I think it would be very problematic for

the US to win. Let's say we take the

most sort of fantastical scenario where

if you control AI, your military is

better than anyone else in this world.

What is the game theory optimal response

of China to blow up TSMC? If we get to a

place where we have a meaningful

superiority, particularly from like in

terms of a military national security

perspective, I think that's very

dangerous for the world.

So Ben, if you can believe it, how long

it's been since we last did this. The

world was very different. No AI at the

time. Uh we talked about aggregation

theory mostly, which I'm sure we'll hit

at some point today. I thought a fun

place to begin since the world has

changed so much is to hear what you

think it would mean for the US to win

the AI race. I think it would be very

problematic for the US to win. Let's say

we take the most sort of fantastical

scenario where if you control AI, you

basically your military is better than

anyone else. You can like somehow it

fixes our manufacturing all these like

things that I don't think AI is

necessarily going to do because they

sort of deal with the real world. But in

this world, what is the game theory

optimal response of China to blow up

TSMC? like it and to me this is like

game theory can get very sort of

convoluted and complex. To me this one

actually isn't that complicated. Uh so I

there's a just a fundamental disconnect

that I have with a lot of the rhetoric

coming out of Silicon Valley coming out

I think of one of the labs in particular

where if we get to a place where we have

a meaningful superiority particularly

from like in terms of a military

national security perspective I think

that's very dangerous for the world. But

in that state, how how much does it

extend beyond TSMC being blown up?

Because in that state, I would assume we

figured out how to build fabs here in

the US, you know, to some degree and are

less reliant on that one choke point. I

think there's a little bit of magical

thinking which I just invoked in terms

of manufacturing and whether it be fabs

whether that be actuators like all these

sort of precursors like the I think the

degree to which we are dependent on

China is underappreciated

and is not something that is going to be

fixed outside of a conflict just because

fixing so many of these things is going

to be dramatically like dumb. Like if

your competitor is sourcing from China

and you're going to start sourcing or

getting things from the US, you're going

to be at such a disadvantage relatively

speaking that you're just not going to

do it. So you do it when you have

literally no choice. And that works for

like very big headline items like you

can browbeat Apple to move some of their

iPhone manufacturing to India for

example. But even that is a good example

because Apple is not moving truly moving

out of China. like they're diversifying

to an extent, but the problem it would

just cost so much and it's like paying

an insurance policy that if you don't

have to pay it and it's astronomically

expensive, you're just not going to pay

it. It's one of those sort of hypotheses

that I just have a hard time even

gawking because in what the only world I

see where we truly

pull out and have no dependency on China

such that if they want to blow up

Taiwan, who cares? has no impact on us

is seems pretty fantastical to me and I

think there's a bit of facing reality in

this regard that is not present in these

conversations.

>> So put yourself put yourself in their

shoes like what do you think the

motivations are?

>> Everyone

can use a good bogeyman. I think from

the AI trade perspective nothing works

better than we have to be China. And I

do think we need to be China. We need to

be competitive. I despair at the extent

to which over the last few years in

particular so many of our responses for

particular from a political perspective

has been to like try to be like China. I

think we should be going the other

direction. Uh more openness, more

innovation, less top down control, less

restrictions on speech and things along

those lines. America succeeds by being

on the leading edge and by leading into

that. You said probably the US being

purely dominant and AI is not the right

end state for the world. What is your

ideal equilibrium for how this goes

worldwide? There's a bit where AI right

now is kind of like the Taiwan situation

in that it feels

the current status quo actually doesn't

seem so bad. And the question is how

sustainable is it? But maybe it's

sustainable for longer than we think. So

the the way I think about it right now

is I think OpenAI and Anthropic are

clearly on the frontier. Who knows

what's happening with Google and then

Grock and Meta are chasing them.

Meanwhile, the Chinese are very capable,

very smart, and also definitely

distilling these models to sort of stay

about 6 to9 months behind. And it feels

like a pretty good equilibrium that I

think is generally favorable to the US.

Now the question is how long can it stay

this way right and there's lots of

questions out there like can the Chinese

actually pull ahead I'm still a little

skeptical that you know for various

reasons getting to the leading edge I

think that last 6 to9 months is very

difficult I think we'll see how it's

going to be instructive how meta and gro

do in terms of actually actually

catching up is that sort of because

especially as we get to the world of AI

improving itself using AI to make the AI

better which I think is definitely a

real thing I think you see a real

acceleration

from both uh OpenAI and Anthropic

recently which and that was sort of

theorized and it seems to be coming true

and to the extent that's true can you

actually catch up and I think the other

question about this by the way is to

what extent does that apply to cost to

serve to marginal costs if you can apply

AI to optimizing your stack to figuring

things out to analyzing all the data can

is your cost to serve sort of

structurally lower than anyone else this

is the thing about the the open- source

models the talk about them being free is

bizarre to me because it's marginal

costs, right? You still have to run

inference like GLM or Kimmy. Kimmy is

very expensive to serve. The cost per

answer is significantly higher. So the

everyone referring to these as free. It

feels like in the narrative it's in

people's head that free is free. Now I

can use AI for free. No, you can't use

AI for free. You're not paying

necessarily the R&D to

create the AI, but you're definitely

paying the inference to sort of run it.

So right now I kind of like where we are

and the push back would be oh that's

right now it's not going to stay that

way. Um which I think is fair push back

but I don't know if you can learn

anything about the future of how this

will go to be more confident in like

where where the equil equilibrium will

end up. What is it? Is it like the

length of the S-curve? Like how far up

the S-curve we are of at some point

these things presumably will level out,

maybe not. What would be the thing you'd

want to know that would give you a

better sense of what the future might

look like? I am concerned that with like

the scare around like people freaking

out about mythos and like this hugging

face incident that the actual

implication of that is not that we

reduce these dangers but we just stop

releasing stuff and we on the outside

>> start to lose any sense of like where

exactly what is actually the frontier

and where it is And it and there becomes

sort of a false sense of security

because like right now everyone's basing

their understanding of mythos on fable

but how good is fable actually relative

to mythos right like that sort of gap is

only going to I think increase over time

and so I think that that's that's a real

question that that I'm not sure about

this question of the AI the

recursiveness and AI sort of making

itself better like does that lead to

sort of some sort of takeoff? And at the

end of the day, there's timing questions

in lots of different ways. I'm worried

about the timing mismatch in terms of

the actual return on investment

producing enough revenue to fuel

investment. Like we're we we're working

our way down the capital curve. Like we

we started with free cash flow, then

like the speed with which the tech

companies blew through the debt markets

is kind of incredible. like it took like

a year and now Google's issuing equity.

Nvidia's putting together the you know

the these

>> this $500 billion thing

>> this $500 billion thing to tap into like

pension funds and insurance floats and

things like that and the what's after

that? Where's the money come after that?

Well, ideally we actually flip back to

free cash flow funding this. But if

there's a gap there, if we don't get

there soon enough, then we could have a

big blow up, right?

But at the same time, even if we have

this blowup, the AI is not going away.

