that's least talked about.

>> Least talked about.

>> Right? And so, listen.

When I look at the bull bear case on the

IPO, right? The bears are looking at

last year's revenue. Say it was $18

billion

and they're looking at the forecast from

the banks of $160 billion, you know, 3

years from now and they're saying,

"Listen, not many companies in the

history of the world have basically 8x

their revenue over 3 to 4 years." Right?

So that's where, you know, I think and

people get nervous about the valuation.

When I look at this, again, when you

break it down as an analyst first

principles, part by part, which is what

I tried to do here, right? When you look

at Starlink, it looks totally doable.

When I look at what they're building in

AI compute terrestrially, looks totally

doable over the course of next 3 years.

When I look at the model itself after

the acquisition of Cursor, you know,

combining those things around the

compute they have, that looks to me like

it could be an upside surprise. So I

would say that I think that uh you know,

in the IPO, but I think when you look

back 3 years from now, there's a decent

chance that everybody's like, "Oh my

god, that was super obvious." Right?

Even though today all of these things

have risk associated and back to where

we started. I'm not, you know, none of

us are here to pump the IPO at 1.77

trillion. It's really to just break it

down as we do inside our shop and to

say, "What is that distribution of

future probabilities? What's the

probability that it's higher from here?

What's the prob" And I think we're all

pretty AI pilled. And if you're AI

pilled, that means we got to build a lot

more compute than the world thinks and

that these models are going to be a lot

more valuable than people think. You

combine that with their core business. I

don't know another entrepreneur or

another business that's a better bet on

the future,

right, than SpaceX. And so I think for

most institutional investors, it's a

must buy, a must own, a set it and

forget it, right, in order to have a

real bet on both the space and the AI

future.

>> From your lips to God's ears.

>> I mean, listen, I I again, I think that

I I I think that you're going to have to

wait, but you know, we had this chart

last week, right, that came out.

Everybody was sending around Twitter,

conveniently timed, and you know, it's

like shows the average max drawdown post

IPO for like 20 companies from Facebook,

Twitter, Alibaba, Shopify is, you know,

over 50%. And so maybe that again will

will will end this section here. You

know, Gavin, you and I've been doing

this a long time. We know it's going to

be bouncy around the IPO. Um,

you know, how do you as a manager try to

try to manage that? Um, do you try to

trade around the IPO? Do you set it kind

of and forget it? I would say from an

Altimeter perspective, what we tend to

do is we take a base position that we

set and forget, right? And then we may

size up or size down depending upon how

the market reacts in, you know, in a

particular moment. Um, but any thoughts

on on this chart or

you know, how how people you guys are

thinking about it in particular. You

obviously own a lot going into it.

>> First agree with absolutely everything

you said and I actually think about it

the same way, set it and forget it.

You've talked about you have ballast,

you move around and you move the ballast

to one side of the ship when you want to

the ship to lean into the wind to go

faster and you move it to the other side

when you don't want the ship to tip

over. I think that's a great analogy.

Think about all

important companies in the portfolio the

same way. So 100% agree. I mean, this

this chart is a bummer. What I would say

is, you know, this data on IPOs, but

what I would just say is this is a

really unprecedented situation.

>> Yes. We've never had an IPO this big.

We've never had an IPO that's going to

go into an index this quickly.

We

simply do not know how much selling

there will be from investors.

I would hazard a guess. I mean, I'm I

don't know.

But Elon, I don't think he needs

liquidity and I think he owns What does

he own, Foxy?

>> It's

50%

>> 50% of the company.

>> way, he's locked up for 365 days or 366

days. So, we know he's not selling,

right?

>> So, I just think it's an unprecedented

situation and the right answer

>> Yeah.

>> is I don't know what's going to happen

in the short term. And the right answer

that I would just, you know, encourage

every investor making their own decision

is to just think exactly [clears throat]

the way you articulated it. We have

these different levers. We have these

different variables. Think about each

one of them from first principles. Make

your own decision.

Do your own due diligence. Be

thoughtful. But, there are a lot of

variables here and that it is a little

funny to me that uh you know, it was 100

times trailing TTM revenue. Well, after

the deals they signed, I think it's at

39 times.

>> That can change fast.

>> So, they added $29 billion in a month.

>> Yes. Now, it's

>> [laughter]

>> By the way, have you ever seen that

happen?

