We have a whole new technology. That's

the most important technology ever. And

you need a whole new infrastructure.

>> Normally when we talk about the

infrastructure world, we're talking

about the servers on the storage and the

network. Here it goes all the way down

to the mines of copper min. That's how

widespread this thing is going to be.

>> It used to be when you built something,

it was an engineering problem. And here

it feels like it really is a resource

limitation. So whether it's tokens or

not, we're pouring a ton of money into

systems and then those systems are

producing a result. And right now we're

bottlenecked on the systems ability to

actually match the resources we're

pouring into them.

>> The leading memory vendor said the

demand they have today will take them 3

years of capacity to supply.

>> If this fund does what we think it will

do, how do we see the world in 5 to 10

years?

>> America wins in the infrastructure

[music] and that would be awesome.

>> Ben Martin Ragu, welcome.

>> Thank you.

>> All right. Thank you.

>> I want to start with a Mark quote to

introduce this new fund. This is the

biggest technological revolution of my

lifetime. This is clearly bigger than

the internet. The comps on this are the

microprocessor, the steam engine, and

electricity, or maybe the wheel. Guys,

the machine age fund. Please introduce

it. Ben, start us off.

>> Well, um, basically what's uh happened

is we have a whole new technology that's

the most important technology ever. And

what happens every time um there's a

dramatic new way of using all of the

things that we love infrastructure um

you need a whole new infrastructure and

never has it been more high impact as it

is on this one. So not only do we need

new chips, new system software, we need

new ways of doing power, we need to

replace copper. I [laughter] mean like

it's absolutely everything. So it's a

very exciting time. So you know

particularly for the kind of hardware

aspects of of this new era um we needed

a new approach.

>> Yeah I would agree. I mean normally when

we at least in the computing when we

talk about the infrastructure world we

talking about the servers and the

storage in the network here it goes all

the way down to the mines copper mines.

That's how widespread this thing is

going to be.

Um that's number one and number two I

think what we have seen over the last 3

years is the steady increase of the

capabilities of the models where the

model is no longer the bottleneck and in

fact using AI these models are getting

better faster and faster and faster. Now

the bottleneck is all what I call south

of the model and so that's why we need

to work on that. You know, the only

thing I'd add very quickly is like we

tend to follow founders and we've been

watching over the last couple of years

is the number of very strong teams going

after complex hardware problems has

increased. I don't know the actual

numbers but I was trying to estimate it

over the weekend. So I think we'd get

you maybe 5% of the deals from top

founders would come in would be hardwood

before. Now I would say north of 20% or

30% right now. So like the founder

community which tends to be much smarter

than the VC community has identified

this as a very active area for

innovation and they're responding. I

think 5% is probably generous.

>> Yeah, it's very low. Is very low. Yeah.

3%. Yeah.

>> And explain some of the macro conditions

that have led to this this trans this

change in terms of the surplus of

founders pursuing these idea. Like what

are they seeing that's that's enabled?

>> Well, I mean the obvious is like you

know the the demand for AI is basically

infinite and as a result of that every

part of the supply chain is under under

duress. I mean everything including like

materials used to make things like like

memory. Um it's also very interesting

whether there's something unique about

AI. Um which because the demand is

infinite and growth is infinite. Um uh

what you tend to worry about is the

margin of companies which is how

efficient it is. Like normally you worry

about growth like can I just you know

can I just get people to buy this stuff.

You don't have to worry about that here.

The question is is can you do this in a

way that's profitable? And a lot of the

um efficiencies are actually strictly

[snorts] a physical limitation of

hardware. And so even the business model

of the AI wave is really putting a lot

of stress on the existing systems

because they weren't built for AI. They

weren't built for those workloads. And I

think there's just this you know this

global observation that we actually need

to change the core components to get

that efficiency to help drive the growth

and to drive the value of the

businesses.

>> Yeah. And how did [clears throat] we

know that demand is actually outpacing

supply here rather than this being you

know another hype cycle? Well,

[laughter] I mean,

>> yeah, there are any number of cities

today. Um, firstly, it is that some of

the smartest judges of demand are

cutting huge purchase orders. I mean, if

you look at the hyperscalers, right?

