Hi, this is Steve Eisman. You know, in

the great financial crisis, people

sometimes ask me, why did I foresee what

was going to happen? And the answer in

some ways is that the center of the

univer universe back then was the entire

financial sector and that was my area of

expertise. So, I knew what was going on.

The financial sector has not been the

center of the universe for a very very

long time. The center of the universe

right now is semiconductor and

semiconductor equipment companies

because that is where the entire AI

infrastructure is being built out. And

so today we're going to talk to a

recurring guest, Stacy Rasgen of

Bernstein, who covers semiconductors and

semiconductor equipment companies. And

we're going to discuss what's going on,

why it's going on, and what could

possibly derail it. And afterwards, I'll

be back with some closing thoughts.

>> [music]

>> Hey, this is Steve Eisman and welcome to

another episode of The Real Eyesman

Playbook. And today we're going to

discuss a group that I would say is the

center of the universe right now, which

is semiconductors and semiconductor

equipment companies. We're going to talk

about it with a recurring guest now,

Stacy Rasgen of Bernstein, who we spoke

to

>> maybe seven months ago.

>> Seven Seven months ago. Stacey, welcome.

>> Good to be here.

>> So, Stacey, you are the center of the

universe. How does that feel?

>> Like, it's semis are always interesting.

There's always something going on. Um,

>> maybe. So, but you've never been the

center of the universe.

>> It is nice to be popular. Like, I'll

I'll I'll I'll leave it at that. I

always joke,

>> you know, I I do I do well at at

Bernstein. I'm, you know, but it's it's

not just my sparkling personality,

right? I mean, it is a fact that the

group is of great interest both to

specialists and generals alike now. And

just given the rise of AI, I mean it's

it's not just semis, right? AI is sort

of dragging everything in in I mean in

semis and out of semis along with it

right now. Yes.

>> It kind of feels like it's the only

thing that's supporting everything else

that's going on. Um so yeah, it's nice

to be popular.

>> I'm glad you're happy to be popular.

We'll see how long it lasts. But

[laughter]

so let's take a step back. Give me a

summary of of all the stocks that the

group is a group like what's been since

the last time we met. Yeah. when and

when seven months ago, it's not like

things weren't good seven months ago.

They were pretty hot then, too.

>> Yeah.

>> What's what's happened in the last seven

months?

>> Yeah. You bet. I mean, it's gone into

overdrive and and and what really what's

happened is is like I said, AI has

gotten so big, it is now dragging

everything along with it. And so,

probably 7 months ago, we were looking

primarily at at the compute and

accelerator names, the NVIDIA and the

Broadcoms of the world. Now, it's

everything in semiconductors. um whe

whether it's you know was the

accelerators and now it's it's memory

and it's semicap and it's optical and

it's power semis and it's CPUs now AI is

dragging everything everything is is it

it you know what it really is is

one at a time all of these different

parts of the industry have sort of

become the the constraint as AI has

gotten bigger and bigger and bigger

different things have now become like

the bottleneck

>> the stocks have actually ripped because

investors as you know investors love to

play bottlenecks

>> right Right. Um and so one at a time

we've seen these different kind of um

different areas of of the broader

semiconductor space take off. And

frankly you could have owned anything in

the space um and you would have been

just fine. Um I think semi now year to

date I don't know what the exact numbers

now but at least up 60% year to date

probably more. And the issue

>> Micron's up over well over 100. Oh, and

look, you companies like like say a

SanDisk or do my I don't cover, my

colleague covers, but they just guided

to a quarterly EPS that is higher than

the stock price was when it went public

like 18 months ago. And so this is what

I find. Yeah.

>> Say it again slowly.

>> SanDisk just guided to an EPS number for

next quarter.

>> Just the quarter

>> one quarter.

>> I I can't remember. It was 31 or

something like that. 31 32 whatever the

number was but it is higher than the

stock price was when it went public like

18 18 months or two years ago

>> that's unbelievable

>> unbelievable and so if you look at the

run that the space has had yearto date

it's actually all earnings multiples

broadly if you take like the socks index

as a prox the socks index is a broad

index of semiconductor companies that

people use

>> the multiples actually come down a

little bit

>> it's actually come down a little bit

right and and so all of the growth we've

seen here today has been earnings right

and so if you are worried about

sustainability and people people I get

it. But the the thesis you would have to

articulate is why are earnings

unsustainable? Because it's not like and

the sector is not cheap, but valuations

are it's it's not egregious at all. Like

we haven't gotten anywhere near nuts

yet, right?

>> In terms of valuation.

>> In terms of valuation, not not at all.

And and and so and

>> so give me what's what's like the the PE

of Micron right now.

>> Oh. Well, so the memory names are

probably trading at single-digit PE.

Micros and that's because you know look

memory is is known to be a very cyclical

industry and typically in cyclical

industries when earnings

>> wait what's the difference between a

memory chip and a CPU chip

>> different um types of processing

different types of applications um

different economics in the industries uh

CPUs are what what they're a subset of a

broader space which is known as logic

they tend to use very advanced um

manufacturing technologies um and

they're used for computation right

memory chips um I mean they're to to to

for memory for memory they used to

store.

>> So Micron's a memory chip company.

>> Micron is a memory chip.

>> So if Micron's a memory chip company,

which are your companies are CPU

companies?

>> Sure. I cover uh well, a lot of them

I've got some CPUs and some that that

want to be CPU companies. So the

traditional CPU companies I have are

Intel and AMD. Intel and AMD.

>> AMD.

>> Um Nvidia, who makes accelerators and

GPUs, they actually make a lot of CPUs

as well. And they are articulating a CPU

story that could be even bigger than the

other two right now. I've got other

names like Qualcomm for example which is

known as to be uh they make um chips for

mobile phones mostly. Um they are now

trying to get into the data center space

and they have CPUs as well.

>> Okay.

>> So yeah.

>> So let's start with

the granddaddy of them all Nvidia.

>> Sure. So,

you know, for my weekly rap, I went over

the numbers with like a actually I

actually went over numbers really

carefully and I I would say if I mean

you'll give me more details, but I would

say if you could boil down the whole

story to like a sentence, it would be

the revenue growth was 85% and two

quarters ago was 65%. It's actually

accelerating

>> and so it's accelerating and the gross

margin a year ago was 60% and it's now

75.

>> Be careful. The gross margin a year ago

had an impairment in it. So it was too

low. It was 75ish or 70 I can't remember

72 or 73 without the impairment.

>> Okay.

>> So my first question is Nvidia gave some

different disclosure this time.

>> What was it and what does it reveal? And

let's start with that question.

>> Okay. You bet. So they did two things.

