What used to matter a lot was execution

was very very [ __ ] difficult and

ideas were cheap. Now ideas are cheap

and plentiful but execution is very

easy. So really only the good ideas are

the ones that can justify the spend on

super cheap implementation.

You told me this incredible story about

how your own team's use of tokens has

changed dramatically this year. Yeah.

Retell that story and what it is

teaching you about what's going on in

the world.

>> Last year we thought we were heavy users

of AI. Everyone's using chat GPT.

Everyone's using cloud. Everyone's got

you know I'm providing whatever

subscriptions anyone wants on the order

of spend of like tens of thousands of

dollars for our firm. This year the

spend has just skyrocketed and and it

really started in late December with

Opus that included Doug who's president

uh Doug Olaflin. He's very much like

leading the charge in the sense of like

non-technical people using uh AI for

coding. Um and so he's basically pled

the whole firm slowly over time. I think

he's been the the leader in doing that.

Obviously the engineers were using it

anyways but spend in January just

started to inflect and rocket and rocket

and rocket and rocket. Um, we signed,

you know, an enterprise contract with

Anthropic and it's gone to the point

where now, um, I think when I last

talked to you it was 5 million spend

rate. It's actually 7 million spend

right now.

>> That was last week, by the way.

>> A lot of that is just the usage, right?

What's what's really, you know, people

people who are have never coded before

are using cloud code and spending

thousands of dollars sometimes a day,

but across a firm, we're spending $7

million a year now on cloud code at the

current rate. um versus our salary

expense being in the neighborhood of $25

million. So, you know, we're north of

25% of spend on cloud code as a

percentage of salary. And if this

trajectory continues, then you know,

we'll spend more than 100% by the end of

the year. Uh which is a bit terrifying.

Thankfully, I don't have to decide

between people and AI because our

company's growing so fast. It's, you

know, more so like, okay, well, I don't

have to hire nearly as fast and I can

spend a lot more on AI and it works and

we just grow faster. But I think other

folks will start to reckon with the fact

that, huh, if this person can do the

work of five to 10 to 15 people uh using

cloud code, then all of a sudden I

should probably cut people. But right

now, I think the use cases are so broad.

For example, one thing is we have a

reverse engineering lab in Oregon that

we've been building for a year and a

half. We have a bunch of, you know,

fancy microscopes, scanning electron

microscopes. The whole purpose of this

is you reverse engineer chips. You get

uh the architecture out of it. you get

the materials that they're using to

manufacture and this is some of the data

we sell. This is a very slow process of

analyzing that data. Instead, um one

person on the team, they've been able to

spend with a couple thousand dollars of

cloud tokens. They've been able to

create this application that is GPU

accelerated runs on a server that we

have at Coreweave and anytime we send it

an image, it's able to take the picture

of the chip and overlay where every

single material is. Oh, this part is

copper. Oh, this part of the gate is uh

tantelum. This part of the gate is

germanium. This part of the gate is

cobalt. And so you can do a finite

element analysis of the entire stackup

of the chip very very quickly visual

with a dashboard guey it's everything

few thousand dollars would took claude

the person previously worked at Intel

and he said that was an entire team's

job to build that and maintain that now

rack that up across you know the entire

firm it's it's insane another example

that I think is super fun is Malcolm

who's an economist at a major bank

before um their economist department was

like 100 or 200 people what he built was

the most incredible thing ever. He piped

all of this different data, you know,

FRED data and all these other data,

right? Employment reports and all these

other things from various APIs. We

signed a couple contracts with folks to

get API access to data. Pulled it all

in, started running regression, started

looking at the impact of various

economic revolutions on the economy um

from a deflationary inflationary

perspective. The BLS has this entire um

Bureau of Labor Statistics has this

entire like set of like 2,000 tasks. And

so he did that with AI, which ones can

be done by AI, which ones cannot, and

grading them across a rubric. You know,

about 3% are doable now with AI. Um, and

so he's created this like metric so that

you can measure things that can be done

by AI, what what the massive

deflationary uh, you know, what the cost

of being able to do those with AI and

therefore the deflationary aspect of it.

You know, output can go up. It's called

phantom GDP is what he called it.

Phantom GDP. Output can go up, but

because cost falls so much, actually GDP

theoretically shrinks. So he created

this whole analysis and a brand new

benchmark of uh language models um a set

of evals across 2,000 different evals.

Right.

>> This all by himself.

>> This is all by himself. Yeah. And he's

like dude this would have taken the team

of 200 economist a year. He's just like

he's like completely cracked out on

claude. He's like everything has

changed.

>> How do you think about as a business

owner going from close to zero to 25%

accelerating towards whatever percent of

total spend? Like at what point are you

like, whoa, I need to put the brakes on

this and be careful how much we're

spending. Maybe we don't need to spend

on the most cutting it on Opus 4.7,

which came out today. Maybe I can

throttle it back to something that's a

little bit cheaper.