>> Yeah.

>> Their capex spend has been exploding.

Next year supposedly, it's going to

reach a trillion dollars collectively

across the big hyperscalers. This year,

it's about 700 billion dollars, right?

And if you think about the hyperscalers

position in the industry, they see

demand from everywhere, right? They see

obviously the frontier labs um wanting

their compute, they see the AI native

companies, they see the enterprise, they

see the US geography, the international

geography. So if anybody has visibility,

it is down and they've been jacking up

their capex like it's never been seen

before, right? So that's a clear clear

sign. And secondly, if you look at the

companies that we see on a day-to-day

basis, they are all ripping. All the

application companies, the growth is

insane. The frontier labs, the growth is

insane. It's been documented. So I would

say on the demand side, the signals have

never been clearer that this is not a

hype. It's to top it all, all of it is

just and prices are going up.

>> Like we've never seen prices go like up

on chips.

>> You keep your prices went down. They

always go down. Yeah. It always goes

down. If you look at the price curve, it

it went like this and then went back

this.

>> Yeah.

>> And we know only like 5 10% of the

address for the market to stop today.

>> I mean, the the the supply, if you look,

if you look at the supply across the

board, it's basically all booked out to

2028.

I mean, it's so bad we've actually seen

multi-day auctions for a few thousand

GPUs.

Um, you know, the other side of that, of

course, is demand. And as Ragu said,

we've seen the fastest growing companies

we've seen in the history of the

industry.

>> But also the unit of work that AI can

do,

the value of that unit of work keeps

increasing. But underneath the covers,

the number of tokens that are consumed

is going by orders of magnitude, right?

If it's one token for I mean 100 tokens

for chat or an agent, it's thousands of

tokens, right? So you got expansion of

both sides of demand. One is the unit of

work is becoming more and more

consumptive

>> of tokens and then secondly the number

of people therefore that are going to be

benefit it's not just the developers

it's going to be all knowledge workers

and then all of beyond that so that's

that's what we see

>> you said the key components in supply

are sold out to 2027 maybe in 2028

>> what does it mean for an entire industry

to be sold out that that far like

>> I don't know this has ever happened

before do you guys recall I mean

remember in the Um, in in in the

internet days when we were doing massive

buildout, the majority that was actually

being put in the ground was speculative

and was dark. Remember the dark fiber?

And here basically every GPU that's

being created is already pre-sold. So

>> yeah, we weren't quite there. I mean

there was

there was a lack of bandwidth

like in the 989 time frame, but

there wasn't there there wasn't that

much real demand for it because there

just weren't that many people on the

internet. So like it was a two-sided

thing and the companies were all rushing

there and needed more bandwidth

theoretically, but there weren't

the users on the other side to consume

it necessarily. And then to really

consume a lot of bandwidth, you have to

do high bandwidth things like video,

which weren't really viable for, you

know, a number of reasons that that had

nothing to do with how much bandwidth

was in the data center. So it smelled

similar, but it wasn't this. This is

like we're flat out and people are

reselling GPUs for four times what they

bought them for and this kind of thing.

Like it's just not. And then,

>> you know, we're we're also out of power

and cooling. Uh and then on top of that,

it's really hard to build because

there's these incredible political

headwinds going into it. So, it's it's

really unprecedented in my career. uh

that we've had anything anything like

this. No,

>> I want to give you a quick anecdote. So,

this I was talking to a CFO of a large

company, large public company who had

historically been very resistant about

going into the cloud. So, they had a lot

of servers and they were doing an

inventory uh check and they realized

that the memory in their servers had

increased so much it could uh fund the

entire migration to the cloud. So, I

just feel like we're in a very unusual

situation.

>> Yeah, [laughter] that's right. We're out

of many things. power, cooling, memory,

GPUs, like you name it, we're out of it.

>> Yeah. So, the flagship uh conference for

the industry is the one called hot chips

is going on in Stanford and the leading

memory vendor said the demand they have

today it will take them 3 years of

capacity to supply it. It's just today

it's not even future demand.

So in terms of being about everything

simultaneously, is it because people

just underestimated how good the models

would be, how how useful they would be,

they just couldn't have foreseen the

demand?

