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And I think we're all pretty AI pilled.

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And if you're AI pilled, that means we
got to build a lot more compute than the

3
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world thinks. And that these models
are going to be a lot more valuable than

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people think. You combine that with
their core business, I don't know

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another entrepreneur or another business

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that's a better bet on the future,
right, than SpaceX. And so I think for

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most institutional investors, it's a

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must buy, a must own, a set it and

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forget it, right, in order to have
a a real bet on both the space and the AI

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future.

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All right, here we go.
Early morning Silicon Valley, BG2 is

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back. We're chopping it up on all things
tech and markets. To do that, I have

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none other than GB in the house, Gavin
Baker from a Treaties. He's brought his

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main guy, Andrew Fox. And of course,
I had to draft Clark Tang into the mix, my

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partner, um to talk
to to talk about some of the

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big questions of the day. You know,
how should we be thinking about the SpaceX

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IPO? You know, what are the big levers?

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There are big numbers out there
for what's going to happen over the course

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of the next few years. So, let's break
that down a bit, help simplify it for

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folks. I Mythos launched yesterday. I want
to talk a little bit about like who's up,

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who's down in the race
for superintelligence. Where are we? What

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did we learn with the Mythos launch? And

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Clark was in uh in in in Taiwan last

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week um with Jensen at Computex and GTC.

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So, what was our takeaway there? What's
going on with GPUs, memory? Where are

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the bottlenecks? And where do we go
from here? To start everything off, um you

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know, maybe just kick it over to you,
Gavin, talking about the SpaceX IPO. The

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IPO is in 2 days. Uh you're a big
shareholder. Congratulations. We're also

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a shareholder. Uh you know, we also we
expect to be buying in the IPO. The Wall

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Street Journal's reporting

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um you know, the Goldman Sachs are both

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saying 160 billion in revenue in 2028.

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

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1.77 trillion. Um so

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when we think about kind of what the big

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levers are, there's so many moving parts

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in this IPO. Um nobody's better than
you at just breaking it down, simplifying

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it. What are the key levers that we
ought to be thinking about that you're

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thinking about over the course
of the next few years?

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>> Sure. Um so great to be here. Thank you
for having me. I thought we're going to

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call it B BGG B,
but [laughter] we can stick with BG2.

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I've been your house. >> hey.
All subject to revision.

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

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big levers or variables that I think
people should focus on. And you know,

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I'm not going to comment on where
I think um those variables go.

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But one is um you guys have this chart.

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Um did you did you post this on X?

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>> I did I did before and then we also
included a new addition with uh XAI's

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new deals as well. >> Yeah. >> Yeah.

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

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uh made a did a great analysis here.

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And he shows that XAI's deal um with

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Google for cloud computing

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uh generates more operating profit per

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gigawatt um than Anthropic, than Meta,
than Google, than OpenAI. Uh their deal um

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>> [clears throat] >>
actually with uh Anthropic

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also generates probably more operating

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profit than anyone but um Anthropic.

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And so you know, um the your your
colleague at Altimeter, Freida, also she

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calculated a 55% IRR >> Mhm.
>> on Claude's one.

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

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

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I'm not the most sophisticated
thinker, but that math maths.

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>> Right. >> And so I
think the most important

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variable, one of the two most important,
is how quickly they bring on terrestrial

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data centers. >> Mhm.
>> We do know from Jensen that uh Elon

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brings data centers up faster than
anyone 122 days. Speed is literally cost

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because every day you're paying
electricians and plumbers. That's cost.

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And they're now monetizing them at

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arguably the highest rate. And so I
think, you know, everybody should run

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their own math on that,
but that is a massive variable.

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>> Yes. >> Truly massive
variable. The second thing

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is, you know, we have a chart and it's
wildly out of date now. It's kind of

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freaking amazing. This chart is I think

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is this chart from 10 days ago?

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But it like the 10 or 12 days since
this chart since we made this chart

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which shows the Pareto curves for Opus

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4.7 for coding

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for Codex from OpenAI. And now we've had

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Opus 4.8. It was already out of date.

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And now we have Fable >> Totally.

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>> and Mythos, which is freaking wild.
In 10 days >> Yes.

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>> like we would have had
to update the chart twice.

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>> Right. >> But what
the Pareto curve sure shows is

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how much intelligence you can get
for a given amount of cost. And I do think

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being all revenue will accrue
to the Pareto curve. All at least kind of

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frontier model revenue will accrue
to the Pareto curve. And this is Pareto

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curve for code coding.
And what I think is so impressive

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is that you can see
in the chart that Composer 2

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was Pareto dominant or,
you know, at the lowest level of

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intelligence with very little training.

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This just reflects, and I know you know
Cursor well. I think you know Cursor

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a lot better than I do.
A vast amount better than I do.

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[laughter]

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But my understanding is that Cursor and

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Anthropic have more tokens
of proprietary coding data than anyone

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else. And they have more tokens
of proprietary coding data than exist on

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the public internet.

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And so they fed Cursor fed

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um used ChemK 0.25, used their own
private data, did some RL, some

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supervised fine-tuning and they
got a really good model. And

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then they spent 3 weeks in the Colossus

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2 cluster and they got a model that

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12 days ago was Pareto dominant
with Composer 2.5. That's on their own

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benchmark. Um Cursor bench,
so maybe take it with a

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grain of salt. But I think this just

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suggests that the Cursor data is very

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valuable for coding and when it is

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trained, you know, to Chinchilla
optimal or beyond Chinchilla optimal with

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reinforcement learning, you know,
I think it suggests that XAI

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and SpaceX AI has a shot
of being a real player in coding.

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>> I mean, I think one of the
interesting things is, you know, we

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you know, the way he answered
the question, right? We didn't talk about

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launch. Right?
We didn't talk about Starlink or

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communications. Those up until
really 6 months ago were the business.

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

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in x.ai and we merged in Cursor and then

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we announced these deals where it
was very clear he was kind of building AWS

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right under our nose, you know, in in in
in terms of this. But what I want to do

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is go to go to Fox. Give us the breakdown.
Three big lines of business,

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right? We've got the the the
communication Starlink launch business,

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we've got the, you know, AI compute
business and then I want to come back to

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x.ai that you were just clicking on. But
if we just go to the core business, what

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do we have to assume goes right in the
core business both with launch and with

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Starlink in order to achieve
the numbers that are out there?

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>> Yeah, sure. So, look, I think
the thing that's foundational to

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everything is the launch business.

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>> Right. >> Right? This is the kind of

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crown jewel of SpaceX. Um it's something
that no one else really has, notably

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reusability. >> Right.
>> And soon rapid reusability. Right? This

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is, I think, what you need to believe in

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to get to the economics in AI that make

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orbital compute something that's
very economically attractive.

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>> Right. >> Outside
of the idea that we are in

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shortage for power, shortage for chips.

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Right? Um so, I think rapid reusability
is the main thing that we're watching

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for and I think most
people should watch for. Um,

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you know, Elon talks about it a lot,
but getting these rockets to fly at a

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cadence that's comparable to an airline,
right? And and Gavin has used this

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analogy before, but um,
the old rocket industry was kind of

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like, imagine boarding a plane, flying
to California, getting off the plane,

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the plane explodes

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after. Um, so I think what SpaceX are

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ultimately trying to achieve is have a

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Starship fly both stages,
not just the booster. Um,

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uh, 30, 40, 50 times

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before you have to retrofit that ship.

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Um, and when you do that,
you're amortizing

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the cost of the vehicle over many
flights, right? And that's what brings

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the cost down significantly. Um,
>> But that's a really hard problem to

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solve. >> Extremely
difficult and look, I think

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the company, you know, have been
loud and clear, they're going to

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attempt to bring back
the second stage >> Right.

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>> of Starship later this year.

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

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re-fly the second stage next year. Um,

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and from there, ramp up the cadence. But
at the end of the day, driving down the

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cost of launch is what
enables all of these other

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businesses and is what makes them
so attractive relative to incumbents.

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>> So how many how many times are we
Starship just launched Starship 3, you

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know, just launched. How many launches
are you know, do you think kind of the

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consensus out there is assuming, you know,
two or three years from now? Like

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what is the launch cadence? Are we
launching one of these every day or we

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launching one of these every week
or every month? Like where are we in terms

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of expectations? >> Yeah,
so look, I think expectations for

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

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know, call it 160 165 launches last year

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up into the high hundreds of launches
in several years and, you know, getting

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into the thousands of launches
probably in the next 3 years thereafter.

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>> Okay. >> Um I think
the company have aspirations.

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>> Thousands of launches,
you're launching,

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you're doing two or three launches a day.

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

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what is this enable? Obviously,
you know, I'm here in Silicon Valley. I

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can't even I can't even keep a call
on Sand Hill Road, two decades into the

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mobile revolution. I mean, it's the
craziest thing. It's like a third world.

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>> It's It's a major business problem
when you're freaking out here. [laughter]

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>> It's crazy. It's crazy. Right by the
Starwood dead zone. And I'm like, how

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can this possibly be? So almost like
it's a joke. It's the epicenter of

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technology in America and you can't
maintain a call. Okay, so we're all

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going to switch to Starlink mobile when
it comes along because I don't want to

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lose that call on Sand Hill Road. So

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walk me through a little bit just again

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high-level. Um it's a big portion of the
revenue growth expected in the business

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over the course of the next two to three
years. My hunch is a lot of this is

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driven uh by uh by direct to cell
connectivity. Walk me through a little

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bit those economics. >> Yeah,
so look, it's actually interesting. Um

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the broadband business is still very
early stage when you think about um the

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percent of households that have actually
been penetrated to date. You look at the

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percent of global households with
Starlink, it's less than 1%. Uh and

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that's the broadband, you know, you kind
of have a base terminal at your house,

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on your car, on your boat, um and then

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airlines now as well.
Um so I actually think broadband can scale

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to hundreds of millions of terminals,
hundreds of millions of users. And today

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the subscriber base

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>> Hundreds of millions if if they get

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rapid reusability of Starship,
which is really hard.

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Um you know, if if
there's not competition.

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Um hundreds of millions, um it's possible.

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

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I love seeing PM and kind of analysts
in in this situation. It's exactly what I

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00:11:31,200 --> 00:11:34,357
do with Clark. Clark will say something,
I'll say the future is a distribution of

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00:11:34,440 --> 00:11:37,757
unknown probabilities. It's either more
likely or less likely, so give me the

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00:11:37,840 --> 00:11:40,557
distribution. Are we talking 20% 30%?

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00:11:40,640 --> 00:11:43,037
It's hilarious. It's the same >> Well,
no, 100% same thing. And like I've

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watched Elon do many hard things and
this is a really hard thing. So, I think

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00:11:46,840 --> 00:11:50,717
it's reasonable to think that they're
going to succeed with rapid reusability,

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00:11:50,800 --> 00:11:54,877
but just I just think it's
important to acknowledge that like

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orbital compute

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00:11:56,600 --> 00:11:58,397
you know, Starlinks, you know, Starlink

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V3, Starlink direct to cell, we need we

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need first reusability for Starship V3
and then rapid reusability unlocks a lot

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00:12:06,840 --> 00:12:09,757
of this. >> Right.
When I see when I see the models

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that the banks are putting out there,
right? And Wall Street Journal,

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00:12:12,440 --> 00:12:15,917
everybody's reported on these.
These same things have been widely leaked.

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They they they largely have the revenue
on connectivity, so let's call it

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00:12:20,240 --> 00:12:22,397
Starlink direct to cell, etc. Going

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

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00:12:24,800 --> 00:12:27,957
to 50 billion uh by 2028. And so, I'm

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not asking you guys to react to, you know,
to tell me your specific numbers,

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

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trying to size up is order of magnitude.

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Do we think we can 5x the business over
the course of the next 3 years? Is there

224
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enough TAM both in terms of broadband
and direct to consumer? And I think the

225
00:12:45,320 --> 00:12:47,957
answer to that is yes. >> Yeah,
here's what I just say very simply

226
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is I have um

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I I travel with Starlink.
Um I'm I'm a big video gamer and very

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consistently wherever I am in the world,
Starlink is the best connection.

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

230
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and I do think once they get to rapid
reusability, it's also going to be

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they're going to have the cheapest cost

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per gigabyte or megabyte delivered um

233
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and better faster, cheaper
has been a winning formula.

234
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And so, 50 billion, that's, you know,
0.3% penetration of the global telecom

235
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market. Now, maybe there's some
deflation with Starlink pricing.

236
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Um but that's the way I'd frame it up.

237
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>> Yeah. I like betting
on better, faster, cheaper.

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>> Um, Clark, I would say probably
the biggest surprise of the last six six

239
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weeks is that Elon,
you know, we talked about

240
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it on all-in podcast, we called it EWS,

241
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Elon web services, right? That that he
struck these huge deals with Anthropic

242
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and Google. I don't even
think people were thinking

243
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about SpaceX in the AI compute game,

244
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right? We If you looked at the models
as of a few months ago, it was

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connectivity, so Starlink, and then it

246
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was x.ai, the model.

247
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But this whole category of taking all of

248
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this compute, which he's uniquely
good at standing up, right? And then

249
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reselling it in a way that's highly
profitable was not in a lot of people's

250
00:14:10,400 --> 00:14:13,277
forecast. Now it's a major component of

251
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the forecast. You know, you and I did
this podcast with Jensen, where Jensen

252
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said Elon is an N of 1. >> What
they achieved is is singular. Never

253
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been done before. Just to put
in perspective, 100,000 GPUs, that's

254
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you know, easily the fastest supercomputer
on the planet as one cluster.

255
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Um, a supercomputer uh,

256
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that you would build would
take normally 3 years to plan.

257
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>> Right. >> And then they
deliver the equipment, and

258
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it takes 1 year

259
00:14:44,320 --> 00:14:49,117
to get it all working. Yes.
We're talking about 19 days.

260
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>> Wow. >> N of 1 is right.
Elon is an N of 1.

261
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>> And his ability to secure supply, stand

262
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up the supply, you know, deploy it in a

263
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way that's uh, you know, coherent and

264
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effective for both himself and I guess

265
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now for others. So, walk us through kind

266
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of that. It looks to me again like
this is a major component of the revenue

267
00:15:09,960 --> 00:15:13,557
story. >> Totally.
I mean, so we we were all at

268
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the macro hard data center, and it
was just very evident the amount of

269
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engineering that was that had
gone into building these sites.

270
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Um, you people always
talk about Google and

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their ability to to build a TPU
and sell the TPU to Anthropic to generate

272
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revenues for AI.

273
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I think it's a pretty similar dynamic

274
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here with Elon able to secure power, uh

275
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build these sites faster than anyone

276
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else and also be able now to monetize it

277
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to um to the this massive
AI market that's ahead of us.

278
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Um if you look at the the relationships

279
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that he's forged with a lot of his
suppliers, you know, be it Jensen, be

280
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it, you know, all of these different um

281
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different sites that actually want
xAI as a tenant. Um his his ability to

282
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finance these deals at at very
attractive uh financing rates relative

283
00:16:07,760 --> 00:16:12,357
to a lot of the other players in in the
space, you know, these are advantages

284
00:16:12,440 --> 00:16:15,997
that compound over time. And when you've
built the credibility to stand up these

285
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sites and monetize at these levels, um
you know, it's very it's actually a very

286
00:16:20,560 --> 00:16:22,357
attractive uh

287
00:16:22,440 --> 00:16:27,517
very attractive proposition for for a lot
of folks involved. Um and actually,

288
00:16:27,600 --> 00:16:31,077
you know, if you look
at the these deals in particular,

289
00:16:31,160 --> 00:16:33,557
Gavin, you you pointed out, but,
you know, they're they're actually

290
00:16:33,640 --> 00:16:38,637
monetizing, you know, perhaps better
than other players in the space by selling

291
00:16:38,720 --> 00:16:40,197
this infrastruc- >> a lot higher.