>> Ultimately, like I'm in the information

business, right? That that is, you know,

we sell analysis, we sell, we do

consulting, we create data sets. I don't

see why this wouldn't be completely

commoditized on a pretty rapid basis if

I'm not constantly improving. my first

product that I was selling as a data set

actually it is you know like there's

more people trying to do it now we've

made it constantly better and better and

better and more detailed and so

therefore it sells a market but the way

we were doing it in 2023 is not terribly

different than you know is it's it's

basically what everyone else is doing

now if I don't move up the bar then I

will be commoditized if I don't move

fast enough I will also lose my edge so

the question is yes AI commoditizes

things just like it commoditizes

software those who can move fast and

keep control of their customers and keep

providing them an awesome service and

keep improving the service won't shrink.

They'll grow. They'll grow faster. Those

who are incumbent and not doing

anything, they're going to lose. And so,

it's a bit of an existential like if I

don't adopt AI, someone else will and

they will beat me. Uh, another easy

example is the energy space. So, we've

had a few energy analysts for a couple

for like a year now. We've been trying

to build out this energy model. It's

very complex. Energy's data services

market is something like $900 million.

So obviously a huge market for me to try

and break into but it has you know we

really hadn't broken into the energy

data services business despite a year of

having multiple people on the team. Um

then cloud code psychosis hits one of

the people who leads the data center

energy and industrial sort of business

at semi analysis uh Jeremy hits him and

now all of a sudden in 3 weeks um he

spent a lot he was spending like $6,000

a day. It was an insane amount but he

scraped every single power plant in the

US every single transmission line above

a certain voltage. um and created this

entire mapping of the entire US grid as

well as a lot of demand sources all from

various public sources of data. Um and

we've shown it to and and we built and

it's got like this dashboard where you

can view and check you can see all the

micro regions of the US where there's

power deficits and surpluses. Um all of

these details built in a handful of

weeks we started showing some of our

customers who buy our data center data

set but are energy like traders. We

showed some of them and they're like wow

how long did this take you? This is

really good. this is better than XYZ

company and then we like get dig deeper.

XYZ company has 100 people and have been

working on this for a decade now.

Obviously our thing is not fully robust

as robust but in some ways it is better.

I'm going to commoditize these energy

services companies, data services

company. Who's going to come commoditize

me if I don't move faster? And so the

question from a business owner's

perspective is yeah I'm spending a lot

but what does that spend getting me? Is

it getting more revenue? Yeah.

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Are you worried that in the limit the

people that control capital and

investing capital who are often hiring

you for for what you do will just say,

"Well, we have analysts too who are

really smart about this. Like, we'll

just build this ourselves." Like if it's

getting that easy, at what point does it

just all pull into the investment firms

that stand to gain the most because they

have the most leverage on top of the

data or the insights that that they

glean?

>> First of all, any information services

business, obviously I don't generate as

much value as my customer does from such

information. Uh because if I sell you

information for a dollar, you're only

buying it for a dollar because you know

that information helps you make a

decision that lets you make more than

$1. And so therefore, you have you have

arbit you you have made more money off

of me than I did from the information

myself. An investment fund, these

investment funds all have their own

information services, you know,

especially like the super like the Jane

Streets of the world and the Citadels.

They're they're really detailed on their

data. And yet, um, these sort of folks

also purchase data from us and continue

to do so and continue to grow with us

because I think there's just some some

it factor, right? We move faster, we're

more nimble, we're a smaller team that's

focused on just one specific thing. uh

AI infrastructure and and the huge

revolution that causes in AI um and

tokconomics and all these things and and

we sort of really see where it's headed

and so we're moving faster and building

faster. Um I think investment

professionals just would you know yes

they'll try and build some of the stuff

we do and um more likely they'll just

buy the data from us and it's cheaper

for them to buy the data from us and

then to build and then build on top of

it than it is to build it themselves.

But ultimately some may try. I feel like

every conversation I have with you, what

I'm always getting at is just supply and

demand of tokens like that's the thing

that's interesting to me in the world

right now. What has this experience

taught you about the demand? Has it

changed your view on the demand side of

that equation? Just feeling it

viscerally yourself.

>> If we take a step back and look at the

macro lens, right? Enthropic has gone

from 9 billion revenue to what they're

at 3540 billion now. Probably by the

time this airs 40 45 billion, who does

ARR? Their compute has not grown to the

same degree. Um, and if you do the

calculations and you assume they didn't

decrease their research and development

compute, they clearly didn't. Their

release, they have Mythos, they have up

is 4.7. So they clearly didn't decrease

their research compute spend. Um, so

ultimately what they've done, even if

you assume all incremental compute

they've gotten has gone towards

inference, their margins are at a floor

of 72%. In reality, some of that

incremental compute they've got probably

went to research and development. It may

be higher than 72% gross margins. To be

clear, at the start of the year, they

started uh there was um there was a leak

by someone from their funding some some

of their funding round docs. Someone

leaked it 30 something% gross margins.

Where on earth does a business like this

grow margins like that? And it's in

principle, right? Their demand is so

high. They're able to cut back on usage

limits, rate limits, all these things.