>> Well, I don't even think it's that. I

mean, this stuff came out of nowhere,

right? We we're only four years into

this. So even if we had a perfect

oracle, once it started working, I don't

think

>> we could have built the capacity.

>> We could have built the capacity.

There's no way. And we're talking about

like chip cycles, which tend to be three

to four years. We're talking about

breaking ground and building data

centers which is you know four to five

years we're talking

>> and connecting breaking down and

building them and having uh power

source. So like you either have to build

your own power or like usually both

you've got to build your own power and

have a power source which is not easy.

Yeah. the ML industry that's used if

it's growing at 20 30% it's a great

growth rate right and it's being

connected to an AI software industry

that's like triple digits is the base

you know

>> so you can see the disconnect right so

it's it's just wide bank

>> and so why didn't this fund exist you

know five years ago or seven years ago

or why was it not a great category to to

invest in in in the same way Brad

>> well I would say we're probably I'd like

to think for just in time, but you know,

we probably would have been uh well

suited to have it at least a couple

years ago.

>> I will say you could actually point on

on on basically every epoch to uh an

independent company that came up, right?

Clearly the move from the client from uh

in mainframe to the client server, we

saw a bunch of companies come up. Um the

move to the internet, this we got Cisco

and Juniper. uh even in the mega data

centers which by the way was largely

driven by the incumbent cloud providers

verticalizing you saw the uh arising of

Arista so there has been the ability to

invest in you know silicon and hardware

but it's been relatively minor because

the change has been relatively minor

like one chip company one switch company

where here everything is and so I I

agree with Ben we're probably you know

we probably could have started a little

bit earlier um but the amount of change

is so high now that it's just an obvious

thing

And the other thing is the demand for

intelligence is so vertical um with

really no end in sight. I mean cuz every

company that's adopted it is growing

very fast in its usage and then most

companies haven't adopted it to a high

degree and then consumers are just

getting started.

And so it's going to probably the demand

for tokens is probably going to grow

close to a,000% a year, which you cannot

grow supply that fast. Like like we're

not

>> like the amount of just work we're going

to have to do across the board to get to

the point where we can grow like infr at

that kind of rate is is pretty vast. So

I think there's a lot of investing

opportunity on the way. And by the way,

the other thing is like all the

architectures

of the hardware systems were built for a

whole different era of computing. And so

more than just we need more capacity, we

need capacity to build there there's

lots of opportunities to build different

kinds of infrastructure. Yeah, they're

they're all reaching their the physics

limits for what they were designed,

right? Like what Ben was talking about

copper and so on and so forth. And you

could go across every one of these

categories and you could find, okay,

this is the limit of this type of

technology. So now you got to get some

technical breakthroughs to get to the

next one.

>> Yeah. I want to dive deeper on the

demand side for a second. As we've moved

from chat bots to reasoning to agents to

to multi- aents, each step has

multiplied the number of tokens a single

task takes up by orders of increasing

orders.

>> Yeah, nobody likes to use AI more than

AI. [laughter]

>> So, um why does that keep happening

instead of a leveling off? Do you just

see that happening you know indefinitely

just continuing to

>> uh well so there's a couple as that the

first one is is for sure right now if

you look at like the the way we're

achieving scaling the way we're doing it

is through a lot of inference so through

a lot of token right if you think about

what RL is you know it's it's a lot of

inference if you think about chain of

thought it's a lot of inference u think

longunning agents of course it's a lot

of inference and so that's just

basically been one of the approaches

that we've been using to um uh to

scaling um I think if you want to step

back and say kind of what is the macro

trend here it used to be when you built

something it was an engineering problem

and you throw a bunch of engineers at it

and that doesn't scale and that would

have a natural law of engineering

physics uh which is what the mythical

manmouth came from and here it feels

like it really is a resource limitation

so whether it's tokens or not we're

pouring a ton of money into systems and

then those systems are producing a

result and right now we're bottlenecked

on those position those systems ability

to actually uh match the resources we're

pouring into them. And so I think like

tokens right now is probably where we

are on the scaling curve, but we don't

have a natural regulator like

engineering like we did before. So I

think we should expect this to continue

and we have to build a supply to support

it.

