What was happening in AI was I think the

most extraordinary moment in the history

of capitalism, the history of American

business. Anthropic they added 11

billion of ARR. The three highest

profile SAS companies founded in the

last 10 12 years are Palunteer,

Snowflake and Data Bricks. And these

three companies spent 10 years building

their businesses. Anthropic added their

combined businesses in one month.

That's just nothing like that has ever

happened in the history of capitalism.

Forget my career. Just the flatout

history of capitalism, the history of

business.

All right. So, this is our uh sixth time

doing this, if you can believe it, which

puts you back into first place uh or at

least tied for first place with Girly.

And I think even since last time when we

did this, which was so exciting and

spectacular, I think we're in an even

more interesting time now. Maybe just

start by riffing on how it felt for you

living through March and April of this

year, which which felt to me just like a

completely unique economic, technology,

and market environment. and you're the

biggest student of of of history and of

these times. So what does it feel like?

>> I would say broadly speaking there are

two kinds of draw downs. They're

drawowns where you're wrong, a company

misestimates,

your hypothesis was invalidated and you

have to take your medicine and you

crystallize that loss. And then there

are draw downs or periods of

underperformance where you you're

underperforming because of companies you

know really really well and where you

profoundly disagree with the price

action and you can lean in and instead

of crystallizing

uh negative performance you can kind of

build pent up alpha pent up future

performance and for me that is what

March felt like. It felt like uh you

know the NASDAQ was selling off and at

the same time what was happening in AI

was I think the most extraordinary

moment in the history of capitalism the

history of American business and what I

just mean by that is that anthropic they

added 11 billion of ARR and what is

astonishing to me about this is that

the SAS and cloud revolution it created

we'll call it between5 and 10 trillion

dollars of value and I would say

Arguably the three highest profile SAS

companies to have kind of been founded

in the last 10 12 years are Palunteer,

Snowflake and Data Bricks. And these

three companies have spent employ

thousands of people, tens of thousands

collectively. They've all spent 10 years

building their businesses. And Anthropic

added their combined businesses in one

month.

That's just nothing like that has ever

happened in the history of capitalism.

Forget my career. Just the flatout

history of capitalism, the history of

business. I wild. And then Krishna comes

on this show and shares some stats. 500%

in DR.

>> Yeah. You do the math on that for three

years. Insanity.

>> We So there's just no precedent for

this. And we, you know, tech tech

investors, you hear a lot of discussions

about S-curves and investing in

exponentials. I've just never seen an

exponential like this. It felt even more

extreme than Deepseek,

>> which was a very similar setup. If we go

back to 25

and there's a huge sell-off on Deepseek,

which was very strange because the paper

gets published 7 days before Deepseek

Monday. got published,

I believe, on a Monday that was a

holiday in America. And I read it, I

thought, hm, you know, this this feels

like it might not read

>> that positively for um you know, the AI

trade. You I took action. We had

DeepSeek Monday where AI really imploded

a week later and that was really strange

because by DeepSec Monday it was super

clear that this was going to be the most

positive thing that had ever happened to

compute demand. Prices in the AWS

available availability zones in Asia had

already like doubled. You were seeing

GPU availability go down. And this was

just the first time we saw how much more

compute-hungry reasoning models are

during inference than non-reasoning

models. And so that was a similar setup,

but you you had to do some work to see

that. I mean, it's not that hard to say,

oh wow, stocks are selling off. The

price of DRAM is going vertical. The

price of GPUs in Asia going vertical. U

GPU availability is going down. And then

like two or three days later, you know,

GPU prices in in America started going

up, GPU rental prices. All you had to do

in in March was just simply observe what

was happening to Anthropic. There

there's all these people who seem to

regret,

you know, not buying during 22, not

buying during COVID, not buying during

Deep Seek. you had the same valuation

setup at the beginning of April and an

even clearer AI inflection

and so there have been all these chances

to buy into AI and then of course what

complicated it was the straight of

foremost I became a believer and am a

believer that I think maybe one thing

that the market was mispricing and I'm

I'm no macro expert I do do a lot of pro

national security investing

And so I do have access to people who

are experts and are

excited to share their thoughts and

opinions with me that the straight of

horm being closed is actually relatively

awesome for America.

>> Why?

>> Because particularly for the goals of

the current administration.

So electricity is a very important

industrial or manufacturing input. The

key input into American electricity

prices which feeds into AI is in G1

natural gas on Bloomberg that was down

20%. And natural gas in Asia, Europe,

everywhere else doubled or tripled. So

our relative manufacturing

competitiveness

improved overnight and for better or

worse that is what the Trump

administration seems to care about. They

are very focused on America's relative

position. And I think a lot of people

had memories of the 1970s.

And what made the 70s so traumatic was

it wasn't just that prices went up, it's

that there were actual gas shortages.

And then you go through, okay, well the

US economy is dramatically less energy

intensive than it was. US econ the

United States is now the world's largest

producer of oil and gas and we've become

now the world's largest exporter of oil

and gas and then on top of that there's

this relative manufacturing advantage

and so that made it I think easier to

stay focused on AI fundamentals stay

focused on what were historically

attractive valuations I think on a

relative basis

tech essentially got as cheap as it's

been versus the rest of the market has

at any point over the last 10 years and

just think about that in the context of

market efficiency. We have the most

extraordinary moment in the history of

capitalism that's wildly bullish for AI

and you get a chance to buy AI

at really attractive valuation. What do

you make of the multiples that

specifically Anthropic and OpenAI, which

in my mind are like the reference assets

that are the most pure play takes on

this trend really being not that crazy?

Like if you just look at the sales

multiple and compare it to maybe what

data bricks and snowflake and these

companies traded at at their peak like

how do you make sense of it? I do think

OpenAI and Enthropic are pretty

different animals from a capital

efficiency perspective. And Enthropic

clearly is has a dramatically lower cost

per token than OpenAI. They just do. And

you can just see that in the amount of

money that they have burned to get to a

roughly similar revenue scale. I think

have have they burned maybe 80% less

than OpenAI.

>> So as businesses, they clearly have very

different structural ROIC's. I think

OpenAI is doing a lot. I think Sarah

Frier is one of the most exceptional

CFOs. I think they're doing a lot of

things to try to improve this

>> and they've secured a lot of compute

more more than

>> they've secured a lot of compute. That's

another big difference. Um it turns out

being aggressive really paid but yeah I

just anthropic at 900 billion for 50

billion and you know ARR and you know I

>> growing a thousand%.

>> Yeah, growing at ridiculous rates. Maybe

a true statement is that if Anthropic

had all the compute, they'd probably be

doing well north of hundred billion

dollars today,

maybe 150.

And I do, you know, they have clearly

deprecated the intelligence of Claude.

There's an analysis Claude is even on

Opus is generating 70% less tokens for

the exact same question. And you know,

as we talked about last time, token

quantity equals quality of answer and

quality of thinking at some level. you

know and there is an intelligence

density per token that also matters you

know I think I felt that as as a user so

I think they would be doing materially

more 100 150 maybe 200 billion so you

might be buying it at more like five

times

unconstrained I'm going to make up a new

number urr unconstrained run rate

revenue yes

>> why do you think they don't raise $und00

billion at a $3 trillion valuation or

something like this. Like if you were

the anthropic CFO, uh Krishna is

awesome. We just had him on. Or if

you're the open if you're Sarah,

certainly if if the inbound I received

following the Krishna episode is any

indication, everyone I've ever met is

trying to invest in in both these

companies.