They took their data center segment

which they used to split up into compute

and networking. Now they're splitting it

up into what could you could take as

hypers scale versus sort of non-hypers

scale. So hypers scale would be the

large

>> the large

>> the Googles and the metas of the world

>> the ones that are building the massive

data centers

>> and then non-hypers scale would be

everything else. So the enterprise

customers and the neoclouds and the

software

>> would that be anthropic anthropy there

or would they be in hyperscalers? Well,

it depends on I think where Anthropic is

getting the compute from, right? Because

they're Anthropic may be building some

and they may be also um going to the Neo

clouds, but

>> okay.

>> Anyways, but um but yeah, so that was

one thing they did and they g they kind

of because there's this big concern with

Nvidia about customer concentration.

>> So what they actually said and they

showed it is is the hyperskll and

non-hyperskller are about equal sized

>> in terms of revenue.

>> In terms of revenue for data center

revenue, they're about 50/50.

>> Okay. And and data sentence is what

percentage of total revenue? Uh oh d

it's 90% right used to be used to be

gaming.

>> Oh yeah yeah yeah 10 years ago was

almost

>> get to my second change in the

disclosure but but 10 years ago was

gaming and there was crypto and all that

nonsense

>> right. Yeah but um but they're giving us

some visibility and saying you they

still have concentrated customers but

they also do have they're not quite as

concentrated as maybe you might think.

>> They do have a long tale of of other

customers and and and those are also

growing very rapidly. just as rap almost

just as rap

>> the data center business is basically

divided equally between hyperscalers and

and everybody else

>> last quarter it was equal they they

bounce around a little bit quarter but

but it's pretty big

>> okay

>> second is they took all of their other

segments which is gaming professional

visualization like workstation stuff

automotive and and this other bucket um

and they lumped those all into a single

segment which they're now calling I

think it was edge computing or edge AI

>> and that's a small percentage of the

total revenue

>> pretty small yeah so they're telling you

two thing what one well the biggest

thing is they're telling None of that

stuff really matters anymore. Right.

Right. But the other I I think it gets

to the the longer term. They they've

talked about

>> longerterm drivers, physical AI and

robotics and and and even the autonomous

driving. And so I think they're

expecting hopefully if we're looking

out, you know, 5 years, 10 years, maybe

that other bucket will get bigger as

some of that other stuff takes up. But I

don't think it'll be gaming. It'll be

things like robotics and and and

automotive hopefully. But but they're

taking all the other segments and

lumping them together. They did not move

stuff in between the segments or

anything like that. Sometimes when

companies resegment, they play games and

they move stuff from one segment to the

other and it's hard to They didn't do

any of that. They just took the big one

and split it up and they took the other

smaller ones and shoved them together.

>> I see. Before we move on, just define

for us the difference between GPU and

CPU.

>> Sure. So, they're both logic. They both

use advanced transistors. Um they and

they both do processing, but they

process differently. Um I'm going to

grossly simplify, but CPUs,

>> please do.

You can think about um CPUs as doing

computations sort of serially like one

after the other, right? Um you can think

about a GPU as doing computations in

parallel. So for example, a lot of these

um compute chips have compute different

a certain number of compute cores on

them like they've got different pockets

of transistors on the chip that can

handle logical operations. And a CPU

might have anywhere from, you know, a

few cores to a few hundred cores on it,

depending on what that thing is is is

being used for, a laptop chip or a

server chip or whatever. GPUs would

typically have thousands of cores on

them. Um, smaller each core would be

less performant than a CPU core, but

there's a lot more of them. And the GPUs

tend to do certain types of math

exceedingly well, but not as useful for

like the general purpose math that a CPU

would would would do. Um

GPUs are tend to be used I this is why

they were used in in gaming and other

things. They tend to be used for um uh

uh it's called matrix manipulation

matrix multiply and addition and I don't

want to go into what those are but it's

the type of of compute operations that

were very useful for graphics. Okay.

>> And as it turns out are actually

exceedingly useful for artificial

intelligence and machine learning. It's

the same kind of math. That's why GPUs

which are developed for graphics turned

out to be very useful for for artificial

intelligence applications.

>> Okay. So quick question on Nvidia's

stock since the

the entire story basic I mean if you

look at you know CPUs, memory chips,

semicap equipment, the entire business

basically hinges on Nvidia. In other

words, if Nvidia is growing revenue 85%.

>> Everybody else is going to do great.

>> Yeah. And then we could have a

discussion about who's who who's doing

better here or there, you know, what's

pricing. You know, you you get get into

the weeds. If tomorrow Nvidia announces

that revenue goes from 85% to 120%,

everything goes up. If it announces that

revenue growth going from 85% to 40,

everything's going down. So my question

is, why is Nvidia only up 14% this year

and sells on a multiple that's much

lower than a lot of these other

companies? whose entire businesses hinge

on them.

>> It's a great question. It gets to what I

said earlier about

>> and I'm very upset about it because I

own Nvidia and I don't get it.

>> Yeah. I mean, try not to be upset, but

you're right.

>> I I'm not that upset.

>> It It's lagged. Um now, to be fair, it's

up I I don't know what it is.

>> It's up a crazy percentage. Whatever.

You know, that's talking about this

year.

>> So, there's a few reasons. So, that that

is one. It had a big run already. Fine.

Um, secondly though, and it it gets back

to what I said earlier where I said AI

was sort of dragging all of these other

segments along. Um,

and investors have been rather than

playing like the GPU or the compute

names, they've been playing the

constraints, right? And again, remember

what I said for just to pick on memory

for example, some of the earnings

revisions we've seen in in some of the

memory names, um,

>> where you've gone up an order of

magnitude or or or even more in terms of

the earnings power in the stocks, you're

just not

>> because pricing has gone crazy.

>> Exactly. you're not seeing that kind of

a of a thing from from Nvidia. And so

the investors who love to play

constraints have have been playing the

constraint names and and it's they've

been going from one to the other like

like I said memory to semicap to optical

to power right

>> to to CPUs. So that is part and and it

interesting because it brings up this

very interesting divergence between the

two because one of them has to be wrong

to your point. The other stuff cannot

work if

>> doesn't work impossible. So

>> I that's what I I think that there is an

opportunity because I I do think that

that has to normalize one way or the

other. Either the constraints are going

to go down or Nvidia I think has to come

up. The valuations I think have to

normalize

>> but there's no constraint on GPUs right

now.

>> Well there there is and there isn't. So

there there's leading edge logic and and

what's called coas the which is the

packaging technology and we can talk

about that if you want to put the chips

together. That's always tight, but

Nvidia and memory is is tight, but

Nvidia has been very good at securing

supply across the value chain. They they

they saw this coming, right? Um so if

there's anybody out there that has

enough

>> J is an excellent CEO.

>> Oh, yeah. Oh. Oh, yes. Oh, yes, he is.

Um so they've been very good at at at

securing the supply that they need to to

meet the growth so they can accelerate.