>> Yeah. Like the simple way to think about

it is

any problem that you have can be solved

with enough

infrastructure [laughter]

>> basically

>> GPUs and power and money. Uh and so

until we run out of problems, we're not

going to run out of demand. And that's

the that's the uh challenge. I

>> mean AI's answer to getting better and

better is to use more AI, right?

inference is one basic building block

that it keeps using over and over and

over again and so that's why these

tokens multiply at each step.

>> Yeah, even the autoc catalytic effect so

even the idea of using AI to create more

AI like creating a GPU kernel of course

is just using more AI um as part of the

process. So again, one way that we think

about it is in the past money would come

in, you have an engineering problem, we

know that it takes two years, normally

fails, you know, it's a national

governor and then you get the product on

the other end. This there's there's

there's nothing between the money going

in and then the hardware, you know,

creating intelligence. And so now we're

just limited by our ability to create

supply. It's a very very different

>> be more GPUs and our sol.

>> Yeah, that's right.

>> So that's the cycle. as long as you have

the money, the GPUs, and the data, you

know, for the foreseeable future, you'll

be able to scale these things.

>> And it's it's fascinating because, you

know, over the last decade, it feels

like there so many, you know, people and

the pervasive sentiment was there's too

much money going to startups. We're

overfunding these startups. There's too

much money in in in in venture capital.

Say more Ben about what that means

because there there used to be this um

sort of skepticism that the more money

you put into into the industry that you

know there would be bigger outcomes and

now you know we were saying at the

offsite that there's to some degree the

the market is as big as we collectively

contribute to it.

>> Yeah. So, this is this look, the one

thing we all knew with in startup world

is that if I have a two-year lead on you

and you try and catch me by hiring a

thousand engineers, you're going to

wreck your company. Like, that never

works. It's a mythical man month. Nine

women can't have a baby in a month. That

like that's it. Like, that never works.

Okay, now that works. [laughter]

But it's not hiring a hundred thousand

engineers. It's taking $3 billion

and like lighting up a magnificent

cluster and then all of a sudden, you

know, whatever Grock can come out of

nowhere and like, oh, all of a sudden

it's real or or a Kimmy or or what have

you.

It's just like these leads um you can

throw money at the problem and you can

throw money at almost any problem and

that works. And so that is just

completely different than anything we've

ever lived through. So we're, by the

way, we're all psychologically adjusting

to this. The Chad GBT app has a billion

weekly activives. There's about 30

million uh developers um who are using,

you know, relatively a big portion of

compute demands. How do we think about

compute compute demands needs now and in

the future in light of what people are

actually doing with AI?

>> That's the progression, right? So Chadic

was a casual app and it's coding for

professionals right now using coding you

have now built amazing tools for

knowledge workers so that's the next

frontier and now there are over a

billion knowledge workers in the line

right and with that it's a long ways to

go for that demand and by the way the

work that they do all this workflow and

automation and so on and then you get to

the back office which is all the agents

So progressively each of these things

unlocks

uh I would say order of magnitude more

demand. I mean just at the start of this

>> well and now you have Grockbot which is

kind of uh you know what uh happened

with coding is kind of happening with

all use of computer viaot and so we're

in a whole another wave of demand and

most certainly there's going to be more

to come. Uh so it does seem quite

unlimited at the moment and we haven't

even gotten into embodied AI or robots

uh which are going to be another source

of demand.

>> Martinez is expert but my understanding

is that part uses computer use which is

just like

>> human being sitting inside the computer

typing away.

>> I literally used it over the weekend to

to update my credit card with a bunch of

services that I'd been like lazy to do

and cancel a bunch of subscriptions. I

mean, this is not coding or whatever.

This is true computer use.

>> All of a sudden, you're creating like

half a billion knowledge workers except

they're all sitting inside of the

computer [laughter]

doing what?