It's not going to stop improving. It's

going to keep sort of progressing and in

a way that we look back on the dot era

or we look back on the railroad era or

we look back on whatever bubbles through

history ultimately immaterial in terms

of the broad scope of humanity even if

they were very devastating to lots of

people.

>> What did the railroads teach us? Do you

think

>> it's now the last bigger buildout,

right? In terms of percent of GDP or

getting

>> I think we might be bigger at this point

or it's like it was the biggest

>> in the ballpark. Yeah.

>> Yeah. You know, the railroads had a real

duration mismatch. It like to build a

railroad and make money off it was a

decade or multiple decades long

endeavor.

and the so whereas you had to issue

money to pay for it in the short term

and the world ran out of money right and

I think that is that's probably the the

aspect I think that's why people reach

for the railroads because everyone talks

about are we going to have enough

compute are we going to have enough

electricity maybe the nearest term

question is are we going to have enough

money which is kind of a bizarre thing

to think about like that's what happened

in in the 1870s like we the world just

ran out of money. But the the the funny

thing is is the railroads kept operating

and they expanded the west and they the

the their contributions to GDP was

astronomical. They're still contributing

to GDP. Railroad money is what's going

into Google right now from Bergkshire

Hathway. Like like

>> very funny.

>> It's it's quite literal.

>> It's literally Bergkshire Hathaway has

this problem to me. This Nvidia deal is

very much paired with the Google equity

issuance which I thought was was that I

mean that one was shocking what had

happened.

>> Why was it shocking?

>> Because it's Google they can't raise

money like why are they issuing equity

right? Like the the why are they giving

away the their you know reducing their

upside if they believe so strongly in

this. But the Bergkshire the Bergkshire

Hathway comparison is interesting

because

in to a rough approximation they make

they have seized candies famously right

tremendously high high margin business.

The problem with a lot of high margin

businesses is you can your your the

percentage profit you can make is very

high but the absolute profit you can

make is reinvestment runway.

>> That's right. Like you just you're just

accumulating cash. And so the brilliance

of the BNSF railway thing was basically

they took the seas candy profits and

said here's another industry whose

margins are way worse but the absolute

dollar amounts are so large that those

way worse margins result in absolute

profits that are much larger. like BNSF

in 2025 or something, their the amount

of free cash they've threw off in one

year was more than Candies had thrown

off its entire lifetime. Even though

you're talking about a low margin

business compared to a very high margin

business and there's a I think there's

an aspect from Bergkshire Hathway where

if you're once your capital gets so

large, you start operating in a world of

like absolute numbers as opposed to

percentage numbers. And the reason why I

thought that was so interesting that

story is it seems to capture where

Google itself might be going. And so it

was very symbolic for them to invest in

Google. Google has this unbelievable

high margin business of search. One of

the most perfect beautiful business

models of all time and the purest

aggregator of them all. Like scales in

every direction. Doesn't have to invest

any money to do it. Everything's zero

marginal cost. It's amazing. And

meanwhile there's this AI opportunity

which requires just astronomical it's

just a cash incinerating cash but you

can imagine if AI is intelligence and

it's TAM is basically all white collar

work

>> and eventually with robotics more

everything potentially like why like the

absolute profits available here even if

the margins are lower is so much larger

that

will we look back and Google search was

seized candies and I it feels like

that's what's happening and and in that

world even yeah you use all your free

cash flow they've done that you tap the

debt markets to the tune of hundreds of

billions of dollars they've done that

you issue equity because like the what

is what does an equity issues do it

dilutes your interest in your interest

your shareholders so you have a smaller

percentage of the pie Well, if you have

a smaller percentage of an

astronomically larger pie, at the end of

the day, no one's going to be

complaining. And I I just thought it was

very symbolic. Bergkshire being the

symbol of that equity issuance in that

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I'm curious setting aside the commercial

and competitive components of this like

you're describing how AI pill on the

pure technology would you say you are

relative to other people thinking about

this space I have a view that is both

super bullish and less bullish in some

respects so

I am not fully convinced about the

generalizable argument like AI is

clearly incredible at coding. It kind of

blows my mind that people were doing

this a year ago, like actually like

writing out code. It's very good at math

obviously, but the obvious a you know

repost is that these are sort of

verifiable do domains and what is the

evidence or where is the compelling

evidence of being very good at

verifiable domains

cleanly translates to being very good at

sort of unverifiable domains or domains

that take have a very long sort of

verification loops and I think that's

still a little bit to be determined and

it's interesting because I raised this I

raised this question and there were some

people at the labs that were on a panel

and I was kind of annoyed at the answer

cuz the answer took me for an AI bear

and they're like oh well people thought

we couldn't solve chess or we couldn't

solve go and we solved those easy enough

and I'm like I thought we could solve

chess I thought we could solve go

because they're knowable domains and you

know scale was the answer to both of

those but also both of those were

bounded right what what is the go-to

example that's not chess that's not

That's not go that is genuinely in a new

space that's sort of an unknowable space

where it's doing things that were not

not possible. So that is sort of the I'm

not fully convinced sense. However, AI

trained

at a rough approximation trained on all

the data of the internet. All the data

of the internet that is like that's dist

distillation. It distilled all of human

thought.

>> No it didn't. It distilled all of the

end state of human thought, the actual

typing it on on Reddit. It doesn't have

the traces, right? It doesn't actually

have the thought, the emotion or

whatever that went into typing that

comment or typing writing that essay.

What if we like say neural link whatever

what if the actual payoff from neural

link is actually capturing the traces of

of human thought that actually

dramatically expands the capabilities of

these models in this world. My concerns

about verifiability is like well we

solve verifiability by getting more

data. My sense is that a huge number of

jobs, a huge amount of economic activity

does not exist in these domains that I'm

not convinced that AI is good at.

Actually, there's a lot of people in the

world who are kind of like sentient AIs

to a certain extent. They operate very

well in verifiable domains. They're

given jobs. They do them. And that

it's almost like a somewhat pessimistic

view of humanity uh to a certain extent.

But I think that market is so huge and

so large that if the models did not

improve at all from where they are right

now, the economic opportunity is

actually massive. I wrote an article a

while ago, you know, there's the whole

like accelerationist movement and I what

I call myself was a reluctant

accelerationist.

I think we need to push forward because

we can't go back and the worst thing we

can do is get stuck where we are. So I'm

very AI peeled in terms of its impact on

the economy its sort of upside in terms

of monetization. I'm not sure about the

timing. What would be like the gradient

towards it? Like imagine law or medicine

where I don't know whether or not you

would consider those verifiable like law

is like a code of some sort. Medicine we

have a certain state understanding of of

things. I mean, I think medicine is like

by far one of the biggest opportunities.

Yeah. Like it's both one of the biggest

opportunities and also one of the most

challenging ones because of all the

regulations and all the access. Like if

you could turn an AI turn machine

learning onto all the medical records,

the number of discoveries and improved

treatments we could come up with in a

very rapid amount of time would be

unbelievable. So that is a very

optimistic view. On the flip side, like

when is that gonna happen, right? I

think the the the

optimistic frame I put on humans is our

capacity to create needs is sort of

unlimited. So I think we'll do a very

good job of creating new opportunities

and jobs sort of in the fullness of

time. The sort of more pessimistic way

to put it is our ability to create red

tape and muck is also fairly unlimited.