Um, what really matters is having an

anthropic rep and having an enterprise

contract with them and getting the rate

limit increases that you need because

otherwise tokens are ultimately super

super in demand. Whoever whoever can pay

for them anthropic has the same problem,

right? Like I mean not problem, it's

it's just the reality of how capitalism

works. Yes, people are spending sending

them $40 billion AR in tokens and but

those tokens are generating way more

than $40 billion in value. Various

businesses will have different value

generation per token. But as we get more

and more intelligent, what really

matters is access to these most

intelligent tokens and leveraging them

at things. You as a person deciding what

is the best way to leverage these tokens

to grow business and generate value

because a lot of folks will want tokens

and generate tokens. Uh but the shitty

SAS startup and and and and SF who is

using Claude to generate, you know,

their software product is not

necessarily actually creating a ton of

value and therefore they're going to get

priced out of tokens uh soon enough.

>> Are you at all surprised that I I had

this experience just today where on the

flight here I got rate limited out on

something I saw 4.7 came out and what I

immediately wanted was like to be on 4.7

that second and I was it just I couldn't

think about using 4.6 anymore. or not.

This 47 is out. I was perfectly happy

with 4.6 for the last many weeks. It's

amazing. Are you surprised that people

are so insistent on going to the most

expensive leading edge thing to the

degree they are?

>> Without a doubt. One of my funniest

memories in the past month and a half is

myself and a buddy of mine, Leopold,

being on our knees in front of an

anthropic co-founder begging him for

access to Methos and then pretending it

doesn't exist cuz we knew it existed.

were like, "Please give us access." And

he's like, "I don't know what you're

talking about."

>> What was your reaction to that rate card

or that eval card coming out?

>> It was rumored in the Bay Area.

Everyone, you know, we sort of like knew

it was supposed to be really good, but

um if you just look at the benchmarks

and obviously benchmarks change over

time, Mythos is potentially the biggest

step up in model capabilities in like 2

years. I think that's really really an

an important detail that you know it

it's so good that they're like don't

want to release it even though they're

they they already announced the price to

their people that they did a selective

release for cyber for and it's like five

or 10x the token cost. They just don't

want to release it um because they're

worried about the like impact on the

world and they're releasing a shitty

worse version of open 47 to us and they

explicitly said in the model card hey we

actually preferentially made it worse at

cyber. I don't know if you read that.

whoever you are, if you have enough

capital, you should get a freaking

enterprise cloud uh enterprise anthropic

subscription where you pay per token,

not with these like subscriptions

because then you won't get rate limited

much. And then you must you need to

figure out how to leverage those tokens

to the highest value task um and make

money off of it because ultimately what

you're doing maybe maybe like a year

from now or two years from now the

business is actually just arbitrageing

tokens, right? The tokens are amazing,

but let's figure out what direction to

point them in and then three or four

years from now the model will know, you

know, what to do with the tokens and how

to make the most value. You know, you

can look at this retroactively. Pick any

benchmark. The cost to hit a certain

capability tier used to cost X and now

it cost 1/100th or 1/ 1,000th of that.

Deepseek, for example, on GPD4 was

1/600th the cost. And since then, the

costs have fallen further for GPD4 class

models. Of course, no one gives a crap

about GP4 class models. They want the

frontier because the frontier lets them

create the economically valuable things.

But GP4 class models can still be used

in like stuff and so people are using

them in some like tiny use cases. It's

just the cost have fallen so fast. It's

it's not really what's driving the

demand. What's driving the demand is is

all these new use cases. Yeah. Current

4.6 opus or 4.7 opus tier models a year

from now my spend for the same exact

quality of the model would probably be

like 70k. I bet you it'll be 100 times

cheaper. irrelevant because I'm going to

be using a way way way better model

which can do way way better things.

Enthropic mythos is more expensive as a

model but it spends a lot less tokens to

do the thing and therefore it is

actually cheaper in most tasks than 46

opus because it's just way more

efficient even though each individual

token is smarter.

>> When I last saw you Methos had just come

out maybe the day before or something or

the the card had just come out and you

said something like uh it actually made

you feel like a little scared it was so

good. What did you mean by that?

Anthropic's whole like goal in 2025 was

and and even a lot of 2024 they're like

hey by the end of 2025 we need an L4

software engineer uh in our model and

and they by and large achieved that with

46 Opus. What they didn't say is that

you know and if you look at Mythos and

if you compare like the benchmarks it's

like an L6 engineer. So L4 is like

pretty new. L6 is like quite well

experienced. I think Anthropic said that

the model internally was available in

February. So in two months they've gone

from L4 engineer to L6 engineer. Uh

what's next? Um you know when when you

think about the model progress it's only

accelerated. Enthropic release cadence

has compressed. Open's release cadence

has compressed. Why? Because these

models generally to make a better model

you need a few things right. You need

amazing compute. Compute is very

expensive and it has a time scale that

we you know we track and it's like you

know it's growing but like you know it's

it's sort of set in stone for the next

you know short short term. it's like

kind of set in stone what you've already

signed. Um there will be delays and

shifts and some somehow you can find a

little more but it's generally pretty

set in stone. There's amazing

researchers that people are paying tens

of millions of dollars for. And then

lastly there's implementation.