>> Mean supply from like Taiwan

semiconductor. Well, Ty and also the

memory guys and also the the packaging

and now we know he's he's getting into

the optical and and scing like lasers

and all kinds of other stuff, right? Um

but he's been very good at at at doing

that. So they have supply. Um but this

divergence has been very interesting.

That's that's another reason it hasn't

worked as well. And I think the third is

just I mean look I can't remember what

the market cap is. Is it 5 trillion 6

trillion now?

>> It's over 5 trillion.

>> It's it's big, right? And it's you know

it's 8% of the S&P or something. And so

for some of the large like especially

the large longies it's hard for them to

own more of it because they're already

there. It's even hard to be a market

weight because it's so big

>> and so that's you know it's like okay

it's 5 trillion is it going to go to you

know

>> posing just so our viewers understand if

Nvidia is at 8% of the S&P there are

plenty of institutions out there

>> who have rules that say you can't do

anything

>> more than more than x%. So they can't

they have to tech so by their own rules

they have to underweight Nvidia.

>> Yeah. Okay.

>> So that there so there's some technical

reasons I think as well.

>> At the same time look I mean the thesis

on it has been pretty simple like number

go up right. [laughter] It hasn't you

haven't needed any more than that. Um

and I think it started to work a little

bit better the last few weeks. Part of

the reason there is you know people get

very excited about the CPU names and

people realize oh wait a minute they

sell CPUs too. And then on the last

earnings call they were trying to

articulate they could have been a little

clearer about I think we'll hear more at

competit. They were articulating a CPU

thesis and I

>> what's their CPU thesis?

>> Oh so so he was articulating a a CPU

opportunity. They're going to do they

think $20 billion worth of CPU revenues

this year. So that's a basically all in

the second half.$20 billion is about as

big as Intel and AMD's CPU businesses.

>> And why are they going to be selling so

many CPUs?

>> So two reasons and this is this is one

reason. Now, why are they going to sell

them, not somebody else?

>> Well, other others will as well. So,

there's two things that they're selling.

One one is in in the large GPU racks

that they sell.

>> So, their their current mainstream

product is something called a Grace

Black. Well, it's like a GB300 NVL72.

They have all these, but it's a big rack

and it's got 72 GPUs and

>> 72

>> GPUs and it has [clears throat] 36 CPUs.

Now, these are Nvidia's own CPU design.

It's called a Grace CPU. It's based on

the ARM architecture. We could talk

about that if you want, but every GPU

rack they sell has these CPUs. Okay, so

that's one thing. However,

>> there is a much bigger demand for CPUs

now just in general. And the reason is

the rise of something called agentic AI.

So this is over the last couple years,

the the big push in AI has been what's

known as generative AI. So

>> LLM

>> LLMs and you know I'm I AI slop like I

ping the thing and it makes a photo or a

video or like what it's generating

content

>> which is interesting and nice but

ultimately it's not

>> aentic AI would be I want to go do

something I want to I want to go I want

to plan a trip to Paris.

>> Exactly. Or

>> go book a do give me the whole

itinerary.

>> Yeah. Or or you know what's where you

use now is for coding like I want to

write an app to go do something like

that. And [clears throat] the thing is

like when when I'm generating AI that's

that's running on the on the GPU, right?

When I'm doing something agentic, I've

got an actual an agent that is the model

is actually like like creating an agent

to go out and do a task and most likely

that task is a real world task and it's

running on a CPU. And so just to give

you to use your

>> why would a Gentic AI run more on a CPU?

>> So to do your your travel,

>> right, my travel to pass.

>> Yeah. So I I ask my my model, I want

plan me a trip to Paris. I want to go on

these dates. I want you to look for

pricing of tickets between these range

and I want to stay in four-star hotels

in these cities. So what is that? And

and and it's going to have an agent

that's going to orchestrate this. So

that agent is going to go spin up a

bunch of sub aents and each of those sub

agents, they're going to go ping, you

know, American Airlines and and Delta

and United and whatever and all the

different hotels.

>> There's a lot of computation going on.

>> A lot of it. All of those things are

going to be running on on CPUs, right?

>> Why not GPUs?

>> Well, there's a GPU that that's running

all this as well, right? But American

Airlines server is not running on a on a

GPU. Like when it's actually going to do

the physical task to make the traveler

agent to look at that's running on CPU.

Another example, I'm using Claude to to

make an app and I'm I'm coding something

and I tell I want to do this. So that

agent is going to go spin up 20 sub

agents.

>> The agents can spin up agents. Each of

those sub engines is going to open up a

a virtual machine like basically a

representative

um uh uh computing environment that's

running on a CPU or more likely on on a

on a compute core on a CPU someplace and

each of those sub aents is going to be

writing a block of code and there'll be

other sub aents going to put all the

stuff together and and orchestrate it

and review it and everything and so that

one GPU with that one test can be

spinning up a ton of CPU um uh compute

while it's running. The way people tend

to think about this, they tend to think

about it in terms of attach rate. So,

for example, in in the old style, if you

go back a couple of years, the CPU

attached was like one to date. I had

like, you know, I had like eight GPUs

and one CPU in in this box in a server

in these and then it went to a 4:1. And

then if you look in these big GPU racks

in Nvidia cells, it's 2:1. A lot of

people talk about, oh, it's going to

with with agents, it's going to go to

one or even

>> so you'll need more CPUs per GPU. By the

way, I think that the the attach rate

model is wrong, but it's useful, right?

It's not I don't think that these CPUs

are necessarily directly attached to the

GPUs,

>> but the GPU when when it's orchestrating

these kinds of agentic tasks, it's it's

going to require the usage of a lot of

CPUs. So, the attach rate model like

like all models are wrong, some are

useful. I think that's a wrong model,

but it's maybe a useful model. We need

more CPU content as you as you do real

world tasks with these models. So that's

one reason why the demand for CPUs is

going up.

>> And and Nvidia is actually not only are

they selling CPUs in their racks,

they're actually going to sell

standalone CPU racks now.

>> Okay, that's interesting. Okay, let's

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So, Michael Bur many many months ago um

put out a thesis about not per se Nvidia

but but about all the hyperscalers

arguing that all the hyperscalers had

lengthened their depreciation from like

three or four years to five to six years

and

>> mathematically speaking

>> that increases obviously earnings that

you can't argue with that that's a fact

>> and he thought that that was

illegitimate because Because the speed

because because the GPU because you keep

hearing from from Jensen, I got a new I

got a new GP every six months I got a

new GPU. So what does that how what

could that possibly say about the old

GPUs? They're obsolete. That was the

argument. What do you make of that?

>> Um that was the argument. I do not think

it's it's correct. Um I although I

understand the logic and I'm not going

to knock on on Bur, you know, fine, but

I don't think it's it's accurate in in

the current environment. In fact, you

know, you can look at the this is data

that exists like you can look at the

rental prices of GPUs by generation,

>> okay?