292
00:16:40,280 --> 00:16:45,157
>> Um Google is obviously paying SpaceX
a huge premium for this compute. Fox, you

293
00:16:45,240 --> 00:16:47,117
said something that I thought was really

294
00:16:47,200 --> 00:16:49,637
important, which is, you know, it may

295
00:16:49,720 --> 00:16:52,277
very well be that in order to get, you

296
00:16:52,360 --> 00:16:56,957
know, first in line on space compute,
which Google certainly wants to do, that

297
00:16:57,040 --> 00:17:01,357
they're willing to pay a premium
for their terrestrial compute. And so, to

298
00:17:01,440 --> 00:17:04,597
me, that's how you kind of square
the circle as to why the premium. Any

299
00:17:04,680 --> 00:17:06,597
thoughts? >> Yeah, look,
I think there's some of that

300
00:17:06,680 --> 00:17:10,997
embedded there, but um look, at the end
of the day, SpaceX can stand up compute

301
00:17:11,080 --> 00:17:15,237
quickly. They can stand it up coherently.
And they can stand up a lot of it in one

302
00:17:15,320 --> 00:17:18,317
place and have it readily available.
So, look, I think that's most of the

303
00:17:18,400 --> 00:17:22,757
premium, but outside of that certainly
people are going to space over time.

304
00:17:22,840 --> 00:17:25,197
>> I have to pay a little call
option to get first in line for space.

305
00:17:25,280 --> 00:17:27,117
>> There you go. Good one.
>> We've all been investing in the neo

306
00:17:27,200 --> 00:17:30,837
cloud space. So, like there's
a fundamental belief around this table I

307
00:17:30,920 --> 00:17:34,117
that that we lack the compute needed to

308
00:17:34,200 --> 00:17:37,957
continue to push the frontier on
intelligence. So, we have to build a lot

309
00:17:38,040 --> 00:17:42,517
of compute, okay? Now there's a there's
competition going on. On one end you

310
00:17:42,600 --> 00:17:46,037
have the hyper scalers who are building
out that capability. Then we have AI

311
00:17:46,120 --> 00:17:48,557
dedicated clouds that are building out

312
00:17:48,640 --> 00:17:50,197
that capability. And now literally in a

313
00:17:50,280 --> 00:17:53,197
matter of weeks, right, we have a a you

314
00:17:53,280 --> 00:17:55,677
know a giant that's emerged in this

315
00:17:55,760 --> 00:17:58,477
category which is SpaceX.

316
00:17:58,560 --> 00:18:00,277
The question to you Gavin is can they

317
00:18:00,360 --> 00:18:03,277
consolidate this market, right? Because

318
00:18:03,360 --> 00:18:07,757
if I think about a marketplace, Elon has
a unique ability to get the supply. He

319
00:18:07,840 --> 00:18:11,957
has a unique ability to cut deals on
the other side and nobody can stand it up

320
00:18:12,040 --> 00:18:16,757
like he can stand it up. So, I think
there might be a real consolidation in

321
00:18:16,840 --> 00:18:20,757
the AI compute market where you have the
hyper scalers on the one hand and on the

322
00:18:20,840 --> 00:18:25,717
other hand, you know, he may emerge
as the largest, strongest player in the AI

323
00:18:25,800 --> 00:18:29,197
compute market. >> Yeah,
so I think they're are they the

324
00:18:29,280 --> 00:18:32,277
number four number five hyper
scaler today after the Google deal?

325
00:18:32,360 --> 00:18:36,277
>> Um it will be number four.
>> Kind of wild.

326
00:18:36,360 --> 00:18:40,597
>> Yeah. >> In 30 days we
went from not being an AI hyper

327
00:18:40,680 --> 00:18:45,717
scaler to being number four. And we passed
a lot of companies including Oracle.

328
00:18:45,800 --> 00:18:47,437
>> Coreweave is a huge business, right?

329
00:18:47,520 --> 00:18:51,437
That we're we're investors in, you know,
and have been investors in, right? But

330
00:18:51,520 --> 00:18:54,317
there are a lot of other players, the
Nebius's of the world, the Iron's of the

331
00:18:54,400 --> 00:18:57,677
world. And I would say that they're
probably 50 neo labs being funded in

332
00:18:57,760 --> 00:19:01,437
Silicon Valley right now as we speak
because of the shortage in compute.

333
00:19:01,520 --> 00:19:06,557
>> Absolutely. So, that's kind
of crazy in 30 days. That's just

334
00:19:06,640 --> 00:19:09,637
extraordinary. What I would
say is there I think there

335
00:19:09,720 --> 00:19:12,877
is a belief that these data
centers are commodities.

336
00:19:12,960 --> 00:19:15,677
>> Mhm. >> And I do not share that belief.

337
00:19:15,760 --> 00:19:19,157
Um I don't think anybody around
this table shares that belief.

338
00:19:19,240 --> 00:19:23,477
And in the same way that Elon was able
to re-engineer a rocket from first

339
00:19:23,560 --> 00:19:27,437
principles and make it reusable,
he engineered an electric car from first

340
00:19:27,520 --> 00:19:30,517
principles. You know, everyone else
was trying to, you know, make an electric

341
00:19:30,600 --> 00:19:34,877
car like an internal combustion engine
car and he thought about it differently.

342
00:19:34,960 --> 00:19:39,597
And um I think he looked
at data center design

343
00:19:39,680 --> 00:19:43,677
from first principles and he designed
something fundamentally different. And I

344
00:19:43,760 --> 00:19:45,637
did actually ask the team. I said, "Hey

345
00:19:45,720 --> 00:19:51,037
guys, maybe I'd be a little less public
about things that are very obvious to

346
00:19:51,120 --> 00:19:54,557
you [laughter] >> Right.
>> about how to design a data center, but

347
00:19:54,640 --> 00:19:59,237
are revelations to other people
because I think what you're doing is

348
00:19:59,320 --> 00:20:02,157
maybe um more differentiated
than you perhaps

349
00:20:02,240 --> 00:20:05,117
realize cuz what you're
doing is so logical to you,

350
00:20:05,200 --> 00:20:08,957
but maybe lot not logical to everyone
else. And that's how he was able to do

351
00:20:09,040 --> 00:20:12,197
it in 122 days. >> Yeah,
to I mean to that point, Brad,

352
00:20:12,280 --> 00:20:14,997
yesterday we were meeting one of our
portfolio companies and we were talking

353
00:20:15,080 --> 00:20:18,397
about behind the meter and we're, you
know, really thinking about it. There's

354
00:20:18,480 --> 00:20:21,277
only maybe two or three two or three

355
00:20:21,360 --> 00:20:26,237
players now that can actually reliably
engineer behind the meter data center.

356
00:20:26,320 --> 00:20:29,037
And you know, there's real engineering
work that goes into all of this. So, if

357
00:20:29,120 --> 00:20:31,677
you think about this,
if you're a gas combustion if you're

358
00:20:31,760 --> 00:20:33,757
Vernova and you say we only have a

359
00:20:33,840 --> 00:20:38,837
certain number of gas combustion engines.
Now, we can sell them to x.ai

360
00:20:38,920 --> 00:20:42,277
or we can sell them to one of these
startup neo clouds. Who are you going to

361
00:20:42,360 --> 00:20:44,437
sell them to? >> Well,
and there's another dynamic.

362
00:20:44,520 --> 00:20:46,317
Everyone starts making more money when

363
00:20:46,400 --> 00:20:49,797
the GPUs get energized and sold faster.

364
00:20:49,880 --> 00:20:55,037
So, literally speed is money for all
of the suppliers. Power, land, turbines.

365
00:20:55,120 --> 00:20:57,917
So, I think it's we'll we'll see.

366
00:20:58,000 --> 00:21:00,717
>> Right. >> Hey Brad, man.
>> And but but this is just we're just

367
00:21:00,800 --> 00:21:02,797
talking terrestrial. I do I do want to

368
00:21:02,880 --> 00:21:04,837
hit on and then you can flip it back on

369
00:21:04,920 --> 00:21:07,837
me. Talk to me, okay, So, let's let's

370
00:21:07,920 --> 00:21:10,637
assume, right, that they continue to

371
00:21:10,720 --> 00:21:13,157
build out the terrestrial landscape.

372
00:21:13,240 --> 00:21:15,597
They continue to find buyers for that.

373
00:21:15,680 --> 00:21:19,877
Um, walk us through, you know, what
this unlocks, you know, and how this is

374
00:21:19,960 --> 00:21:23,357
related to space data centers because I
think, you know, once you start talking

375
00:21:23,440 --> 00:21:27,477
terrafab capacity and beyond. So, we're
talking 1,000 gigs, right? And this year

376
00:21:27,560 --> 00:21:31,397
what what we're doing, 25 or 30 gigs
just to put it all in perspective.

377
00:21:31,480 --> 00:21:33,677
>> 20, yeah. >> Right? >> 20, 25 gigs.

378
00:21:33,760 --> 00:21:36,757
>> Okay, so so once we start scaling up,

379
00:21:36,840 --> 00:21:41,237
walk us through, do we have
to have space data centers in

380
00:21:41,320 --> 00:21:46,357
order to get excited about buying the IPO,
right? And then there's obviously

381
00:21:46,440 --> 00:21:49,837
this this debate in the world. I I heard
Jeff Bezos say, you know, I think it's

382
00:21:49,920 --> 00:21:53,397
more like 6 years, but Elon's going
to say three because if if he says six,

383
00:21:53,480 --> 00:21:57,037
then it will take even longer. So, say
three and we may get it in four or five.

384
00:21:57,120 --> 00:22:00,357
But are space data centers integral and

385
00:22:00,440 --> 00:22:04,717
essential to, you know, the IPO? And what
do you think the timeline is, Erin?

386
00:22:04,800 --> 00:22:07,237
There are you guys. >> So,
I don't think I think if you think

387
00:22:07,320 --> 00:22:10,717
about those variables around what

388
00:22:10,800 --> 00:22:15,157
Crusher could mean for XAI.
And we do have an existence proof that

389
00:22:15,240 --> 00:22:18,477
once you really get
on that Pareto frontier,

390
00:22:18,560 --> 00:22:21,917
revenue can scale rapidly and it's
called Entropic. And there does seem to

391
00:22:22,000 --> 00:22:26,437
be an exhaust There seems to be a lot
of demand for coding. And I do think I'm

392
00:22:26,520 --> 00:22:29,157
John Massad posted
something very interesting.

393
00:22:29,240 --> 00:22:32,837
>> The The founder of Replit.
>> The founder of Replit. It he called it

394
00:22:32,920 --> 00:22:35,317
bitter lesson adjacent that coding may

395
00:22:35,400 --> 00:22:40,357
be the fastest path to AGI and ASI
because if you really go to coding, you

396
00:22:40,440 --> 00:22:43,837
can write code if a model's good
at coding to do anything. So, I think

397
00:22:43,920 --> 00:22:46,597
that's a profound point and I think
coding is going to continue to be very

398
00:22:46,680 --> 00:22:49,797
important. So, I think if
you think about that variable,

399
00:22:49,880 --> 00:22:51,277
if you think about Starlink direct to

400
00:22:51,360 --> 00:22:54,157
cell enabled by Starlink V3, and you

401
00:22:54,240 --> 00:22:57,357
think about how quickly they can or

402
00:22:57,440 --> 00:23:00,197
cannot bring on terrestrial compute, I I

403
00:23:00,280 --> 00:23:02,637
think orbital compute is is

404
00:23:02,720 --> 00:23:06,437
is necessary for the IPO valuation,

405
00:23:06,520 --> 00:23:11,237
but it's certainly important and it's >>
Well, maybe another way to say it is you

406
00:23:11,320 --> 00:23:15,157
think you you may think
we're going to get to ASI

407
00:23:15,240 --> 00:23:17,077
faster than we're going to get to

408
00:23:17,160 --> 00:23:19,037
orbital compute. That may take us from

409
00:23:19,120 --> 00:23:24,197
300 IQ to 400 IQ, 500 IQ, and beyond.

410
00:23:24,280 --> 00:23:28,517
Um and the ability to scale it up
to consume 10% of, you know, global GDP,

411
00:23:28,600 --> 00:23:30,997
but maybe maybe that's
where we should move next.

412
00:23:31,080 --> 00:23:34,717
>> No, no, I think on the orbital compute,
I think Foxy would be great our Clark to

413
00:23:34,800 --> 00:23:39,197
lay out the math from first principles on,
you know, Clark has this great chart

414
00:23:39,280 --> 00:23:42,557
on, you know, the gigawatts it costs,
you know, the dollars per gigawatt.

415
00:23:42,640 --> 00:23:44,957
>> Right. >> Walk us
through the economic case.

416
00:23:45,040 --> 00:23:48,357
>> Yeah. Yeah, so I mean, on this point of

417
00:23:48,440 --> 00:23:51,637
is orbital key to investing here? I

418
00:23:51,720 --> 00:23:55,677
don't think it is and I'll first
point I'll make is what are the implied

419
00:23:55,760 --> 00:23:59,157
monetization rates based
on expectations today for the AI

420
00:23:59,240 --> 00:24:02,197
business? And you know, I think you
threw out the $160 billion number that's

421
00:24:02,280 --> 00:24:04,557
been leaked out there
that people are talking about.

422
00:24:04,640 --> 00:24:08,877
The implied monetization rate on that
number is something like $14 billion per

423
00:24:08,960 --> 00:24:11,397
gigawatt per year for the AI business.

424
00:24:11,480 --> 00:24:13,997
They just signed Anthropic at 22 to 23.

425
00:24:14,080 --> 00:24:15,477
They just signed Google at 50.

426
00:24:15,560 --> 00:24:17,917
>> Right. >> Right. So,
I I think you can invest

427
00:24:18,000 --> 00:24:22,797
behind the AI business terrestrially
and still be excited about it. But with

428
00:24:22,880 --> 00:24:24,957
orbital >> an important point.
Excited about it if

429
00:24:25,040 --> 00:24:26,357
they can get the land and the power.

430
00:24:26,440 --> 00:24:30,477
>> Right. But but but I mean I I think
for most investors, right? They get They

431
00:24:30,560 --> 00:24:35,837
have an easier time getting their head
around how SpaceX wins terrestrially.

432
00:24:35,920 --> 00:24:39,037
Like can they go get land, power,
and chips? The answer to that is high

433
00:24:39,120 --> 00:24:43,557
probability yes, okay? And what we're
saying is at the rate they're monetizing

434
00:24:43,640 --> 00:24:47,237
that, that gets you to the numbers
that are being leaked out there before you

435
00:24:47,320 --> 00:24:50,277
even have to take the leap of faith
that they're going to extend the lead with

436
00:24:50,360 --> 00:24:52,637
orbital data centers.
But take us there on that, too.

437
00:24:52,720 --> 00:24:58,477
>> Sure. Yeah, so so look, with orbital,
I think the key thing is um two-stage

438
00:24:58,560 --> 00:25:02,757
reusability. >> Yeah. >> And beyond
that, rapid two-stage reusability.

439
00:25:02,840 --> 00:25:05,517
>> Yeah. >> So, today
with Starship, they've shown

440
00:25:05,600 --> 00:25:09,437
that they can successfully
reland the booster.

441
00:25:09,520 --> 00:25:12,317
The second stage, we'll see what
happens later this year. I think they're

442
00:25:12,400 --> 00:25:16,517
attempting to bring that back
and then make it reusable by next year.

443
00:25:16,600 --> 00:25:20,357
Um but the thing that's important about
two-stage reusability when it comes to

444
00:25:20,440 --> 00:25:22,917
the economics for orbital compute,

445
00:25:23,000 --> 00:25:25,917
right, is the cost per kg comes down

446
00:25:26,000 --> 00:25:27,517
significantly. You know, we're talking

447
00:25:27,600 --> 00:25:29,957
about going from $1,500 per kg on

448
00:25:30,040 --> 00:25:33,477
Falcon, somewhere in that range, to 250

449
00:25:33,560 --> 00:25:38,517
per kg, something lower. Um and the
more that you can reuse the rocket,

450
00:25:38,600 --> 00:25:39,757
the more that price comes down.

451
00:25:39,840 --> 00:25:43,277
>> Right. >> Right, cuz you're just
depreciating the cost of the launch.

452
00:25:43,360 --> 00:25:46,237
And eventually, you asymptote
to the cost of the fuel.

453
00:25:46,320 --> 00:25:49,397
>> Right. >> Right. Assuming
you can use a rocket for forever.