Historically has been very difficult. If

I have an idea now I have to implement

it. Implementing is hard. Now ideas are

there. Implementation is very easy. It's

expensive but it's very easy. So how do

you how does one decide what ideas to

implement? And it turns out if your

implementation is just so much easier

now you can just implement more ideas

and move on the treadmill faster and

faster and faster. Whether that is AI

model research and so now your model

release cadence is shrunk to down to 2

months from where it was 6 months before

or hey I want to I want to take every

power plant in the US and every

transmission line and model it and run

regressions and see the micro supply and

demand. I can also do that. The idea is

cheap. You know which idea makes sense?

which idea is worth the capital that you

have to spend on the tokens because the

implementation is there. It's it's

that's the I think the key learning and

if implementation costs continue to tank

which they are um we don't even have

mythos yet. It's only been you know a

handful of hours since Opus 47 launched

but you know my team is pretty excited

about it internally. What now comes to

the world uh it's a complete reordering

of how like economies work. What used to

matter a lot was execution was very very

[ __ ] difficult and ideas were cheap.

Now ideas are cheap and plentiful but

execution is very easy. So really only

the good ideas are worth are the ones

that can justify the spend on super

cheap implementation.

>> So are you actually scared or are you

just is it just does it just introduce

an uncertainty that's hard to grapple

with?

>> Uncertainty is there. Um but I do I do

think that causes some fear in terms of

how does society reform itself? How does

one

exist in a world where actually any you

know your ability to implement something

is not actually that important. Your

ability to choose the correct idea for

AI to implement and then your ability to

sell that idea or sell what the AI has

implemented is what matters. Your

ability to garner capital towards that

is what matters. And going back to the

point of like it's very important to

have the newest model always. Who's

going to have access to the newest

model? Anthropics project. I know it's

not called earwig, but I troll anthropic

people by calling it earwig. Um,

glasswig anthropic earwig, you know,

where they only release mythos to

certain companies for cyber. That's just

going to be something that continues.

Models will have less broad and less

broad deployment. I know I know Open AI

and Enthropic and all these people are

like, we want to have great AI for

everyone. AI is very [ __ ] expensive.

Who's going to pay for the trillion

dollars of infrastructure? People who

have money and can can build useful

things with AI. And then you don't want

people to distill your models. So you

don't release them broadly. Uh you

release them to a fewer and fewer set of

customers. Those customers are also now

wrestling over the tokens unless

anthropic jacks them. You know, they

could double their pricing on Opus and I

would continue to pay and I bet most

users would continue to pay.

>> I bet that wouldn't solve their

humongous capacity problem that they

have. So then the question becomes where

does this cycle end where you know token

usage and therefore the benefits of

those tokens the additional value

generated on top of those tokens

aggregates among fewer and fewer and

fewer companies. I don't have mythos.

You know who has mythos? Top freaking

banks. Um now they're only using it for

cyber security. But at some point I can

envision a world where hey maybe I

because I have an enterprise enthropic

contract and because enthropic people

kind of like me they're willing to give

us like slightly earlier access or

slightly higher rate limits or something

for a model. I hope that's what happens.

And then my competitor whoever that is

doesn't have that and I'm able to

[ __ ] crush them. There are people who

are like Ken Griffin of Citadel is like

super well-connected and super rich and

he's like he he just signs, you know,

who knows? He goes and signs a deal with

Open Arenthropic that's like, "Yeah, I'm

going to get access to your models. Um,

and I'll buy the first $10 billion worth

of tokens each year. So, whenever you

release the model, you know, I'll spend

the first 10 billion tokens and then

everyone else can get the model after

that." And it's like, okay, well, now

what does that do? Well, now he's going

to crush everyone in the market. That's

just an example. Could be cyber like

Anthropic is worried about, oh, now I

can hack people. could be information

services business like myself where I

crush someone else. I think you know it

it's it's such a broad base. We don't

know what these models can do. Anthropic

doesn't know what these models can do.

No one knows what these models can do.

It's up to the end user to figure out

where they can leverage the tokens to

see what they can build and imagine

which is tremendously productive and

uplifting for humanity. But then what

happens to the concentration of

resources and usage of it?

>> Presumably right now robotics or robots

consume relatively zero tokens versus

everything else. Do you see what's your

view of that? If that's like a second

demand curve that could start to

ratchet, there's a new startup every

single day, you know, within a mile of

here trying to build something

interesting in robotics.

>> So there's this concept of software only

singularity, which is that the world

has, you know, AI singularity, but only

in software. And now what about the rest

of the world? Vast majority of the world

is physical. You can see the world

orient around hardware, not software.