>> Never. Never. And you know, it just goes

to show

first um Elon is not only a great

engineer.

He and Gwen and the team are great at

business.

>> And Brad,

>> They they they understand what needs to

be done to raise the capital to get to

the next phase. They have a long-term

mission in the business. And so, to me,

again, what we saw in the course of the

last few weeks with cursor, what we saw

with these deals that they cut, I don't

know that any of the mag seven could

have moved that quickly to adjust the

business that they did. It's

exceptionally entrepreneurial at scale,

which we very rarely see in businesses.

Two other things I would just say

>> you a hug, Brad?

>> Two Two other things I I I I would just

say. Number one is people talk a lot

about the total amount of capital being

raised. If you add up the capital here,

right, for Anthropic what they may

raise, what OpenAI may raise, what, you

know, SpaceX may raise, let's call it

$250 billion.

That's 1% of the Mag 7.

Okay, it's 1% of the Mag 7.

>> I Yeah. And

And we will as well. You know, like that

to me is like a bet on the future that

we all believe in. And so, if I said,

"Where are we out of consensus? What is

our variant perception?" We actually

think it's going to be bigger, faster,

and we've thought that for a couple

years. Um so, first, it's only 1% of the

Mag 7 market cap. And then you

referenced it, the amount of selling. Um

I've got a chart we'll post here. This

is, you know, the the dribble share

release for SpaceX shareholders. You

know, so there's not a lot that can be

released um up until after the first

earnings. This We saw this in the

Cerebras IPO. Um there's a version of it

here in this IPO. And so, again, I think

the banks have been thoughtful here,

knowing that this is a very large IPO.

And I'm not saying that won't trade

down. Like there's possibility, you

know, these things trade down. But

again, for me, telescope out, is there

any company better positioned as a bet

on the future? I think what they've

shown over the course of last 5 weeks,

they're

they're they're probably number one. But

let's move on.

>> No, no, can I just say one thing about

the employees? I think another thing

that's unprecedented here is the

employees

>> Yeah.

>> and to a large degree the investors here

have had liquidity every 6 months.

>> Exactly.

>> the last 10 years.

>> Yes.

>> So, if you're a SpaceX employee or

former employee, and you wanted to sell

you've had whatever that is,

close to 20 chances. And it is a matter

of historical record that large

investors have been able to sell. So

I would think a lot of the people

>> they've

>> chosen to own it. Now, there's a new

valuation and we'll see what they do,

but just this is utterly unprecedented

and we'll see.

>> Yeah, I know. It's It's It's a great

point. We have in fact called these

companies quasi-public. Um you and I

both know that SpaceX and I'd put

Anthropic in in in this category as

well, Databricks in this category. These

things in many ways have been more

liquid over the course of the past 3

years than some public biotech companies

we know. Right? And so there's a

continuum of liquidity here. We We treat

it as a binary, private versus public,

but it's really about this continuum.

You know, let's keep going on models.

You know, um Anthropic launched Fable 5,

which you referenced um yesterday, which

is basically Mythos um with some

classifiers and safeguards um around

cyber and biology, chemistry,

um and distillation. When those things

get triggered, it fails back [snorts] to

Opus 4.8. Um you know, there was a

Copart tweet about this yesterday. He

said, you know, it sold on all the

benchmarks, but what really makes it

special is long-running tasks. Okay? You

retweeted our good friend, you know,

Noam Brown. Um you know, ChatGPT 5.5

also exhibited these capabilities.

Um you know, it it led Noam, right, to

suggest that it's not very relevant to

do these snapshot benchmarks anymore.

Yeah, like the x-axis has to be time or

tokens or compute because we can solve

most problems now if we just let these

frontier models for a very long uh point

in time. So, Gavin, what is this new

class of model, right, Fable Fable 5,

ChatGPT 5.5? What does it mean for the

race in superintelligence? Who's up?

Who's down? Who's still on the frontier?

Um give us your thoughts.

>> I mean, it's hard to say that

Anthropic's not up.

>> Yeah.

>> Like after

the revenue numbers they've put up,

after the Fable 5 release, and Mythos is

evidently even better.

But I just think that Gnome Brown post

from yesterday, polynomial, is so

profound.

And just the idea that we do not know

how smart these models are.

And we made

>> Say more about that. Why don't we know

how smart they are?