>> So I think it's wise

it the future is uncertain.

you are clearly in a very capital

intensive game even if you are you know

Enthropic

um I'm sure is at very positive gross

margins on inference today I think

probably starts generating cash this

year if they are not already generating

cash which I think is probably the case

but still you probably want to be able

to raise more capital access more

compute the world is uncertain Ukraine

is starting to really really win how is

Russia going to respond ond, you know, I

think there's still a lot of uncertainty

in Iran. All this uncertainty, I think,

probably amplifies geopolitical

uncertainty over Taiwan. So, it's an

uncertain world. If if I think about

Elon, Elon has always made investors

money. He treats it like a sacred

covenant. And as a result, because he's

made people money for now 20 years, he

has a superpower. And that is he can

essentially raise as much capital as he

wants, whenever he wants. And I think

it's wise that these companies are

taking I don't know if that's how they

think about it, but I do think being

focused on making investors money is

wise and creates benefits that don't

just last for like a year or two. They

can last for the next 20 to 30 years.

>> And the way Elon did this was sort of

systematically underpricing SpaceX or

whatever else. Like what is the actual

method?

Just never being greedy on valuation,

never pushing valuation.

>> Just that simple.

>> You know, my friend Antonio pointed out

SpaceX compounded, you know, low 30% per

year for whatever that was a decade. And

and that was just because Elon was, I

think, focused on preserving the

superpower and having trying to strike a

fair balance between investors and

employees.

But I I think it's wise. But could

Anthropic raise money at probably at

least a 100% premium

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works.com to get started. Let's get to

the Watson wafers part of the

discussion. Always my favorite thing to

talk about with you. Uh the importance

of this infrastructure buildout. I feel

like every time I feel like it's getting

overheated and then the next time I talk

to you, it seems like we should have

done way more than we did. And you've

studied S-curves and the steepness of

those S-curves a lot. Uh and you know a

lot about history. Talk us through how

you're thinking about Watson wafers

today as the key to inputs into this

whole thing. Yeah, I would say I think

capitalism is going to solve the Watts

shortage

>> absent big regulatory political blowback

which I think is a real possibility. the

head of kind of data center infrain

investing at one of the big PE firms.

You know, I think Blackstone, Apollo,

KKR said it used to be energy and chips

were our biggest gating factors. Now

it's zoning and approval much more

important. And I think a lot of

companies are waiting till after the

midterms to take action in terms of

maybe workforce reductions. Nobody wants

to be, you know, piñata during the

midterms,

but you know, you've seen a lot of

companies that make turbines significant

announce of plans to significantly

increase capacity. There's like two of

these machines that can cast these big

blades. We haven't made one in 80 years

in the West. We don't know how to make

them anymore, etc., etc., etc. All of

that is true. And I and and by no means

am I minimizing, you know, the

industrial engineering, you know, magic

and artistry that goes into those, but

capitalism is very good at solving

problems like these over time. There's

other sources of energy besides these

turbines with a longer time frame. So I

think the watts shortage will probably

begin to alleviate 27 28 and then I

think orbital compute will really solve

that. And I do I do want to like reframe

orbital compute because I think when

people hear data centers in space they

which we discussed on our last episode

they picture a pentagon sized building

in space. They're like well we can't do

that. That's not what it is. A blackwell

rack weighs you 3,000 lbs. It's 8 ft

high. It's 4 feet deep. 3 feet wide.

It's racks in space. And SpaceX has

showed you an illustration and it's a

rack. That's the satellite. Uh but it's

probably about the size of a blackwell

rack. It has these solar wings that are

probably 500 ft long on each side. You

keep it in a sunsynchronous orbit. So

those solar panels are always in the

sun. And then because it's in an exactly

sunsynchronous orbit, the radiator which

extends behind it for hundreds of feet.

>> This is a common criticism. Yeah. how

you going to go.

>> I've spent a lot of time at Starbase

over the years and I've talked to a lot

of SpaceX engineers and I do think it is

the most talented group of engineers on

planet Earth and they're very confident

they have solved this and they're not

always confident like I think probably

you know there's some engineering that

needs to happen to turn the starship

into a Mars colonial transporter. Will

they do that? Absolutely. What are they

more focused on? I would say probably,

you know, the repair and maintenance.

>> These are the two big, you know, the two

big responses, the radiator and and how

do you repair the whatever issue goes

wrong in the rack.

>> And the answer is like until you have

probably an, you know, floating

optimuses. You don't. Now, I do think

Starship is going to change the space

economy in ways we cannot imagine.

Particularly if regulation becomes a

constraint to data centers, none of it's

going to matter. you're going to sell as

much orbital compute as you can make.

And then obviously you link these racks

using lasers traveling through vacuum

which are already on every Starlink. And

it's just it's just mindblowing to me

that SpaceX operates the world's largest

satellite fleet which is like 98 or 99%

of all satellites in orbit. Every

Starlink they're cooling it today. And

you know, I think Starlink V3 is going

to operate at 20 kW. A Blackwell rack is

only 100 kows. And people talk a lot

about density. Well, if you're

connecting the racks with lasers through

vacuum, you know, you can make the rack

bigger physically. You're focused on

weight, not size. In a data center on

Earth where you're trying to connect

racks, ideally using copper, minimize

lengths, etc., etc. Cabling is a big

cost. um you do want that rack to be

small because you know copper when you

can, optics when you must. But in space,

you know, there's all sorts of things

that SpaceX can do that I think maybe

some of these naysayers are not

contemplating, but it's just they

operate more satellites than you want.

They have a 20 kow satellite today, so

maybe you just scale that up to 60

kilowatts to start. They seem very

confident they're going to go right to

100 to 120. And they also the same

company now also operates the largest

data center on earth. They have the

world's best hardware engineers and all

sorts of people almost all of whom are

not smart enough or practical enough to

work at SpaceX are these armchair

skeptics.

You know, I don't want to quote Larry

Ellison, but somebody was, you know,

being skeptical and Larry and Larry was

just like, "Listen, he's out there

landing rockets. I don't see anybody

else landing rockets. And the reality is

is that 10 years later, no other company

is consistently capable of landing and

fully reusing an orbital rocket. And

none of this works makes sense without

reusability. That means you have to land

it. I would like to redefine orbital

compute has racks in space, not giant

floating pentagoniz

data centers in space, which is just,

you know, that's silly. But you can, you

know, what makes a data center is you're

connecting these racks with lasers. So

it'll be racks in space that are

connected with lasers into a virtual

data center. And and if you think about

that state of the world, let's say that

all happens and we're really good at

getting these things up economically,

running matrix multiplication all over

space. What does that mean for

terrestrial data centers? Someone once

said, um, you know, America was going to

suck as hard as it can on every energy

source it can get. And I just think the

same is true of compute.

>> It's why I'm probably less worried about

like an edge AI barecase than I was.