And you know how much of our economy is

actually we've managed to create more

and more jobs that is just sort of like

make busy and make slow to a certain

extent. If I go back to the early 2010s

or you know maybe the aggregation theory

was stewing in your brain and then you

published it in 2015. I think it's fair

to say like that theory that idea maybe

you could just quickly remind people

what it is defined the winners and

losers of that era of technology. I'm

really curious how you're thinking about

what theory or or principles will define

this era of winners from like a

financial perspective and market cap

perspective.

>> It's a good question. I go back and

forth even just on the question of of

aggregation theory itself. How much does

that ex you know apply in this current?

>> Yeah. Cuz like like a push back that

people have is one of the key components

of aation theory is zero marginal cost

and zero marginal cost uh shows in lots

of ways. The one that I sort of focused

on the beginning was distribution. Like,

and people say, "Oh, I don't have

distribution. I have to pay Google for

friends." Like, "Well, no, you have a

website. Your problem isn't that you

have distribution. Your problem is you

don't have demand." And you're paying

for demand when you're paying for ads

and things on those because the

aggregators control demand. And they

control demand because in a world of

abundance, the hard problem is not

distribution, it's discovery. How do you

actually find what you're interested in?

So, the companies that solve discovery

in their domain come to dominate that

market. They get a virtuous feedback

loop. that sort of aggregation theory in

in a nutshell.

And the other thing is transaction

costs. There's no transaction cost.

Google can scale to the whole world. And

they can scale to the whole world. Not

just on the user side, but also on the

monetization side. The vast vast vast

majority of advertisers on Google or

Meta never inter never talk to someone

at Google or Meta. They just go up and

they buy ads. It's all done by

computers. And those computers from a

business perspective

>> cost zero dollars. AI obviously that

changes significantly like be their

inference costs are real. Uh but then

again sort of how real are they?

>> They're real right now.

>> They well how real I don't know are they

>> depends on the company but they're

they're way more real than the those

prior examples.

>> Well like if you look at gross margins

or something

>> for sure but but you have this

incredible spread. So you have people I

think the vast majority of people who

are using ad today are using it as

basically a Google substitute or like a

recipe maker or whatever it might be.

And my suspicion is that the cost to

serve those people is extremely low and

low in the basically similar to serving

them a web page like I would imagine

it's it's marginally higher but not not

that much higher. Then you have on the

other extreme people who are actually

leveraging test time scaling right. So

it used to be we just scale by making

the models bigger and bigger. Now you

can scale as far as time. How long do

you think about the answer? Well, you

could think about the answer for days or

weeks or months. And that is a d that is

directly marginal cost. Like every

second longer you're thinking is costing

more money which speaks to like we think

about AI and inference as this one

question. And that's I was sort of being

a bit, you know, pushing back on you.

But actually the marginal cost question

for the different user, the user using

free chat GPT and the user trying to

solve a a math theorem. They're not even

remotely in the same universe. And and I

think you see this challenge actually in

the enterprise in a very interesting

way. So Microsoft recently, you know,

they they are shifting their enterprise

plan, right? So they come out with like

an E7 plan, $100 per user per month that

includes some amount of usage, but then

they also are charging for usage on top

of that. And this is kind of a really I

think this is a kind of a fraught

position for Microsoft to an extent

because the positive way to think about

Microsoft is they do everything you need

as a business. Every individual

component might not be the best, but you

get it all for one price and they all

mostly work together. And if you're, you

know, particularly a small or

mediumsized business or even a large

enterprise, there's real value in that.

That's right. It makes life easy. The

moment you start having to think about

how much you're paying, it's not just

that that's a new decision. Number one,

that is untethered from headcount,

right? Microsoft got the benefit is when

you were hiring a new employee, you

would think about the cost of that

employee and baked in the cost of that

employee is $100 a month or $50 a month

for their their license. It was kind of

a thoughtless

revenue stream for Microsoft. Now, if

you think about usage, you have to think

every single month, how much do I want

to spend? And that introduces two

problems. Number one, most companies

aren't set up to do this. They make

budgets like once a year. This idea

we're going to be thinking about through

our budgetary aotment on like a monthly

basis doesn't compute. There's an aspect

where they're used to thinking about

capex decisions or one-time costs. And

there's a bit where what I'm talking

about this employee like the loaded cost

of employee. It's not capex but it's

kind of like capex. It's like you make

the decision up front then you don't

think about it anymore if the decision

is sort of already made. But if you're

thinking about usage you're doing it

again. But the the final thing is if

you're every month you're looking at

your Microsoft bill and how much do I

use? You start thinking about what am I

paying for? Like how good is each of

these products? Should I actually just

start thinking about and spraying this

out? And I think they had to do it

because

that extreme of user who uses a ton of

tokens and is actually leveraging AI

costs way more to Microsoft than $100 a

month. They can't support them. But they

want to hold on to the set cost for the

vast majority of employees who can fit

in that because they need to ask their

customers to think a little bit for

those extreme employees, but they don't

want them to think too much because that

sort of breaks the model in very sort of

surprising ways. surprised at all that

the the recipe builder user that is very

low cost to serve that there hasn't been

a great business model that's emerged

around them just yet like you know

business Google and Facebook are sort of

business perfected in this in this prior

era. They haven't seemed to figure this

out at all. I'm frustrated but not

surprised.

This is obviously a market that should

be supported by advertising like that.

That is why advertising is always the

consumer business model. Consumers don't

want to pay there. So there's two things

to understand about consumers that

Silicon Valley has to relearn about

every 10 years. Number one, consumers do

not want to pay for software. And number

two, consumers do not care about being

productive. And this is like we went

through this in early SAS. Like the the

canonical company for this in my mind is

Dropbox.

>> So Dropbox, unbelievable product. Like

especially when it first came out in

business school, I was one of the first

people to use Dropbox and that was went

off like crazy. I have so much storage

still like my free Dropbox cuz I gave

out of my code to like so many people.

So Drew Hston makes his amazing product

so easy to use, just absolutely

seamless. And I think very clear about

this. He wanted to build a consumer

company and there's that famous story of

him meeting with Steve Jobs and I think

you know Apple was interested in

acquiring Dropbox and they're like oh we

want to build a company and Steve's you

know your feature not your feature not

not a company and you know which that

plain Jane just file sync Apple did make

a feature as far as like sort of iCloud

drive and Dropbox they grew very fast

and then they had like a 2-year lull and

in that 2-year year low. What they had

to do was basically completely rebuild

the app from the bottoms up because the

people not enough consumers are going to

pay for it. They needed enterprises

could see the value, they would pay, but

if you want enterprise, you need

permissions. You need control. You need

someone else to be able to set all these

sorts of things. And their app wasn't

even created to do that at all. So, they

had to rebuild the whole thing and

realize the only way we're going to make

money is by selling to companies. Why do

companies pay? Because companies are

paying employees. So to the extent they

can make their employees more

productive, they're getting a greater

return on their investment. It's the

complete inverse of a consumer. A

consumer is like, I spent all day

working. Why do I want to come home and

be more productive? I want to sit on the

couch and watch reals. And and the the

and you see that with AI and you also

have this overarching just skepticism of

advertising. You know, I've gotten so

much traction on trajectory by being an

advertising appreciator. And I go back

and read my early articles about

advertising that were kind of

directionally correct, but also like

were not very good at all. But I got so

much traction doing it because I was the

only person writing about advertising.