>> Because nobody has run Mythos for a year

continuously. And we may never know how

smart each generation of models actually

is or was, but because we don't have

time to appropriately evaluate their

intelligence before the next model comes

out. I mean, this is a profound

statement. And just just imagine, okay?

So, I always say like when you think

about FSD,

just imagine a human being who never

gets distracted, never gets tired, never

talks on the phone in the car, never

drinks and drives, never yells at their

kids, never has to go to the backseat to

give their baby a bottle.

And like of course you would think that

over time that is superior to humans who

are distracted.

I don't know how long How long can you

think deeply about one topic, Brad?

>> What do you Give me an hour. Give me an

[laughter] hour. Give me an hour.

>> A BIT.

THAT MAKES me feel terrible cuz I think

I can think deeply about one topic

continuously before having a stray

thought enter my mind

for like maybe 5 minutes. Then I can

come back to that.

Imagine if Albert Einstein

had been able instead of, you know, and

maybe that maybe I

maybe he could think for 3 hours at a

time. Clearly an exceptional intellect.

But imagine Albert Einstein had just

thought about fundamental physics

24 hours a day.

He doesn't have to eat, he doesn't have

to sleep, he doesn't have to relax, he

doesn't drink,

>> never gets old,

>> never gets old,

>> never has diminished intelligence,

>> and he thought for 1 year. I mean, we

might already, you know,

>> have solved a lot of these intractable

problems.

>> So, I just think that's an extraordinary

thought. And just my takeaway was

however

bullish I was on compute before then,

I'm just a lot more bullish.

>> Right. Right. Right. So, so, so that is

a, you know, we saw when

that was probably what really unlocked

Opus 4.6. It was the first really

long-running model that could maintain

that context, maintain that memory, um

solve some of these longer-running

problems, right? For us, the signal was

in January. We knew we felt like that

was a big moment, but then when you

started to see the revenue go up, we

knew that lots of people were voting

independently, that that was a profound

moment that they became much, much more

useful. So,

but one of the things that the consensus

going into this year, right? So, the big

question going into this year was was

the AI revenue going to show up? Were we

going to get to these thresholds of

intelligence that caused enterprises and

consumers to use them more? And I think

the consensus at the time, at least on

this podcast, um the the the debate with

with my with with with Bill was the

open-source models, cheap tokens, were

catching up on the frontier, that

perhaps these models were beginning to

asymptote, um that people wouldn't

really pay for premium tokens,

and it seems to me that the evidence on

the field, 6 months into the year, is

just the opposite, right? That frontier

tokens are capturing the vast majority

of all the revenues,

and that in fact, if you believe in the

long-running capabilities and more

compute allows you to do that, they may

actually be extending their lead, right?

On some of these models that were built

on distillation. So, I just open it up

to anyone around the table, what are

your thoughts on whether or not, you

know, have we challenged this thesis

that cheap open-source tokens are going

to always, you know, close the gap on

these frontier models, or are they

extending their leads?

>> I I think this debate, like this same

debate has existed since the beginning

of

since we started training these models

to begin with, which was hey, we're

always kind of three, six months behind

the frontier. But empirically, like you

can just see all of the revenue has

actually just accrued at the frontier.

And that I think that's because every

time we release the frontier,

a whole new like slew of use cases

>> Right.

>> that that previously we could have never

tackled before, like coding.

Um but also just, you know, you know,

we've we've just been locked at our desk

for the last last day just, you know,

hammering Claude because, you know, it's

just fascinating the things that now we

can do with fable five that we could

just couldn't do with opus 48 just a day

before.

>> So what are some of those things, man?

I'm curious.

>> So So I think it's really really good at

multi-agent orchestration. So they they

Anthropic released a um a blog post

about like different uh agent um six

different agent like orchestration

patterns that, you know, they've they've

talked about. But really like once you

start being able to manage all these

agents, the harness and the model itself

is being arled with one another, they're

actually being,

you know, fused closer and closer

together, but the model can understand

the, you know, the extent of your work.

So, you know, one of the things, for

instance, is um

I just threw in like seven of our models

and just said, "Okay, like I want to

create a master view of like my beliefs

given all of these assumptions of all

these companies, TSMC capacity, like and

then and then produce me a report on all

this stuff." And you know, the the model

is able to reason through all of our

assumptions. Like actually, if you

believe this

>> Right. What are the contradictions

exactly?