>> And take take the older generations, the

Hoppers, which which Hopper came out in

in 2022. You can even look at amp

ampers, which is the generation, the

Nvidia generation before Hopper. And by

the way, just to level set you, the

current generation is the current

generation that's about to come out this

year is called Reuben, the Ruben.

>> The current one that's shipping is

called Blackwell,

>> right?

>> The one that came before that was Hopper

2022. The one that came before that was

Ampear 2020.

>> Okay.

>> Okay. And we can look at the rental

prices because these GPUs are out there,

>> right?

>> They're available if you want to rent

them. Rental prices for even for the

older stuff are going up, not down,

>> right? And and even for the stuff that's

fully depreciated, they're they're very

profitable to use. So these GPUs do not

disintegrate after 3 years, right? They

are still usable. So the question is, is

it economically viable to continue using

them when you have better GPUs that are

out there? And at least at the moment it

is absolutely economically viable to use

and you can see it in the data like it

it's I think it there's there's no

question um the rental prices are going

up not down why is that because demand

is off the charts and demand is so

strong we need every bit of compute that

we can possibly get right now and

>> even old comput

>> even whatever is there and and that's

been the interesting thing is more and

more computers with with every

generation you get more compute out of

it right so you've had exponentially

more compute coming online and yet the

demand has been accelerating even more

than than that. And so we need every bit

of compute that we can have and the

stuff is getting monetized no problem.

If we run into a scenario where demand

falls off and I don't need that old

comput then I worry about lifetime and

depreciation but we're screwed anyway so

like who cares, right? I do not think

that that is a there if we want to

articulate viable bare cases on AI we

can. I do not think that that the

depreciation point is a is a viable bear

case.

>> Okay, we're going to come to the

potential bare cases later. Let's keep

going down some of the companies. Um,

let's talk about AMD.

>> Yeah, look, that stock has done great.

So, so I can't argue with it,

>> but AMD is a smaller company

>> than than Nvidia.

>> Almost every company is small.

>> Almost every company is small, but quite

a bit smaller.

I I can't remember what AMD's revenue

growth was in the first quarter.

>> Oh, it was pretty strong.

>> How strong? I can't say. I don't think

it was

>> I got I I'm not sure. I'm not sure

because they've got other businesses. So

just articulate the AMD. You're

recommending it.

>> We just upgraded it.

>> I saw you.

>> To be fair, I will say that my track

record with AMD bull cases is horrific.

So we However,

>> we all have our crosses.

>> We do. I've been much more wrong by not

recommending it. I mean, clearly like we

we've been we upgraded on on earnings.

Um, and I'm actually kind of kicking

myself because when we had previewed the

quarter a week and a half before they

were, I came this close to upgrading it

into the print and I I chickenened out.

I lost my nerves. So like, no guts, no

glory. Okay,

>> I'll tell you the reasons we were

looking to upgrade and then the reasons

why we did though.

>> Okay,

>> into the print

>> again. People were getting very excited

about this this agentic CPU

>> story and they have very good CPUs and

not not only is there is their data

center their CPU business growing a lot.

They actually have really good products.

um they're taking a ton of share from

Intel and and just just to give you an

example for how strong CPU demand is

right now. Intel had about 200 basis

points of margin upside last quarter

because they were selling previously

written off garbage that was like lying

around by by their own admission they

their their server products are not

competitive.

>> Customers right now do not care. The

stuff was written off. It was lying

around. They just they could sell

customers like we'll take it. They'd

already written off to zero. So they

were sold it at 0% cost basis.

>> AMD actually has good products that

customers want. They've been taking they

took out oodles of share. AMD 10 years

ago, AMD's market share in x86 servers

was.1%.

>> Say that again.

>> AMD's market share in x86 servers chips

10 years ago in 2015, 2014, 2015.1%.

Until 99.9% market share,

>> 99%

>> this on a revenue basis. Um,

>> so basically 100% versus zero.

>> Zero. Today AMD's revenue share in x86

servers is like I can't remember low to

mid 40s

>> and Intel

>> uh it's the opposite the 60s but AMD's

taking care of course they actually have

products that that customers want to

buy. Um that's and then the other thing

that we were looking at is you know they

are selling they also have GPUs

>> and they've signed in a couple of big

deals OpenAI and Meta um and this buy

side I think was there but the sell side

for whatever reason had not put Meta in

the numbers yet. So I started to look

into 27 and and for the first time in a

while my numbers were quite a bit above.

We we we didn't upgrade. We didn't pull

the trigger on it at that point because

the street was already modeling this the

CPUs up 50% this year and I figured well

people must know Meta is coming. Um it

looks like now this the server CPUs for

them are not going to be up 50% this

year. They'll be up 70% like maybe more.

>> Okay,

>> we'll see.

>> And how are they doing in the GPU part

of the business?

>> They're doing okay. Um a couple of

things. So

>> because that was the story. It still is

part of the story, but you have to

remember so the the the GPU thesis on

AMD goes something like they're going to

go from being a marginal player to a

slightly less marginal player.

[laughter]

They go from 4% market share to 10 or 11

in a market that's growing to a trillion

dollars plus

>> right

>> now to to be fair, you know, to to the

bears on AMD to sign these big deals and

they signed two of them right now. Open

AAI and Meta multi-gawatt multi-billion

dollar deals. They they basically had to

give away chunks of the company to get

there. They they gave warrants.

>> Yes.

>> Each one was about 10%. If if they're

all fully exercised, there are purchase

commitments required for the warrants.

So, there's also stock price thresholds.

Um I will be honest, I would rather see

them sign big deals without having to

sign the warrants, but I understand the

warrants. The reason they they did it is

I mean like they had to be on the rocket

ship, so they bought their ticket. It

was an admission on their part that they

the the products as they stand today are

not good enough yet to get the kind of

share that they need. But they have to

build scale rapidly. It's not just

enough to have the parts. You have to

have the developers and the ecosystems

and everything behind you. And it's a

chicken in the egg, right? Developers

are not going to develop for your

product if you don't have anything in

the marketplace. So, they're doing

everything they can to get the products

into the marketplace to build that

ecosystem. And I I don't like it, but I

understand it. And frankly, if the stock

goes to $600, which was the high end of

the of the stock price threshold to

exercise the warnings, I figured

investors are not going to care anyways.

And the stock price looks like it's

maybe going there. So, they reported

earnings. Everything looked good. Our

server estimates, which I thought were

high, looks like they needed to go

higher. Um, and I started running my

numbers and it was like, well, now I'm

not the street had been at like 11 bucks

for next year. I had been at 13 and

change. Now I'm at like 14 and change.

And if Lisa, the CEO, if Lisa Sue is

correct, and and the and the server CPU

thing does not level off if it keeps

going because she also doubled her

estimate for where she thought the CPUs

could go.

>> Okay.