454
00:25:49,480 --> 00:25:51,477
>> Yes. >> Right, which
will take a very long time

455
00:25:51,560 --> 00:25:54,717
for us to to really achieve that.
But, um and at that point, we're talking

456
00:25:54,800 --> 00:25:58,037
about something well south of 250 per kg.

457
00:25:58,120 --> 00:26:03,037
So, then you look at the specs of these
AI satellites. You know, Elon did a great

458
00:26:03,120 --> 00:26:05,957
>> Yeah, that pod that that pod
was incredible that he laid out the other

459
00:26:06,040 --> 00:26:07,677
day, the specs on the satellites.

460
00:26:07,760 --> 00:26:10,117
>> It was really great
because I think they

461
00:26:10,200 --> 00:26:12,917
are finally showing people, here's how

462
00:26:13,000 --> 00:26:17,317
you could viably design
one of these satellites.

463
00:26:17,400 --> 00:26:21,477
And how heavy is the satellite? How many
could you fit into a Starship launch?

464
00:26:21,560 --> 00:26:27,077
And when you back into the numbers,
you get to something like 5 MW of capacity

465
00:26:27,160 --> 00:26:29,237
per Starship launch. >> Right.

466
00:26:29,320 --> 00:26:33,077
>> There's 100 metric tons in one of those
Starships. So, you can back into the

467
00:26:33,160 --> 00:26:37,397
math of how much will it cost per gigawatt

468
00:26:37,480 --> 00:26:39,917
to launch these satellites into space.

469
00:26:40,000 --> 00:26:43,597
>> Right. >> Launch
this compute into space. Um

470
00:26:43,680 --> 00:26:45,037
and the math that you get to before you

471
00:26:45,120 --> 00:26:47,517
account for things like

472
00:26:47,600 --> 00:26:50,717
bad GPUs, bad satellites, right, these

473
00:26:50,800 --> 00:26:52,517
will all be things that happen.

474
00:26:52,600 --> 00:26:54,797
But the math you get to is it's about $5

475
00:26:54,880 --> 00:26:59,877
billion per gigawatt of CapEx
to put these in space.

476
00:26:59,960 --> 00:27:03,237
>> Right. >> For comparison,
terrestrially,

477
00:27:03,320 --> 00:27:04,997
talk about the switch gears, the

478
00:27:05,080 --> 00:27:08,517
generators, the transformers, the shell,

479
00:27:08,600 --> 00:27:10,477
getting the power, that today is about

480
00:27:10,560 --> 00:27:13,677
25 20 to 25 billion per gigawatt.

481
00:27:13,760 --> 00:27:19,997
So we're talking about a 5x reduction
in cost on half of your bill of materials

482
00:27:20,080 --> 00:27:21,157
>> Right. >> for the data center.

483
00:27:21,240 --> 00:27:22,437
>> Right. >> Which is a huge number.

484
00:27:22,520 --> 00:27:26,797
>> Yeah, just just very simply, I mean
just to say that put it cost $60 to put a

485
00:27:26,880 --> 00:27:30,237
gigawatt on the ground today.
And we'll call it

486
00:27:30,320 --> 00:27:34,437
35 of that is are the GPUs and the
silicon that's doing the training and

487
00:27:34,520 --> 00:27:38,677
the inference.
And 25 billion is the land, the shell,

488
00:27:38,760 --> 00:27:42,797
the power, and the cooling.
I would hypothesize that those elements

489
00:27:42,880 --> 00:27:44,157
are probably going to be inflationary,

490
00:27:44,240 --> 00:27:47,437
so that 25 billion may not go down.

491
00:27:47,520 --> 00:27:52,757
And because space, power, cooling are

492
00:27:52,840 --> 00:27:57,037
effectively free in space, and when
I say space, I mean land. You know,

493
00:27:57,120 --> 00:28:00,560
there's no land in space,
but there is space.

494
00:28:00,760 --> 00:28:04,557
Um you're you're talking about putting a

495
00:28:04,640 --> 00:28:07,117
gigawatt into space for 30 billion and

496
00:28:07,200 --> 00:28:11,877
having lower operating costs.
Now the dynamic versus 60 billion that's

497
00:28:11,960 --> 00:28:15,917
inflationary, and that third and that 30
billion, that five may be deflationary

498
00:28:16,000 --> 00:28:19,437
over time. But what we need to consider

499
00:28:19,520 --> 00:28:21,317
is you know, the reliability and the

500
00:28:21,400 --> 00:28:25,037
maintenance. And so as long as you know,

501
00:28:25,120 --> 00:28:26,717
everybody can do the math,

502
00:28:26,800 --> 00:28:30,117
but as long as these satellites in space

503
00:28:30,200 --> 00:28:33,517
aren't failing at an at an astronomical

504
00:28:33,600 --> 00:28:36,717
rate, the math maths. As you can see,

505
00:28:36,800 --> 00:28:40,917
and by the way, we know GPUs melt
and lasers fail. We know this happens in

506
00:28:41,000 --> 00:28:44,437
data centers, particularly
during big training runs.

507
00:28:44,520 --> 00:28:47,037
And yeah, I mean GPUs melt.

508
00:28:47,120 --> 00:28:50,237
Um so as long as the reliability and

509
00:28:50,320 --> 00:28:54,757
maintenance is not dramatically lower,
the math is there once we have

510
00:28:54,840 --> 00:28:57,757
reusability and then rapid
reusability for Starship V3.

511
00:28:57,840 --> 00:29:00,317
>> I when you when we look
at this, okay, so we we

512
00:29:00,400 --> 00:29:04,837
went through Starlink and we said,
"Okay, like it it just stands to reason

513
00:29:04,920 --> 00:29:07,677
we're going to have direct to cell
on Starlink." Like the assumptions there

514
00:29:07,760 --> 00:29:11,757
are, you know, again, seem like you can
get your head around. Then when it comes

515
00:29:11,840 --> 00:29:15,517
to building terrestrial data centers,
again, not a hard one to think that

516
00:29:15,600 --> 00:29:18,677
based on these couple deals that Elon's
going to build a much bigger Starlink's

517
00:29:18,760 --> 00:29:22,437
going to build or SpaceX is going
to build a much bigger business there. And

518
00:29:22,520 --> 00:29:25,837
then you have this call option on space
that would drop the price even further.

519
00:29:25,920 --> 00:29:27,877
The one thing we haven't talked about is

520
00:29:27,960 --> 00:29:32,997
their model, right? And I find this
surprising, right? Six Six months ago,

521
00:29:33,080 --> 00:29:36,117
x.ai was competing, they were doing
pretty well, but they've done something

522
00:29:36,200 --> 00:29:38,797
dramatic over the course
of the past couple

523
00:29:38,880 --> 00:29:40,037
couple months, which is they bought

524
00:29:40,120 --> 00:29:44,557
Cursor, right? Cursor is 700 800 people

525
00:29:44,640 --> 00:29:48,837
was already doing incredibly well
from a revenue perspective. Our own

526
00:29:48,920 --> 00:29:52,557
projections were that they could
exit this year at up to $10 billion

527
00:29:52,640 --> 00:29:56,117
of revenue, so they were growing very fast

528
00:29:56,200 --> 00:29:59,837
one of the leading coding agents, but
they also had this incredible team with

529
00:29:59,920 --> 00:30:03,237
the potential, right, to really build
a frontier level model, but they were

530
00:30:03,320 --> 00:30:05,557
compute constrained. So all of a sudden,

531
00:30:05,640 --> 00:30:07,837
they get bought by X. X has massive

532
00:30:07,920 --> 00:30:10,917
compute that they can now train on.

533
00:30:11,000 --> 00:30:13,877
And when I think about the revenue in AI

534
00:30:13,960 --> 00:30:15,797
that like if I look at that line item in

535
00:30:15,880 --> 00:30:18,557
the models having it go from $10 billion

536
00:30:18,640 --> 00:30:23,197
to $150 billion, yes, a lot of that will
be the core weave type business that

537
00:30:23,280 --> 00:30:28,277
they have, but the question is how much
of that is going to be the core x.ai

538
00:30:28,360 --> 00:30:31,877
business that's really powered by the new
team from Cursor. So any thoughts on

539
00:30:31,960 --> 00:30:34,997
that, Kevin? >> Right now,
so Composer 2.5 was Pareto

540
00:30:35,080 --> 00:30:39,637
dominant 12 days ago. It was trained
on the Kimmy K2.5 base model.

541
00:30:39,720 --> 00:30:43,437
>> Right. >> Now, what's
happening is the Grok 4.3

542
00:30:43,520 --> 00:30:46,717
1.5 trillion parameter model is training.

543
00:30:46,800 --> 00:30:48,997
One would hypothesize based on scaling

544
00:30:49,080 --> 00:30:51,957
laws that that will might be a better

545
00:30:52,040 --> 00:30:56,957
base model. And then the cursor data
is being injected into the pre-training

546
00:30:57,040 --> 00:30:59,677
process, not just reinforcement learning.

547
00:30:59,760 --> 00:31:03,597
And we'll see, and I think that is going
to be a very important data point when

548
00:31:03,680 --> 00:31:07,597
that comes out. And I just think
everyone should keep in mind that once

549
00:31:07,680 --> 00:31:12,597
you are at multiple places on that
Pareto curve, if you have compute, you

550
00:31:12,680 --> 00:31:16,517
can scale really rapidly. >> You know,
that that to me is if I had to

551
00:31:16,600 --> 00:31:20,437
say what the one piece that's
being lost in the story,

552
00:31:20,520 --> 00:31:23,277
right? Like it's easy for everybody
to get excited about the deals with

553
00:31:23,360 --> 00:31:26,597
Anthropic because you can put your hands
around that. You know how much revenue

554
00:31:26,680 --> 00:31:30,517
it is. I see debate about, you know,
the 90-day termination and how long they

555
00:31:30,600 --> 00:31:33,917
last and what multiple do you put
on those revenues. But I think the thing

556
00:31:34,000 --> 00:31:38,477
that's getting lost is I think they've
dramatically advanced their capability

557
00:31:38,560 --> 00:31:42,157
when it comes to building a frontier
model. People outside Silicon Valley may

558
00:31:42,240 --> 00:31:45,877
not know, you know, Michael and the
team at Cursor as well. This is an

559
00:31:45,960 --> 00:31:48,357
extraordinary team that he just

560
00:31:48,440 --> 00:31:53,117
downloaded, right, into SpaceX. SpaceX
was already building good models. And

561
00:31:53,200 --> 00:31:56,797
what they have is they have
this way to monetize compute

562
00:31:56,880 --> 00:32:00,997
that gives you this call option that you
can pull all that compute in-house,

563
00:32:01,080 --> 00:32:04,397
right, to train a model and then to run
the model. I suspect if there's an

564
00:32:04,480 --> 00:32:08,477
upside surprise, if we went around the
table, I'd say this is the place that's

565
00:32:08,560 --> 00:32:11,437
getting the least amount of attention
and could have the biggest upside

566
00:32:11,520 --> 00:32:14,477
surprise. Any any thoughts, Clark, on

567
00:32:14,560 --> 00:32:18,997
what you think is being overlooked
or areas that you think are misunderstood

568
00:32:19,080 --> 00:32:23,477
about the business today?
>> I I would say I would say

569
00:32:23,560 --> 00:32:28,437
what the last few weeks
have proven is that Elon, um,

570
00:32:28,520 --> 00:32:31,917
their team can stand up all this compute.
Actually, if you just, you

571
00:32:32,000 --> 00:32:36,317
know, went back 1 and 1/2 years,
you know, they were behind in the race to

572
00:32:36,400 --> 00:32:40,797
stand up compute. They were you know
they they didn't have that many H100s.

573
00:32:40,880 --> 00:32:44,717
They brought in Colossus. Then they
brought in Colossus 2 at a scale much

574
00:32:44,800 --> 00:32:49,237
larger than anyone else.
And now you know as we gear for Vera Rubin

575
00:32:49,320 --> 00:32:52,197
you know from you know
a lot of my conversations

576
00:32:52,280 --> 00:32:54,037
it looks like they've you know secured

577
00:32:54,120 --> 00:32:57,197
maybe up to 20% of Vera Rubin capacity

578
00:32:57,280 --> 00:33:00,197
especially in the early days of you know

579
00:33:00,280 --> 00:33:06,117
when you know these these chips are very
scarce that that they're going to have a

580
00:33:06,200 --> 00:33:11,037
a a lead on all of this because you know
people think that they can stand up this

581
00:33:11,120 --> 00:33:14,637
compute better. So I think they'll all
you know what what the last few weeks

582
00:33:14,720 --> 00:33:18,397
have actually shown
is that Elon you know Elon will

583
00:33:18,480 --> 00:33:23,237
take you know take a shot at hitting
the frontier but if it you know if for

584
00:33:23,320 --> 00:33:26,117
whatever reason um they

585
00:33:26,200 --> 00:33:28,557
they have over procured some capacity

586
00:33:28,640 --> 00:33:30,917
this is a very scarce asset that they've

587
00:33:31,000 --> 00:33:35,877
shown that they can monetize at actually
you know best in class margins and

588
00:33:35,960 --> 00:33:38,757
payback periods.
>> The irony is like you know

589
00:33:38,840 --> 00:33:41,717
you and I've been doing this long enough
to know I mean that's why Bezos built

590
00:33:41,800 --> 00:33:46,037
AWS. Right? He had to build
capacity for Black Friday.

591
00:33:46,120 --> 00:33:47,677
>> Yeah. >> Right?
But then the rest of the year he

592
00:33:47,760 --> 00:33:50,837
sat on all this capacity they had
to build and he figured out a really

593
00:33:50,920 --> 00:33:56,077
incredible way to monetize this. And by
the way investors at the time 2009 2010

594
00:33:56,160 --> 00:33:59,277
when he was building out
the capability around AWS hated it.

595
00:33:59,360 --> 00:34:01,117
>> Of course. >> Because he
was consuming all that free

596
00:34:01,200 --> 00:34:05,637
cash flow. My meanwhile he was digging
the biggest gold mine in the history of

597
00:34:05,720 --> 00:34:07,077
the world. One of the biggest.

598
00:34:07,160 --> 00:34:10,117
>> One of the biggest. >> Among them among
them at the time was probably the biggest.

599
00:34:10,200 --> 00:34:14,017
>> Yeah Google search might want to have
a we'll have a we'll have a discussion.

600
00:34:14,100 --> 00:34:16,197
>> [clears throat] >> By the way
I do think it is important.

601
00:34:16,280 --> 00:34:18,677
Grok 4.3 I think the cursor if they

602
00:34:18,760 --> 00:34:20,637
acquire it that may end up being very

603
00:34:20,720 --> 00:34:23,437
important. But Grok 4.3 was on the

604
00:34:23,520 --> 00:34:25,957
Pareto frontier and has of 10 or 12 days

605
00:34:26,040 --> 00:34:28,477
ago and this these things move fast. But

606
00:34:28,560 --> 00:34:33,677
most intelligent 500 billion parameter
model in the world. And they were on the

607
00:34:33,760 --> 00:34:36,157
frontier and there are four
companies on the frontier.

608
00:34:36,240 --> 00:34:39,997
xAI, SpaceX AI, Google one with Gemini

609
00:34:40,080 --> 00:34:43,837
3.1 Pro, and then the rest of it
was dominated by Anthropic and OpenAI. But

610
00:34:43,920 --> 00:34:46,717
they were on the Pareto frontier and now
we'll see what they do with Cursor.

611
00:34:46,800 --> 00:34:49,237
>> Yeah. Um I want to come
back to that in a second.

612
00:34:49,320 --> 00:34:50,597
>> way, man, I want
to ask you some questions.

613
00:34:50,680 --> 00:34:53,477
>> go go go. What do you think? So you
think the biggest source of potential

614
00:34:53,560 --> 00:34:55,917
upside is the model?
>> Yes. >> What do you think?

615
00:34:56,000 --> 00:34:58,757
>> I think that's the I think that's
the thing that's least talked about.

616
00:34:58,840 --> 00:35:01,957
>> Least talked about.
>> Right? And so, listen.