>> Yeah, it's it was fascinating. And and

and you know, before we'd never do that,

but but now, you know, I think we're

just step one into multi-agent

orchestration. We're going to do this

even further and that's one example.

I've also dumped all my all my notes

into it and it's reason across all my

notes from the last 3 years and said,

you know, here are some of your ideas

that were consistent. Here are like, you

know, the sources that were actually the

highest signal to what actually played

out, you know, and then it is actually

just super fascinating what you could do

and we've just blown through our blown

through our limits.

>> mean it's it's it's unlocking all this.

I mean like they gave examples yesterday

and the release Anthropic did, you know,

50 million line Ruby code base at Stripe

that was, you know, refactored in a day

versus many weeks with many people. You

think about where this is impacting

biology and life sciences just across

the spectrum.

Um

and to me it really gets back to this

fundamental point. Number one, if you

believe this to be true about

long-running agents, then we're going to

produce and consume more tokens in the

future as far as the eye can see. So the

world this gets me back to, you know,

terrafab and space orbital and all this

because

we we we may in fact unlock real

thresholds of intelligence, but we're

going to have to let these horses run

for a long time in order to get there.

Yeah, I would just say two things I two

things can be true.

>> Mhm.

>> The majority of economic value may

continue to accrue to the frontier and

man has it ever accrued to the frontier

thus far and for sure the first 6 months

of this year, but the majority of tokens

consumed in the world may be open

source.

>> And they are

>> today.

>> Yes. I and I think that this current

state is likely to persist. Harvey had a

great blog post that they put out on X

and they used and it's just amazing how

everything gets out out of date like in

5 days, you know.

But they used their own proprietary

legal data to do reinforcement learning

and supervised fine-tuning

with Fireworks on an open source model

and then And used a router and a router

being something that picks which model

you send which query to, and which model

you use to check which model. And they

got better outcomes than Opus 4 either

4.7 or 4.8 at a lower cost.

>> Yes.

>> And I think that is the future. And the

reality is

they were still consuming a lot of Opus,

but a majority of the tokens they were

processing probably were in their own

open-source models.

>> We heard the same thing. We did a We did

um

We did an enterprise survey that we'll

post of 300 companies how which ones

were optimizing, so these are folks who

are kind of looking at model routing and

saying we're going to send certain

tokens over here, which ones are

thinking about optimizing, which ones

aren't optimizing yet, and then what is

their expected use of frontier model

tokens, right? And they're all expecting

to consume a lot more even though

they're already in the process of

optimizing. Think of it in the in in the

context of JP Morgan. If they're doing

some back of the house stuff, right, on

customer service or whatever, they may

very well use an open-source model. Now,

I think they're loath to use Chinese

open-source models, so they're waiting

on kind of US open-source models to, you

know, be able to really deliver the bang

that they need, but my hunch is for

these enterprises, a lot of that back of

the house stuff will get rooted there.

That will probably be a majority of the

tokens, but I think the really

high-value stuff, you know, coding as an

example, they don't want to write

second-tier code. I think the vast

majority of that will continue to be on

the frontier.

>> Um

>> You don't need Albert Einstein to book

you a trip. You don't need Albert

Einstein to do KYC.

>> But but but this is the debate we had at

literally at this table two years ago.

However, if you just look at the revenue

curves, right? What bill What what folks

concluded when they said that, they

said, "Therefore, the frontier models

will not accrue most of the revenue."

And what we're seeing right now, it's

90% of the

>> That has been decisively wrong. Probably

more than 90%, and it may continue to be

decisively wrong. Frontier might be 90%

of the

economic value. Open-source

>> might be 80% of tokens.

Something that I think is very important

on open source

is that you know, I think there's this

belief that it's bearish for AI.

It's actually it may be very bearish for

the frontier models. There's that bear

case you talked about. It's actually

really bullish for compute and hardware

because if the frontier models are

capturing less of the margin, then

you're going to spend more on compute.

So, the better open source does, the

better it is for compute providers.

>> And I yeah, I I will say it there is a

very

I would say between um spending time in

the heart of like the West, Silicon

Valley, and also spending time in Asia,

there is like a very

big um

like a deep-seated belief in one versus

the other, which is like if you spend a

lot of time here, it's like all closed

source, cloud, every all traffic is

going to go, you know, by way of this

direction. And then you spend time in

Asia, you know,

the the overwhelming belief is that

we're going to find the right model to

the right workload, and we're not going

to overspend.