That's actually why I think software

only singularity is like just a blip and

not like a you know we we do get

everything else because once software is

super easy what makes robots really hard

it's like programming microcontrollers

and actuators and controlling all this

stuff is very difficult right now the

interesting thing about models AI models

is they're actually really inefficient

in learning it's just we're able to give

them so much data that they're able to

learn and surpass us in certain ways

robots currently the robot models um

VA's uh vision language action models

which is very popular right now is

probably not going to be the thing that

ultimately scales beyond. They are

inefficient in data um and we can't

scale the data for them fast enough.

There is going to be some way to large

scale pre-train robot models where just

like humans see all this data throughout

their lives. And what's interesting is

humans the reason why we're so good is

we're sample efficient. One example, two

example, we're good. And so applying

that to robotics. So once you once you

have this software only singularity

implementation is super cheap. anyone

can start to build these mo people can

start to build models that now robots

are actually useful and so I think in

the next six to 18 months we'll start

seeing real breakthroughs in robotics

that enable few shot learning i.e.

there's a pre-trained robot model and

now there's a robot that you have hired

or bought or whatever. You show it a few

examples and it's able to do it. You

tell it to stack these two things or you

tell it, hey, this can can actually like

balance perfectly, you know, and and it

starts doing these things.

>> Nicely done.

>> One shot.

>> No, trust me, I've spilled many of

times.

>> So, I think I think robots will get fot

learning right now. Now, you know,

there's a lot of companies doing robots

for like, you know, advertisement or

robots for like simple stuff like that,

but it'll be like, oh, folding clothes,

but it's going to get really niche like

robots just for cleaning chalkboards.

Um, and it's a rental service or, you

know, it'll be it'll be a model package

that you download onto your standard

robot that then does that, right? And

and you pay for that. And anyways, there

will be a huge explosion in physical

good acceleration and and deflationary

effects there. But and and so that's

that's ultimately going to keep token

demand going crazy. I I don't think

token demand slows down personally.

>> Did you learn anything else about the

world based on Mythos's results and how

it was built? My way of asking like the

you know if you break down the the

components of the scaling laws like the

>> So Methos is a materially larger model

than prior models and so yes it is a

much larger model. Now whether or not

it's it's what chip it's trained on is

not really relevant. It's the scale and

obviously you know to a 100,000 black

wells is equivalent to hundreds of

thousands of prior generation chips.

TPUs and tranium have their different

release cadence. So it's not exactly

like mirrored one to one. Um but

ultimately yes mythos is a significantly

larger model. It's proof that the

scaling laws still work. Um everything

about it shows the trend line continues

of models. More compute into model makes

model better. And along the whole way

it's not just more compute into model

makes model better. along the whole way

we're also getting these compute

efficiency wins which are you know as as

all this research compute that the labs

are spending is actually turning into if

I want x capability tier model every 6

months that cost or every two months

that cost is dramatically decreasing but

then if I scale it up massively I get a

humongous capability jump as well and so

yes it's it's proof that this is still

happening Google and anthropic are not

heavy heavy users of GPUs on the

training side but openai they'll they'll

start having their new class of models I

think they're taking a more sensible

principled approach to scaling uh in

small steps. Enthropic really went for a

huge jump. We'll see better and better

models throughout the year and the

release cadence is only going to get

faster.

>> We've gone a long way in the

conversation with saying almost nothing

about OpenAI which would have been so

strange.

>> So, so this is this is the interesting

thing. Everyone's like, okay, so

Anthropics just won, right? You know,

they had Methos in February. They never

even released it cuz they didn't feel

the need to. They're already sold out.

Their revenue is already adding $10

billion a month. Um and then you've got

Opus 47 today all before open eyes you

know um alleged Spud release which you

know media such as the information and

others have have posted about. So

clearly Anthropic is in the lead right

and OpenAI is cooked. What's interesting

is because Anthropic has such bounds on

compute and they can only grow it so

fast and sort of to the point of you

know you know Daria Daria used to gloat

about how OpenAI was being too

aggressive on compute and Anthropic was

more sensible in their scaling and now

Enthropic is like [ __ ] we should have I

wish we had a lot more compute. OpenAI

is able to pay the bills perfectly fine.

In fact, they've raised a ton of money

to get incremental compute in addition

to the irresponsible levels of compute

that they were buying from Oracle and

Core and SoftBank and all these people

and Microsoft uh you know such as

Tranium. Now they're getting tranium as

well from Amazon. Um so so they've done

this like insane thing on compute and

they need know they also know they need

more. But what's interesting is if you

were to say Opus 46, you know, let's

ignore models getting better over time.

Let's just take diffusion of this

technology. You and I may get jump on

the model immediately day one, but other

businesses take time and it takes time

for people to learn and the spark of oh

[ __ ] claude psychosis moment doesn't hit

everyone at the same time. And so by the

end of the year, let's say a 46 opus

tier model the economy would spend

$und00 billion on. I don't think that's

unreasonable. It's spending $40 billion

right now.

>> That's like a linear extrapolation.

>> It's a linear extrapolation, not a not

an exponential. To get the exponential,

you need the better models. Enthropic

won't have enough compute to do that.