>> I I do I do think that that that Mark

Mark Andre is right. It's like the right

analog here is like the steam engine or

electricity in the following way. Like

we've we've introduced this new thing

that you can turn to work and there are

some very obvious applications now, but

there's probably 30 40 years of throwing

computer at problems. anything with a

clear reward signal and we we're just

starting like we've got language and

code that's it and just starting

computer use but like what else are we

looking at we're looking at uh in terms

of science materials biology I mean of

course creativity is a massive use and

so listen we're at the very very early

part of a very long journey and we've

removed this key bottleneck which is you

know traditional software engineering

now of course you know bottlenecks will

move and there'll be kind of more

complexity elsewhere but I think we're

at a very early in a very long run of

throwing computer problem. So let's

expect you know this compute need to

persist for decades.

>> But because we mentioned it u Marty talk

about grabbot um because we um we're

talking at the offsite about how you

know what what struck you about it.

Obviously we're involved in every

possible way you could you could be

involved but what um yeah what what did

you find so interesting about it? So I

think we've I think we've as an industry

gone through kind of multiple

realizations for how AI enters our

lives, right? And and uh very early on

we're like okay well you add AI to a

product and it's like whatever it's like

a search bar and then you kind of you

know you you do chat with it and it

chats back because that's kind of the

traditional way to do it. Um uh and then

Open Clock kind of showed up and that

was it earlier in the year. And with

Open I say, "Okay, well maybe like it

just being like Google but better. Maybe

that's not the full embodiment of it.

How about we'll have it be a standalone

thing but it'll be an extension of you

and it'll share your keys and it'll know

your passwords and it'll just kind of do

stuff that you would do, right? So it's

kind of an extension of you but it's

more like a human, an extension of you."

And then [snorts] what I think Rockbot

got really right is no, how about it's

actually an employee. So now you have

this thing that's an entity and it

doesn't have like special access to your

keys or whatever. It has its own

computer and it has its own browser and

because these are the smartest models in

the world, it can do whatever an

employee can do. And it's kind of

interesting because now actually if I if

I want something done my first thing I I

think is like well can do it for me and

and often the answer is yes even if it's

something you wouldn't you know expect

it. So, the obvious ones are like

whatever. It'll like manage my calendar.

It'll like book a meeting, but there's

also non-obvious ones as well. Like, so,

for example, I'll have it um uh read

through my email and do triage. And and

I did I don't tell it how to do that,

but it will know to check with me before

actually doing the triage. So, like

these things are sophisticated enough

that you can give a relatively high

level task and it'll do kind of, you

know, like sophisticated things as a

result.

>> Ben, I know you're thinking a lot a lot

about this and how this you know, works

in the organization. you think a lot

about culture of course. What are your

thoughts here?

Well, I mean, I think if you just look

at us, um,

you know, it's like having a new kind of

employee and

there's going to be a lot of them and we

have to just like, you know, with our we

spent many, many, many years figuring

out how to work with our kind of regular

human employees and now we've got these

other kinds of employees

and, you know, there is a learning curve

with them. So um they can burn a lot of

tokens and spend a lot of money and get

nothing productive done. Um they can

forget stuff. They can make stuff up. Um

you know they can have good behavior.

They can have bad behavior. They can

>> they can create security problems. So

like there there there's all those

aspects to it, but like they can also be

like super duper productive. And so I

think figuring out how to integrate them

in, have them work nicely with the

people that they're working with, um,

the actual humans, uh, is all something

that we're learning how to do. I mean, I

I don't want to sit up here and say I've

cracked the code. We've got this more of

a sloop and [laughter] the whole firm is

just completely automated now, and I'm

going to slowly get rid of all the

humans because I can. Like, that's not

at all where we are. were much more

going like, okay, how do we make all our

humans superhuman um without like

wrecking the place um because the bots

got out of control?

>> Yeah. And it's interesting because we've

done we've tried a couple of different

ways as how best to get agents into the

system if you will and eventually

it was Martin's insights just treat them

as people and get it done. And that's

what we're doing. that's turned out to

be the most durable way of getting this

thing going inside of an organization.

>> I want to go back to the supply side and

go deeper into the the bottlenecks. You

we were talking about how you know in

terms of the data centers, the chip

architecture, system software,

facilities themselves that none of them

were designed with with AI in mind. What

would it look like for them to be

designed with AI? Like what is sort of

the mental model for thinking about what

that could mean?