>> We're going to consume as much compute

as we can. And inference I think is very

sensible for orbital compute. Training

will be done on Earth for a long time.

So I don't think that this is super

bearish for terrestrial data centers. I

think those are going to be valuable for

my lifetime.

But I do think if you are in this

ecosystem of power production and

cooling and you are massively ramping

capacity and you know a lot of these

capacity ramps are going to be hitting

just as I think you know all of the

silly skeptics start to understand that

orbital compute is very real like I

think it's worth thinking long and hard

about that if you're one of those

companies and then all sorts of cool

stuff is happening in the interim you

know we're getting really good at

repurposing jet engines. You know,

there's that boom aerospace that is

doing this.

>> So, there's a lot like capitalism is

hard at work

>> on on watts. On wafers, though, it's

just this group of, you know, flinty

older humans in Taiwan who are the most

important humans in Taiwan. They are the

overwhelming fraction of the country's

GDP, water usage, electricity usage.

They talk about the Silicon Shield. They

all view themselves as inheritors of,

you know, Morris Chain's sacred legacy.

I vividly remember like visiting Science

Park more than 20 years ago and, you

know, talking to them. Do you think you

could catch Intel? And they said, "This

is such a beautiful dream, but it's a

dream for our grandchildren."

>> And they did it. partly because of

Intel's self-inflicted wounds, but just

they don't they think very differently.

You know, one reason, you know, Jensen

flies over there so much is he wants

them to expand capacity. I do think it's

wild that Jensen has never had a

contract with Taiwan Semi. They do

business on what seems fair in

handshakes. Just fascinating. No

contract. It's going to be fair over

time. We're partners. We're going to be

fair to each other. And the truth is,

you know, based on every every prior

market precedent for a foundational new

technology like AI, you've always had a

bubble. You know, Carleta Perez wrote

this great book about this. And

basically, markets are efficient. They

correctly understand that this is a

foundational technology.

There's what Mobison calls a breakdown

in diversity.

Everyone becomes bullish on this new

technology. And I am beginning to worry

a little bit about a diversity

breakdown. And then you get a bubble.

That bubble funds the buildout of this

new technology, but supply gets ahead of

demand. And you get a crash and it's a

particularly severe crash if it's a

debtfueled buildout like the year 2000.

And one thing really happy about really

good about the current buildout is it's

still overwhelmingly funded out of

operating cash flows which is a a really

important fundamental difference versus

the year 2000 has is valuation has is

the fact that every GPU is running at

100% utilization when 99% of fiber was

unutilized. So there's all these

fundamental differences, but we do have

to history doesn't repeat, but it rhymes

and and as investor, we have to be very

cognizant of it

and recognize that based on the last two

or 30 hundred years, you know, forget

the internet bubble. We had a railroad

bubble, a canal bubble, we should expect

a bubble. And that's terrifying. Like

nobody wants a bubble. A bubble is

terrible. reason it's terrible is if

you're valuation sensitive, you like

massively underperform. You get fired by

probably all your clients. George

Vanderhiden, who um is is is no longer

with us, great port uh fidelity

portfolio manager, he fought the bubble

in 99 and he retired in two in early

2000 because I think he just couldn't

couldn't take it.

>> He knew it was wrong and you know his

his clients were deeply skeptical.

George, you're out of step. you know, he

had he had white hair. He's truly great

man. I I only overlapped with him

briefly, but he was a very important

mentor and friend to my good friend and

mentor Jennifer Urick. So, I have a lot

of Vanderhiden DNA through her. He was

the same person who said being early is

the same thing as being wrong. George

retires because he can't take the

underperformance and he can't take

clients saying what's wrong with you?

You don't get it. and he has like 40% of

his funded tobacco, 40% did homebuilders

and literally he underperfor he probably

outperformed the NASDAQ

by like 20 or 30x over the next three

years. Okay. And I have been optimistic

that this fundamental shortage of wafers

which really today is controlled by

Taiwan Semi will prevent one. If Taiwan

Semi did what Jensen wanted, I think

Nvidia could sell two trillion dollars

of GPUs in 26 in 26 or 27, maybe two.5

trillion, maybe three trillion, but

there is a limit where consumers would

consume so much that you probably would

be in an overbuild. And so Taiwan Smi,

if we don't get a bubble, like we need

to throw a party for them because they

will have single-handedly prevented a

bubble. Okay, you are starting to see

companies go to Intel

and Samsung.

>> Let's just assume TSM stays super supply

constraint versus you know the latent

demand like what what happens?

>> Well, one of you know the history

markets is I don't know who but one of

Intel and Samsung they're not going to

stay disciplined. They will break and

then at some level that will force

everyone else to break.

So like I think a lot of this may come

down to the degree to which Taiwan SIM

can maintain a lead over Intel and

Samsung. You got to remember it's

whatever it is it's 9 12 15 months.

>> Sort of like the leading node edge. You

mean

>> exactly you know the pace at which they

expand capacity. Like if I were to watch

one thing to understand whe there's a

bubble it's Taiwan Simmy's capacity

decisions. And I think there's a

Goldilocks zone where they expand enough

they make it hard for Intel or Samsung

to really truly emerge as like a um at

scale second source with something you

know well north of 30% market share. And

yet they also keep this fundamental

constraint on wafers

that you know helps us avoid a bubble.

And then obviously I think the terapab

um is going to play into this too.

>> Say more about that for people that

>> the turfab it's a SpaceX I believe

Tesla's involved as well um joint

venture to build the world's largest fab

here in America and I'm I think they're

going to be successful. on they have a

partnership with Intel which is very

important um because they're getting

access to 50 years of institutional

knowledge that's just you know a nine

months a few quarters 12 months 3 to

five quarters behind the front that's an

advantage it's also an advantage that I

believe that terafab is going to get

attention from the a teams at all the

semicap equipment companies like one big

reason Taiwan semicought up is ASML and

KA tenor and lamb research and applied

material materials. They wanted them to

catch up. They didn't they don't like

having a monopsiny and so the A teams

were in Taiwan working. Intel made some

mistakes and presto. And so the A teams

will will be here because of Elon's

reputation in in hardware engineering.

And then just to a degree that I think

is u maybe hard for people to imagine in

America um where you know politics has

replaced religion because Elon had his

fora into politics that makes it hard

for some people in America to see him

clearly which is sad because I do think

you know he's probably doing more for

America than any other American. You

know he's single-handedly bringing

manufacturing back to America. He's

revived Dince Tech. SpaceX is in some

ways the most important defense

contractor in America. You know, what

he's doing with Starlink is amazing for

the world. He's creating all these blue

collar manufacturing jobs, which is like

a goal, I think, of a lot of liberals

and good for America. He's done more

than any living human to decarbonize the

world. And if you are upset about data

sitters on Earth for environmental

reasons, well, here you go. You know, so

it's it's sad, but he is a living deity.

in China, Taiwan, South Korea, and

Japan.

And having watched him for a long time,

what he's going to do is they're going

to recruit the best people because the

best engineers want to work for Elon,

especially in hardware engineering. He's

going to recruit incredible engineers.