And so and and presumably OpenAI and

Google will hit that tier soon enough.

Whoever hits that tier next, sure,

Enthropic may get to charge 70 plus%

gross margins, but if OpenAI hits it

next, they charge 50% gross margins.

They still get all of this incremental

demand. And probably they also won't

have enough compute to serve all the

users. And so, sure, maybe Mythos is a

model where if the world had enough

compute, it'd be $500 billion of revenue

or something crazy. There is such demand

for these tokens and such limitations on

compute, you know, and we see this with

H100 prices skyrocketing and the useful

life of these GPUs continue to extend.

It's pretty clear even the tier 2 lab is

going to be sold out of tokens, let

alone the tier one lab. The tier one lab

will have better margins, but the tier

two lab will be sold out and probably

the tier three lab will also be close to

sold out. Economic value that the best

model can deliver is growing faster than

our ability to actually serve those

tokens to people via the infrastructure.

And so this gap will continue to grow

and the model labs will continue to have

expanding margins until people in the

hardware supply chain infrastructure

supply chain are like wait no why don't

I just jack up my margins. So suffice to

say I think the assessment today or your

assessment of the demand side is

completely explosive in your own

particular example here at semi analysis

but just more broadly that as people

fall in you call it AI psychosis as

people fall into this experience of what

they can do the implementation

difficulty going completely away I I've

certainly felt that you know my own

token spend is just through the absolute

roof just in the matter of weeks so that

that feels like a pretty good assessment

anything we're missing on the demand

side

>> if you don't use more tokens you'll

never escape the permanent underclass

just expand on that.

>> So either either you use more tokens and

you generate economic value outsized

economic value for the use of those

tokens. Um a lot of people are doing it

the boring lazy way. Oh, I guess I'll

just work one hour a day instead of

eight hours a day and I'll have AI do

most of my job. That's the boring way.

The cool way is I'll still work eight

hours a day and I'll I'll do 8x the work

and maybe I'll make 5x the money. Um

maybe not you can't do this with a job

obviously. There's people who have

multiple jobs. Um there's people who

like start companies and start selling

stuff. get that economic value on on

this AI before everyone is using it and

it's table stakes. Uh because it's still

not table stakes if you don't use more

tokens and generate the value from them

and capture that value. These there's

three different problems here. Using

more tokens, generating value from those

tokens and capturing value from those

tok uh from the value that you created

from the tokens. Uh if you don't do

these three things, you'll never escape

the permanent underclass i.e. as models

continue to skyrocket in capability and

the concentration of resources

potentially happens.

>> Okay, let's talk about supply. what is

going on like how would you describe the

frontier of what's changing or what is

changing at the frontier of supplying

the the entire stack that's required to

serve all these tokens as the demand

curve explodes

>> as demand skyrockets prices are going up

for everything on the supply side um

whether it be the NGPUs

uh their prices are going up in addition

their useful life is extending

>> H100 prices look like this

>> yeah exactly there's people who have

argued GPU's full lives are less than 5

years complete nonsense

Um there are clusters now resigning

three or foury old hopper clusters

resigning for 3 or four more years. Um

there's A100 clusters that are resigning

for another couple years. So the useful

life is clearly not 5 years. It's maybe

even seven or eight years. Um arguably

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

see when Hopper gets there, but it it's

clearly not 5 years. So the useful life

is extending and the prices are going up

on that renewal. So in effect the gross

margin was not 35% on a cluster, it's

beyond that. Um so margins are expanding

in the in the cloud layer. Margins are

um extremely healthy on the hardware

layer with you know Nvidia still

charging 75 or whatever percent gross

margin as we move down the stack. Memory

obviously margins have skyrocketed

there. Places like optics and logic

there are large prepayments um and

margins are growing slowly um more so

the companies that are making chips like

Nvidia are paying huge prepayments. So

in effect the cast of capital or timing

of cash flow return on invested capital

is going up even if the gross margin

isn't. And you see this across the whole

supply chain. You see ASML is completely

sold out and they need Carl Zeiss to

expand faster. Everywhere along the

chain

everyone's either sold out and margins

are going up or they're getting

prepayments increases the return on

invested capital because the invested

capital is lower. And so this is like a

consistent trend across any part. It's

it's even like you know a PCB to make a

PCB requires copper foil and that copper

foil is sold out and people are making

prepayments for it. It's like anything

and everything that like has a pulse and

is like sold out. People are like

jumping to get more incremental supply

and fighting over the supply for the

years after.

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What do you think are the most important

bottlenecks? Like typically in economic

history when there's this kind of

demand, supply reorients and rises very

very quickly to meet the demand. It

seems like it's almost impossible for

supply right now in this moment to keep

up. You know, famous last words, every

every shortage is followed by a glut

historically. But what are the most

interesting bottlenecks to you on across

the supply side?