>> Yeah. So um I mean if you

start with a statement that you just say

hey original model

um of infrastructure on any of these

models has to change you can go se

category by column and see how it bricks

right and then you start uh unlocking

the bottlenecks in each one of these

things. So eventually you have to get to

a system where if you look at what an

inference engine does, right? It takes

up a lot of memory, it generates new

tokens along with the compute. And so

you can just think about how do I

optimize all of this? What does the

memory need to be? What does the compute

need to be? How do they need to talk to

each other? How much power does each of

them need? And if they need all of these

power, how do you cool each of these?

Right? And then how do you put the

collections of these things together?

That is the exercise that's underway in

the industry right now with a lot of the

founders. So they're breaking down the

problem into its fundamental components

and saying what is the exact nature of

the compute that's getting done. Okay,

it's how it's going to be matrix

multiplications. How do I optimize my

compute around that kind of a scenario

and then they all need very

progressively to generate these tokens?

What is the best way of hierarchically

arranging this memory right and then how

does the power consume I mean then you

got to connect it together what are the

ways of connecting it on the same chip

but across chips and across data centers

how much power does each of these data

transmission take so you have to

progressively break it all down and

rebuild it from these fundamental

building blocks and that's what we see

underway and that's where we see the

opportunity

>> let me give you an interesting mental

model to think about how the landscape's

changed so um uh so today to build a

frontier model costs let's say $3 to5

billion right so and let's you know and

and that's to train it and so the

inference has to pay back at least that

of course right you know in order for

any of this stuff to be viable let's say

two times that so let's say that now

inferred has to to make $10 billion so

if you can save 20% of efficiency on

that that's $2 billion and you can

easily build an ASIC for $2 billion

Right. So, so we've actually gotten to

this interesting point in the industry

where it actually makes sense to build

an ASIC per model just because the

amount of capital investment in that

model and then unlike traditional

software, traditional software has a lot

of state and a lot of you know is very

dynamic. These models are fixed. The

model weights are fixed. And so we don't

know if the world goes to per model A6.

But it gives you a great mental model of

how you would evolve the architecture to

be far more bespoke for these massive

capital investments we're doing. Like I

don't think in the history of the

industry we've ever created a digital

artifact with something like $5 billion

that went directly into that artifact.

And so you know like this I think is

going to put the greatest demands on

hardware that we've ever seen. Well, to

that end, rack power requirements are

moving from roughly 5 to 10 kilowatts to

100 to 50 kilowatts. Compute density is

climbing something like 70x.

>> Cooling is moving from air to liquid as

a requirement. What are the investment

opportunities as a result of this?

>> Well, first of all, when you get to that

level of power per rack, AC power

doesn't work anymore. [laughter] So,

like that's a pretty wild thing. Um, so

now now you're into DC power, which by

the way also requires its own cooling.

Um, and is like,

uh, by the way, super dangerous.

Uh,

which is kind of ironic because this

was, um, Edison promoted DC power by

claiming how dangerous AC power was in

demonstrating it [laughter] by like

electrocuting animals and things.

>> The horse. Yeah.

>> The horse. Yeah.

So but he was right but around his own

kind of power which is extremely

powerful is the good news. Um so you

know just starting with power uh yeah

that's going to be like very very

different I think with cooling. So and

this gets into

so yes we're going air cooling to liquid

cooling. I think we're already at liquid

cooling for any state-of-the-art data

center. Like that's already kind of a

done thing.

>> But it gets into okay, you know, given

the political environment and so forth,

you like liquid cooling isn't enough.

It's got to be eco-friendly liquid

cooling. Um, and you know, kind of DC

power is not enough. It's got to be um

power that contributes uh to the power

of society, not takes away from it. And

so you have data centers who have been

behaving badly. Um small percentage

actually probably 10%

wasting a lot of water. Um you know not

as much as pistachios or almonds and so

forth as people demonstrate on the

internet but like they could be a lot

more efficient with that. Uh and then

there are ones that you know kind of are

uh parasites of power and don't

contribute power back. I think all

that's going to end. it's going to have

to end just because like like that we

we've kind of gone through a one-way

door on that. Uh so that requires like a

level of engineering um that you know

many haven't invested in yet. So that's

coming. Uh and then you know like if

racks are that dense um there are other

things that like the way the floors are

designed have to support that kind of

weight. uh you know that kind of thing

is is actually for real. Um and then you

know I I think that there's

you know you just need a lot of

everything and so there's going to be

you know kind also by the things are

really loud so you have to build the

data center with thicker walls or you're

going to disturb the peace in the

neighborhood which is not going to be

acceptable like I don't think any

state's going to allow that.