And then they'll be next to next to

Turfab, they'll be a Taiwan town. Oh,

these are your favorite restaurants. I'm

going to move them and their whole staff

from Taiwan to Texas and we're going to

make everything the way they like it.

And then we'll have Japan Town. Same

thing. Then we're going to have Korea

Town. We're going to have all these

things exactly but dialed to recruit the

best engineers. And that's just not the

way that the people who run Intel at

Seung think. So he's going to have the

best talent. He's going to have the A

teams at the wafer fab equipment

companies. He's he has intel which is

important. It's so good for all of any

administration's political goals. And I

think it's different enough that it will

not alienate Taiwan SMI.

>> And these have long lead times, right?

So like Terrafab is going to be pumping

out Nvidia G or whatever GPUs, whatever

chips like quite quite a long time from

now.

>> Elon tends to do things differently.

Everybody else has taken three years to

build a data center. He built one in 122

days. You know, Samsung had to give him

an office in their fab in Texas because

he was so unhappy about like the pace at

which they're expanding a building.

We'll see. Are you surprised by you

mentioned Deep Seek earlier? The simple

reaction to that was okay, these models

are just going to get 95% as effective

for some tiny fraction of the cost to

still Chinese open source models. Like

we'll be able to use these for most of

what we want to do. Fast forwarded a

little bit of time, you know, two years

from now, there's no reason I have to

spend a million dollars a year in my

small little firm on on tokens or

something. But then the actual reality

seems quite different than this. And I'm

curious why there's that dissonance in

your mind.

>> I do think it's the fascinating the

returns to the frontier, all the

economic returns to AI at the model

layer, not all of them, but an

overwhelming amount of them have been at

the frontier, which is surprising to me.

And I think it's been surprising to a

lot of people and I think this is one of

the most important questions to be

answered and you need to have a

hypothesis on it as an investor. Are

frontier tokens going to continue

capturing the overwhelming majority of

economic value created at the model

layer? And it is surprising like I just

I remember when Gemini 3.1 Pro came out

and it was it was mind-blowing to me. It

was so good. And today it's intolerable.

>> Intolerable.

And you know there's probably a little

bit of a dynamic where companies

prototype with Frontiers then when they

put something into production you're

hearing a lot of people do use Vertex or

you know open source. But still it is it

is a fact today that the overwhelming

majority of these economic turns come

from Frontier tokens. And that's

surprising and whether or not it

continues I think is a very interesting

question. And I'm much more open-minded

to that having had the experience I've

had with Gemini 3.1

and then Opus. Um, and then I do use Gro

4.3. It is on the paro frontier. like

the companies that are on the paro

frontier are and this is by the way a

big change in a a consequence of what we

talked about last time. Google losing

their percost token leadership as a

result of making very conservative

design decisions with TPU V8 to try and

take it away partially from Broadcom and

Nvidia um continuing to make aggressive

choices. Uh but Google dominated the

prao frontier. The prao frontier being

intelligence versus cost. And I think

this is the most important thing to look

at to analyze AI labs. Google dominated

that nine months ago. They at every

point on the paro frontier. OpenAI, XAI

and Anthropic were inside of them. Now

the Paro frontier is dominated by

Enthropic, OpenAI. And then Grock 4.3 is

on the paro frontier. It's clearly like

the, you know, the best lowest cost 500

billion parameter model. And then Gemini

3.1 is like hanging on to the paro

frontier. And if I were to bet or bet

that they're subsidizing that out of

pride, I would just say one a violation

of Richard Sutton's bitter lesson is for

sure the biggest risk to this trade

>> to all of AI. Now the closer someone is

to AI, the more skeptical they are this

will occur. One thing I think

contributed to weakness in March was,

you know, a much more stupid version of

DeepSeek, which was this thing called

Turboquant. and Turbo Quan is some

Google memory optimization that was

written up in a paper a year ago. And

then during the middle of an agreement

while Google was negotiating with

Micron, Samsung and Highex to sign, you

know, some LTA that would lock in really

high prices for a long time. They

released this. You know, what people do

is always more important than they say.

And they just kind of publicize it on X

and it goes viral like, "Oh my god, DRM

is cooked. Here's this DRAM

optimization." I was unable to find a

single AI engineer on planet earth who

believed that turbo quant would have any

impact on DRAM demand but nonetheless a

violation of Richard Sutton's bitter

lesson you know more compute will always

outperform human algorithmic ingenuity

more computing data or chin beyond

chinchilla optimal I guess what what

people increasingly do today that's a

real risk man and I think the people who

are building these models are skeptical

of that risk the reason I am a little

less skep skeptical is I think we are

very close to ASI and who knows if the

bitter lesson holds for 400 IQ models

just you know or maybe we get a

temporary

period where these you know if you get

to ASI the first thing it wants is

probably to be smarter and have more

resources. How does it do that? It makes

itself more efficient. I think that is

an actual risk that humans the bitter

lesson literally I believe includes

humans in it. So we're about to find out

whether the bitter lesson we'll find out

if it applies to 300 IQ ahis then 400

then 500 and 600 and at some point we

may have like a temporary violation of

the bitter lesson based upon AI and ASI.

So I'm curious how you think about some

other parts of the innovation around the

model continual learning and memory

being two that see people seem to be

most focused on as things that might

create yet another you know new paradigm

that we would enter. What do you think

about the role of those two things?

Yeah. Well, I think we've done a lot

with memory through these harnesses. And

it turns out that harness engineering is

not as important as the model, but it

really matters. And these harnesses in

these models are increasingly being

co-developed. One of the big things a

harness does, which you just think of as

like a a runtime that the model operates

in, knows where the pool tools are. It

like creates context, memory, state, um,

you know, has very specific,

you know, prompts or instructions and

just

>> makes a huge difference. Even simple

versions,

>> it makes an incredible difference. I

think the last time I was on here or one

of the other times I just said like,

"Hey, as an investor, it's very

important that you pay for the $250 a

month version to get like your own

intuitive sense." that's no longer

possible to understand what frontier AI

is capable of today even for like a

non-coding use case you need to have

cloud code or codeex and you need to be

on an enterprise plan and the reason for

this is and this is another I think this

is another dynamic that's enabled by

Google losing their cost leadership is

these AI models just shifted to

usagebased pricing and if you're on that

$250 or$300 or $280 month plan or

whatever it is you are getting severely

rate limited. You are getting a

labbotomized version of the AI because

like we talked about Claude now produces

70% less tokens. You want the tokens

that Claude and its harness really think

it needs to produce to get you a good

answer, you need to be on a usage based

plan. And by the way, this is so bullish

for AI. I was a telecom analyst in ' 05

to07 and cellular had been a great

growth industry really for the last 10

years and the reason was you had a

combination of fixed pricing you had 900

minutes for whatever it was and then

usage based pricing over that and when

did cellular stop being a great growth

industry when everybody just went to all

you can eat. And and by the way long

distance was the same thing. AI is just

shifting from all you can eat to pay by

the drink. And it turns out people

really like to talk to their friends

long distance. They really like to talk

to their friends on the phone. And

people really like to use AI and

particularly now that one person can

have a 100 agents working. So I think

the shift to usage based pricing is

probably why you will see OpenAI and

Anthropic exceed well over $200 billion

in ARR this year. because not only is

more compute going to become online, but

they're going to be able to push

frontier token pricing with these usage

enterprise models, but it's it's sad.