>> Right.

>> And I think, you know, I would say I

would say

the next year is probably going to be

the most indicative of which way this

falls

um because

I think I think the reason why

uh closed source models have captured so

much of the value is because um the

models actually get the intention and

actually carry through the work. And

this is the first year where we actually

had agents that actually carried out

user intention from just answering a

chatbot request to actually producing

useful work.

>> Right.

>> Um now the the the level of this

intelligent has scaled so rapidly, and

we continue to push against like the

most economically valuable tasks, which

are coding and finance and all these

like knowledge work tasks. But like for

the long tail of tasks, if open source

continues to maintain a 6-month lag, we

might actually see a lot more open

source used for

you know, our everyday tasks that we

might actually

>> basically Jensen's argument, right?

Jensen's argument is you're going to

have model routing

and we're just in a moment in time where

the frontier models gain the advantage

can do long-running tasks that open

source models couldn't do it very well

and so they're accruing all of the

value, but as soon as the open source

models can do the long-running tasks as

well, which is not far away that they

too will grab a bunch a bunch of this

revenue.

>> Are you about to burst into reflection?

>> I'm not.

>> Okay. No, no, no, no are we, but I'm

very impressed by Misha and and the team

and what they're doing. I very much want

a frontier open source US lab to win. We

know that, you know, I heard you say

recently and I believe it to be true

Nvidia any day that they really wanted

to, right? They already have some great

open source models. They could

absolutely build a frontier open source

model whenever they chose to do it and

so it's not a question in my mind as to

whether or not the US is going to have a

frontier open source model. It's just a

question about timing and then like at

that point in time is that you know,

let's say let's assume they get these

long-running capabilities.

Have the frontier labs now achieved

something yet again that allows them to

keep keep the the stranglehold on the

revenues?

>> Yeah, and I just think it's if you're

Wow, that's a cute ASIC you've built

there. That is so cute. How would you

like

open source to join the frontier?

>> Right.

>> How would you like that? How do you like

them apples? So, I mean I'm not sure

that's the explicit calculation, but I

do think Jensen

>> Say more. Just double click on that for

everybody at home.

>> Yeah.

>> If you were if they were to put an open

source model out there, how does that

impact the ASIC landscape?

>> Well,

you might not have the revenue

to fund [laughter]

to fund that the revenue of the margins

to fund that ASIC.

And I do think Nvidia is highly likely

to be the world's dominant provider of

open source AI. And I do think Jensen

will bring open source,

you know, right now it's whatever, 6

months behind the frontier.

>> Yeah.

>> We might see it

creep closer and closer and closer.

And I do think Jensen has a big business

decision. I see this, you know, chart

here, so let's, you know, chop it up

about Nvidia, as you say.

But if all of his customers

are going to compete with him,

>> Yes.

>> then

why not compete with his customers? And

we have all these neo clouds.

>> Right.

>> So that's a cloud computing business

that can compete with all these cloud

computing businesses.

He has his own models that are really,

really good. Nematron 3 or 3.1 was

actually really, really cool from a

computer efficiency perspective. And

he's always careful to release small

models so as to not tread on Anthropic

and OpenAI,

>> Right.

>> Google's toes.

But I do think that is a choice he is

making.

And just, you know, at if if the

economics change,

>> Right.

>> I think Nvidia can join the frontier and

become one of the world's largest cloud

computing companies much faster than

people think.

>> Interesting. Interesting. Clark, walk us

through this

this chart.

>> Yeah, so so I think one of the takeaways

from spending time in Taiwan was there

there is certainly a lot of excitement

around the next wave of ASICs.

Um, but I think I think it's like a very

clear moment now where Nvidia

it used to be an argument of Nvidia

versus ASICs one or the other and, you

know, total domination one or the other.

Now I think it increasingly every year

every every one assumed that Nvidia was

going to lose share dramatically on a

revenue scale, on a gigawatt scale, on a

unit scale. And actually, if you

actually look at the last few years, you

know, they've actually maintained their

share very, very handsomely.

Um, actually,

um, if you accounted for the fact that

Anthropic

was not really using Nvidia. They

probably actually gain share against

if not for in 25 26. So, I think I think

what was very interesting though was a

new class of

accelerators or ASICs.