617
00:35:02,040 --> 00:35:06,037
When I look at the bull bear case on
the IPO, right? The bears are looking at

618
00:35:06,120 --> 00:35:09,237
last year's revenue.
Say it was $18 billion

619
00:35:09,320 --> 00:35:13,717
and they're looking at the forecast from
the banks of $160 billion, you know, 3

620
00:35:13,800 --> 00:35:15,957
years from now and they're saying,
"Listen, not many companies in the

621
00:35:16,040 --> 00:35:18,237
history of the world have basically 8x

622
00:35:18,320 --> 00:35:20,637
their revenue over 3 to 4 years." Right?

623
00:35:20,720 --> 00:35:25,877
So that's where, you know, I think and
people get nervous about the valuation.

624
00:35:25,960 --> 00:35:29,677
When I look at this, again,
when you break it down as an analyst first

625
00:35:29,760 --> 00:35:33,717
principles, part by part, which is what
I tried to do here, right? When you look

626
00:35:33,800 --> 00:35:35,957
at Starlink, it looks totally doable.

627
00:35:36,040 --> 00:35:39,957
When I look at what they're building
in AI compute terrestrially, looks totally

628
00:35:40,040 --> 00:35:41,997
doable over the course of next 3 years.

629
00:35:42,080 --> 00:35:45,757
When I look at the model itself after
the acquisition of Cursor, you know,

630
00:35:45,840 --> 00:35:49,397
combining those things around the compute
they have, that looks to me like

631
00:35:49,480 --> 00:35:53,637
it could be an upside surprise. So I
would say that I think that uh you know,

632
00:35:53,720 --> 00:35:57,517
in the IPO, but I think when you look
back 3 years from now, there's a decent

633
00:35:57,600 --> 00:35:59,517
chance that everybody's like, "Oh my

634
00:35:59,600 --> 00:36:02,117
god, that was super obvious." Right?

635
00:36:02,200 --> 00:36:06,397
Even though today all of these things
have risk associated and back to where

636
00:36:06,480 --> 00:36:10,397
we started. I'm not, you know, none
of us are here to pump the IPO at 1.77

637
00:36:10,480 --> 00:36:14,077
trillion. It's really to just break
it down as we do inside our shop and to

638
00:36:14,160 --> 00:36:17,237
say, "What is that distribution
of future probabilities? What's the

639
00:36:17,320 --> 00:36:19,117
probability that it's higher from here?

640
00:36:19,200 --> 00:36:22,997
What's the prob" And I think we're
all pretty AI pilled. And if you're AI

641
00:36:23,080 --> 00:36:26,437
pilled, that means we got to build a lot
more compute than the world thinks and

642
00:36:26,520 --> 00:36:29,797
that these models are going to be a lot
more valuable than people think. You

643
00:36:29,880 --> 00:36:33,677
combine that with their core business.
I don't know another entrepreneur or

644
00:36:33,760 --> 00:36:37,837
another business that's
a better bet on the future,

645
00:36:37,920 --> 00:36:41,677
right, than SpaceX. And so I think
for most institutional investors, it's a

646
00:36:41,760 --> 00:36:44,557
must buy, a must own, a set it and

647
00:36:44,640 --> 00:36:49,637
forget it, right, in order to have a real
bet on both the space and the AI future.

648
00:36:49,720 --> 00:36:51,037
>> From your lips to God's ears.

649
00:36:51,120 --> 00:36:54,717
>> I mean, listen, I I again, I think that
I I I think that you're going to have to

650
00:36:54,800 --> 00:36:58,397
wait, but you know, we had this chart
last week, right, that came out.

651
00:36:58,480 --> 00:37:00,117
Everybody was sending around Twitter,

652
00:37:00,200 --> 00:37:02,717
conveniently timed, and you know, it's

653
00:37:02,800 --> 00:37:05,837
like shows the average max drawdown post

654
00:37:05,920 --> 00:37:08,477
IPO for like 20 companies from Facebook,

655
00:37:08,560 --> 00:37:10,997
Twitter, Alibaba, Shopify is, you know,

656
00:37:11,080 --> 00:37:16,037
over 50%. And so maybe that again will
will will end this section here. You

657
00:37:16,120 --> 00:37:19,037
know, Gavin, you and I've been doing
this a long time. We know it's going to

658
00:37:19,120 --> 00:37:21,877
be bouncy around the IPO. Um,

659
00:37:21,960 --> 00:37:25,357
you know, how do you as a manager try to

660
00:37:25,440 --> 00:37:27,677
try to manage that? Um, do you try to

661
00:37:27,760 --> 00:37:32,677
trade around the IPO? Do you set it kind
of and forget it? I would say from an

662
00:37:32,760 --> 00:37:36,477
Altimeter perspective, what we tend
to do is we take a base position that we

663
00:37:36,560 --> 00:37:38,677
set and forget, right? And then we may

664
00:37:38,760 --> 00:37:41,397
size up or size down depending upon how

665
00:37:41,480 --> 00:37:43,877
the market reacts in, you know, in a

666
00:37:43,960 --> 00:37:46,917
particular moment. Um, but any thoughts

667
00:37:47,000 --> 00:37:51,717
on on this chart or you know,
how how people you guys are

668
00:37:51,800 --> 00:37:54,797
thinking about it in particular.
You obviously own a lot going into it.

669
00:37:54,880 --> 00:37:57,397
>> First agree with absolutely everything
you said and I actually think about it

670
00:37:57,480 --> 00:37:58,717
the same way, set it and forget it.

671
00:37:58,800 --> 00:38:01,957
You've talked about you have ballast,
you move around and you move the ballast

672
00:38:02,040 --> 00:38:05,077
to one side of the ship when you want
to the ship to lean into the wind to go

673
00:38:05,160 --> 00:38:08,157
faster and you move it to the other
side when you don't want the ship to tip

674
00:38:08,240 --> 00:38:09,677
over. I think that's a great analogy.

675
00:38:09,760 --> 00:38:13,237
Think about all important
companies in the portfolio the

676
00:38:13,320 --> 00:38:16,277
same way. So 100% agree. I mean, this

677
00:38:16,360 --> 00:38:20,877
this chart is a bummer. What I would
say is, you know, this data on IPOs, but

678
00:38:20,960 --> 00:38:22,917
what I would just say is this is a

679
00:38:23,000 --> 00:38:24,677
really unprecedented situation.

680
00:38:24,760 --> 00:38:28,037
>> Yes. We've never had an IPO this big.

681
00:38:28,120 --> 00:38:32,717
We've never had an IPO that's going
to go into an index this quickly.

682
00:38:32,800 --> 00:38:36,477
We simply do not know how much selling

683
00:38:36,560 --> 00:38:42,197
there will be from investors.
I would hazard a guess. I mean, I'm I

684
00:38:42,280 --> 00:38:45,197
don't know. But Elon,
I don't think he needs

685
00:38:45,280 --> 00:38:48,997
liquidity and I think he
owns What does he own, Foxy?

686
00:38:49,080 --> 00:38:52,597
>> It's 50% >> 50% of the company.

687
00:38:52,680 --> 00:38:57,557
>> way, he's locked up for 365 days
or 366 days. So, we know he's not selling,

688
00:38:57,640 --> 00:38:59,677
right? >> So, I just
think it's an unprecedented

689
00:38:59,760 --> 00:39:02,357
situation and the right answer >> Yeah.

690
00:39:02,440 --> 00:39:05,917
>> is I don't know what's going to happen
in the short term. And the right answer

691
00:39:06,000 --> 00:39:09,197
that I would just, you know, encourage
every investor making their own decision

692
00:39:09,280 --> 00:39:12,917
is to just think exactly [clears throat]
the way you articulated it. We have

693
00:39:13,000 --> 00:39:15,837
these different levers. We have these
different variables. Think about each

694
00:39:15,920 --> 00:39:19,517
one of them from first principles.
Make your own decision.

695
00:39:19,600 --> 00:39:22,757
Do your own due diligence.
Be thoughtful. But, there are a lot of

696
00:39:22,840 --> 00:39:24,397
variables here and that it is a little

697
00:39:24,480 --> 00:39:27,157
funny to me that uh you know, it was 100

698
00:39:27,240 --> 00:39:31,677
times trailing TTM revenue. Well, after
the deals they signed, I think it's at

699
00:39:31,760 --> 00:39:33,917
39 times. >> That can change fast.

700
00:39:34,000 --> 00:39:36,397
>> So, they added $29 billion in a month.

701
00:39:36,480 --> 00:39:39,317
>> Yes. Now, it's >> [laughter] >> By
the way, have you ever seen that happen?

702
00:39:39,400 --> 00:39:42,677
>> Never. Never.
And you know, it just goes to show

703
00:39:42,760 --> 00:39:46,717
first um Elon is not
only a great engineer.

704
00:39:46,800 --> 00:39:50,117
He and Gwen and the team
are great at business.

705
00:39:50,200 --> 00:39:52,477
>> And Brad, >> They they
they understand what needs to

706
00:39:52,560 --> 00:39:56,997
be done to raise the capital to get
to the next phase. They have a long-term

707
00:39:57,080 --> 00:40:01,277
mission in the business. And so, to me,
again, what we saw in the course of the

708
00:40:01,360 --> 00:40:05,397
last few weeks with cursor, what we saw
with these deals that they cut, I don't

709
00:40:05,480 --> 00:40:09,757
know that any of the mag seven could
have moved that quickly to adjust the

710
00:40:09,840 --> 00:40:14,677
business that they did. It's
exceptionally entrepreneurial at scale,

711
00:40:14,760 --> 00:40:16,637
which we very rarely see in businesses.

712
00:40:16,720 --> 00:40:19,397
Two other things I would
just say >> you a hug, Brad?

713
00:40:19,480 --> 00:40:23,557
>> Two Two other things I I I I would
just say. Number one is people talk a lot

714
00:40:23,640 --> 00:40:27,317
about the total amount of capital being
raised. If you add up the capital here,

715
00:40:27,400 --> 00:40:32,517
right, for Anthropic what they may raise,
what OpenAI may raise, what, you

716
00:40:32,600 --> 00:40:35,997
know, SpaceX may raise,
let's call it $250 billion.

717
00:40:36,080 --> 00:40:40,077
That's 1% of the Mag 7.
Okay, it's 1% of the Mag 7.

718
00:40:40,160 --> 00:40:43,437
>> I Yeah. And And we will
as well. You know, like that

719
00:40:43,520 --> 00:40:47,117
to me is like a bet on the future
that we all believe in. And so, if I said,

720
00:40:47,200 --> 00:40:50,917
"Where are we out of consensus? What
is our variant perception?" We actually

721
00:40:51,000 --> 00:40:53,877
think it's going to be bigger, faster,
and we've thought that for a couple

722
00:40:53,960 --> 00:40:56,757
years. Um so, first, it's only 1% of the

723
00:40:56,840 --> 00:41:01,357
Mag 7 market cap. And then you
referenced it, the amount of selling. Um

724
00:41:01,440 --> 00:41:05,037
I've got a chart we'll post here.
This is, you know, the the dribble share

725
00:41:05,120 --> 00:41:07,957
release for SpaceX shareholders. You

726
00:41:08,040 --> 00:41:12,517
know, so there's not a lot that can
be released um up until after the first

727
00:41:12,600 --> 00:41:16,917
earnings. This We saw this in the Cerebras
IPO. Um there's a version of it

728
00:41:17,000 --> 00:41:21,077
here in this IPO. And so, again, I think
the banks have been thoughtful here,

729
00:41:21,160 --> 00:41:23,437
knowing that this is a very large IPO.

730
00:41:23,520 --> 00:41:26,437
And I'm not saying that won't trade down.
Like there's possibility, you

731
00:41:26,520 --> 00:41:31,077
know, these things trade down.
But again, for me, telescope out, is there

732
00:41:31,160 --> 00:41:34,317
any company better positioned as a bet
on the future? I think what they've

733
00:41:34,400 --> 00:41:36,837
shown over the course
of last 5 weeks, they're

734
00:41:36,920 --> 00:41:39,797
they're they're probably number one.
But let's move on.

735
00:41:39,880 --> 00:41:42,237
>> No, no, can I just say one thing
about the employees? I think another thing

736
00:41:42,320 --> 00:41:45,837
that's unprecedented here
is the employees >> Yeah.

737
00:41:45,920 --> 00:41:50,277
>> and to a large degree the investors
here have had liquidity every 6 months.

738
00:41:50,360 --> 00:41:52,037
>> Exactly. >> the last 10 years.

739
00:41:52,120 --> 00:41:54,877
>> Yes. >> So,
if you're a SpaceX employee or

740
00:41:54,960 --> 00:41:57,397
former employee, and you wanted to sell

741
00:41:57,480 --> 00:42:02,597
you've had whatever that is,
close to 20 chances. And it is a matter

742
00:42:02,680 --> 00:42:04,157
of historical record that large

743
00:42:04,240 --> 00:42:08,477
investors have been able to sell. So

744
00:42:08,560 --> 00:42:11,677
I would think a lot
of the people >> they've

745
00:42:11,760 --> 00:42:14,637
>> chosen to own it. Now, there's a new
valuation and we'll see what they do,

746
00:42:14,720 --> 00:42:17,557
but just this is utterly
unprecedented and we'll see.

747
00:42:17,640 --> 00:42:20,317
>> Yeah, I know. It's It's It's a great
point. We have in fact called these

748
00:42:20,400 --> 00:42:25,157
companies quasi-public. Um you and I
both know that SpaceX and I'd put

749
00:42:25,240 --> 00:42:29,277
Anthropic in in in this category as well,
Databricks in this category. These

750
00:42:29,360 --> 00:42:32,117
things in many ways have been more
liquid over the course of the past 3

751
00:42:32,200 --> 00:42:36,077
years than some public biotech companies
we know. Right? And so there's a

752
00:42:36,160 --> 00:42:38,957
continuum of liquidity here. We We treat

753
00:42:39,040 --> 00:42:43,197
it as a binary, private versus public,
but it's really about this continuum.

754
00:42:43,280 --> 00:42:45,197
You know, let's keep going on models.

755
00:42:45,280 --> 00:42:47,517
You know, um Anthropic launched Fable 5,

756
00:42:47,600 --> 00:42:49,877
which you referenced um yesterday, which

757
00:42:49,960 --> 00:42:52,237
is basically Mythos um with some

758
00:42:52,320 --> 00:42:54,997
classifiers and safeguards um around

759
00:42:55,080 --> 00:42:59,397
cyber and biology, chemistry,
um and distillation. When those things

760
00:42:59,480 --> 00:43:01,277
get triggered, it fails back [snorts] to

761
00:43:01,360 --> 00:43:06,517
Opus 4.8. Um you know, there was a
Copart tweet about this yesterday. He

762
00:43:06,600 --> 00:43:09,597
said, you know, it sold on all
the benchmarks, but what really makes it

763
00:43:09,680 --> 00:43:13,477
special is long-running tasks. Okay? You

764
00:43:13,560 --> 00:43:15,397
retweeted our good friend, you know,

765
00:43:15,480 --> 00:43:18,997
Noam Brown. Um you know, ChatGPT 5.5

766
00:43:19,080 --> 00:43:21,677
also exhibited these capabilities.

767
00:43:21,760 --> 00:43:24,397
Um you know, it it led Noam, right, to

768
00:43:24,480 --> 00:43:27,117
suggest that it's not very relevant to

769
00:43:27,200 --> 00:43:29,077
do these snapshot benchmarks anymore.

770
00:43:29,160 --> 00:43:31,957
Yeah, like the x-axis has to be time or

771
00:43:32,040 --> 00:43:34,517
tokens or compute because we can solve

772
00:43:34,600 --> 00:43:36,797
most problems now if we just let these

773
00:43:36,880 --> 00:43:39,597
frontier models for a very long uh point

774
00:43:39,680 --> 00:43:42,877
in time. So, Gavin, what is this new

775
00:43:42,960 --> 00:43:46,357
class of model, right, Fable Fable 5,

776
00:43:46,440 --> 00:43:49,717
ChatGPT 5.5? What does it mean for the

777
00:43:49,800 --> 00:43:51,597
race in superintelligence? Who's up?

778
00:43:51,680 --> 00:43:54,877
Who's down? Who's still on the frontier?

779
00:43:54,960 --> 00:43:59,317
Um give us your thoughts. >> I mean,
it's hard to say that Anthropic's not up.