>> She thought by 2030, they had analyst

day a couple of months ago. They said,

"We think in 2030 it'll be$60 billion

TM. Now she thinks 120."

If that's true and things don't

moderate, they continue to grow, all of

a sudden I'm looking at 2028 and I'm

pretty close to 20 bucks in earnings,

which was their 2030 target. And at 20

bucks in earnings in 28, you can start

to underwrite quite a bit of upside. So

I admitted it. It's late. Like I get it,

but better late than never. Um, and so

we we pulled the trigger on it after

earnings and upgraded it.

>> Let's talk Intel.

>> Yes.

>> 7 months ago, Intel was here and now

it's up here.

>> What the hell happened?

>> Couple of things. Number one, the CPU

story as well is working for them,

>> right?

>> And I think they're getting bailed out a

little bit, but look, you take lucky

over good. It's fine. Like I said, their

products by their own admission are not

competitive, but it doesn't matter right

now. So, they're selling stuff that

ordinarily they probably would not be

selling, but that is helping. It's

clearly helping number one. Number two,

a lot of the deals that they've signed

like with the government and and you

know, Nvidia and others took a stake. It

got the balance sheet in in place. They

actually they had had this um

>> deal with private equity in they sold

half of their Ireland fab to Apollo for

11 billion.

>> And remember, the private equity guys

don't work for free. So, it was actually

a very earnings dilutive deal for them

which they needed to sign at the time.

>> Um, however,

>> private equity yourself a sweetheart

deal. Really?

>> Yeah. Yeah. Again, if you're sitting

down with private equity, you're

probably not walking out with the better

side of it. They needed it, you know,

few years ago when they signed it. They

don't really need it now. And so, their

balance sheet is good enough that they

were able to buy their way out of it.

Again, at a cost. It didn't come for

free, right? um they gave Apollo a

pretty good return over the three years

or whatever that they had it but but

they bought their so you got rid of that

potential earnings dilution from from

that um and then I would just say in

general the narrative is going their way

so beyond just the CPU thing there's

been some incremental uh narrative

around the foundry deal the

manufacturing

right um and they said two things they

said um uh their I'm going to talk a

little about their process and some of

the nomclature they they have different

process technologies The current version

that they're starting to ramp now is

something called 18A and they make a a

notebook product on it called Panther

Lake.

>> Okay. And they've got a next generation

process that is in the works which is

called 14A. Okay. Um and they said a few

things. They said and and but they've

had issues ramping the what's called the

manufacturing yields like how many good

products are you getting out of the fab.

They've had issues ramping it. They said

the 18 yields which were not great. They

said they're they're ramping better than

they thought. and they said 14A yields

which is in development are better than

18A was at this point.

>> Okay,

>> now these were very carefully worded

statements because I would say 18A may

be ramping better than they thought but

you can look at their margin guidance

and kind of make some guess the yields

are still not good clearly. Okay,

>> maybe they're

>> it's still a company that's not great

>> and and 14A you know better than 18 at

this point but at this point in its term

18A yields were pretty close to zero. So

I mean fine but there were a few other

things like you had um uh you know

there's been rumors that they may be

getting some foundry customers. Apple

was mentioned again it will be it will

be small

>> but people hope that that will lead to

something else. And then one thing that

I think is a positive for them is

there's a lot of demand for this on the

AI side for what's called packaging.

Again, when you look at an AI chip, it's

not a single chip. It's a bunch of chips

that are all put together.

That putting together part is called

packaging and it's difficult and it's

actually one of the constraints and

Intel actually has decent packaging IP

>> and so they may actually get some

packaging revenue and so that's all

helped. And then finally, like there

there's been one overarching bullcase

which is why we have not been short the

stock even though I've made my career

being negative on it, right? It it it

was a gift he keeps on giving for like

15 years until like a few months ago,

right?

>> But I think the overarching that I've

been afraid of from the shorts is look,

Trump wants the stock to go up. I mean,

let's be honest, he took a stake. He

tweeted out pictures of himself

literally watching a chart of the stock

price going up. Right.

>> And the right thing to do, frankly, in

hindsight, was to buy it as soon as

Trump took the stake.

>> Right.

>> Clearly. So interesting. Okay.

>> I would say then they still have a lot

of wood to chop. However,

>> I do like the CEO. I like Lipu.

>> You do?

>> I do. Absolutely. Um, he's doing the

right things. You know, look, and he's

People would say sometimes he's

following Pat Pater, the old CEO who got

fired,

>> right?

>> Following Pat's strategy and oh my god,

he's getting all the credit for it. I

actually think Pat's strategy was not

the wrong one. I think Pat's execution

of that strategy was horrendous.

>> Pat came in and he acted like Polyiana.

He said everything's perfect. He started

hiring. He blew out the cost structure

and then he had to fire everybody,

right? I mean, Libu at least came in.

>> This is what Pat should have done. Who

who comes into a turnaround and act like

Polyiana? Like, I don't get it. So you

come in, he's underpromise and overd

deliver versus the other way around. He

he actually had to do a layoff. He got

the cost structure in in in place. Um

and he's doing what you what you need to

do in a in a turnaround. And so I think

Pat's strategy was actually very good. I

think the execution of it initially was

was not great. Um Lipu is executing on

on the groundwork that was laid to

execute on that strategy better. So they

still got a lot of wood to chop. I think

a lot is getting priced in at these

stock prices, but the narrative is going

>> rising tide. the narrative like is the

narrative and the things right now are

trading very much on narrative. Got it.

The narrative is going their way.

They'll have an analyst day in the

second half sometime and they'll

hopefully have something to say.

>> What is the difference between I can

never keep track and Qualcomm?

>> Totally different. I I know they're

different, but I can I can never keep it

straight in my head.

>> They were almost the same. You know,

Broadcom tried to buy Qualcomm a few

years ago. They went hostile. Yes.

>> Actually, he would have succeeded. Um

just for idiots. What does Broadcom do?

What does Qualcomm? Let me talk Qualcomm

first because a little a little simpler.

So Qualcomm primarily makes chips for

smartphones. Um processors and radios,

they're called modems,

>> right?

>> Um as well as connectivity, Wi-Fi and

stuff and and RF and so that's the bulk

of their uh 70 70 or 75% of their chip

business. Qualcomm also has a an

automotive business relatively small but

growing and they have what they call

IoT. So this is like networking and and

industrial stuff and they're also trying

to get in other markets. They have a

very nent PC business. And then now the

big story for Qualcomm is data center,

right? So they

>> everybody's trying to make

>> but how are they getting what product do

they have?

>> We've got a few products. Um they do

have a CPU.

>> Okay.

>> And they they talked about a win with

humane which is the Saudi Arabia consort

AI consortium like a year ago. We

haven't heard anything yet but we have

that.

>> They do have um AI racks. They get they

have a 200 megawatt deal again with

Humane. Okay.