It's sad for the world and because it

just means if you can't afford that,

you're not at the frontier. But yeah,

continual learning, man, I mean, if we

solve that,

>> how do you conceptualize that? Like

>> there's so many mysteries about the

human mind, like we're such sample

efficient learners relative to AI. Like

I forget what it is, but like an AI

needs orders of magnitude.

>> Yeah. Many orders of magnitude. Now we

have a crude variant of continual

learning today when something is

verifiable and that's just, you know,

reinforcement learning during

mid-training. But yeah, continual

learning is a model that dynamically

adjusts its weights or adjusts in some

way in real time. Like as a human,

>> that's what you do.

>> Yeah. Like if I the first time I touch

or you know put my hand in a fire, I've

learned I never put it in there before.

That model today needs to put its hand

in the fire a million times and then

have, you know, the designers

effectively put a fire in the next

training run or an RL gym for it to

learn. I think it has to be dynamically

updating the weights, but I think people

are working on really smart techniques

beyond this. But if we get that then we

have a really fast takeoff and people

seem

confident that continual learning is

kind of just around the corner. And I do

think this is like the third big

question. Bitter le violation as a

result of ASI or less likely human

ingenuity. Will Frontier tokens still

command the premium they do? And will we

get continual learning? And if so, when?

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What is the role of new chip companies

in all of this? Like we talked a lot

about Nvidia and you know their their

sort of relationship with TSMC and Intel

and all these sorts of things. There's a

thousand flowers blooming. I think

literally probably a thousand flowers

blooming trying to create a new chip to

address some part of this bottleneck.

I'm curious how you process this space,

this opportunity, what role it will

play, what role they'll play.

>> So, I think this is good and healthy for

the world. It's good for Jensen too. Um,

you know, because a different

administration might take a different

view. Competition, I think, is good for

everyone. In in tank design, they talk

about the iron triangle. The iron

triangle of tank design is that all

designers of a tank, they have to make

trade-offs between attack, defense, and

mobility. And you know, for obvious

reasons, the more defense you have,

which is just armor, the heavier the

tank is, the less mobile it is. So you

have to live in this triangle and make

tradeoffs. Okay? Like the marava in

Israel, it's optimized for defense.

Russian tanks and like the Leopard are

generally more optimized for mobility.

Chip design is the same. And you there

there are these fundamental constraints

imposed by the laws of physics has

embedded in the Taiwan semi design rules

that you need to live within and you

have TPU tranium and AMD which are all

um you know essentially trying to be a

better GPU and today I think probably

Tranium is doing the best. Nobody's a

better GPU, but Trrenium is is I think

their, you know, they're they're tugging

on Superman's cape

>> and and this hadn't started yet. The

Tranium 3 needs to ramp into production

because it has a switch scale up

network, which you really need to

economically inference models. You know,

a lot of companies have a Taurus

architecture. Um that that's where

Google was and AMD. We'll see. The

MI450, we don't know yet. We'll see. We

probably know more about Trinidium 3

than the MI450, but that's a hard game

to play. So you have to do something

different and you have to do something

different that is also hard to do. So I

think the best path for these startups

like my rule of thumb is 1% market share

is going to be worth 100 billion. 100

billion is a pretty good venture

outcome. I think what Jensen would say

is like, okay, if something somebody

does something different and it gets to

one or two or 3% share, we'll make that

chip and that's that's coming for

everyone. But if you're trying to make a

better GPU, good luck. If you were doing

something different, it also needs to be

hard to do. And you can make different

trade-offs. you know, the disagregation

of prefill and inference really have

opened the aperture um for making these

different trade-offs because you can

make very aggressive trade-offs for

decode, aggressive trade-offs for

prefill.

>> Prefill being taking in the context,

decode being, you know, write the

output.

>> Yeah. I have a great colleague named

Andrew Fox who said, "Picture, you know,

a British naval ship from the 18th

century. Prefill is loading the cannon,

decode is firing it." And what prefill

literally is is just the model

understanding the question, the prompt

and then kind of keeping track of its

own dec.

And that is fundamentally a memory

capacity bound problem. Decode is a

process of generating new tokens and

that is memory bandwidth constraint. And

so if you're a chip designer, this gives

you a richer canvas to to paint on. But

even so, it needs to be hard because if

you make different trade-offs in that

iron triangle to optimize for memory

capacity, and they're not hard

trade-offs to make, well then Nvidia is

going to make those same trade-offs.

They get better prices from Taiwan Semi

than you're ever going to get. Um, and

good luck. Good luck. And they have the

advantage of working with every model

company and optimizing their designs.

And by the way, another very funny thing

is if you're a VC

and you're investing in semiconductor

company that is telling you they are

going to have an advantage because of a

Taiwan semi process that they have

special access to. I promise you that

Jensen saw that process when it was a

twinkle in Taiwan Simmy's eyes and it

they know more about it than this little

company with 200 people can imagine.

Taiwan, CMI, everybody in the supply

chain is showing Jensen everything the

same way they're showing Amazon

everything, AMD everything, TPU

everything. And that's another reason

don't go try to make a better GPU. So

you can do something different. You can

paint in the pre-filled canvas. You can

paint in the decode canvas, but you also

have to do something hard because if it

gets to scale, you're going to have

those four companies has very fast

followers. My firm was a was a um

venture investor in Cerebras. What

Cerebrris has done is something hard and

fundamentally different way for scale

computing. And it it comes with a set of

trade-offs, but that architectural

decision they made was hard and lets

them do something that no one else can

do. And we'll find out how big that is.

And you know, they're working on really

cool things like um one of the problems

Cerebras has. Once you start needing to

glue a lot of chips together and scale

up networks or scale out networks, you

need a lot of IO and IO is bound by

what's called the shoreline, the sides

of the chip. And so Cerebris has an

overwhelming ratio of onchip computed

memory relative to shoreline IO. Well,

they're really smart people. They did

something really hard. They're trying to

see if they can put an optical wafer

right on top of that and then that

solves that problem. Um, I'm sure

they're looking at hybrid bonding of

DRAM, you know, to get around these

alleged limitations that are not true. A

cerebrus machine can theoretically run

any size model. There are sizes of

models where they're much better than

other sizes. So, Cerebras, what I think

is interesting is they did something

different that's hard to do, really hard

to do wafer scale computing. So, I do

think there's a role for these and you

know, I would just encourage them all

make a different trade-off

and try and do something hard. because

everybody's going to get funded after

the Cerebrus IPO. It's not going to be a

problem. But it took it took Cerebras

three generations of chips to get it

right. And it's really hard. Like Andrew

Feldman, the CEO, you can just see

>> how hard it was

and that whole team did

>> to get where they are today. And they

need to have the grit to do that, the

resilience. This first chip is a

failure. It happens. Can you come back

and make a second chip? But the one last

thing on this topic, this is going to be

amazing for the useful lives of GPUs and

may single-handedly save private credit.