780
00:43:59,400 --> 00:44:03,117
>> Yeah. >> Like after
the revenue numbers they've put up,

781
00:44:03,200 --> 00:44:05,517
after the Fable 5 release, and Mythos is

782
00:44:05,600 --> 00:44:07,317
evidently even better.

783
00:44:07,400 --> 00:44:10,397
But I just think that Gnome Brown post

784
00:44:10,480 --> 00:44:15,037
from yesterday,
polynomial, is so profound.

785
00:44:15,120 --> 00:44:19,197
And just the idea that we do not
know how smart these models are.

786
00:44:19,280 --> 00:44:22,277
And we made >> Say more about that.
Why don't we know how smart they are?

787
00:44:22,360 --> 00:44:25,957
>> Because nobody has
run Mythos for a year

788
00:44:26,040 --> 00:44:28,997
continuously. And we may never know how

789
00:44:29,080 --> 00:44:31,597
smart each generation of models actually

790
00:44:31,680 --> 00:44:36,197
is or was, but because we don't have
time to appropriately evaluate their

791
00:44:36,280 --> 00:44:39,557
intelligence before the next model
comes out. I mean, this is a profound

792
00:44:39,640 --> 00:44:42,757
statement. And just just imagine, okay?

793
00:44:42,840 --> 00:44:45,277
So, I always say like
when you think about FSD,

794
00:44:45,360 --> 00:44:48,157
just imagine a human being who never

795
00:44:48,240 --> 00:44:50,957
gets distracted, never gets tired, never

796
00:44:51,040 --> 00:44:55,357
talks on the phone in the car, never
drinks and drives, never yells at their

797
00:44:55,440 --> 00:44:59,557
kids, never has to go to the backseat
to give their baby a bottle.

798
00:44:59,640 --> 00:45:03,117
And like of course you would think that
over time that is superior to humans who

799
00:45:03,200 --> 00:45:06,637
are distracted. I don't know
how long How long can you

800
00:45:06,720 --> 00:45:08,597
think deeply about one topic, Brad?

801
00:45:08,680 --> 00:45:11,277
>> What do you Give me an hour. Give
me an [laughter] hour. Give me an hour.

802
00:45:11,360 --> 00:45:15,157
>> A BIT. THAT MAKES me
feel terrible cuz I think

803
00:45:15,240 --> 00:45:18,717
I can think deeply about one topic
continuously before having a stray

804
00:45:18,800 --> 00:45:22,397
thought enter my mind for like
maybe 5 minutes. Then I can

805
00:45:22,480 --> 00:45:26,517
come back to that.
Imagine if Albert Einstein

806
00:45:26,600 --> 00:45:29,837
had been able instead of,
you know, and maybe that maybe I

807
00:45:29,920 --> 00:45:34,037
maybe he could think for 3 hours at a
time. Clearly an exceptional intellect.

808
00:45:34,120 --> 00:45:38,557
But imagine Albert Einstein had just
thought about fundamental physics

809
00:45:38,640 --> 00:45:41,997
24 hours a day. He doesn't
have to eat, he doesn't have

810
00:45:42,080 --> 00:45:46,077
to sleep, he doesn't have to relax,
he doesn't drink, >> never gets old,

811
00:45:46,160 --> 00:45:48,957
>> never gets old, >> never
has diminished intelligence,

812
00:45:49,040 --> 00:45:53,037
>> and he thought for 1 year.
I mean, we might already, you know,

813
00:45:53,120 --> 00:45:55,317
>> have solved a lot
of these intractable problems.

814
00:45:55,400 --> 00:45:57,997
>> So, I just think
that's an extraordinary

815
00:45:58,080 --> 00:46:01,037
thought. And just my takeaway was

816
00:46:01,120 --> 00:46:05,317
however bullish I
was on compute before then,

817
00:46:05,400 --> 00:46:09,037
I'm just a lot more bullish. >> Right.
Right. Right. So, so, so that is

818
00:46:09,120 --> 00:46:11,397
a, you know, we saw when

819
00:46:11,480 --> 00:46:14,317
that was probably what really unlocked

820
00:46:14,400 --> 00:46:17,117
Opus 4.6. It was the first really

821
00:46:17,200 --> 00:46:22,277
long-running model that could maintain
that context, maintain that memory, um

822
00:46:22,360 --> 00:46:26,357
solve some of these longer-running
problems, right? For us, the signal was

823
00:46:26,440 --> 00:46:29,037
in January. We knew we felt like that

824
00:46:29,120 --> 00:46:33,757
was a big moment, but then when you
started to see the revenue go up, we

825
00:46:33,840 --> 00:46:37,877
knew that lots of people were voting
independently, that that was a profound

826
00:46:37,960 --> 00:46:41,677
moment that they became much,
much more useful. So,

827
00:46:41,760 --> 00:46:45,837
but one of the things that the consensus
going into this year, right? So, the big

828
00:46:45,920 --> 00:46:51,157
question going into this year was was
the AI revenue going to show up? Were we

829
00:46:51,240 --> 00:46:55,317
going to get to these thresholds of
intelligence that caused enterprises and

830
00:46:55,400 --> 00:46:57,757
consumers to use them more? And I think

831
00:46:57,840 --> 00:46:59,477
the consensus at the time, at least on

832
00:46:59,560 --> 00:47:02,477
this podcast, um the the the debate with

833
00:47:02,560 --> 00:47:05,237
with my with with with Bill was the

834
00:47:05,320 --> 00:47:09,797
open-source models, cheap tokens,
were catching up on the frontier, that

835
00:47:09,880 --> 00:47:11,637
perhaps these models were beginning to

836
00:47:11,720 --> 00:47:13,997
asymptote, um that people wouldn't

837
00:47:14,080 --> 00:47:16,877
really pay for premium tokens,

838
00:47:16,960 --> 00:47:19,317
and it seems to me that the evidence on

839
00:47:19,400 --> 00:47:21,637
the field, 6 months into the year, is

840
00:47:21,720 --> 00:47:26,997
just the opposite, right? That frontier
tokens are capturing the vast majority

841
00:47:27,080 --> 00:47:30,957
of all the revenues,
and that in fact, if you believe in the

842
00:47:31,040 --> 00:47:34,677
long-running capabilities and more
compute allows you to do that, they may

843
00:47:34,760 --> 00:47:37,117
actually be extending their lead, right?

844
00:47:37,200 --> 00:47:40,597
On some of these models that were built
on distillation. So, I just open it up

845
00:47:40,680 --> 00:47:42,397
to anyone around the table, what are

846
00:47:42,480 --> 00:47:45,477
your thoughts on whether or not, you

847
00:47:45,560 --> 00:47:48,477
know, have we challenged this thesis

848
00:47:48,560 --> 00:47:52,597
that cheap open-source tokens are going
to always, you know, close the gap on

849
00:47:52,680 --> 00:47:55,517
these frontier models,
or are they extending their leads?

850
00:47:55,600 --> 00:48:00,997
>> I I think this debate, like this same
debate has existed since the beginning

851
00:48:01,080 --> 00:48:05,157
of since we started training these
models to begin with, which was hey, we're

852
00:48:05,240 --> 00:48:09,597
always kind of three, six months behind
the frontier. But empirically, like you

853
00:48:09,680 --> 00:48:13,397
can just see all of the revenue has
actually just accrued at the frontier.

854
00:48:13,480 --> 00:48:14,997
And that I think that's because every

855
00:48:15,080 --> 00:48:21,477
time we release the frontier,
a whole new like slew of use cases

856
00:48:21,560 --> 00:48:24,517
>> Right. >> that that
previously we could have never

857
00:48:24,600 --> 00:48:28,637
tackled before, like coding.
Um but also just, you know, you know,

858
00:48:28,720 --> 00:48:33,037
we've we've just been locked at our desk
for the last last day just, you know,

859
00:48:33,120 --> 00:48:36,797
hammering Claude because, you know, it's
just fascinating the things that now we

860
00:48:36,880 --> 00:48:40,837
can do with fable five that we could
just couldn't do with opus 48 just a day

861
00:48:40,920 --> 00:48:42,517
before. >> So what are
some of those things, man?

862
00:48:42,600 --> 00:48:45,117
I'm curious. >> So So I think
it's really really good at

863
00:48:45,200 --> 00:48:47,757
multi-agent orchestration. So they they

864
00:48:47,840 --> 00:48:50,357
Anthropic released a um a blog post

865
00:48:50,440 --> 00:48:55,237
about like different uh agent um six
different agent like orchestration

866
00:48:55,320 --> 00:48:58,797
patterns that, you know, they've they've
talked about. But really like once you

867
00:48:58,880 --> 00:49:01,837
start being able to manage all these

868
00:49:01,920 --> 00:49:06,797
agents, the harness and the model itself
is being arled with one another, they're

869
00:49:06,880 --> 00:49:09,357
actually being, you know,
fused closer and closer

870
00:49:09,440 --> 00:49:13,637
together, but the model can understand
the, you know, the extent of your work.

871
00:49:13,720 --> 00:49:15,917
So, you know, one of the things, for

872
00:49:16,000 --> 00:49:21,077
instance, is um I just threw
in like seven of our models

873
00:49:21,160 --> 00:49:22,717
and just said, "Okay, like I want to

874
00:49:22,800 --> 00:49:26,397
create a master view of like my beliefs

875
00:49:26,480 --> 00:49:31,397
given all of these assumptions of all
these companies, TSMC capacity, like and

876
00:49:31,480 --> 00:49:35,757
then and then produce me a report on all
this stuff." And you know, the the model

877
00:49:35,840 --> 00:49:39,117
is able to reason through all
of our assumptions. Like actually, if you

878
00:49:39,200 --> 00:49:42,397
believe this >> Right.
What are the contradictions exactly?

879
00:49:42,480 --> 00:49:46,477
>> Yeah, it's it was fascinating. And and
and you know, before we'd never do that,

880
00:49:46,560 --> 00:49:50,277
but but now, you know, I think
we're just step one into multi-agent

881
00:49:50,360 --> 00:49:54,197
orchestration. We're going to do
this even further and that's one example.

882
00:49:54,280 --> 00:49:56,157
I've also dumped all my all my notes

883
00:49:56,240 --> 00:49:58,877
into it and it's reason across all my

884
00:49:58,960 --> 00:50:03,197
notes from the last 3 years and said,
you know, here are some of your ideas

885
00:50:03,280 --> 00:50:07,197
that were consistent. Here are like, you
know, the sources that were actually the

886
00:50:07,280 --> 00:50:11,717
highest signal to what actually played
out, you know, and then it is actually

887
00:50:11,800 --> 00:50:16,117
just super fascinating what you could
do and we've just blown through our blown

888
00:50:16,200 --> 00:50:18,477
through our limits. >> mean it's
it's it's unlocking all this.

889
00:50:18,560 --> 00:50:21,757
I mean like they gave examples yesterday
and the release Anthropic did, you know,

890
00:50:21,840 --> 00:50:24,077
50 million line Ruby code base at Stripe

891
00:50:24,160 --> 00:50:29,197
that was, you know, refactored in a day
versus many weeks with many people. You

892
00:50:29,280 --> 00:50:32,677
think about where this is impacting
biology and life sciences just across

893
00:50:32,760 --> 00:50:37,157
the spectrum. Um and to me
it really gets back to this

894
00:50:37,240 --> 00:50:39,877
fundamental point. Number one,
if you believe this to be true about

895
00:50:39,960 --> 00:50:44,117
long-running agents, then we're going
to produce and consume more tokens in the

896
00:50:44,200 --> 00:50:46,677
future as far as the eye can see. So the

897
00:50:46,760 --> 00:50:51,237
world this gets me back to, you know,
terrafab and space orbital and all this

898
00:50:51,320 --> 00:50:55,357
because we we we may in fact unlock real

899
00:50:55,440 --> 00:50:58,997
thresholds of intelligence, but we're
going to have to let these horses run

900
00:50:59,080 --> 00:51:00,877
for a long time in order to get there.

901
00:51:00,960 --> 00:51:03,757
Yeah, I would just say two
things I two things can be true.

902
00:51:03,840 --> 00:51:07,677
>> Mhm. >> The majority
of economic value may

903
00:51:07,760 --> 00:51:11,277
continue to accrue to the frontier and
man has it ever accrued to the frontier

904
00:51:11,360 --> 00:51:16,117
thus far and for sure the first 6 months
of this year, but the majority of tokens

905
00:51:16,200 --> 00:51:17,797
consumed in the world may be open source.

906
00:51:17,880 --> 00:51:18,797
>> And they are >> today.

907
00:51:18,880 --> 00:51:21,397
>> Yes. I and I think that this current

908
00:51:21,480 --> 00:51:24,997
state is likely to persist. Harvey had a

909
00:51:25,080 --> 00:51:28,357
great blog post that they put out on X

910
00:51:28,440 --> 00:51:33,477
and they used and it's just amazing how
everything gets out out of date like in

911
00:51:33,560 --> 00:51:37,077
5 days, you know. But they
used their own proprietary

912
00:51:37,160 --> 00:51:40,277
legal data to do reinforcement learning

913
00:51:40,360 --> 00:51:44,677
and supervised fine-tuning with
Fireworks on an open source model

914
00:51:44,760 --> 00:51:47,677
and then And used a router and a router
being something that picks which model

915
00:51:47,760 --> 00:51:51,837
you send which query to, and which model
you use to check which model. And they

916
00:51:51,920 --> 00:51:57,157
got better outcomes than Opus 4
either 4.7 or 4.8 at a lower cost.

917
00:51:57,240 --> 00:52:01,357
>> Yes. >> And I think that is
the future. And the reality is

918
00:52:01,440 --> 00:52:04,557
they were still consuming a lot of Opus,
but a majority of the tokens they were

919
00:52:04,640 --> 00:52:08,237
processing probably were
in their own open-source models.

920
00:52:08,320 --> 00:52:11,117
>> We heard the same thing.
We did a We did um

921
00:52:11,200 --> 00:52:15,917
We did an enterprise survey that we'll
post of 300 companies how which ones

922
00:52:16,000 --> 00:52:19,957
were optimizing, so these are folks who
are kind of looking at model routing and

923
00:52:20,040 --> 00:52:22,637
saying we're going to send certain
tokens over here, which ones are

924
00:52:22,720 --> 00:52:26,037
thinking about optimizing, which ones
aren't optimizing yet, and then what is

925
00:52:26,120 --> 00:52:30,317
their expected use of frontier model
tokens, right? And they're all expecting

926
00:52:30,400 --> 00:52:33,757
to consume a lot more even though
they're already in the process of

927
00:52:33,840 --> 00:52:37,517
optimizing. Think of it in the in in the
context of JP Morgan. If they're doing

928
00:52:37,600 --> 00:52:42,197
some back of the house stuff, right,
on customer service or whatever, they may

929
00:52:42,280 --> 00:52:46,117
very well use an open-source model.
Now, I think they're loath to use Chinese

930
00:52:46,200 --> 00:52:50,397
open-source models, so they're waiting
on kind of US open-source models to, you

931
00:52:50,480 --> 00:52:54,317
know, be able to really deliver the bang
that they need, but my hunch is for

932
00:52:54,400 --> 00:52:57,757
these enterprises, a lot of that back
of the house stuff will get rooted there.

933
00:52:57,840 --> 00:53:00,797
That will probably be a majority
of the tokens, but I think the really

934
00:53:00,880 --> 00:53:04,277
high-value stuff, you know, coding
as an example, they don't want to write

935
00:53:04,360 --> 00:53:07,797
second-tier code. I think the vast
majority of that will continue to be on

936
00:53:07,880 --> 00:53:11,117
the frontier. >> Um >> You don't
need Albert Einstein to book

937
00:53:11,200 --> 00:53:14,997
you a trip. You don't need
Albert Einstein to do KYC.

938
00:53:15,080 --> 00:53:18,717
>> But but but this is the debate we had
at literally at this table two years ago.

939
00:53:18,800 --> 00:53:23,597
However, if you just look at the revenue
curves, right? What bill What what folks

940
00:53:23,680 --> 00:53:25,557
concluded when they said that, they

941
00:53:25,640 --> 00:53:30,717
said, "Therefore, the frontier models
will not accrue most of the revenue."