>> We haven't seen anything yet. And then

they just announced a hypers scale ASIC.

ASIC stands for application specific

integrated circuit custom chip.

>> Okay,

>> they got some type of of AI ASIC. We

don't know what the part is. We don't

know who it's selling to. We don't know

how big it is. We don't know when it's

coming. We don't know anything except

they have a a win. But that

>> but that single announcement actually

sent the stock up 70%. Tells you how

okay

>> tells you how nuts things are.

>> And they're going to have they have an

analyst on on in the middle of June, end

of June. We'll hear more about. But

that's Qualcomm. Mostly smartphones

today trying to diversify away.

>> Okay. They also have a licensing

business, but I should mention um

Qualcomm owns a lot of the cellular IP

and other stuff that's out there. So,

they get a license on every 3G, 4G, and

5G like smartphone device or and other

that are sold in theory whether or not

their chips are in it.

>> Okay.

>> Okay. So, that's a pure profit. Now,

let's go to Broadcom. So, Broadcom is a

lot of things. Broadcom has a

semiconductor business and a software

business. And their semiconductor

business is AI and non AI. So, let me

take these these bits one at a time. And

I would say Broadcom historic, let me if

I put the AI piece aside for a minute,

which that's actually the bulk of we'll

be the bulk of the company pretty soon.

But if I put that aside for a minute, um

Broadcom historically was um uh grower

through acquisitions,

>> right?

>> And historically they did two

transformative acquisitions and a bunch

of little ones. Broadcom originally, I

think it spun out of where did it spun

out of HP and Agyant, I think, way way

back in the days when they went public

in like09. And back then they did, you

know, they did RF parts for smartphones

and and and some other things. And they

they they bought a lot of companies.

They bought um LSI Logic which got them

into storage and other things. And they

bought um PLX and and SCOPtics which got

them into optical. And the biggest one

back it was called a Vago back then I

should say. That's why the ticker is

still AVGO today.

>> I was wondering.

>> Yes. Um but Avago bought what I would

call classic Broadcom. [laughter]

and classic Broadcom did um wireless

connectivity which was Wi-Fi and

Bluetooth and GPS and they did storage

and they did broadband cable modems and

DSL but the the crown jewel was a

networking they made chips for switches

and routers and that got them a bunch of

scales. So if I just look at the nonAI

piece of Broadcom's business and but

it's a complicated company that's why

I'm going to go through but the nonAI

piece they've got four or five segments

they have wireless which is um uh RF

filters for smartphones as well as that

classic Broadcom Bluetooth Wi-Fi GPS

>> they have storage so they do um um hard

drive controllers and SSD controllers

and and storage adapters and things like

that they have again that broadband

business from classic broadcom the cable

cable modems and DSL and pawn and

everything else. Um, they have a

networking business, the the merchant

silicon switching and routing. They also

do networking custom chips.

>> And what's the AI part of the business?

>> I get there in a minute.

>> Okay.

>> And then they they have a small

industrial piece. That's the non AI.

>> They also have a software business. And

the reason here is they had remember I

said they tried to buy Qualcomm. When

that failed, they started buying

software companies instead for a while.

And they bought um

>> uh CA Technologies, which does a

mainframe.

They bought um semantic. Semantic that

was it. They bought their enterprise

security business and then they bought

VMware which was the big VMware

>> which which got which got them virtualiz

and then so before the AI took off they

were roughly 60% semi40% software.

>> Okay.

>> Now we talk about AI um so they do two

things. They do networking and they do

custom chips

>> right

>> like Google for example makes what they

call TPUs. This is a tensor processing

unit. It's Google's own internal custom

AI chips.

Broadcom effectively works with them to

make that chip. So, and they've they've

been doing these chips, by the way, for

15 years. It just it wasn't that big

until fairly recently. They've been

working with Google for for 15 years,

but now with AI, it's just taken off.

And so, this overall AI business across

the uh the custom chips and and the AI

networking, they guided for next year

for that to be a hundred billion

dollars, which is way bigger than the

entire company was, you know, a year or

two ago.

>> Wow.

>> Right. Um and they'll by the way they'll

probably do a lot better than 100

billion would would be my my guess. Um

but that is

>> how how well has this stock done lately?

>> It's been like Nvidia um it's kind of

lagged.

>> Why?

>> Same reasons as Nvidia. Um people have

not wanted to buy the accelerators. Um I

also think because they have the

software business. Software has been in

the toilet, right?

>> I've got a colleague of mine that covers

it that I mean it looks to me like he's

ready to slit his wrists, right?

>> [laughter]

>> We we a couple we about a month ago I

had I had the software analyst from

Beard on Rob Oliver who was great and

>> the joke that we had was that it's the

only group I said this I said it's the

only group I've ever seen that goes down

on good news bad news and medioc just

just news.

>> Yeah. And and there's people worry about

the rise of of AI and agentic AI. You'll

you won't need all these these SAS

companies, right? Because you're going

to replace it. And and by my guess is I

I think for some of them you can argue

about what the terminal value of those

businesses are. There's probably some

babies getting thrown out with bath

water as well. And I would put broadcom

software business in that. Broadcom

doesn't do anything on the it's on the

application. It's all infrastructure.

>> Okay.

>> So this is nobody's replacing the

virtualization. The AI runs on the

virtualization layer.

>> Okay. Let's turn but it got impacted by

that as well.

>> Got it. Let's turn quickly to ASML, Lamb

and Sure. Semicatch. What do these guys

do? Explain what they do.

>> That so there's a whole separate sector

in semis which is the guys that make the

equipment that make the tools that make

chips. So there's the big five. There's

applied materials,

>> right?

>> Lamb research, KIAC here in the US and

then there's ASML in the Netherlands and

Tokyo Electron in Japan. Okay,

>> those big five have 70% low 70% of the

total what's called WF wafer fabrication

equipment market.

>> They have 70% plus of the WV market.

That percentage has kind of been going

up over the time and they tended they do

different things. Um the AAT and the

Lambs and the Tokyo Electrons of the

world do I I I should step back. When

you're making a chip, you're doing four

broad kinds of processes. So these chips

are made on on a on a silicon wafer.

It's a slice of silicon, leading edge 12

in, 300 millimeters, about that big

around. And and what you do is is I do

four things. I put stuff on that wafer,

>> right?

>> I pattern the stuff because I want to

define areas where I want stuff to be

and areas where I do not want stuff to

be. I take stuff away and I monitoring

and I watch what I'm doing. I monitor

and control the process what I'm doing.

And you repeat those things over and

over and over and over again. and you

build up the different layers of the

chip of of the circuitry that make the

chips. And if you were to look at a

cross-section of a chip, it looks like a

layer cake,

>> right?

>> I've got the transistors at the bottom

and I've got, you know, I can have 30,

40, 50 different layers of metal wiring

separated by insulating materials to

wire all those transistors together. And

the features are very small at the

bottom and they get bigger as you go up.