In a world of everyone want to have a

blog in Twitter, no one want to talk

about advertising. But even now there's

in Silicon Valley there's this sort of

embarrassment about the fact that the

valley is in many respects monetized by

advertising and particularly during the

last sort of eight years there was a

Facebook's icky and like all these best

engineers don't want to go work on this

problem

>> and so you literally had open AAI

replaying the Dropbox story but at like

100x size being like no we're going to

sell subscriptions to consumers and they

did. They sold a lot, but they didn't

sell enough. If you're going to be in

the consumer market, you have to be

doing advertising. And now they're doing

advertising now. It's a little weird

they finally pivoted to doing

advertising. At the same time, they're

like, "Oh crap, we need to go for the

enterprise cuz Anthropic is kicking our

so quite sure what they're doing there.

They have been rolling out ad features

very rapidly like things like like copy

and the the connections with retailers

so you know if a purchase went through

so you can do all the tracking and

things like that. So I'm very interested

to see how that goes. There's a bit

where had they leaned into advertising

immediately as soon as Chat GPT was a

hit, I think they would have a killer ad

product right now. I think that Google

would be in much bigger trouble. I think

meta would be in much bigger trouble

because if you have this flywheel, the

thing about advertising with consumers

is your ability to monetize the consumer

>> goes up in because the advertisers

bearing the price increase. So there's

zero elasticity issues. If you're

charging consumers a price, if you want

to raise the price, like Netflix, this

is their problem with with the

subscription plan. They have to be how

much can they raise prices before

consumers rebel and drop drop a tier or

give up the service entirely, right?

Charging people money is hard.

>> Giving people things for free is easy

and it's very frustrating that OpenAI

did not pursue this sooner. I know

you've been spending time with, you

know, some of the big money firms and

sources of capital. What is your sense

of their appetite right now and how

they're thinking about the future?

Because I think this year it's going to

be 800 billion or something that we're

going to spend in capex. Next year it's

supposed to be 1.3 trillion I think is

the current estimate. It's going to

keep, you know, keep going up from

there. We're burning through all the

compute that gets installed like

basically immediately. It's such a

strange circumstance that we can use the

capacity right away as soon as it's

online.

>> Well, that's the thing though. So

there's a few timing mismatches that are

happening right now. All the bulls on

Twitter is always like we're we don't

have enough comput. We don't have enough

compute. Well, we don't have enough

compute because there was insufficient

investment made in 2023 and 2024, which

yes, absolutely. And by the way, if you

think there's not enough compute, TSMC

decreased their rate of growth in 2023

and 2024 and 2025. So like we're our

shortage of compute is going to get

worse in the next few years because a

fab the lead time is even greater than a

data center. So all today when we say

there's not enough compute, it's not

like all the money that the companies

are putting in today

>> manifest in comput. No, it all manifests

in compute in 2028 and 2029. So you have

like on the calls you have both Andy

Jasse and Sadella are out there saying

look we're just building data centers

like these are the shells. We might not

use them now, maybe we'll use them in

the future and we only buy GPUs when we

know there's demand for them. That is a

great story to tell. I'm not sure how

much that I think is a lot of BS because

the reality is is if you've built the

shell that money is sitting there.

You're not going to let it just sit

there. Like if you have if you invested

a fixed cost and this is the whole logic

of commodity markets. I think tech in

general doesn't understand commodity

markets. tech is by and large focused on

if I produce a highly differentiated

product and that differentiation could

be like you know software it could be uh

a network in terms of developers it

could be a social network sort of thing

where peerto-peer where I'm highly

differentiated then my ability to charge

higher prices provides sort of my profit

margin so the the classic example is

like Apple right they have their

ecosystem and they have their software

and they have third party and all those

sorts of things and so they can charge

they have 50% margins is on their

iPhone. Everyone looks at Apple as like

the ideal business model. That's how you

run a business. But in a commodity

market, the price is set by the marginal

supplier.

>> Cost to serve is all that matters.

>> That's right. And so I had a good friend

in Taiwan who was in shipping. Um

fascinating industry. It's kind of like

the airlines too. Another industry that

I love to look at, but like you you you

buy a ship and the cost of that ship is

depreciation and your marginal cost is

actually quite low. It's the fuel to run

the ship and the cost of the crew and

like your port fees. Not that much. What

that means is you are going to run that

ship

>> as full as human

>> basically. No, you're going to run it no

matter what. And you're going to bring

down the price of a container as low as

it needs to be to cover your marginal

costs. Now, your paper losses in this

situation might be very large because

your accounting loss includes

depreciation, but the depreciation is an

accounting figment. you already paid the

money and so you're you're going to run

that ship at whatever the market will

bear and the container the beauty of the

container is it is a pure commodity and

so the cost of the market is going to be

the marginal cost now if it gets low

enough at some point people will exit

because their marginal cost actually

can't like they're actually losing money

on a shipment right uh not just paper

money but like actual real money they

will exit but then the supplies

diminished So then the price the price

will go back up and you get this

interplay of sort of coming in and off.

But then let's say the market's very

high like it was during co it's like wow

we're making so much money right now cuz

there's not enough supply. There wasn't

enough supply of ships. So containers

went from like usually being like $3,000

$4,000 to 17,000 $18,000. Like the the

amount of money that these shipping

companies made in a very short amount of

time was insane. And so what happens

though? Well, more ships.

>> Imagine if we had more ships, right? The

problem is it takes 2 years to build a

ship.

>> So by the if everyone makes this

decision simultaneously, then the ship

you suddenly have a lot of ships, price

plummets, etc. Where we see this is in

components, in memory in particular.

Memory very famous for boom and bust

cycles. Uh people entering the market

late. But to what extent are data

centers

going to be memory makers where right

now everyone can see we don't have

enough compute. So everyone's like we

absolutely have to be investing because

there's so much money to made and look

at our payback period. The problem is

you're measuring your payback period in

a time of scarcity. Is that payback

period going to hold in a time in a time

of abundance? Uh and and the sort of the

bulls would say there's never going to

be a time of abundance. AI short time

scaling we're going to be short forever

which maybe we will be my concern is

even if that's right we could still have

an air gap

>> in that there's so much money going into

it right now and not enough has come

online to actually make sufficient

revenues to cut to handle the situation

where we run out of capital that like I

again I believe in AI I think it's a

real thing I think the economic impact

is going to be astronomical I think all

the concerns about societal impact are

very real and are going to come to bear

in a major way. You can believe all that

and still be worried about are we going

to make the bridge to this actually

generating the level of returns

necessary to continue to fuel this sort

of going forward. Can can you zoom in on

TSMC and the and the maybe the some of

the component makers where fabs are

involved and so far at least my

understanding is that they've been quite

conservative in their willingness to

expand capacity, build new fabs, meet

the market's demand with similar growth,

which they have not done. And if that if

that just rate limits this whole thing

and prevents us from getting one of

these giant overbuilds.