>> Say more about that. What what do you

mean by the private credit? Well, just,

you know, private credit, they're in

pain from these SAS loans and however

much they're marked down, they probably

need to be marked down more because if

the public companies are struggling to

adapt, how's like a debtleaden company

going going to adapt um and invest in

what is a very different margin

structure business? But there's a lot of

private credit and GPUs too. They were

underwriting that to I think three or

four years. And the disagregation of

inference means that I think these GPUs

are going to have 10 or 15 year lives.

The AI skeptics are like, oh, these

companies are all cooking their books.

You know, the useful life of GU GPU is

only a year or two. The useful life of a

CPU is only four years because the rapid

technological change. No. What rapid

technological change has done with the

disagregation of pre-fill and inference

is mean that you you know you can put a

cerebra system or grock LPUs that Nvidia

acquired effectively in front of a

hopper or even an ampier use that hopper

and ampear for prefill and extend the

useful life of that GPU

until it melts. Now they do melts they

do melt so they have a time but you know

maybe you you don't have to run them as

fast. This is going to be really good

for the whole private credit industry.

It's going to help finance the AI

buildout because if you can start to

finance GPUs at more like you know 5% or

6% instead of I think Corw's lowest

financing was like low sevens that

actually mathematically changes the cost

to finance this buildout. We had this

technological innovation that it's going

to lower the cost of financing extend

the useful life of compute on Earth. And

then I do think the one last thing

that's interesting about that is um my

friend Jamon from Kotu just did a

podcast and Cotu had a deck and they

talked about hey you know the sellers of

shortage are doing so much better than

the buyers of shortage. Buyers of

shortage being you know the the

hyperscalers

but if you own a giant installed base of

what is currently in shortage that's

also a very very good place to be. And

we're hearing, you know, CPUs are way

more important than they were in an

agentic world. They do all these things

around orchestration, tool calls, etc.,

etc., etc. The biggest CPU fleets in the

world sit at the hyperscalers. So, I

think some of these hyperscalers may

have,

>> you know, may may catch up a little bit

to the sellers of shortage.

>> I want to talk about this idea of

different and hard applied outside of

the infrastructure piece of this. So,

now you're starting to interact with new

founders, um, existing CEOs and founders

that have to adjust to this new world.

What are you seeing like the most AI

native founders that aren't building

chips or infrastructure or models, but

just people using this technology to

build other stuff? How do they feel the

most different to you if if you've

observed differences?

>> Well, one, I do think this is just for

chip design. To me, it's always been a

fundamental question for venture. So,

there are different ideas that are

obvious to everyone on planet Earth as

soon as they hear it. And if that's

where you are in venture, if it's not

hard to do, if it becomes obvious to the

world before you have built um scale,

scale is the ultimate advantage, you're

in trouble. And the great thing Amazon

had was um you know, I think it was

obvious to a lot of people, but it

wasn't obvious to the retail CEOs. And

Amazon, they were very smart.

Any e-commerce company that VCs invested

in, they would destroy. They'd be like,

"Oh, that's so cute. We're gonna we're

gonna take our margins of that to

negative 10,000%."

And that's what like like the guys at

Wayfair, they did something hard and

Amazon tried to kill them and they

failed. Those are like tough

operationally

like really competent CEOs. For me in

venture, I always look, is this going to

be obvious to the world before this

company could build scale

or is this both not obvious, different,

and really hard to do? I think a lot of

founders are really struggling with this

>> in AI like I think people are

becoming worried you know today in that

in Jensen's five layer cake of AI

and the profits they're acrewing to

energy they're crewing to data centers

they're crewing to chips they're

acrewing to models they're not really

acrewing to the applications cursor and

cognition you know got to a scale you

know they focused on coding

you know 18 months ago the people were

focusing on coding. OpenAI was doing

everything under the sun. The people

focused on coding were cursor cognition

and um anthropic and it was really right

to focus on code. Um I'm MSAD the

founder of Replet tweeted something that

I thought was so smart just it was

something like you know bitter lesson

adjacent is the fact that coding might

be the shortest path to ASI and useful

AI because if you're really good at

coding you can write yourself code to do

anything. So I think it was really smart

of those companies to focus intensely on

coding and I think they all probably got

to a scale where they they have a place.

I think cognition is doing something

really really different but I think a

lot of founders are really struggling

man they're really struggling

and you know I think they're trying to

get confidence that in nichier areas

>> that they can get to them and get like a

you know a data moat

>> before the model companies get to that

niche or that it's a small enough niche

that the model companies won't do it

themselves but it can still produce a

venture outcome. Is this related to what

you would call like the token path? I

know you've used that phrase with me

before.

>> Yeah, I he comes from a guy um at

alttimeter, Jamon Ball. He just said if

you're a software company or an AI

company of any kind, you have to be in

the token path. So, data bricks that's

in the token path. Comparable companies

are in the token path. If you're not in

the token path

and you're not in some really niche

thing, life may be hard. And even for

these vertical niches, I think if you

talk to the people at the model

companies, they're even skeptical of

some of these because all of the data

that's, you know, being generated in

these niches come from humans. But then

you're betting that you're able to use

that proprietary data in this narrow

vertical to train a model that's lower

cost than the frontier labs can ever get

to. And maybe that's a good bet, but I

just think you have to be very very

careful. Now on the other hand, if the

returns to these frontier tokens

relative to other tokens come down,

there's going to be an explosion in

value creation at the application layer.

And I think another really important

point is

I have a belief that whenever he wants,

Jensen can probably get pretty close to

the frontier

>> with his own model.

>> With his own model, they're doing some

really cool things. Neimatronics

>> commoditize your compliment as

>> say I don't think he wants to do that

that is what open AI and you know

anthropic are kind of trying to do to

him unsuccessfully

but so it's just like he's a very

logical thinker this is the logical

counter move

>> and I think you will see that like

opensource frontier which today consists

of you know Chinese models with stolen

American tokens you know Somebody told

me that like Deep Seek

uh the latest one or maybe the original

one was only 150,000 reasoning traces.

There's many ways to launder this if

you're a Chinese company. You know, you

can hit all these different APIs. You

can make it hard. Now, the American labs

are working really hard on

anti-distillation technology. But I I

just think Chinese open source, they're

doing really impressive things in a very

resource constrained way. But there's a

lot of distillation. And this is why I

think in addition to there not being

enough compute to serve Mythos

just they did not want it to be

distilled. they wanted to use Mythos,

you know, distill it themselves, use it

to RL their next model, whatever it is.

And then I think what they and

eventually I think if OpenAI gets to,

you know, economics feel good about

anyone on the frontier will do is just

say, you know, there's going to be some

very interesting game theory because

it's it is it's a new kind of prisoner's

dilemma. You know, we talked about the

old prisoners dilemma being just around

like, hey, you you're in a prisoner's

dilemma where you have to spend. The new

prisoners dilemma is going to be if you

were at the frontier, do you release

that model via API or not?

>> And if everyone at the frontier agrees

not to do that, then Chinese open source

is quickly

>> if one person defects, they're going to

have the best model. They're going to

have a lot of revenue and cash flow and

then of course resources equal

intelligence. So they'll start to pull

ahead and then that will lead to, you

know, everybody else releasing it. So

it's a new game theory. It's kind of the

same game theory that you have with

Taiwan semi Samsung and Intel. The

reality is like if if a company like

Nvidia were or AMD were to ever really

really use one of these other

foundaries, that foundry would get

better really quickly. So I do think

Jensen is going to keep open source a

certain time frame behind the frontier.