MediaTek with their

with their new V8T

versus, you know, Broadcom's V8I for

TPUs

actually was a big topic of discussion.

And, you know, I I think for ASICs

the argument now is that more and more

will look custom to the actual workload

and that is like one vector that people

are moving in versus Nvidia now is

has kind of shown itself as the the

predominant provider of compute to

a lot of the world and for, you know,

internal internal workloads,

perhaps they will go more and more

custom and more and more down the stack.

And I I remember just, you know, 1 year

ago when it was kind of a Broadcom or

Nvidia battle. It seems there's a lot

more nuance now to, you know, what type

of accelerators will fit which workloads

and fit which customers and fit which

business models. Um

and yeah,

I thought I thought that was a

a new topic.

>> It's actually

>> New realization though, I think we all

kind of shared this view for a long

time.

>> Yeah, I was just shocked. I mean, I'm

I'm out here. I did a board meeting with

one of our companies

and just, you know, their biggest one

thing they emphasized is

we thought the world would be have be

consuming less Nvidia than it is and if

anything, Nvidia is accelerating and

they just continue to out execute their

competitors. And I think a lot of people

are indexing to this

OpenAI gigawatt and you know, Nvidia has

10.

Broadcom has 10.

Um

who has six?

AMD AMD has six and they have warrants.

And then Cerebras has

our shared portfolio company

has a gigawatt.

And I just that is what's on paper.

>> Right.

>> What actually gets deployed, let's see.

I will be very surprised if you know

that 10 out of 27, what's that math?

Let's see who's best at math. What

percentage market share is that?

>> 30% yeah.

>> Yeah. I'll be very surprised if that is

where they land. I think that is an

extremely unlikely outcome.

And especially as long as we're in a

watt constrained world, if you can get

more tokens per watt, which is literally

revenue with Nvidia

than a lot of alternatives just if you

build your factory with another chip

you may save some money, but you're

going to have less revenue and the

margins may be lower and that's a point

that Jensen keeps hammering and I think

is a really important. And by the way,

credit where credit is due

the most important the most one of the

most surprising things to me in this

ASIC landscape

>> I'd say Meta and Microsoft have been

probably disappointing.

>> Yes.

>> You know who made a good ASIC?

>> Yes.

>> Well, I know you know.

>> Yes.

>> Jalapeno

>> Yeah, exactly.

>> from Open AI. They made a great chip.

>> Yes.

>> Now, unfortunately needs to run at a

much lower temperature than the Nvidia

GPUs, which means you need to spend more

money on cooling and that consumes more

power. They made a great chip.

>> Well, we can I mean I think the question

there and the question for everybody is

going to be is that the highest and best

use of your time? Right? Like I you

know, I tend to think that the frontier

companies like there's this belief that

they got to be vertical vertically

integrated. But if you believe like I do

that the race to super intelligence

particularly as we get these recursive

loops working may be over in the next

two to three years, then I think focus

focus focus focus. You exist to build

the best intelligence in the world and

to deliver the best intelligence in the

world and you that means you have to

have all the revenue. Because if you

want to build out the compute that's

going to be required to continue to push

the frontier, you have to have the

revenue in order to support it. So I

think you know, subject to the focus

question, I think they certainly did.

This all brings me back to kind of a

reality check, though.

Um

you know, we just got done talking about

test time compute, inference time

compute, long-running agents. This is

really the thing that's unlocked the

revenue this year. Um it all pushes us

in the direction of more CapEx. Google

just raised $80 billion,

right? We've now taken the Mag 5 or Mag

7 free cash flow, you know, down

dramatically, 80% um from just a few

years ago. Um and Morgan Stanley, you've

got this chart in front of you, up to

their 2027 CapEx forecast from 950

billion to 1.1 trillion. I mean, we were

talking about this with Jensen. That was

his forecast 2 years ago. You know,

obviously, this doesn't even include

SpaceX, CoreWeave, etc. So, I think the

number on 2027 is likely closer to 1.5

trillion.

And if we compare this to the total

incremental inference revenue, so the

thing that the market gets worried

about, you know, back to my Sam Altman

podcast, you know, in October of last

year, can we really afford to spend 1.5

trillion of CapEx a year if we're only

generating X amount in inference

revenue? The thing I think that lit the