>> Supply chains are usually very fast to

react. Um, one unique thing is that our

supply chains now are more complex than

ever. and the things we're building are

more complex than ever and therefore the

lead times are longer. Um, and it's not

like we haven't seen 18-monthl long lead

times in other industries. It's just

building incremental supply didn't take

years. Um, and this is the case with

memory, right? Memory can only grow

capacity, you know, low double digit

percentages a year, right? 20s 30% a

year. Um, even less for NAND, a little

bit higher for DRM. Even though the

demand signal was very strong at the end

of 2025, the memory companies

immediately sort of started reacting.

None of that incremental capacity really

gets here until the second that they've

decided to do in addition to the typical

20 to 30%. You know, they can stretch a

little bit, but really the true

incremental supply doesn't come till 28,

which is a very unique thing. Even if

they wanted to build as fast as

possible, it doesn't come till 28 uh

early late 27 at best. And so the result

is memory prices have, you know, gone

through the roof. And guess what?

they're going to double and triple

again. Um, at least on DRAM especially,

people are like, "Oh, the memory storage

is overplayed. Everyone gets it." And

it's like, "No, no, no. You don't get

it." DM will double or triple from here

still because that's that's how much

capacity is required and they have to

steal capacity from somewhere else. And

the only way to steal capacity from

somewhere else in a in a capitalist

economy is demand destruction via higher

pricing. We're not like rationing stuff

here. And so ultimately, that's what's

going to happen. And so margins continue

to go up. Um, I think Logic also has

humongous uh capacity problems. TSMC

just had their earnings. Uh, they keep

upping capex. Ultimately, you know, it

takes them quite some time to build

fabs. Um, they're trying to do

everything they can to squeeze every

little output out of every fab that they

have. But ultimately, they're not

raising prices fast because they're good

people. It seems like, um, you know,

singledigit price increases instead of,

you know, tripledigit price increases

like the memory guys have had. And so

you ultimately have like this like

market where yeah TSMC is a great

company but are they are they actually

going to extract all the value? I

mentioned things like copper foil, glass

fibers for PCBs, lasers. These are

things that are like well understood and

niche supply chains but they're very

very tight. Um and ultimately upstream

the semiconductor wafer fabrication

equipment supply chain is one that like

I still think is it's gone up a lot but

it's still very underappreciated. TSMC

capex this year they say 56. Uh we've

had 57.4 4 billion since January. Um,

and we may up it slightly more just

because we see some some ways that they

can get incremental capex. But what

people aren't focusing on is what does

that mean next year and what does that

mean the year after? And it turns out 3

years from now TSMC is going to spend

hundred billion on capex. U maybe two

years from now, right? Maybe 28.

Sincerely, they may spend $und00 billion

on capex in 2028. And people like just

can't fathom that. But what does that

mean for their downstream supply chains?

um you know companies like Lamb Research

or Applied Materials or ASML or their

further downstream supply chains like

MKSI and and all these other companies

the tail whip just gets whipped harder

and harder and harder and ultimately

that's a shortage if you know TSMC wants

to spend $100 billion in 2028 which is a

real possibility I think people would

think that's insane but that's a real

real possibility

>> what about other parts of the chip

ecosystem where GPUs have been

completely dominant what about like CPUs

or AS6 or things that start to pop out

as both opportunities and bottlenecks

beyond just like Nvidia's GPU dominance.

>> Yeah, I mean AS6 are obviously taking

off, but I'll sort of pivot away from AI

chips to talk about these other things.

There's a project we did on FPGAAS and

there turns out there's 120 FPGAs per

per um next generation rack um AI rack

and then like what about all the FPGA

names CPU wise all these reinforcement

learning environments plus all the slop

code you and I are generating that is

now running on some you know Versell

instance or whatever it is um or some

AWS instant or some bucket that we've

spun up all of that requires CPU and so

CPUs are completely sold out and demand

is skyrocketing there

>> help people understand the role that CPU

plays and everything.

>> Yeah. So there's two there's two main

reasons why you need tons of CPUs. One

is when you're doing reinforcement

learning um the CPU is very critical to

that. So so before you would throw all

the internet's data into the model,

train it, spit it spits and it it spits

some stuff out. Now you train all the

world's internet you put all the

internet data into the model. Then you

put it in this environment. This

environment is like hey model try this

out and it tries stuff out. It tries a

bunch of different things and in the end

there is an environment which scores

whether or not what it tried out is

successful and it grades it. And these

environments can be anything. It can be,

hey, check if the text was outputed in

the right way, structured outputs. It

can be very simple stuff. It can be very

complex stuff. Um, and people are

starting to get into very complex

things, right? Like, hey, I want you to

open this file, change it, edit it,

update it, submit it to this website. I

want you to open up this physics

simulation from Seammens and edit this

CAD model. So the environments can get

more and more complex and those

environments run on CPUs. They don't run

on GPUs. They don't run on AS6. The AS6

run the model that takes the input data

from the environment, runs it through

the model. The model creates outputs of

various different trajectories, right?