I think that's going to be a very

interesting thing to watch. And then by

the way, open source gets monetized.

There's this misnomer that open source

is free. Open source tokens, they cost

energy. They, you know, they cost energy

to produce. You need to make up on GPUs

and the open source model companies

almost always get a revenue share.

>> How are you preparing a trades for the

world of Mythos 3, Mythos 4.

>> We're just trying to overinvest in cyber

security. You know, something I've like,

you know, said in multiple forums and I

really believe is you everybody needs to

have a safe word. Everybody needs to go

leave your digital devices behind.

Literally go to the ocean and have a

family safe word or a company safe word.

And it can't be one that can be like

socially engineered. And this is just to

avoid like cyber crime where like what

looks like your son or your daughter or

your your grandparents or your parents

or whatever facetimes you. It's an

utterly accurate

simulation of them. they know everything

and can extrapolate based on what

they've said, what they're likely to

say, and says, you know, wire me a

million bucks.

>> That's defensive. What about what will

you still be able to do that it won't be

able to do, I guess,

>> on the analytical side.

>> So, it's a good question. I did just

have I I just watched The Last Samurai

and I asked um people at my firm to

watch it. And The Last Samurai, if you

haven't seen it, I highly recommend

watching it. It's actually a movie

that's aged really well. Tom Cruz movie

from 20 years ago. You know, the conceit

is Tom Cruz is this like bitter, washed

up Civil War veteran who's actually a

very good soldier. He's bitter and

washed up because he feels like he

participated in negative actions against

the Native Americans. He's hired by

Japan to train just during the Miji

restoration. And he's hired by the

modern elements of the Japanese

government to train like an army of

peasants

>> how to fight the samurai. There's a

first battle. Of course, the samurai win

even though they don't have guns. He

fights valiantly. So the samurai decide

not to kill him, take him to their

village. He becomes a samurai. It feels

like the civil war to him. So he fights

on the side of the samurai.

And at the end, he's massacred by a

peasant with a machine gun. And like the

machine gun is here and if we do not all

become masters of the machine gun, we're

going to get mastered. So I am trying to

become a master of the machine gun. And

then, you know, I'm optimistic. There's

a long period of time where just like if

you were a 50year-old samurai veteran of

many wars, I fought many wars, master

dwarf. Um, you will have advantages

using the machine gun. And I'm

optimistic as a lifelong student of

investing. I'm going to be able to

master the machine gun, this new

technology, um, integrate it into my own

process, integrate it into our firm's

process in ways that, you know, let me

contribute value as a human being for a

long time. But, you know, like everyone,

like, you know, I have agents running

all the time now.

>> What's your most useful agent? The most

useful agent honestly is as and I think

I told you this and I don't want to hurt

your business, but my single most useful

agent is a really good summary of the

points that would be interesting to me

from podcasts. There's like six hours a

day of stuff that I feel like it's in my

job description to watch, you know,

every time every time somebody from

OpenAI, XAI,

Google,

you know, Cursor,

Fireworks, Bin, I say nothing of like

Jensen, Elon, Daario. Um, I feel

compelled to watch and I just don't have

that much time. And there's some real

needles and hay stacks. There's a set of

things I always like to see like I'm

very sensitive to management

compensation. What are they incented to

do? They do they just have stupid RSUs

or do they have PSUs? And if they have

PSUs, what are those PSUs incent them to

do? I think systems that do a very good

first pass at that and you know that

saves people a lot of time. It frees

them up for more creative work than like

you know going through the proxy pulling

the PSU thing looking at how it's

changed versus all the proxies because

there's signal in that and that's very

labor intensive and that's so good for

an AI and there's obviously all sorts of

same things within investing. This is

the most exciting thrilling time to be

an investor

>> and there is and it is I am a little I'm

getting a little bit worried

>> the diversity breakdown thing. Yeah, I'm

getting

>> Say just like a little bit more about

like the kinds of people that are

>> I don't know anyone like me who's not

really bullish on DRM.

>> No one.

>> No one. There's all these interesting

things happening with AI right now. So,

one is cross-sectionally the valuations

do not make sense. They just flat out do

not make sense. They cannot all be true.

You have semicap equipment companies

trading at 40 times next quarter's

annualized earnings and DRAM companies

trading at mid-s single digit. at the

peak of the last cycle that was like

five verse 12. At one point it was like

three verse 45. Those can't both be

true. And yes, semiconductor capex

business models have improved more than

the memory business models. We don't

know how much HBM is going to improve

memory business models yet. Yes, they

have some element of recurring revenue

with parts and maintenance, but it's not

worth a,000% multiple gap. I think it's

hard to square like the valuation of

something like Nvidia which is still you

know in in in early April was

essentially as cheap as it gets relative

to the market like in the last 10 or 12

years or whatever it is and very cheap

absolute it's very hard to square that

valuation with something like GE

Vernova's valuation

>> because it builds in like an

unfathomable amount of share loss for

Nvidia. So valuations cross-sectionally

are really different because we are in

shortages.

The lowest quality companies are doing

the best. So if you're an oil and gas

investor or you know a mighty investor,

natural resources investor and you're,

you know, you're well versed in thinking

of costs, this is very intuitive to you.

In a real bull market for a commodity,

the commodity suppliers with the highest

costs go up the most because it's the

most beneficial to them. They go from on

the verge of bankruptcy to gushing cash.

And this is, I think, one reason

commodity investing is really, really

hard because quality outperforms during

the cycles, but you get all of the

outperformance during the downturns when

the high-cost guys that moon during the

shortages and the commodity bull

markets, you know, go bankrupt or

whatever. You're seeing that happen in

every industry. the lowest quality

players in, you know, these different

industries that are hated and detested

by the hyperscalers and the buyers

because they have high costs, they're

unreliable, the parts fail at a high

rate, etc., etc. They're sold out and

raising prices. Um, and then that

activity gets the interest of like these

retail accounts on X and these stocks

get bid to the moon. whereas some of the

higher quality expressions

have like actually really underperformed

and you know as an investor it's it's

hard because you know within a like

shadow of a doubt that that thing that's

moved you know 10x in 3 months or 6

months is going to go right back down

subject to what they do with all the

cash. But like these low quality

companies really do smart stuff with

cash. And so it worries me a little bit

that people who were very skeptical a

year ago are no longer skeptical. But

then I just contrast that with like the

valuations of these like highquality

companies which are just not extended

and it makes me feel better. But it does

kind of feel like, you know, I always

thought it was funny in 24 and 25 that

anyone asked about an AI bubble or

talked about it because it's like you

have this nuclear bubble and this

quantum bubble right here, right in

front of you. What are we talking about?

This is so real. Some of that nuclear

quantum silliness is maybe spread into

more speculative, lower quality, smaller

cap names where if you have a big

presence on X or Reddit, it's easy to

move them. And that frightens me a

little bit, but I just wish there were

more AI bears. Like I wish there were

more memory bears. You know, one reason

I'm um you know, Astera is a stock I've

been close to a long time. There's a lot

of bears on that. I love that. Great.