>> Well, we can talk about a few different

ones like we'll start with memory.

memory used to have tons and tons of

memory makers and every time there'd be

sort of a boom memory makers sort of

like reenter the market um or like new

countries would come in like Taiwan used

to have like a memory market and but you

would get these exact dynamics if

there's a shortage of memory there's so

much money to be made because no one you

can't bring capacity on immediately

we're like it's the same as shipping

it's the same as what we're seeing right

now and so what would that that would do

is that would spur sort of people to

come in the market, you get too much

capacity, prices would plunge and people

would just get blown out cuz the issue

is the upfront cost for these is so

large. Just like buying a ship, like

building a fab is even more so. And

memory now, like the leading edges of

memory are using things like EUV

machines. So the costs are getting into

the billions of dollars for these lines.

And what happens is every time these

boom bus cycles, some people would

enter, more people get washed out. You

go through there's like these these

famous historical moments for these

memory cycles and like companies just

get blown out. One of the most

interesting actually memory stories is

how Samsung sort of took over memory was

they saw it as an opportunity and they

had studied history and they realized

that actually the way to take over the

market is to invest into downturns so

that you're ready when the next cycle

comes around which requires a ton of

guts and a ton of discipline and a ton

of money but they did that and basically

wiped out the Japanese. That's when the

sort of the South Koreans generally took

over the market in a major way. But it

got down to three. And the problem is

three, it's not a monopoly, but it's

kind of an oligopoly. And they all got a

lot more discipline about let's not make

the mistakes of the past. And we're not

colluding, but we all are on the same

page about let's not do that. And I

think that dynamic sort of ran head on

to the current moment where it just took

a while for them to realize no there is

a secular shift in memory demand that

didn't exist for for a very long time.

And so I I think the memory solution

will be solved eventually. The other

thing they the risk they run is Apple

like Apple's lobbying to get Chinese

memory, right? uh and what is the number

one focus of like al algorithmic

changes. How can we use less memory? I

think the memory makers probably screw

themselves in the long run by creating

such a massive target on their back.

I've analogized memory makers to Iran.

Like the issue with the straight of

moose is it's very effective.

It's more effective if you don't use it

cuz then it's always hanging out there

as something you could do. Now they did

it. Turns out it worked. But like the

UAE and Saudi Arabia, they're going to

build pipelines. They're going to build

new ports. They're not going to let this

happen again. It's very painful right

now. But say Iran wants to close the

straight of our moves in 2035. It's not

going to have any effect because it will

have been built around. My concern for

the Merry Makers is they might have done

the same thing. Like no one's going to

let themselves get in this situation

again as far as memory goes. TSMC is

arguably worse because there's only one.

Uh there's one company on the leading

edge. Um, obviously Intel and Samsung

are trying to get there and it's the

same thing like like the all markets

carry risk and a lot of the question is

who ends up holding the risk and what I

think a lot of the tech companies didn't

fully appreciate is the extent to which

TSMC has offloaded risk onto the big

tech companies. And the way they've done

that is the risk that TSMC is worried

about is over capacity. If we build too

much, it's not just that we built too

much and we have all these fixed costs

that are not being fully utilized, but

if we build a fab, we expect that fab to

run for 30 years. We've like baked in

too much capacity into the system for

years and years and years. So, they are

very biased towards being much more

conservative. And there's a little bit

of a culture component to this too. One

of the most interesting TSMC stories,

it's kind of analogous to that Samsung

story was Morris Chang retired in like

the late 2000s and new leadership took

over and there was the great recession

and so they pulled back their plan

spending. He comes in, fires everyone

and he's like, "The iPhone just

launched. This is the biggest

opportunity we've ever seen. We need to

be investing, not cutting." And they

invested through the Great Recession and

through that downturn. That's what laid

the foundation for them taking over sort

of leading edge semiconductors in that

time. Morschang is what a one of one

like on the Mount Rushmore in my mind of

the greatest sort of and most impactful

tech executives of all time. The entire

fabulous model is so critical to to what

tech is and what it does and also just

the guts to do that right at that time

particularly in you know someone who

lived there a culture that doesn't

necessarily tend to make those sorts of

bets. TSMC, they were pretty

conservative to be totally honest. And

so what happens though? Where' the risk

go? TSMC's like, "Well, we we don't want

to take the risk." Risk doesn't

disappear. It just moves. The risk is

right now where you have every single

big tech company realizes if we had more

compute, we could be making more money.

So there's lots of foregone revenue and

foregone profits. That is the

manifestation of the risk that TSMC

handed off to them. Risk doesn't

disappear. It just gets handed off. And

sometimes that risk doesn't manifest in

losing money. It manifests in not making

money. And there's money not being made

right now because what happened was they

were very excited about 5G. They did a

big like wave of like investment um in

expanding their fabs in around 2020,

2021, 22. And they're like, "Okay, we're

good." And like I said, 2024

like Chri 2022 big thing in tech in

2023. In 2024, their growth rate went

down. In 2025, their growth rate went

down. In 2026, it's up now. It was very

funny because I, you know, I was writing

about this a while ago and then I think

it was like one or two earnings calls

ago. Suddenly uh CCway the the CEO and

chairman is talking about like the use

cases for AI like the whole earnings

call in a way he never had before. This

is the problem with the why the makers

are scared. Usually there's like a bull

whip and they're worried about being at

the end of the bull whip where the

demand happens and it works its way down

the chain and they're at the end and

then they double down. It's already too

late and they're they're wasting all

their money. And I think the thing with

AI is if it's a bull whip it's like the

longest bull whip of all time. like

there's still so much to be built and it

just took a while for Asia to get the

message where these sort of companies

are. Uh but I think I think they've by

and large gotten it but them getting the

message it then takes several years for

that to actually materialize.

>> Do you have a sense for like how long

you think it will take given the extreme

shortage of compute?

>> Well, the interesting thing is what this

means for Intel and and Samsung sort of

the logic business. So, I've been

writing about the problem of this

dependency on TSNC for years. Um,

actually, one of my first articles in

2013 was exhorting Intel. You I say you

have to build a fab a fab business. You

you like you're not going to be this

desire anymore. You like there's a huge

business in manufacturing chips. And I

thought I was late writing it then.

Their stock goes to the moon throughout

the 2010s as they're riding the sort of

cloud wave. And it wasn't until like

2020 where they finally realized and we

fell behind. By the way, there's this

huge opportunity. We're totally

unprepared for it. We don't have a

customer service mindset or culture

organization or all the IP building

blocks and all these things that TSMC

has. And they need a customer. They need

customers to help them actually build a

real foundry business. And so I would

write about this problem and I'd write

about like the China issue like you're

dependent on on a a company that is 60

miles offshore of our greatest political

you know opponent who thinks it's

theirs. So I these are big problems and

that's where I came to appreciate this

insurance issue for a big tech company

to go to Intel and say Intel you make

our chip and by the way the biggest

benefactor of this is going to be you

cuz you're going to learn how to work

with a partner and the biggest pain is

going to be us cuz we're going to have

to figure out how to work with you. We

could just go to TSMC. They are awesome.