942
00:53:30,800 --> 00:53:32,717
And what we're seeing
right now, it's 90% of the

943
00:53:32,800 --> 00:53:34,237
>> That has been
decisively wrong. Probably

944
00:53:34,320 --> 00:53:38,237
more than 90%, and it may continue to be
decisively wrong. Frontier might be 90%

945
00:53:38,320 --> 00:53:42,557
of the economic value.
Open-source >> might be 80% of tokens.

946
00:53:42,640 --> 00:53:45,437
Something that I think is very
important on open source

947
00:53:45,520 --> 00:53:47,917
is that you know, I think there's this

948
00:53:48,000 --> 00:53:50,157
belief that it's bearish for AI.

949
00:53:50,240 --> 00:53:53,917
It's actually it may be very bearish
for the frontier models. There's that bear

950
00:53:54,000 --> 00:53:58,597
case you talked about. It's actually
really bullish for compute and hardware

951
00:53:58,680 --> 00:54:02,677
because if the frontier models are
capturing less of the margin, then

952
00:54:02,760 --> 00:54:04,437
you're going to spend more on compute.

953
00:54:04,520 --> 00:54:08,517
So, the better open source does,
the better it is for compute providers.

954
00:54:08,600 --> 00:54:12,277
>> And I yeah, I I will
say it there is a very

955
00:54:12,360 --> 00:54:16,477
I would say between um spending time
in the heart of like the West, Silicon

956
00:54:16,560 --> 00:54:20,277
Valley, and also spending time
in Asia, there is like a very

957
00:54:20,360 --> 00:54:24,317
big um like a deep-seated
belief in one versus

958
00:54:24,400 --> 00:54:27,477
the other, which is like if you spend
a lot of time here, it's like all closed

959
00:54:27,560 --> 00:54:32,397
source, cloud, every all traffic
is going to go, you know, by way of this

960
00:54:32,480 --> 00:54:36,197
direction. And then you
spend time in Asia, you know,

961
00:54:36,280 --> 00:54:40,237
the the overwhelming belief is that
we're going to find the right model to

962
00:54:40,320 --> 00:54:43,117
the right workload,
and we're not going to overspend.

963
00:54:43,200 --> 00:54:46,157
>> Right. >> And I think,
you know, I would say I would say

964
00:54:46,240 --> 00:54:51,877
the next year is probably going to be
the most indicative of which way this

965
00:54:51,960 --> 00:54:55,237
falls um because

966
00:54:55,320 --> 00:54:59,517
I think I think the reason why uh
closed source models have captured so

967
00:54:59,600 --> 00:55:04,157
much of the value is because um
the models actually get the intention and

968
00:55:04,240 --> 00:55:07,237
actually carry through the work. And
this is the first year where we actually

969
00:55:07,320 --> 00:55:10,557
had agents that actually carried out

970
00:55:10,640 --> 00:55:15,437
user intention from just answering
a chatbot request to actually producing

971
00:55:15,520 --> 00:55:18,917
useful work. >> Right.
>> Um now the the the level of this

972
00:55:19,000 --> 00:55:22,917
intelligent has scaled so rapidly,
and we continue to push against like the

973
00:55:23,000 --> 00:55:27,077
most economically valuable tasks,
which are coding and finance and all these

974
00:55:27,160 --> 00:55:29,357
like knowledge work tasks. But like for

975
00:55:29,440 --> 00:55:32,477
the long tail of tasks, if open source

976
00:55:32,560 --> 00:55:37,637
continues to maintain a 6-month lag,
we might actually see a lot more open

977
00:55:37,720 --> 00:55:42,397
source used for you know,
our everyday tasks that we might actually

978
00:55:42,480 --> 00:55:43,877
>> basically Jensen's argument, right?

979
00:55:43,960 --> 00:55:46,597
Jensen's argument is you're
going to have model routing

980
00:55:46,680 --> 00:55:50,837
and we're just in a moment in time where
the frontier models gain the advantage

981
00:55:50,920 --> 00:55:54,397
can do long-running tasks that open
source models couldn't do it very well

982
00:55:54,480 --> 00:55:57,677
and so they're accruing all of the value,
but as soon as the open source

983
00:55:57,760 --> 00:56:01,677
models can do the long-running tasks
as well, which is not far away that they

984
00:56:01,760 --> 00:56:04,477
too will grab a bunch
a bunch of this revenue.

985
00:56:04,560 --> 00:56:06,717
>> Are you about to burst into reflection?

986
00:56:06,800 --> 00:56:08,957
>> I'm not. >> Okay. No,
no, no, no are we, but I'm

987
00:56:09,040 --> 00:56:12,237
very impressed by Misha and and the team

988
00:56:12,320 --> 00:56:14,237
and what they're doing. I very much want

989
00:56:14,320 --> 00:56:17,717
a frontier open source US lab to win. We

990
00:56:17,800 --> 00:56:21,757
know that, you know, I heard you say
recently and I believe it to be true

991
00:56:21,840 --> 00:56:25,557
Nvidia any day that they really wanted to,
right? They already have some great

992
00:56:25,640 --> 00:56:29,197
open source models. They could
absolutely build a frontier open source

993
00:56:29,280 --> 00:56:33,317
model whenever they chose to do it and so
it's not a question in my mind as to

994
00:56:33,400 --> 00:56:36,917
whether or not the US is going to have
a frontier open source model. It's just a

995
00:56:37,000 --> 00:56:42,317
question about timing and then like
at that point in time is that you know,

996
00:56:42,400 --> 00:56:46,157
let's say let's assume they get
these long-running capabilities.

997
00:56:46,240 --> 00:56:50,597
Have the frontier labs now achieved
something yet again that allows them to

998
00:56:50,680 --> 00:56:53,437
keep keep the the stranglehold
on the revenues?

999
00:56:53,520 --> 00:56:56,037
>> Yeah, and I just think it's if you're

1000
00:56:56,120 --> 00:57:01,437
Wow, that's a cute ASIC you've built
there. That is so cute. How would you

1001
00:57:01,520 --> 00:57:04,517
like open source to join the frontier?

1002
00:57:04,600 --> 00:57:06,077
>> Right. >> How would you
like that? How do you like

1003
00:57:06,160 --> 00:57:10,437
them apples? So, I mean I'm not sure
that's the explicit calculation, but I

1004
00:57:10,520 --> 00:57:13,757
do think Jensen >> Say more. Just double
click on that for everybody at home.

1005
00:57:13,840 --> 00:57:15,757
>> Yeah. >> If you were
if they were to put an open

1006
00:57:15,840 --> 00:57:19,597
source model out there, how does
that impact the ASIC landscape?

1007
00:57:19,680 --> 00:57:23,677
>> Well, you might not have the revenue

1008
00:57:23,760 --> 00:57:26,797
to fund [laughter] to fund
that the revenue of the margins

1009
00:57:26,880 --> 00:57:31,117
to fund that ASIC. And I do
think Nvidia is highly likely

1010
00:57:31,200 --> 00:57:35,797
to be the world's dominant provider
of open source AI. And I do think Jensen

1011
00:57:35,880 --> 00:57:40,437
will bring open source, you know,
right now it's whatever, 6

1012
00:57:40,520 --> 00:57:42,237
months behind the frontier. >> Yeah.

1013
00:57:42,320 --> 00:57:47,237
>> We might see it creep
closer and closer and closer.

1014
00:57:47,320 --> 00:57:50,557
And I do think Jensen has a big business
decision. I see this, you know, chart

1015
00:57:50,640 --> 00:57:53,837
here, so let's, you know,
chop it up about Nvidia, as you say.

1016
00:57:53,920 --> 00:57:59,197
But if all of his customers are
going to compete with him, >> Yes.

1017
00:57:59,280 --> 00:58:02,557
>> then why not compete
with his customers? And

1018
00:58:02,640 --> 00:58:04,317
we have all these neo clouds. >> Right.

1019
00:58:04,400 --> 00:58:08,077
>> So that's a cloud computing business
that can compete with all these cloud

1020
00:58:08,160 --> 00:58:11,277
computing businesses.
He has his own models that are really,

1021
00:58:11,360 --> 00:58:14,957
really good. Nematron 3 or 3.1
was actually really, really cool from a

1022
00:58:15,040 --> 00:58:18,477
computer efficiency perspective.
And he's always careful to release small

1023
00:58:18,560 --> 00:58:22,237
models so as to not tread
on Anthropic and OpenAI, >> Right.

1024
00:58:22,320 --> 00:58:26,597
>> Google's toes. But I do think
that is a choice he is making.

1025
00:58:26,680 --> 00:58:31,117
And just, you know, at if if
the economics change, >> Right.

1026
00:58:31,200 --> 00:58:33,077
>> I think Nvidia can
join the frontier and

1027
00:58:33,160 --> 00:58:37,077
become one of the world's largest cloud
computing companies much faster than

1028
00:58:37,160 --> 00:58:41,397
people think. >> Interesting. Interesting.
Clark, walk us through this this chart.

1029
00:58:41,480 --> 00:58:43,637
>> Yeah, so so I think
one of the takeaways

1030
00:58:43,720 --> 00:58:47,077
from spending time in Taiwan was there

1031
00:58:47,160 --> 00:58:48,877
there is certainly a lot of excitement

1032
00:58:48,960 --> 00:58:51,957
around the next wave of ASICs.

1033
00:58:52,040 --> 00:58:57,317
Um, but I think I think it's like
a very clear moment now where Nvidia

1034
00:58:57,400 --> 00:59:01,197
it used to be an argument of Nvidia
versus ASICs one or the other and, you

1035
00:59:01,280 --> 00:59:03,277
know, total domination one or the other.

1036
00:59:03,360 --> 00:59:07,997
Now I think it increasingly every year
every every one assumed that Nvidia was

1037
00:59:08,080 --> 00:59:12,877
going to lose share dramatically on a
revenue scale, on a gigawatt scale, on a

1038
00:59:12,960 --> 00:59:16,517
unit scale. And actually, if you
actually look at the last few years, you

1039
00:59:16,600 --> 00:59:21,797
know, they've actually maintained
their share very, very handsomely.

1040
00:59:21,880 --> 00:59:26,397
Um, actually, um, if you accounted
for the fact that Anthropic

1041
00:59:26,480 --> 00:59:28,317
was not really using Nvidia. They

1042
00:59:28,400 --> 00:59:31,557
probably actually gain share against

1043
00:59:31,640 --> 00:59:35,437
if not for in 25 26. So, I think I think

1044
00:59:35,520 --> 00:59:39,517
what was very interesting
though was a new class of

1045
00:59:39,600 --> 00:59:43,277
accelerators or ASICs. MediaTek with their

1046
00:59:43,360 --> 00:59:48,557
with their new V8T versus,
you know, Broadcom's V8I for

1047
00:59:48,640 --> 00:59:50,237
TPUs

1048
00:59:50,320 --> 00:59:52,957
actually was a big topic of discussion.

1049
00:59:53,040 --> 00:59:56,477
And, you know, I I think for ASICs

1050
00:59:56,560 --> 00:59:58,357
the argument now is that more and more

1051
00:59:58,440 --> 01:00:01,317
will look custom to the actual workload

1052
01:00:01,400 --> 01:00:03,597
and that is like one vector that people

1053
01:00:03,680 --> 01:00:06,477
are moving in versus Nvidia now is

1054
01:00:06,560 --> 01:00:09,557
has kind of shown itself as the the

1055
01:00:09,640 --> 01:00:12,677
predominant provider of compute to

1056
01:00:12,760 --> 01:00:17,357
a lot of the world and for,
you know, internal internal workloads,

1057
01:00:17,440 --> 01:00:21,117
perhaps they will go more and more
custom and more and more down the stack.

1058
01:00:21,200 --> 01:00:25,997
And I I remember just, you know, 1 year
ago when it was kind of a Broadcom or

1059
01:00:26,080 --> 01:00:27,837
Nvidia battle. It seems there's a lot

1060
01:00:27,920 --> 01:00:30,557
more nuance now to, you know, what type

1061
01:00:30,640 --> 01:00:33,197
of accelerators will fit which workloads

1062
01:00:33,280 --> 01:00:37,837
and fit which customers and fit
which business models. Um

1063
01:00:37,920 --> 01:00:43,717
and yeah, I thought I thought
that was a a new topic.

1064
01:00:43,800 --> 01:00:45,757
>> It's actually >> New
realization though, I think we all

1065
01:00:45,840 --> 01:00:47,957
kind of shared this view for a long time.

1066
01:00:48,040 --> 01:00:50,957
>> Yeah, I was just shocked. I mean, I'm
I'm out here. I did a board meeting with

1067
01:00:51,040 --> 01:00:54,397
one of our companies and just,
you know, their biggest one

1068
01:00:54,480 --> 01:00:59,157
thing they emphasized is we
thought the world would be have be

1069
01:00:59,240 --> 01:01:01,397
consuming less Nvidia than it is and if

1070
01:01:01,480 --> 01:01:06,237
anything, Nvidia is accelerating and they
just continue to out execute their

1071
01:01:06,320 --> 01:01:10,277
competitors. And I think a lot
of people are indexing to this

1072
01:01:10,360 --> 01:01:14,237
OpenAI gigawatt and you
know, Nvidia has 10.

1073
01:01:14,320 --> 01:01:17,277
Broadcom has 10. Um

1074
01:01:17,360 --> 01:01:21,277
who has six? AMD AMD has
six and they have warrants.

1075
01:01:21,360 --> 01:01:25,797
And then Cerebras has
our shared portfolio company

1076
01:01:25,880 --> 01:01:29,877
has a gigawatt.
And I just that is what's on paper.

1077
01:01:29,960 --> 01:01:33,317
>> Right. >> What actually
gets deployed, let's see.

1078
01:01:33,400 --> 01:01:37,477
I will be very surprised if you know
that 10 out of 27, what's that math?

1079
01:01:37,560 --> 01:01:40,717
Let's see who's best at math.
What percentage market share is that?

1080
01:01:40,800 --> 01:01:44,077
>> 30% yeah. >> Yeah.
I'll be very surprised if that is

1081
01:01:44,160 --> 01:01:48,637
where they land. I think that
is an extremely unlikely outcome.

1082
01:01:48,720 --> 01:01:53,037
And especially as long as we're in a
watt constrained world, if you can get

1083
01:01:53,120 --> 01:01:54,877
more tokens per watt, which is literally

1084
01:01:54,960 --> 01:01:57,397
revenue with Nvidia

1085
01:01:57,480 --> 01:02:03,157
than a lot of alternatives just if you
build your factory with another chip

1086
01:02:03,240 --> 01:02:05,637
you may save some money, but you're
going to have less revenue and the

1087
01:02:05,720 --> 01:02:09,037
margins may be lower and that's a point
that Jensen keeps hammering and I think

1088
01:02:09,120 --> 01:02:12,997
is a really important.
And by the way, credit where credit is due

1089
01:02:13,080 --> 01:02:16,997
the most important the most one of the
most surprising things to me in this

1090
01:02:17,080 --> 01:02:20,997
ASIC landscape >> I'd say
Meta and Microsoft have been

1091
01:02:21,080 --> 01:02:22,557
probably disappointing. >> Yes.

1092
01:02:22,640 --> 01:02:23,757
>> You know who made a good ASIC?

1093
01:02:23,840 --> 01:02:24,997
>> Yes. >> Well, I know you know.

1094
01:02:25,080 --> 01:02:26,677
>> Yes. >> Jalapeno >> Yeah, exactly.

1095
01:02:26,760 --> 01:02:28,917
>> from Open AI. They made a great chip.

1096
01:02:29,000 --> 01:02:31,677
>> Yes. >> Now,
unfortunately needs to run at a

1097
01:02:31,760 --> 01:02:34,997
much lower temperature than the Nvidia
GPUs, which means you need to spend more

1098
01:02:35,080 --> 01:02:37,677
money on cooling and that consumes
more power. They made a great chip.