You know, I first invested in the series

C. Good luck thinking you're going to

price that, you know, differentially for

me. You know, good luck thinking that's

a copper loser. And then there's also

you can feel the baskets in the market

and the leverage baskets and what

baskets you're in is really important.

You know, copper, optical, DRAM, NAND.

Um, and a very interesting thing that's

happened this year, um, is in 24 and 25

the AI trade traded together. So like

you could be long GPU compute, scale up

networking, and optical scale across and

like short power. that trade worked from

like a riskmanagement sense because you

know I'm very factor aware that all blew

out in January of this year it's like

you know scale up networking would go

crazy while scale out was going down or

DRM massively underperforming NAND and

HDDs which had not h happened so these

cross-sectional correlations within AI

really fell apart and you had to get

very fine grained you couldn't hedge

your memory

anymore with like some semicap equipment

or nan everything cross-sectionally

really changed and in a very interesting

way in January and I think maybe one

reason for that was you know the AI got

to a quality where it was all of a

sudden really easy for a bunch of people

to get really smart on these different

subsectors start trading them and then

they get put into baskets and those

baskets

>> yeah creating price efficiency Yeah,

exactly. And then it's like if you like

I think some of the biggest

opportunities outside of these higher

quality names that I think can compound

for a long time and they're safe unlike

these lowquality names which are

terrifying is in names that are

miscatategorized

like Astera was in a lot of copper loser

baskets. Astera their biggest product is

going to be a switch. You use both

copper and optics to connect switches to

accelerators.

And so definitionally, if you're a

switch company or an accelerator

company, you cannot be a copper loser

because you're going to be on the other

side of that connection. I

>> I wonder if you could riff just for like

a sentence or two on each of the major

companies. I feel like I always forget

to ask you like Google, Microsoft,

Amazon, you know, the the major players

that are public that all the

conversation is centered around these

exciting new companies.

>> Yeah. So Google um it was incredible

last year because they had that TPU

advantage which is now gone. The reason

I think they're still in a great

position is just they have the most

compute of everyone. We talked about the

value of installed bases being higher as

a result of shortages.

>> They have the biggest installed base of

compute. Yeah,

>> I am a little surprised

by

their inability and Google IO is this um

is this week

>> and um like if they don't release

something that even slightly leapfrogs

open AI

andor um clawed

like that that's interesting and it's

not a disaster. faster for Google. It's

just interesting and it just means this

Nvidia effect we discussed is even more

powerful than maybe I'd imagined. But

I'm very curious to see what the paro

frontier looks like literally in 5 days

after Google's announced its new stuff.

This is a big card for them. But Google,

you know, between um the amount of data

they have and the YouTube data is

actually really genuinely valuable. It's

actually it is valuable in a world of

robotics. The amount of compute they

have and you know the search business

they have. Google's never not going to

be in a good position. And then you see

that with GCP going crazy. You got to

give Zuckerberg immense credit. Um what

he's done in terms of making Meta an AI

first company internally and I do think

he is the only one of those true

internet giants to have done that. And I

give him a lot of credit for that. I

give him a lot of credit for um paying

up when he did for you know all those

you know those billion dollar contracts

that talent

>> and Muse I think was a really big upside

surprise um you know was the first model

from MSL and it's not on the paro

frontier with you know XAI Google's one

entrant and then openAI and claude but

it's pretty close that was very

impressive to me so I think meta is in a

better position. Still not as strong of

an absolute position as Google, but like

they're better position and rates of

change matter more than level as you

know in markets particularly over short

like three-year time frames over like

long time frames level of competitive

advantages tends to dominate but even

within that you know the changes changes

are really matter. Amazon I think is in

a really strong position because of

Trrenium. you're going to see like real

P&L efficiencies from robotics over the

next 18 months in their retail business.

I actually think Nova their internal

models are not where Muse is, but

they're better than they get credit for.

Microsoft, I think Satya is a really

brilliant man, but you know, in in

investor conversations,

people just don't talk about him the way

that they did. I I like Satya. I admire

him. I think he's an exceptional CEO

and I give him a lot of credit for the

decisions he's made, but you know, he

did go from we're going to make Google

dance to being the product manager of

Copilot in like three years. I I would

love to know during the coup attempt

against OpenAI, does Satcha regret his

decisions?

Does Satia wish that he had supported

Ilia and instead of Sam and that kind of

Ilia and Meera were really running

OpenAI today? In his heart of hearts, I

would love to know because I think the

Microsoft OpenAI partnership might look

very different in that world. I think

that's a very interesting question that

we'll never know the answer to.

But I give him a lot of credit like he

is what he is doing now he's taking risk

so they could earn you know this goes to

the decisions you have to make in that

cone of uncertainty are not only how

much you spend but what you're going to

spend it on I think Microsoft flinched

for like a moment in early 25 you know

they have this algorithm we spend this

much capex dollars we get this return

that algorithm was kind of off and if

you flinch you lose position

>> you lose all these allocations and it's

difficult to get it back. So they

flinched and now the decision Satya is

making which the market has punished him

for but I think is the right decision is

we're going to use our compute rather

than making I mean who knows how fast

Azure could be growing if they're

willing to just sell GPUs to OpenAI.

We're going to use our compute

internally to make our own products

better. You know, one reason C-pilot is

so bad or has been so bad is just not

enough compute available. They're fixing

that. He's the product manager of

Copilot. I do think he's a great CEO and

they're trying to use their compute to

train their own models. I don't I am a

little skeptical that they have the

right team to succeed there but you know

they can certainly like just like Meta

they can afford to hire maybe maybe a

different team but I think he's making

good decisions that are risky decisions

to position Microsoft from for this

world where frontier models are are no

longer API accessible

>> and I think it's a really courageous

decision that I give him a lot of credit

for and he is foregoing I Microsoft

probably be an $800 stock today if they

were using their GPUs to serve OpenAI

solely OpenAI and anthropics capacity

instead of using them for their own

products. So I give him a lot of credit

for making a great decision. What's

really interesting is the degree to

which these companies are outward facing

in their decisions. The two companies

who are the most deeply engaged with

startups are Amazon and Nvidia by a

mile. Then there's a really intense

engagement with Google, their next most

intense. Broadcom is engaged in a

different way. They're just, you know,

everybody's favorite AS6 supplier. Like

it's, you know, if you're a startup,

it's considered like a level up if you

get to work with Broadcom for your

second gen chip. And it's considered

mana from heaven if Broadcom works with

you for their first gen chip. And then

you see essentially

zero engagement with startups from AMD,

Microsoft, and Meta. And I just Yeah, I

mean when I say zero, it's a little. And

I just wonder about that decision

because some of the best teams

are no longer at big public companies.

They're at these smaller startups.

And I think it's going to end up being a

pretty big advantage for Nvidia, AMD,

Google right behind them to have this

engagement

that you just don't see from these other

um hyperscalers.

>> As we wrap up, I'm curious for you to

riff on any other like out there

knock-on effects that you've started to

think about for this giant trend. We've

talked about the specific companies in a