They are so great to work with that we

know they're going to do a good job. It

just never made rational sense for

anyone to go work with Intel. That was

their fundamental problem. In a in a

world in a unchanging world, TSMC would

just win forever. But this is where TSMC

in some respects made the same mistake

as the memory makers made the same

mistakes as I ran. If I can continue the

analogy because they didn't invest the

last few years. The shortages are going

to be so acute. big 10 companies that

we're foregoing so much revenue and so

many profits because we don't have

enough compute. We will go through the

pain of getting Intel of getting Intel

up to speed of getting Samsung's logic

up to speed. The scarcity

is what ultimately saved Intel. Um, and

I expect at some point in the near that

they're going to announce like some

major partner for the first time. It's

going to be a big deal. But it was

ultimately TSMC brought it on

themselves. It's the cure for high

prices is high prices thing where we're

going to route around them. Yep. And

like and it's just like there's all

these things as like an analyst sitting

on the side. You can write these things

and it it was one of those things I sort

of learned like no one's going to pay

insurance they don't need to pay when

that insurance expected value is is

negative. The way to solve the

geopolitical problem of dependence on

TSMC

is to come up with a compute use case

that is so massive that everyone is

economically incentivized to bring other

people up to speed and then we get the

sort of geopolitical insurance for free.

>> If you think about the let's say top 10

or 15 technology companies, which ones

do you think have the most interesting

setups today for their business?

>> The answer is always Amazon. Um and the

reason because what Amazon is so

compelling is the extent to which they

build for them. They are their first

best customer like they provide the

scale to get basically anything off the

ground which they then sell to other

people. AWS is the most obvious example.

AWS contrary to sort of popular thought

was not spare Amazon capacity. Actually

it took a long time to get amazon.com

onto AWS. But the re what it drove was

the understanding that we need to have a

scalable. We can't be having so many

meetings like we need to have just

compute that you can plug in purely API

surface. You don't need to talk to

anyone. It's just there. And oh by the

way if we do that for our internal

retail teams we could do that for

anyone. And turns out the retail is so

big we have to start with everyone else.

AWS actually started serving external

customers before it served internal

ones. But now it serves them all. You

got other products like say the

logistics right where it was the

opposite like right now we're using

external providers for logistics UPS and

FedEx and USPS we need to build this up

ourselves and now they built up

themselves they're offering it to third

parties right now other people can use

their use their delivery services and

you see this in market after market like

they're talking about things like some

of their AI products that they're or

their chip products right what's the

beauty of the graviton or the tranium

particular the early versions. The early

versions were terrible. But if you're on

Amazon and you're using some of their

managed services, like say the Redshift

database service, they don't tell you

what the processor is underneath that.

You're just buying a managed service.

>> So they can put all their crappy

processors underneath the services

they're selling and that gives them the

volume and the capacity to iterate them

and get better. And they get to the

point where they can actually sell them

externally. And so because they were the

first best customer for Graviton,

Graviton got better because they were

the first best customer for Tranium.

Tranium got better and now Trrenium is

obviously, you know, running anthropic.

They're doing the same thing. They're

doing the same with AI products. We'll

see if any of them take off. They have

call center software. Their call center

or their customer experience is going

through AI. By the way, it's pretty

good. I don't I don't know if you like

>> I haven't tried it.

>> Well, because I you know, moving back to

to America, I've been buying lots of

stuff and well, every summer I'd buy

lots of stuff in a very brief amount of

time. sometime in like the last year or

so, you can go on and you're clearly

talking to a chatbot, but the chatbot

does a great job and and it actually

does take care of the problem. So, you

could see that actually starting to work

in that in that regard. But they're

going to they're building up these AI

services for their own business that

they're going to make broadly available.

And some of them will work, some of them

won't. But this is it's such an elegant

sort of approach and given they have so

many investments in the real world.

Their core business feels so impervious

to AI for like the model version of AI.

It will benefit from AI but their their

moat feels deeper than anyone as far as

their core business and their ability to

just sort of generate new business lines

organically is is very compelling.

>> What about Apple? They've sat this whole

thing out. It seems

>> it feels like it might be a situation of

better be lucky than good to a certain

extent.

>> I mean, Apple has their whole has their

whole ecosystem and

at the end of the day, they do own

access to customers. So, they can sort

of get suppliers. This is the classic

aggregator play. If you own access to

customers,

suppliers come to you, not the other way

around. and and so they can get

suppliers for their AI sort of as as

needed. And by the way, you know, to the

extent it's true that people don't want

to be productive, they just want a sort

of a chatbot.

Not only can they serve them a chatbot

and with, you know, finally getting a

Siri that works, but you can see a

future where this absolutely can work on

device and they actually don't even need

to pay for inference costs either

because they, you know, they're using

the customers electricity. I mean, I I

don't think we're quite there. There's a

reason they're using Google Cloud and

and Nvidia chips, but you can certainly

imagine a future where where that's the

case and they're in physical goods. Like

actually making phones is is hard,

right? and having retail, having

distribution for physical goods is is

good. So, they're they're more

insulated. The smartphone is so perfect.

It's small enough to fit in your pocket.

It's big enough to watch basically

anything on it. You can run your whole

life on it. All your entertainment is

there. Like when we talk about customers

just want to be entertained. The TV is

now an accessory. It's all on your

phone. And you know, I don't see anyone

taking over the phone. The question is,

is the phone always going to be the

center or is there a bit where

particularly in the home, this is where

I'm very, you know, open's efforts here

are very interesting, uh, where you want

sort of an ambient AI where you can just

talk to the AI and it tells you what you

need. Apple is the best position to

provide that, but can they provide that

without having leading edge models? Can

they provide that if they're so phone

centric or is it like a Microsoft

situation? Microsoft didn't miss mobile.

They were very early to mobile. The

problem is their mobile was a small PC.

They assumed the PC would always be the

center and their phones were going to be

something that was off that Apple

realized no we the f we need to reset.

The phone is not going to be accessory

to the Mac. The phone is going to be the

phone. The iPod helped them realize that

and going with Windows and all that. But

will they fall into a Microsoft like

trap like assuming the phone's so good

it's always going to be the center and

then let's figure out around it or is

this finally the time when actually

ambient the cloud just in general AI

being everywhere it can manifest through

your phone it can manifest through a

device can manifest on your computer is

actually better and is actually

disruptive to them I think it's possible

I also think it's totally valid for

Apple to double down on what they do.

The other thing about the AI stuff is

on what basis should we expect Apple to

be good at this?