Okay? But the the companies that make

the tools make the processes to do

those. And there's different flavors,

different materials and different ways

to put stuff on the wafer, different

ways to take stuff away, right? But

they're all versions of of those kinds

of things. And so applied materials and

lamb research and Tokyo Electron

primarily do the put stuff on the wafer

and take stuff off the wafer steps. ASML

does that patterning step. It's known as

lithography. It's the most critical step

especially for the advanced

semiconductors because

>> mean for the GPUs. Well, the GPUs and

even the CPUs and everything else

because the the most advanced uh chips

have the smallest features, right? And

it's that patterning step, that

lithography step that defines how small

of a feature you can print on the wafer.

So, ASML is does that almost they have

90% market share. They got almost 100%

in the in the most advanced tooling.

Companies like KAC, um CLA do that

process control that monitor monitor the

wafer. um they do in inspection. They

look for problems and defects on the

wafers. They monitor it while it's

running and and there's other companies

that do that. ASAT has a process control

business. There's smaller companies like

Onto and Nova and others.

>> So, which of these is doing the best?

>> Well, they're all doing good. And and so

this is the thing with semicap the

correlations are pretty high. And as I

say, like if if if it's working, they

will all work to greater or lesser

degree. And and you could own all of

them. You you'd be okay. You could own

the basket. There's been divergences.

Lamb has probably done I I definitely

but year over year Lamb's probably done

the best of at least of my three. I

cover AAT Lamb and KLA.

>> Okay.

>> Um Lamb's probably done the best. KLA's

probably done the quote unquote worst,

but I mean they're all up. It it but I'm

going to make up the numbers, but it's

like it'll be like Lamb is up 200%

year-over-year and KLA's up 100% or

something like that. They're they've all

done.

>> Okay, let's switch gears again. Let's

take a step back.

>> Yeah.

>> I mean, this story is insanely powerful.

Yeah,

>> let's talk about what could derail this.

>> Sure.

>> I mean, there are lots of theories out

there. I've heard theories of,

>> you know, people being basically forced

to be token junkies

>> and and and the the companies that that

they're using are are dramatically

underpricing the use of the tokens and

eventually they'll have to charge for

the tokens and when people get charged

for the tokens, they're not going to

want to use the tokens as much. That's

one theory. You know, another theory is

that Open AI is kind of a shell game and

that the guy who runs it a liar and and

it's going to go public and it's gonna

and it's not going to be great. And then

>> when they go public, you have to open up

the kimono.

>> They got to open the kimono.

>> Um and and uh they're the kind of the

weak sister of the whole story and but

they're a big percentage of the whole

industry. So if they fail, think things

go bad. I mean

>> from where you sit

>> Yeah.

>> I mean you've you've heard it all. I

mean, this may go on for the next five,

seven years. It's certainly possible.

Um, but if it didn't

>> Yeah. What would derail it?

>> What would derail it? Sure. You You bet.

So, I mean, you you have to be thinking

about this all the time.

>> And look, I've been thinking about it

since the day it started. Like, you got

to remember like, so Chetch, it's not

been that long. Catch BT November of 22.

Nvidia started its big run in May of 23.

Like, that's And the way it started with

>> I'll give you the date, May 25. I looked

it up May 25th because it's when they

reported

>> the numbers that they actually reported

were very very good but they but they

guided to 11 billion 11 billion so the

street

>> which was 50% higher than where you were

and where your colleagues were at

>> the street had been at seven and they

guided a little

stock was up 24%.

>> And I and and I remember and by the way

you can't even see that move on the

stock price.

>> Not now. Yeah. Yeah. But I remember

actually looking at that press release

and and seeing the 11 and thinking that

I must be reading like the wrong line on

the now it seems very quaint because

where did they just guide

>> you started you started to go let me

make sure I'm on the right line

>> and and to be this for this next quarter

they just guided 91 billion just just to

give you 11 from from 11 in May of May

2023.

>> So in three years they they've almost

10xed the revenue

>> right

>> and and that that that 11 billion was

something that we' never seen before.

The title of my note the next day was

the big bang.

>> Big bang.

>> Right. Yeah. Okay. So I that's It hasn't

been that long.

>> And it hasn't been that long at all.

>> And I've been thinking about it since

that day, right? What could derail three

years? Yeah. So let me talk about like

the I mean the thing you would see is

probably capex numbers getting cut,

right? And and but but by the time you

see it, it's too late

>> clearly. Yes. You know you the day medic

comes out and you know cuts capex or

something like it's all over. But we are

not seeing anything like that. If

anything, the capex numbers keep getting

revised higher and higher and higher.

And in some sense, at least for the big

hyperscalers, it it's not only are I I

do actually do think that they're

getting a return on this, especially

companies like like the metas of the

world that have a lot of internal uses

for this, you know, fine, but in some

sense it's it's also existential for

them, right? Meaning, well, everybody's

spending. I have to spend because if I

don't, they may win and I be I may be

out of business. The meta, by the way,

the meta problem

>> is that

>> Google is spending 180 billion this

year. And actually, I'm very proud of

myself that I actually know these

numbers now. And Amazon's spending 220.

>> The table stakes in this business have

exploded.

>> And Meta, poor little Meta, which

doesn't

>> I'm I'm using Meta just an example.

>> No, I'm just I'm not pick on them, but

here's the problem is Meta, which

doesn't have a data center business, is

spending 135 billion. And people are

upset because they keep increasing the

capex because they can't afford it as

much as some of the other guys.

>> Yeah. I mean [clears throat] to be fair

though this is not 2000201. I mean these

are these are real companies.

>> Real companies with with the most

profitable companies in the history of

of man of humankind right also. So um

and they're not idiots either. And I may

have said this last time I I was here. I

you did. Um they're not idiots. Right.

So they're not spend I don't I don't

believe this. They're they're spending

for no no return. They can see things

that we cannot see. They're not fools.

In some sense, it it sort of is

existential, but they they can they can

run it for a while. So, like I'm not

really worried about capex numbers

rolling over. If anything, we're seeing

capex continue to go up. And my take has

always been capex is too low. I mean,

it's funny, you know, Jensen talked

about in 2030 we might be doing three

trillion plus in in infrastructure

spending. And it seemed like a crazy

number when he first gave us like a

year, two years ago. People were like,

is that a cumulative? No, no. We're

we're doing pretty close to a trillion

dollars this year, right? We're not that

far off. So at that level three trillion

isn't isn't as much of a stretch anymore

as maybe it used to be. But that that

would be the the sign like so what would

drive that? I mean

>> what would drive

>> So clearly

>> I mean obviously comes down to return

>> right but like you said by the time we

actually saw meta report I'm cutting my

capex numbers it would be too late. It

would be but but but the question I

would question is why would that happen?