1099
01:02:37,760 --> 01:02:39,517
>> Well, we can I mean
I think the question

1100
01:02:39,600 --> 01:02:42,197
there and the question for everybody is
going to be is that the highest and best

1101
01:02:42,280 --> 01:02:45,117
use of your time? Right? Like I you

1102
01:02:45,200 --> 01:02:49,317
know, I tend to think that the frontier
companies like there's this belief that

1103
01:02:49,400 --> 01:02:52,757
they got to be vertical vertically
integrated. But if you believe like I do

1104
01:02:52,840 --> 01:02:56,037
that the race to super intelligence
particularly as we get these recursive

1105
01:02:56,120 --> 01:02:58,517
loops working may be over in the next

1106
01:02:58,600 --> 01:03:03,677
two to three years, then I think focus
focus focus focus. You exist to build

1107
01:03:03,760 --> 01:03:07,117
the best intelligence in the world and
to deliver the best intelligence in the

1108
01:03:07,200 --> 01:03:10,717
world and you that means you have
to have all the revenue. Because if you

1109
01:03:10,800 --> 01:03:13,717
want to build out the compute that's
going to be required to continue to push

1110
01:03:13,800 --> 01:03:16,357
the frontier, you have to have
the revenue in order to support it. So I

1111
01:03:16,440 --> 01:03:21,317
think you know, subject to the focus
question, I think they certainly did.

1112
01:03:21,400 --> 01:03:24,837
This all brings me back to kind
of a reality check, though.

1113
01:03:24,920 --> 01:03:28,357
Um you know, we just got done talking
about test time compute, inference time

1114
01:03:28,440 --> 01:03:32,077
compute, long-running agents. This is
really the thing that's unlocked the

1115
01:03:32,160 --> 01:03:34,557
revenue this year. Um it all pushes us

1116
01:03:34,640 --> 01:03:39,197
in the direction of more CapEx.
Google just raised $80 billion,

1117
01:03:39,280 --> 01:03:41,437
right? We've now taken the Mag 5 or Mag

1118
01:03:41,520 --> 01:03:44,037
7 free cash flow, you know, down

1119
01:03:44,120 --> 01:03:47,077
dramatically, 80% um from just a few

1120
01:03:47,160 --> 01:03:49,917
years ago. Um and Morgan Stanley, you've

1121
01:03:50,000 --> 01:03:51,557
got this chart in front of you, up to

1122
01:03:51,640 --> 01:03:56,237
their 2027 CapEx forecast from 950

1123
01:03:56,320 --> 01:03:59,757
billion to 1.1 trillion. I mean, we were
talking about this with Jensen. That was

1124
01:03:59,840 --> 01:04:03,797
his forecast 2 years ago. You know,
obviously, this doesn't even include

1125
01:04:03,880 --> 01:04:09,277
SpaceX, CoreWeave, etc. So, I think
the number on 2027 is likely closer to 1.5

1126
01:04:09,360 --> 01:04:12,677
trillion. And if we
compare this to the total

1127
01:04:12,760 --> 01:04:16,357
incremental inference revenue,
so the thing that the market gets worried

1128
01:04:16,440 --> 01:04:18,437
about, you know, back to my Sam Altman

1129
01:04:18,520 --> 01:04:20,797
podcast, you know, in October of last

1130
01:04:20,880 --> 01:04:23,597
year, can we really afford to spend 1.5

1131
01:04:23,680 --> 01:04:28,037
trillion of CapEx a year if we're
only generating X amount in inference

1132
01:04:28,120 --> 01:04:29,837
revenue? The thing I think that lit the

1133
01:04:29,920 --> 01:04:32,637
fuse this year was Anthropic showed up

1134
01:04:32,720 --> 01:04:35,557
in a major way with revenue, right? And

1135
01:04:35,640 --> 01:04:38,037
so, we have, you know, the AI lab

1136
01:04:38,120 --> 01:04:40,637
revenue everybody combined at around

1137
01:04:40,720 --> 01:04:44,797
$300 billion next year, right? So, can't

1138
01:04:44,880 --> 01:04:48,397
you know, and go roll that out to 2027

1139
01:04:48,480 --> 01:04:50,757
uh or that is 2027, 300 billion. So,

1140
01:04:50,840 --> 01:04:53,117
we're spending 1.5 trillion of CapEx on

1141
01:04:53,200 --> 01:04:55,677
300 billion of inference revenue. Does

1142
01:04:55,760 --> 01:04:58,037
that math math for you? And what would

1143
01:04:58,120 --> 01:05:02,837
cause you, you know, to to get more
nervous again about our ability to

1144
01:05:02,920 --> 01:05:04,157
continue to make these investments?

1145
01:05:04,240 --> 01:05:07,837
Because the second we get nervous about
it, the entire semi complex is going to

1146
01:05:07,920 --> 01:05:10,637
come down a lot. Well, what do you
think the gross margins are on that 300

1147
01:05:10,720 --> 01:05:13,277
billion? Yeah, let's call it 50%.

1148
01:05:13,360 --> 01:05:16,317
>> I I would guess they're probably
a little bit higher than that. I might say

1149
01:05:16,400 --> 01:05:20,157
60 or 70. But, I mean,
that math starts to math,

1150
01:05:20,240 --> 01:05:24,077
and what I would just say is I
think that 300 billion is low, man.

1151
01:05:24,160 --> 01:05:25,797
>> Yeah. Yeah. >> I just think it's low.

1152
01:05:25,880 --> 01:05:29,557
>> From your mouth to God's >> Yeah,
yeah, exactly. I think I think we

1153
01:05:29,640 --> 01:05:34,437
end this year well over 200 billion
in inference revenue, well over.

1154
01:05:34,520 --> 01:05:38,357
And so, I think the math really maths,
and I do think we have to give uh Jensen

1155
01:05:38,440 --> 01:05:42,357
>> Yeah, our friend.
>> some credit because he said some things

1156
01:05:42,440 --> 01:05:44,197
that seemed outlandish. >> Right.

1157
01:05:44,280 --> 01:05:49,037
>> And he was conservative. He was low.
He said a trillion 2 years ago.

1158
01:05:49,120 --> 01:05:50,677
And I mean, he was really low.

1159
01:05:50,760 --> 01:05:52,717
>> Right. >> And so, like,
let's give the guy some

1160
01:05:52,800 --> 01:05:55,317
credit and think about
what he is saying right now.

1161
01:05:55,400 --> 01:05:58,077
>> For sure, for sure. And and listen,

1162
01:05:58,160 --> 01:06:02,637
I would say consistently,
Elon's been taking the over.

1163
01:06:02,720 --> 01:06:04,917
Sundar's been taking the over.

1164
01:06:05,000 --> 01:06:10,277
Sam, Dario, you know, Dario did
the podcast with Dwarkesh when he was

1165
01:06:10,360 --> 01:06:13,437
talking about country geniuses in the
data center. He said that will be here

1166
01:06:13,520 --> 01:06:16,477
by 2028. He said revenues will go into

1167
01:06:16,560 --> 01:06:19,397
the low hundreds of billions by 2028.

1168
01:06:19,480 --> 01:06:23,397
So, let's call that, you know, 3 400
billion of revenue by 2028. And he said

1169
01:06:23,480 --> 01:06:27,717
that a while ago now, so he may even be
revising up his number. And he said it's

1170
01:06:27,800 --> 01:06:31,317
hard for me to see that there won't be
trillions of dollars in revenue before

1171
01:06:31,400 --> 01:06:34,317
2030. And if you're on that revenue

1172
01:06:34,400 --> 01:06:36,517
trajectory, if we're on a trajectory to

1173
01:06:36,600 --> 01:06:38,757
200 by the end of this year, let's call

1174
01:06:38,840 --> 01:06:42,277
it 4 or 500 by next year, and a path to

1175
01:06:42,360 --> 01:06:46,357
trillion plus by 2029,
then the math maths.

1176
01:06:46,440 --> 01:06:50,837
>> And we got to keep in mind
that half of the spending is there to

1177
01:06:50,920 --> 01:06:53,237
you know, for training, maybe a little
less than half. What is it, Foxy?

1178
01:06:53,320 --> 01:06:55,917
>> It's probably that depends on the lab,
but it's I would say it's increasingly

1179
01:06:56,000 --> 01:06:59,637
less than half. >> Yes. >> Okay.
So, we'll call it 35% is spending

1180
01:06:59,720 --> 01:07:02,717
that's not revenue generating, but it's
going to kind of make the next model.

1181
01:07:02,800 --> 01:07:04,677
So, I think the math maths. >> Right.

1182
01:07:04,760 --> 01:07:08,117
>> And there's still this prisoner's
dilemma where if you opted out, that may

1183
01:07:08,200 --> 01:07:11,837
be an existential decision.
>> And I think like coming into this year,

1184
01:07:11,920 --> 01:07:15,477
going back to this kind of what
narratives were violated, you know,

1185
01:07:15,560 --> 01:07:20,237
I think into this year everyone expected
token pricing, uh the price of compute,

1186
01:07:20,320 --> 01:07:24,277
it's all deflationary. And it will be
kind of a smooth line deflationary over

1187
01:07:24,360 --> 01:07:26,917
time. But, I think this year what we've

1188
01:07:27,000 --> 01:07:31,437
seen is the opposite. And you know,
it's all comes back to supply-demand. The

1189
01:07:31,520 --> 01:07:35,517
demand side of the equation seems to be
far outstripping the supply. Right? And

1190
01:07:35,600 --> 01:07:38,637
I think you look at the
deals signed by SpaceX

1191
01:07:38,720 --> 01:07:44,037
and others, the monetization
rates per watt are increasing.

1192
01:07:44,120 --> 01:07:46,237
Um and

1193
01:07:46,320 --> 01:07:48,677
look, that is on a a pretty nascent

1194
01:07:48,760 --> 01:07:54,117
small base of users, right? Like Alex
at Well Rock, he has this great um

1195
01:07:54,200 --> 01:07:55,557
way to frame it.

1196
01:07:55,640 --> 01:08:01,437
Less than 0.2% of people on Earth are
actually using AI in an agentic way.

1197
01:08:01,520 --> 01:08:04,077
>> Right. >> Right?
Like I'm not a technical person,

1198
01:08:04,160 --> 01:08:10,477
but I'm consuming 500 CPU cores
in a VM instance, five GPUs 24/7.

1199
01:08:10,560 --> 01:08:12,477
>> Yeah. >> I mean,
if you draw that out to any

1200
01:08:12,560 --> 01:08:17,157
meaningful percentage of the population,
I mean, we're going to be in, you know,

1201
01:08:17,240 --> 01:08:20,517
this kind of shortage environment
maybe for some time. So,

1202
01:08:20,600 --> 01:08:23,717
I think that is all positive
for this ROI question.

1203
01:08:23,800 --> 01:08:26,817
>> Man, foxy, 100 to one CPU to GPU ratio.

1204
01:08:26,900 --> 01:08:30,037
>> [laughter] >> Kind of agentic workflow.

1205
01:08:30,120 --> 01:08:33,157
>> He said of course.
>> [laughter] >> Five.

1206
01:08:33,240 --> 01:08:35,717
>> Five, yes. >> Yeah.
>> I'm being smart with my phone.

1207
01:08:35,800 --> 01:08:37,757
>> Good, good, good. Excellent.

1208
01:08:37,840 --> 01:08:40,597
>> I I will say also that ratio of 300 to

1209
01:08:40,680 --> 01:08:44,237
1. You know, call it 1.2, 1.5.

1210
01:08:44,320 --> 01:08:47,677
Um there there is also a rate that now

1211
01:08:47,760 --> 01:08:50,237
physically we can only expand

1212
01:08:50,320 --> 01:08:55,077
how much we can produce and how much
we can actually increase that spend by,

1213
01:08:55,160 --> 01:08:59,837
whereas we're seeing the opposite right
now on the on the the willingness to pay

1214
01:08:59,920 --> 01:09:03,317
for these tokens. And actually like when
the willingness to pay for these when

1215
01:09:03,400 --> 01:09:04,997
the monetization per gigawatt is

1216
01:09:05,080 --> 01:09:07,477
actually increasing from, you know, call

1217
01:09:07,560 --> 01:09:11,477
it like 20 20 billion um in the in the

1218
01:09:11,560 --> 01:09:16,837
best best of cases for at the beginning
of the year to now like 30 to even

1219
01:09:16,920 --> 01:09:20,317
pushing 40 >> per
gigawatt >> per gigawatt.

1220
01:09:20,400 --> 01:09:24,877
Um all of that is is a very heavy fixed
cost base, but all of that is like pure

1221
01:09:24,960 --> 01:09:26,517
margin flow through now. And you're

1222
01:09:26,600 --> 01:09:29,437
actually, you know, as we scale like the

1223
01:09:29,520 --> 01:09:33,877
willingness to pay for for all of this
and and now the all of this stipulated

1224
01:09:33,960 --> 01:09:36,957
by like, you know, everything we're
talking about of like how much is open

1225
01:09:37,040 --> 01:09:40,717
source versus not and all of these
different flows, but really like as

1226
01:09:40,800 --> 01:09:43,077
we're climbing this curve, you know, the

1227
01:09:43,160 --> 01:09:48,157
the the revenue is might actually
outstrip our fixed cost base by by

1228
01:09:48,240 --> 01:09:52,437
significant amount. And I think that's
why all the labs are pushing, you know,

1229
01:09:52,520 --> 01:09:57,037
the the the gas to the pedals because
they all they all see like within if we

1230
01:09:57,120 --> 01:09:59,957
continue this curve within like 3 years,
you know, we're just going to be so

1231
01:10:00,040 --> 01:10:04,197
short on all the computer >> It's
a great I'm sorry, but I mean I

1232
01:10:04,280 --> 01:10:07,837
like it's a great point. Like if you
thought you were getting a when you made

1233
01:10:07,920 --> 01:10:11,917
these decisions >> Yes.
>> in November of 2025,

1234
01:10:12,000 --> 01:10:13,477
you thought you were
getting a certain return.

1235
01:10:13,560 --> 01:10:17,197
>> Yeah. >> You may be getting
triple that return today.

1236
01:10:17,280 --> 01:10:20,677
>> At Tropic, no way no way did they think
they were going to be anywhere close to

1237
01:10:20,760 --> 01:10:23,557
break even. >> Yeah. >> Right?
And and and and in this part of

1238
01:10:23,640 --> 01:10:28,397
the curve, and the reason like I I I
called it accidental profitability that,

1239
01:10:28,480 --> 01:10:30,917
you know, people have been talking about
that because they want to spend a lot

1240
01:10:31,000 --> 01:10:33,797
more money on computer. They just had
a hard time doing it. Now maybe with

1241
01:10:33,880 --> 01:10:37,117
SpaceX, you know, they could take some
of those dollars and and and go spend

1242
01:10:37,200 --> 01:10:39,317
them other places. But that to me is,

1243
01:10:39,400 --> 01:10:42,717
you know, a a fundamental change. Um the

1244
01:10:42,800 --> 01:10:46,397
first argument against the frontier
labs was they'll never generate revenue.

1245
01:10:46,480 --> 01:10:48,677
Okay? And then we that got blown up.

1246
01:10:48,760 --> 01:10:51,237
Then it was like even if they generate
revenue, it'll be really shitty gross

1247
01:10:51,320 --> 01:10:54,677
margins, and they'll never be able
to get make money. And then kind of that

1248
01:10:54,760 --> 01:10:58,477
that's blown up. And and you know,
I think now, you know, people are falling

1249
01:10:58,560 --> 01:11:02,277
back and they're saying, "Well, they're
overcharging. This is token maxi." My

1250
01:11:02,360 --> 01:11:05,877
good friend, you know, Chamath has said
there's no ROI on any of this spend.

1251
01:11:05,960 --> 01:11:10,917
It's all this token maxi. My best
evidence for why we all know, of course,

1252
01:11:11,000 --> 01:11:14,557
when somebody puts on this much spend
like at Altimeter, we're not optimally

1253
01:11:14,640 --> 01:11:19,197
spending every single dollar. But,
the question is, why are millions of

1254
01:11:19,280 --> 01:11:23,637
independent businesses, small, medium,
and large, why are millions of consumers

1255
01:11:23,720 --> 01:11:25,717
all choosing to do the same thing?

1256
01:11:25,800 --> 01:11:30,397
They're not dumb. These are, you know,
rational economic actors that are all

1257
01:11:30,480 --> 01:11:34,237
simultaneously saying, "I want to do
this because it makes my life better. It

1258
01:11:34,320 --> 01:11:38,437
makes my business better, etc." To me,
that is the best evidence as to why I

1259
01:11:38,520 --> 01:11:40,157
think this revenue can continue.