Like just in like at the most crude

level, AI is this probabilistic

endeavor? Apple is the king of

deterministic products. like a physical

product. You ship that iPhone, you ship

it once and it's got to be it's got to

be good. If it's bad, it costs you

billions and billions and billions of

dollars. Apple's never had an iPhone

recall, which if you think about it is

actually it's amazing. And that the sort

of care and decision-m and diligence and

you know fierceness in terms of your

supply chain and like making hard

decisions is very very different than

everything that goes into like making

great AI and I'm generally prefer

companies to do what they're good at. So

from my perspective I'm fine with Apple

not doing AI. I I want them to keep

making great devices. of the five, let's

call it five potential frontier AI

winners. So, OpenAI, Ananthropic,

Gemini, let's put SpaceX AI, you know,

Grock, and Meta in that pile. Which of

those firms do you think has the most

interesting setup? Open Eye and

Anthropic obviously are the riskiest,

but also have the biggest upside. You

know, they're just the never discount

number one, the power of belief. They

think they're creating God. like like

the the most impactful things in history

have usually been fueled by religion and

the two religious organizations in

Silicon Valley are the sort of like open

kind of like mainline like they go to

church every Sunday they're sort of like

evangelicals is like that's anthropic

like they're they're all in uh it it is

core their belief that goes a long way

the fact you need to make a business

work for you to survive goes a very long

way Google just needs like search to not

die too quickly, right? Meta has the

huge advertising business in in a world

where Meta was run by anyone other than

Mark Zuckerberg. They would not be on

the leading edge. That is the one of the

purest manifestations of founder sort of

energy for better or for worse. Their

business is so amazing. You see them

just easily sort of doubling down on

that. Like Google, there's a bit where

it it made sus.

They've been doing research in this.

It's like it makes sense why they're

pursuing this meta being like actually

we're going to hire a completely new

team and we're going to start from

scratch. This all again is pretty

insane. So credit to Mark Zuckerberg in

that regard. Again, you could decide

whether that's a good idea or not. And

then SpaceX AI, I mean the

data centers in space is like that is

the theory is there like do they have to

own their own model though to do that?

They'd get better margins if they do. Um

then again if we actually run out

whether through political opposition or

power or whatever it might be if we run

out of data centers on Earth like they

can run whatever model they want as

we're seeing with their sort of you know

selling their capacity to anthropic

right now. So they're all pretty

interesting. I think um probably the

case for SpaceX AI is probably the

weakest because the data center and

space play is so highly differentiated.

Like if that plays out, it I'm not sure

to what extent they need to even have

their own model. So why are you wasting

billions and billions of dollars in the

meantime? Um that's a fair question.

From a tactical perspective, I love the

cursor acquisition. like that makes so

much sense for both companies and so I'm

intrigued to see what they do. Um, Meta

is probably the most interesting just

because you've written a lot about this

recently.

>> The

I think there's a very good case to make

that it is actually more reckless to not

be on the frontier if you're a digital

company. Right? So the the counter to

the counter to Meta is actually

Microsoft. Microsoft is not on the

frontier. The reason why Microsoft has

$20 billion of free cash flow last

quarter, Microsoft paid a $10 billion

dividend last quarter, right? Like

there's there's some money, but their

play is, okay, we're going to play all

these off each other. We're going to

provide middleware. We're going to

provide the platform that enterprises

will build on us and we're going to sort

of inter, you know, disintermediate

disintermediate the models. And I think

it's a I think it's a rational play.

It's the IBM play of the '9s. Like

there's, you know, history sort of

echoes. Everyone talks about Google,

Google like following Microsoft.

Microsoft follows IBM and you can see

that uh to an extent. Uh

>> what did IBM do? What's the what what's

now?

>> Well, so IBM um so IBM had this

dominant, you know, we talked about it

in the 70s. Uh and then you fast forward

to the '9s and IBM is this very sort of

distressed asset and the thought was IBM

needed to break up and all these

different pieces they had. So Lou Gerson

comes in, he takes it over. And I think

Gersonner's real key insight to IBM is

actually everything. We're pretty

mediocre at everything. It's kind of

like what I told Microsoft before. And

that's the price of Monopoly. Once

you've been a monopoly, you kind of lose

your capacity to be good because you're

you didn't need to compete anymore. And

I think a lot of tech incumbent

companies have this problem. They it

didn't matter what they did, they were

going to rake in money. And if you don't

have the pressure, if you don't have the

incentive, if you don't have the fear of

death or the fear of God as we talk

about these mono companies, then you

don't do your best work. And the problem

is that you once you lose that muscle,

it's gone. You're just sort of fat and

flabby. And so what Gersonner realized

is actually the worst thing IBM could do

would be to break it up into component

pieces cuz all those component pieces

are actually not very good.

our biggest asset is that we're big.

It's like what? No. What does it mean

we're big? We can It's the '9s. This

internet thing is coming along. There's

all these companies that kind of know

they have to figure out the internet and

they don't know what to do. They need

someone who can come in, understand

their business, and help them get

online. That's basically what IBM did.

So they built out and this is an echo of

what's happening now huge consultant

force and they put all their time into

building basically it was middleware

where they would go in and they put this

layer between a company's old school

mainframe like which all these companies

had and then modern web services on the

other end so they could have websites

and e-commerce sites and all this sort

of thing. It gave IBM a 30year lease on

life. Yes, in theory you could go get

point solutions from all these hot

Silicon Valley startups but you you

don't understand that. you don't know

how to do that. You know us. We'll come

in. We'll create all this middleware.

We'll give build this big consulting

force to help you implement it and

you'll get online. And IBM basically

brought all of corporate America online.

And that's what Microsoft's playbook.

Microsoft is um will help you figure out

AI. It will help you figure out in a way

where you're not giving away the crown

jewels to these companies. We're going

to build this platform, this harness,

this sort of middle layer where you can

we're dependable. We're stable like you

know us we we have backwards

compatibility to the 80s like you can

build on us and then we'll manage all

the changing models and what's updating

and do all those sorts of things and

does that mean you'll get the absolute

best experience no middleware sort of

saws off the sharp edges right like you

you sort of get a lowest common

denominator capacity but if you value in

this the oldest enterprise sales motion

how did Oracle go to market Oracle went

to market in the 198 the 80s Larry

Allison with this you know another

technology taken from IBM or just IBM

didn't want it relational databases and

they're like you don't want to be locked

into IBM you want to be able to

relational database you could run

anywhere come come with us the the

reason this is a joke is cuz Oracle

locks you in more than anyone right but

the the the all of enterprise sales is

companies whose long-term goal is to

lock you in getting you on board by

trying to make you scared of being

locked into somebody else, right? Like

all the cloud companies are like, "Oh,

portability and whatever. You can be

whatever." And then they're like, "Oh,

just use our service that only runs in

our cloud and now you're locked in." Um,

so that that that's Microsoft's playbook

and it's a very rational playbook and I

think it makes sense and that is the

opposite. That's why they have extra

money because they're not on the

frontier. They are building massive data

centers, but they're building data

centers for inference. They're not

building it for training. And their

story about we're investing in time in

response to customer demand is more

believable in that regard. they're not

having to tell a fungeibility story

where we're building big data centers

for training that will be used for

inference down the road maybe. But go

back to this notion that it's reckless

to not be in the frontier as a

>> digital. Well, so so the the reason why

that's concerning though is at the end

of the day, why are we using Microsoft

products again?

>> Cuz we did before.

>> Like to what extent does it actually

make sense to have all these artifacts,

all these documents, all these like

email inboxes? Can't AI just do that?

Like there there's a real threat here

where to Microsoft's software business

the whole systems of record thing is

it's kind of funny because one reason

why systems of records are so powerful

is it's so hard to move them to

somewhere else because it's a very

tedious repetitive job.