>> Yeah. Yeah. So, I mean, ultimately, it's

going to come down to return. Like,

either they're spending all this money

and getting something out of it at the

end of the day or they're not. And if it

turns out that there's no return, then

the whole thing by the there's nowhere

to hide, by the way, if this were to

happen. If it turns out there's no

return on AI, it's all it's all a shell

game. It's all everything's coming

crumbling down the in semis and out of

semis. The only economy right now,

>> the whole economy,

>> the whole economy. [laughter]

I mean last year I I calculated that it

was something like if you the AI capex

was something like 75% of the growth in

in GDP.

>> I think for these kind of you have to

almost go back to like the build of the

railroads like it's like a percentage of

GDP to find something that's sort of

comparable to what we're seeing. So it's

a lot right. So that that would be that

would be problematic. So look ultimately

like if if it turns out there's no

return and but there's a couple ways

there's there's no return

>> and or or and there's return that's not

so great. Yeah. Yeah. So, and there's a

couple ways that this could happen. So,

one is just by the way just the air

pocket. You know, we even before AI, you

look at the hyperscalers, they would

tend to build and digest and build and

digest and build and digest. So, if

there's a digestion cycle, which I guess

could happen, that would be bad. But if

if you thought it was just a digestion,

you could probably own through it or

look through it, right? The doomsday

scenario would be there's there's no

return or the return is much smaller

than we than we think it is. Only thing

you can do right now is monitor proxies.

But I I mean, look, you are seeing token

usage explode. And maybe you argue,

well, they're using too much, but I

think they are monetizing. I mean, just

as one point example, you can look at

Enthropic.

Enthropic does periodically release

their annual like annualized revenue run

rate.

>> They've gone vertical, right? So, I

mean, the last number they gave, which

was a few weeks ago, they were 40$44

billion annualized revenue.

>> A month before that, it was 30. In

January, it was 14. In December, it was

nine. A year ago, it was like a billion

or like whatever it was. So they've

literally just in the last like few

months done that.

>> Okay.

>> Right. So companies are clearly using

them. We we're seeing layoffs and

companies are are laying off and

spending the money on tokens and in many

cases they're now spending more money on

the tokens than they're spending on the

and to be fair I don't know how many of

these layoffs are actually AI driven and

how much of it is just some of these

companies just stuff themselves full

during co and so it's a convenient

excuse. I don't know but absolutely

we're we're starting to see adoption

rates and but it's it's not like

broad-based like like adoption.

A lot of it is this agentic stuff and

specifically for coding which I think is

a real use case where there actually is

demand and and there's an appetite to

pay right so I like and I don't know

what form you know AI workloads and

usage will take but but agent coding is

clearly one where we're seeing like

we're we're it's it's it's reached

takeoff velocity right

>> um and so this whole idea that there's

no return on AI I I I don't believe I

don't believe it I I'll be fits and

starts And you know, we'll we'll see how

it what the trajectory looks like, but I

think we're already seeing evidence,

clear evidence of use cases.

>> Yeah. So, let's talk about power for a

second. I mean, I mean, some people say

that that's the binding on the entire

industry. What's your thought here?

>> Yeah, you bet. So, if you were to ask

me, you asked me earlier like what what

could blow it up, right? I mean, if you

ask me like let's say the demand is

there, again, Jensen says we're going to

do three trillion plus in what would

stop us from getting there? Assuming the

demand is there, it's it's probably

power. I mean certainly the US

electrical grid is not capable of of

adding what would need to be added

probably for the demand. And so actually

what we're starting to see now at least

here in the US is is local like on-site

generation. So turbine oh even there

like what's the lead time on a turbine

is probably three years. And this is

where the whole idea

>> oh one of the big gas turbines.

>> Yeah. It's at least three years.

>> So you know we didn't even talk about

China but like I I I did a piece of work

um not that long ago and the the title

of it was something like the US has

chips but no power. China has power but

no chips. Like who's bringing more

capacity on them? And by the way,

>> they have all the the

>> they can just throw up a coal plant,

right? They don't they don't care

>> and and and their chips are are not

competitive right now. And actually,

it's partially because I think the

semicap sanctions have been um

successful like they can't make fact I

know there was some announcements from

Huawei over the weekend. They're they're

they're you know in general we're

forcing China to be creative by the way

on on how they make semiconductors

because they can't pursue the options.

They can't buy ASL.

>> Exactly. So they're doing Huawei by the

announcement from Huawei, by the way,

was I think is not the stuff they're

doing is not unknown, but they're

pursuing it now earlier than I think the

rest of the world because they have no

choice. Right. And that that's we're

going off topic, but they they've got

power. They don't have chips. Um but if

they can get chips, they they can power

them because they can throw up a coal

plant like wherever they want. They've

got they got plenty of power. the US and

other places, it's much harder to bring

the the the the centralized um power

capacity online, the grid capacity

online. So, they're doing a lot more

things like like on-site generation,

right, to power these

>> small nuclear reactors.

>> Yes. Mars and I mean, they even turned

on they're turning three Mile Island

back on. So,

>> but yeah, but power is a huge is a huge

controversy, a huge concern.

>> Stacy, thank you.

>> Yeah. Oh, you bet.

>> Great. That was really great.

>> And we're back. So, yes, Stacy does

cover the center of the universe.

There's no question about it. And some

closing thoughts are think from seven

months ago when I saw him last things

are actually accelerating from back

then. So for example, Nvidia just

reported and they reported 85% revenue

growth. But two quarters ago that was

65%. So whatever's going on is

accelerating and this rising tide is

lifting all boats. Nvidia is up only 14%

this year now. It's been a great stock

for many many years. So people can't

complain. But the stocks that have done

the best are the memory stocks and the

CPU stocks. And the reason why those

have done well is that investors are

playing bottlenecks. And the bottlenecks

are in CPUs. The bottlenecks are in

memory. And there's actually an

interesting CPU story in that because of

um Agentic AI, the number of CPUs that

you need per rack is actually

increasing. So the CPU companies have

all gone up enormously. But what's

interesting is that yes, they've gone up

enormously, but the earnings have

actually gone up more. So the multiples

have actually contracted a little bit.

We talked about, you know, what could

derail this story. And he, you know,

really, I don't think he thinks at this

point anything's going to derail it

right now. But long term, what would

derail it is that the returns that AI

creates are disappointing. And if that

becomes clear, people will pull back.

But I think at least according to Stacy,

still too early in the story for that

really to be an issue. And so given that

capex is increasing, he's still very

bullish on the stocks. Thanks for

listening.

This podcast is forformational purposes

only and does not constitute investment

advice. A host and guests may hold

positions [music] in stocks discussed.

Opinions expressed are their own and not

recommendations. Please do your own due

diligence and consult a licensed

financial adviser before making any

investment decisions.

>> [music]