1260
01:11:40,240 --> 01:11:44,317
>> Yeah. And Clark, I think like the point
you made is dead on cuz I mean, you want

1261
01:11:44,400 --> 01:11:47,717
to own asset-heavy businesses
in inflationary environments, and token

1262
01:11:47,800 --> 01:11:49,557
pricing is going up, and supply and

1263
01:11:49,640 --> 01:11:52,037
demand is tightening, so totally agree.

1264
01:11:52,120 --> 01:11:57,067
>> Um you know, as we begin to uh
find our way to the exit ramp and and

1265
01:11:57,150 --> 01:12:00,757
[laughter] and wrap here, one of the
things I you know, you and I've been

1266
01:12:00,840 --> 01:12:04,517
doing this for a long time, Gavin,
a couple decades. Um you may even sketch

1267
01:12:04,600 --> 01:12:08,357
longer than me, even though I'm
a little bit older than you. Um

1268
01:12:08,440 --> 01:12:12,877
you know, we have uh I always like to do
a market check, because I find a lot of

1269
01:12:12,960 --> 01:12:16,557
time that analysts come on these things,
and they talk their, you know, talk

1270
01:12:16,640 --> 01:12:19,117
their book, and you know, there are
a lot of people who listen to these

1271
01:12:19,200 --> 01:12:20,997
things, retail investors and others.

1272
01:12:21,080 --> 01:12:23,437
It's just kind of like, what do
we really think? And so, I always

1273
01:12:23,520 --> 01:12:27,357
characterize as kind of small, medium,
and large. Like, what am I doing? Do I

1274
01:12:27,440 --> 01:12:30,677
have small exposure on? Do I have medium
exposure on? Do I have large exposure

1275
01:12:30,760 --> 01:12:34,357
on? You know, and if you look at what's
happened in the markets, semis ripped

1276
01:12:34,440 --> 01:12:37,637
this year. I mean, like uh you've been
doing this a long time. I don't I've

1277
01:12:37,720 --> 01:12:41,197
never seen it before, right? I've never
seen, you know, the doubles and the

1278
01:12:41,280 --> 01:12:43,557
triples across the board like we saw.

1279
01:12:43,640 --> 01:12:47,757
But, there's been huge dispersion,
right, in the market. Internet's down

1280
01:12:47,840 --> 01:12:52,037
16%, uh software's
down 8% on the year. You

1281
01:12:52,120 --> 01:12:55,837
know, spy and and Nasdaq are up,
but really up because of their components

1282
01:12:55,920 --> 01:12:59,957
that are related to AI and compute.
And so, the market itself has kind of

1283
01:13:00,040 --> 01:13:03,957
struggled. Meanwhile, if you were in
the stuff that we were invested in, we've

1284
01:13:04,040 --> 01:13:07,477
all done pretty well. I think you know,
I've said it a couple times. I think if

1285
01:13:07,560 --> 01:13:11,677
the Anthropic revenue had not shown up
this year, because that was the overhang

1286
01:13:11,760 --> 01:13:15,397
on the market, I think the whole market
could be down this year. Right? Um but

1287
01:13:15,480 --> 01:13:17,317
that showed up. You know, we just had

1288
01:13:17,400 --> 01:13:19,757
these huge months in in in April and

1289
01:13:19,840 --> 01:13:23,517
May. Um for us, you know, because prices

1290
01:13:23,600 --> 01:13:27,557
came up so much, because I have some
worry about, you know, geopolitics, the

1291
01:13:27,640 --> 01:13:32,077
macro backdrop with, you know, with with
what's going on with inflation in the

1292
01:13:32,160 --> 01:13:35,637
short run, and just like, you know,
needing a little consolidation in this

1293
01:13:35,720 --> 01:13:39,077
market to answer some of these questions,
because now expectations are

1294
01:13:39,160 --> 01:13:42,597
higher. You know, we dialed back from what
I would call large for Altimeter to

1295
01:13:42,680 --> 01:13:47,637
something kind of like medium small. Um
again, it's never all or nothing for us.

1296
01:13:47,720 --> 01:13:50,997
It's like, what is the
risk-reward at a given price?

1297
01:13:51,080 --> 01:13:54,517
Um and so, we think this is a,
you know, maybe going to be a period of

1298
01:13:54,600 --> 01:13:59,157
consolidation on way to much higher highs.
Um curious just how you run the

1299
01:13:59,240 --> 01:14:01,677
book, how you think about
it like a portfolio manager.

1300
01:14:01,760 --> 01:14:05,237
>> Very similarly, man. I always think
stocks, the markets, I imagine them as

1301
01:14:05,320 --> 01:14:08,957
runners. Okay? And like in '22,

1302
01:14:09,040 --> 01:14:12,637
that runner had gone downhill.
It had a lot of energy, man.

1303
01:14:12,720 --> 01:14:17,037
Yeah, it was painful. It wasn't fun.
Um but coming out of that, there was a lot

1304
01:14:17,120 --> 01:14:19,557
of kind of pent-up upside in the market.

1305
01:14:19,640 --> 01:14:23,197
And you know, the market, particularly
last 2 months, it has run up a very

1306
01:14:23,280 --> 01:14:26,797
steep hill. And a lot
of companies, semiconductor

1307
01:14:26,880 --> 01:14:30,677
companies in particular, you know,
ironically, you know, Nvidia and

1308
01:14:30,760 --> 01:14:32,757
Broadcom, they they have been laggards.

1309
01:14:32,840 --> 01:14:36,517
>> Totally. >> And so,
but a lot of these, like I do

1310
01:14:36,600 --> 01:14:40,277
see a lot on X about finding the next
bottleneck. I think that was the last

1311
01:14:40,360 --> 01:14:44,557
game. That game is over.
You've had a lot of stocks that forget

1312
01:14:44,640 --> 01:14:47,077
climbing a mountain or a hill. They've

1313
01:14:47,160 --> 01:14:49,557
gone straight up a cliff, okay?

1314
01:14:49,640 --> 01:14:52,877
>> Yes. They're tired. They need to rest.

1315
01:14:52,960 --> 01:14:57,477
And we'll see, do they just rest at the
top of that cliff they climbed? Do they

1316
01:14:57,560 --> 01:15:00,837
hang out on the in their
harness for a while?

1317
01:15:00,920 --> 01:15:03,877
We've seen some.
Or do they need to go downhill for a

1318
01:15:03,960 --> 01:15:07,637
bit? We'll see, but I'm
thinking very similarly to you.

1319
01:15:07,720 --> 01:15:11,557
But it is and I think there's, you know,
the market is seasonal. I think there's

1320
01:15:11,640 --> 01:15:15,717
real real concerns around
inflation and rates.

1321
01:15:15,800 --> 01:15:20,197
>> What was CPI this morning? >> uh 4.2.
I think we added core came in at

1322
01:15:20,280 --> 01:15:23,357
like 0.2 versus 0.3,
so a little bit better.

1323
01:15:23,440 --> 01:15:26,557
Um but you know, clearly
we're we're above four again.

1324
01:15:26,640 --> 01:15:28,557
And um and and there's short-term

1325
01:15:28,640 --> 01:15:31,917
pressure on, you know, core PCE, etc.

1326
01:15:32,000 --> 01:15:35,237
Um and we have some unknown unknowns,
but the market, I mean, if I had told

1327
01:15:35,320 --> 01:15:38,837
you the fact pattern for this year,
that we're going to be in a war with Iran,

1328
01:15:38,920 --> 01:15:42,757
that, you know, oil was going to be
at 100 bucks, that CPI was going to be

1329
01:15:42,840 --> 01:15:46,117
creeping back up, that internet was going
to be down 15%. Software is going

1330
01:15:46,200 --> 01:15:49,797
to be down 8%. You would have said,
"I want nothing to do with that market,

1331
01:15:49,880 --> 01:15:53,677
right?" And here we are. The market's
done pretty good in the stuff that we

1332
01:15:53,760 --> 01:15:58,237
traffic in because the world
underestimated AI revenues and

1333
01:15:58,320 --> 01:16:00,997
underestimated the amount of compute
that was going to be needed.

1334
01:16:01,080 --> 01:16:04,277
>> It's odd to say you know, we're heading
into a seasonally weak period with all

1335
01:16:04,360 --> 01:16:09,037
of these fears. AI has actually been
seasonal for the last three summers.

1336
01:16:09,120 --> 01:16:12,237
Token consumption is kind of plateaued,
slowed down, and that's cuz you know,

1337
01:16:12,320 --> 01:16:15,957
college kids are big AI consumers
and they don't use as much AI, you know,

1338
01:16:16,040 --> 01:16:19,477
hopefully they're all using
it to learn and not cheat.

1339
01:16:19,560 --> 01:16:23,317
But that may happen. It may not
happen because of generative AI.

1340
01:16:23,400 --> 01:16:27,637
>> is building swarms of agents, building
a SpaceX model. He's going to the SpaceX

1341
01:16:27,720 --> 01:16:29,957
IPO with me at the exchange on Friday,

1342
01:16:30,040 --> 01:16:32,637
but I he had to build an AI model using

1343
01:16:32,720 --> 01:16:34,677
AI agents. He had to build a model, a

1344
01:16:34,760 --> 01:16:39,997
DCF before we go to the exchange. He
is mesmerized. He is absolutely and it's

1345
01:16:40,080 --> 01:16:41,517
extraordinary what he's doing.

1346
01:16:41,600 --> 01:16:44,237
>> So he's one kid who's not easy to less
computer >> [laughter] >> or something.

1347
01:16:44,320 --> 01:16:46,037
>> He's burning it. He's burning it.

1348
01:16:46,120 --> 01:16:48,557
>> Yeah, but you know,
if token consumption

1349
01:16:48,640 --> 01:16:50,957
plateaus, if open source takes some

1350
01:16:51,040 --> 01:16:55,917
share, there's a Silicon data index that
has showed, which is an index of kind of

1351
01:16:56,000 --> 01:16:59,277
consumption and pricing. I think there
may have been a little bit of a shift

1352
01:16:59,360 --> 01:17:02,677
over the last 2 weeks to open source
tokens that are cheaper. Like people

1353
01:17:02,760 --> 01:17:06,917
looking at that data as bearish or not
understanding it. But nonetheless, like

1354
01:17:07,000 --> 01:17:09,917
I just think there's reasons,
you know, to look around, be careful, be

1355
01:17:10,000 --> 01:17:12,877
thoughtful. I always assume a bullet
is coming for me. Head on [laughter] a

1356
01:17:12,960 --> 01:17:16,597
swivel. It's the bullet you don't see
that gets you. So, I'm trying to spin as

1357
01:17:16,680 --> 01:17:20,237
fast as I can. But yeah,
it's the market may need to

1358
01:17:20,320 --> 01:17:24,957
take a breather. But man, when I
think about what Noam Brown said

1359
01:17:25,040 --> 01:17:28,997
and when I see the capabilities of Fable,

1360
01:17:29,080 --> 01:17:32,997
it's just hard for me to get too bearish.

1361
01:17:33,080 --> 01:17:37,797
>> I mean, like to me um and we
got two, I think, of the most

1362
01:17:37,880 --> 01:17:41,997
extraordinary guys of, you know,
the next generation, you know, sitting in

1363
01:17:42,080 --> 01:17:45,357
the room. We have at Altimeter, we have
deep admiration for the work that you

1364
01:17:45,440 --> 01:17:47,557
guys do. I always appreciate when you

1365
01:17:47,640 --> 01:17:52,837
send me a note about the work that we do
and we publish. Um but for the guys who

1366
01:17:52,920 --> 01:17:55,877
are newer to the business, they might
think this is the way that it kind of

1367
01:17:55,960 --> 01:17:58,957
always was, right? And like this line,

1368
01:17:59,040 --> 01:18:01,397
the steepening of the line of creative

1369
01:18:01,480 --> 01:18:04,197
destruction, the steepening of the line

1370
01:18:04,280 --> 01:18:07,717
of, you know, scale advantages. Um I

1371
01:18:07,800 --> 01:18:11,757
always believed it was to it was going
to be true. I never thought it would be

1372
01:18:11,840 --> 01:18:14,477
true at this rate. I went back last

1373
01:18:14,560 --> 01:18:16,957
night. In the last 7 years, we've added

1374
01:18:17,040 --> 01:18:19,957
1 trillion of revenue to the Mag 7

1375
01:18:20,040 --> 01:18:22,637
in the last 7 years, okay? To get to a

1376
01:18:22,720 --> 01:18:25,477
trillion, to get to the first trillion

1377
01:18:25,560 --> 01:18:28,157
of, you know, took over 20 years. In the

1378
01:18:28,240 --> 01:18:30,397
last 7, we had another tr- trillion and

1379
01:18:30,480 --> 01:18:33,357
that added 17 trillion in market cap.

1380
01:18:33,440 --> 01:18:35,197
That trillion dollars, okay?

1381
01:18:35,280 --> 01:18:37,637
I The forecast now that we're going to

1382
01:18:37,720 --> 01:18:40,437
add another trillion of revenue in just

1383
01:18:40,520 --> 01:18:45,597
three companies SpaceX Anthropic
and open AI over the next four to five

1384
01:18:45,680 --> 01:18:49,397
years. Okay, like not
seven companies three

1385
01:18:49,480 --> 01:18:54,837
companies and in half the time right
and so I would say that you know, we are

1386
01:18:54,920 --> 01:18:58,157
going to have bumps in the road.
I know that it's going to be like this but

1387
01:18:58,240 --> 01:19:00,197
we're going to higher highs because the

1388
01:19:00,280 --> 01:19:02,757
size of the prize. This is going to

1389
01:19:02,840 --> 01:19:06,877
transform five ten 15% of global GDP.

1390
01:19:06,960 --> 01:19:11,797
There is no doubt in my mind and 10%
of global GDP is 10 trillion dollars. It's

1391
01:19:11,880 --> 01:19:15,077
an exciting future to be a part of it's
fun to do it with you guys. I think

1392
01:19:15,160 --> 01:19:18,357
we're going to have to do our work to do
the things to make sure America wins and

1393
01:19:18,440 --> 01:19:22,597
that we evolve the social contract keep
everybody you know lift the floor take

1394
01:19:22,680 --> 01:19:27,357
everybody with us on this ride
but it's a it's a it's a really exciting

1395
01:19:27,440 --> 01:19:30,077
time to be doing what we're doing
it's fun to be doing it with you guys.

1396
01:19:30,160 --> 01:19:31,477
>> Yeah, I just want
to say Brad thanks for

1397
01:19:31,560 --> 01:19:34,437
having us and thank you for what you've
done with the Trump accounts. I actually

1398
01:19:34,520 --> 01:19:38,877
think it's super important for America
for the world to give people an equity

1399
01:19:38,960 --> 01:19:42,277
stake at a very young age.
They they will see it compound over their

1400
01:19:42,360 --> 01:19:46,237
lifetimes. This is a great thing you've
done for the world. So thank you. I'd

1401
01:19:46,320 --> 01:19:50,317
echo all your comments like deep
admiration for you your team gratitude

1402
01:19:50,400 --> 01:19:55,077
for the collegiality and friendship
between our firms. I know Clark and Foxy

1403
01:19:55,160 --> 01:19:57,317
they hang out like all the time.

1404
01:19:57,400 --> 01:20:00,677
>> That's a people think that you know
and there are people in our business who

1405
01:20:00,760 --> 01:20:04,877
don't want to share anything.
Our view is like we open source it

1406
01:20:04,960 --> 01:20:08,277
but there are very few people who we
actually call and ask their opinion

1407
01:20:08,360 --> 01:20:12,757
because there are very few people who do
the thousands of hours of work that we do

1408
01:20:12,840 --> 01:20:16,317
you know that are adding to that and you
do it and we appreciate that and you do

1409
01:20:16,400 --> 01:20:20,317
as well Gavin we appreciate that. So
with that love fest let's call it a wrap.

1410
01:20:20,400 --> 01:20:23,960
Thanks for being here. >> Thank you.

1411
01:20:47,280 --> 01:20:49,320
>> Mhm.