Oh AI is actually surprisingly good at

that. I'm not sure how good the systems

of record Microsoft isn't so much

systems of record. They do have some

like the dynamics business. It's user

interface. It's like where you actually

interact with the computer. That's the

part that is like when you see codeex or

when you see uh you know claude co-work

or whatever it is like aimed like an

arrow to the heart of what Microsoft h

of what Microsoft has and in the long

run all digital companies are but

Microsoft is very much like there

there's a re their strategy is sound

it's also

desperate in a

existential way and also in a they might

pull it off because they're desperate

sort of way. Meta is not threatened

immediately. But if this is where my

bullish view of AI comes in, I think all

digital

companies are threatened and meta is a

digital company like they they they have

software. Now the sort of one worry is

AI takes up more and more time and that

like time ultimately is is meta's

currency. We saw opening I tried the

sora thing didn't really take off.

Social network is actually pretty hard

also cost a lot of money like it's kind

of really interesting. This came up with

the creator payments stuff. So YouTube

very famously as paid creators kind of

from the beginning and that's a much

bigger drag on the business than people

appreciate because

YouTube has marginal cost to their

content. Now unlike a Netflix they don't

have to put pay that cost upfront. They

pay it after the fact. So they're

sharing revenue. So it's a much it's a

better model than a Netflix model.

Netflix is to pay upfront for content

and then ideally make more money.

YouTube pays along the way. But but

Facebook or Meta

>> pays nothing.

>> They pay nothing. People are like

Instagram is this unbelievable product

that generates all this money for which

Facebook pays zero dollars for content.

It's unbelievable. And so it's funny

because you could see a world where for

YouTube AI generated content could

theoretically be a positive because the

inference cost to generate content could

be less than what they're sharing with

creators. For meta AI generated content

to the extent they're the ones

generating it is actually a worse margin

profile than what they have today. What

they have today is free. So they have

attention. Um there's a bullish world

where meta is actually very well placed

because in a world where we're

interacting with AI all the time the

desire for a human connection becomes

greater and it's sort of like a Meta

going back to their roots. Meta one of

their biggest mistakes actually Meta was

always a social network company. They

killed Snapchat or stop Snapchat's

growth by realizing Snapchat has a great

product. Let's layer it onto our

network. They they took their they

brought their network to bear to kill

Snapchat. uh where Tik Tok the reason

why Tik Tok is just a blind spot for

them is Tik Tok is classified as a

social network and it's not a social

network at all. Tik Tok is an

entertainment product. You it doesn't

matter who you follow on Tik Tok. What

you see on Tik Tok is a function of what

you watched and you're going to get more

more of the same, right? And the it's a

userenerated

content network. And the the insight

from Tik Tok was the way to get the best

content to limit it to your social

network is an artificial constraint.

We're going to give you the best content

from across the whole network. And the

vast majority of content is going to be

crap. But this is like the absolute

question before. Like you don't think

about margins, you think about absolute

numbers. The absolute amount of great

content, even if the margin for great

content is infantessimal, if we have an

a ton of content, the absolute amount of

great content is going to be very large.

And so the then Meta is like we're a

social network. And so Meta is serving

you content from your network of people

you know and Tik Tok serving you the

best content from around the world.

That's why they took a huge chunk out of

them. Meta had to shift. That's what's

happened with with Instagram and with

reals is it's not really a social

network. It is a entertainment product

that pulls from the entire network. And

social networking is like the group

checked. it's possible in AI actually

social network is important again

because like we actually want humans we

want to have some sort of connection to

them that'll be interesting to see how

that plays out then the other thing with

with the models is they're so impactful

on advertising biggest impact of the

models the biggest monetization right

now is probably not anthropic openi it's

it's the incremental gain that is

happening for Google and meta and most

of that most of most of the stuff is

prel but we're getting to LMS whether it

be generating advertising content like

they are the bad like what do we want

want verifiable domains. How do you

verify if a generated image is good for

an ad? Does the ad sell or not? Like

they can they actually can validate

their image creation and their text

creation in a way no one else can. And

their validation is the ad marketplace.

Like running a gazillion AB tests on all

these different things, see what works,

see what doesn't. Most ads are a

throwaway. It's fine. Um like the vast

majority of ads don't convert. So they

have this they have this massive

advantage, this huge liquid market that

is a verification machine where the

verifiers are humans deciding whether

they click on that ad and make a

purchase or not, but they're doing it at

global scale. That can actually have a

feedback loop to make their products

better. You're also going to get a world

where ad matching is actually still

fairly crude. It's like here's the

qualities of the person, here's the

qualities of the ad. And it's like you

create an embedding like a a vector

calculation uh and see what numbers

match and then you sort of match an ad

to the person. What do LM do? LMS

predict like we're going to move to this

world where Meta is going to look at

people and say this person probably

wants to see this next and they're going

to go find that thing and show it to

them. the potential upside in terms of

just showing people better ads that are

more relevant to them. They only need to

increase like a few percentage points

for the returns to be billions and

billions of dollars. This alone is worth

them investing in being on the leading

edge in in having these amazing models.

I think a big problem Meta has is they

don't tell this story. Like it's weird,

but Mark Zuckerberg has the same problem

Sam Alman does. He doesn't love ads.

They have the best ad business in the

world. They have an ad business that I

think is a societal positive. Like you

and I have set up these little content

businesses that make great money, but

we're content is kind of you get a ride

on social media, right? I grew up on

Twitter, people sharing my links. It was

amazing. If you're selling some product,

like the beauty of the internet is there

is a niche out there that wants that

product. The question is how do you find

the niche?

Facebook advertising. That's what it

does. It it connects. It helps products

find the people who didn't even know

they wanted that product, but when they

get it, they're so happy they got it.

And that is tre that's a huge societal

positive. You have new business from a

new entrepreneur making a new product.

You have customers who are happy they

got something that they didn't know they

would get otherwise. Those customers, by

the way, got lots of free entertainment

and they didn't have to pay for it along

the way. And Meta made a bunch of money

for themselves and their shareholders,

which is basically everyone in the

world. Like that this is why advertising

is great. And Meta's advertising in

particular is awesome. And I get

frustrated that Meta doesn't talk about

that. Mark Z has never really talked

about the societal benefits of

advertising except in passing.

>> I see. in 20 years. Like he's handed it

off to other people to take care of. And

maybe there's a bit where him not paying

attention is why

there is a certain like

grit and grind that goes into building

advertising business like and like you

know all the Facebook people get

frustrated or have questions about as

far as data and all those sorts of

things and maybe there was a bit where

he didn't want to be involved in it and

wipe his hands of it. But you saw this

like when when Apple passed ATT app

tracking transparency was one of the

most

one of the worst antitrust violations in

the history of technology like just

Apple unilaterally

obliterating all these business models

while they're simultaneously building

their own as far as advertising goes and

doing like doing all this tracking. Why

trust us? And meanwhile they're running

these advertisements. So remember that

advertisement of people on the bus like

overhearing everyone around them what

they're saying. That was such a

dishonest representation of how

advertising works on the internet. You

had Tim Cook in Congress talking about