Ways that it think it could solve it um

in different instances. those

trajectories are graded slashscored and

the ones that are successful you train

on and you update and you reiterate and

you iterate iterate iterate and so CPUs

are very useful for that one and then

once you have these great models and

you're deploying them those models are

generating code they're generating

useful output that useful output it

doesn't go from a GPU straight to the

human brain um it goes from a GPU or an

ASIC through to you know a deployed app

that you're deploying somewhere that

actually just runs on CPUs so that's

another area where there's a lot of

demand and and things are sold out um in

a large large way.

>> As you continue to assess and try to be

the world's best informed person on both

the trajectory of supply and demand,

what are things that you wish you knew

to make that understanding that you

don't know?

>> I think the hardest area for us um and

for everyone is understanding

tokconomics, economics of tokens. Um, I

think we have a really tremendously like

good insight into how much it costs to

run infrastructure, what the cost of

tokens are, what the cost of models are,

what the margins of these labs are, but

the usage and adoption is what's really

difficult to model, you know,

continuously, right? We we have these

like we had like crazy in January, we

had crazy estimates for February,

anthropic smashed them. How do we

calibrate this model? What are the data

sources for this? February, uh, we had

crazy assumptions for March and then

they smashed them. And everyone sees the

number of 10 billion and they're like

what the how do they add 10 billion in

revenue? Who is using all these tokens?

Why are they using them? What are they

building with them? And then more

importantly with what they're building

with these tokens, how is that actually

diffusing into the economy? And what

value is that generating? Because it's

not really something that you can

capture in any any GDP statistic, right?

all of the value of the tokens that I

use get transformed into better

information which I then sell at a

discount to what people used to sell

information for relatively because um

and therefore that information is now

making its way throughout the economy

and and people are making better

investment decisions or better

competitive decisions if they're a semi

company or data center company or

hyperscaler and now how how much what

what is the value of this and what has

that what has that done to the economy

it's clearly by every subjective metric

amazing Amazing. But where is the

phantom GDP? What is the phantom GDP?

How do we track the real economic?

Because because the GDP metrics are not,

you know, accurate if you were to say

what is the GDP that Dylan Patel is

making. It's tiny compared to what the

value that I think is being created. And

so ultimately, what is the value being

created by these tokens? Not on a basis

of, you know, just simple, you know,

what is the knock-on effect, right? What

is the knock-on effect of all the things

that these things are doing? I think

that's the real uh question and

challenge uh that's hard to measure. I

think we've got a tremendous, you know,

reading on the supply side of things. I

think we've got a tremendous reading on

even a lot of the demand side signals,

but it's it's what is the value these

tokens are generating. That's hard to

quantify and measure.

>> I hope we get a chance to do this like

every 3 months because this changes so

quickly. What do you think is going to

happen next? Like when I when I come

back 3 months from now and we're in San

Francisco together again, what do you

expect?

>> Large scale protests.

>> Really?

>> Yeah. Yeah, I think there will be a

large scale protest against anthropic

>> and open AI.

>> Expand on that a little more.

>> Um, people hate AI. Um, AI is less

popular than ICE, less popular than

politicians. Confused how Pew surveyed

this, but apparently AI is less popular

than politicians. You know, with

Enthropic adding so much revenue, that's

going to start causing business changes

downstream. People are going to get more

and more scared of AI. they'll start

blaming more and more of their own

problems and things that are, you know,

global, you know, have been deep-seated

problems for a long time. Those will

bubble up and be blamed on AI. Um,

probably some politician or some social

media people will start to be able to

take uh influencer will be able to start

taking and weaponizing AI against

people. You look at the comments of news

articles where Sam Alman had a Molotov

cocktail thrown in his house twice in

like two weeks. They're like, people are

cheering it on. Uh, and this is just the

beginning. So, I think I think we'll see

large scale protests against AI in three

months.

>> What is the counterwe to that? Like, how

should the AI industry head that off?

>> First of all, Sam Alman and Dario have

to stop getting on interviews. They're

so uncarismatic.

I don't know what they're doing. Every

interview they do is like, wow, normal

people are going to hate you even more.

Like, Sam being on Tucker Carlson

probably made all Republicans hate

OpenAI. And same with Dario. They just

have no charisma. I think that's first.

Two, they need to start showing

uplifting things that can be done with

AI. Um, three, they need to stop talking

about how the capabilities are going to

change the whole world constantly

because then people are going to get

fear of that capability because they

have no connection.

>> They don't know how to use it. Yeah.

>> There's no connection to it either. It's

like the average person doesn't know an

anthropic employee. The average person

doesn't know an open eye employee.

average person doesn't know who these

people are, what their goals are, and

they just view them as like this like

sneaky cobball of like 5,000 people at

this company that are going to change

the world and automate all the jobs and

and destroy society. That's what they

view it as. And and as people who are

funding the building of all these data

centers and and power plants that are

going to pollute the world, right? They

don't quite understand what's happening.

You know, they have to stop talking

about the future thing that's going to

happen and only talk about present, how

uplifting AI is. I think it's a huge

reorg and rebranding that needs to be

done.

>> I love doing this with you. Thanks for

your time.

>> Awesome. Thanks.

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