>> And so a lot of the ways people have

architected and designed

the buildings themselves are already

completely obsolete. Like once we get to

Fineman, um a much smaller percentage of

the data centers that we have today

work. In fact, I mean everybody talks

about memory prices, but one of the

fastest areas where prices is increasing

is reinforced concrete from for their

sake.

>> The other thing that happens when these

data centers are uh sending 800 volts to

the whack is it's become so dangerous

number one, but secondly, we don't have

enough electrical contractors that have

the expertise to deal with the 800 rules

inside the data center. because this is

high voltage. Only 2% of electrical

engineer or uh electricians in the US

have been certified on DC power. So like

that gives you an idea. Now Meta's got a

whole program to train people up and so

forth which is great. It's like a new

job court where they train people for

free to do this job. But you know it's

funny AI is taking all the jobs. AI is

going to create a lot of new

electricians.

>> Yeah. I think we're just doing something

in space too. the big guys that own the

big cloud data big data centers, they

all are furiously experimenting with the

robots, right? To do the work of

assembling or putting servers into the

data center, etc. And so you will see

that increasing as a result of the

evolution in AI.

>> By the way, to be to be clear on the um

on the actual fund that we're raising,

our focus is on computer science

infrastructure. So anything a model runs

on that's computer science right so

think you know chips network

interconnect storage all the way down

probably to the electricity

>> yeah and and saying more about the

robotics arm in terms of what we'll be

doing versus maybe American dynamism or

how to think about that

>> yeah yeah for sure so um you know again

we we think that any platform that that

AI will run on like one of the the the

great breakthroughs that AI does is it

allows computers to interact with the

physical world, right? It can see, it

can hear, it can talk, right? And this

means new platforms, right? And the

simplest way people say edge device, but

that doesn't really mean anything,

right? I mean, it could be a mobile

device, it could be a CDN, it could be a

laptop, but it also could be an

embodied, you know, device that goes

around. And so again, we we um as you

know, as infrastructure focused

investors don't do heavy regulated

industries um or more verticalized

industries, but any sort of computer

science platform that's going to push AI

further out, we're quite interested in.

>> Yeah. Going back to the data centers, by

2028, new data centers are going to need

something like 44 gawatts of additional

power against maybe 25 gawatt of

expected grid additions.

>> Hold on, hold on. We use that word

gigawatt.

>> No, it's like it's like we'll have 100

gigawatt, [laughter]

>> Martine. What's a gigawatt?

>> I mean I mean how big is it? It's

multiple football fields. I mean it's

massive. It's 50,000 50,000 people.

>> What do you mean? What is it?

>> Like like

>> the equivalent is like 50,000 houses.

>> City

>> 50,000 50,000 homes. 50,000 homes.

I I I grew up I grew up in Flagstaff,

Arizona, [laughter]

which is a town of 40 to 60,000 people,

depending on the universities. We have

less than a gigawatt of power

consumption. I mean, this is

>> so you can basically light up and air

condition your entire town for a

gigawatt.

>> Yeah. I mean, this is

>> just throwing them around. [laughter]

>> No, but by the way, everybody talks

about the gigawatt. There's very few

gigawatt data centers that are actually

up. I mean, we've got a long way to go,

right? But then why can't utilities and

hyperscalers just build faster?

Oh, there's so many things there. Well,

there's first of all, right now, you

need humans to build them. So, there

there's just like the regular

construction, but much more than that,

you need

permits.

Um, you need access to power uh that you

can plug in. So you're either you're

doing a combination of you've got to get

access to power which is a massive kind

of regulatory

bidding struggle. There's very limited

kind of amounts and things you can tap

into in terms of n natural gas power

grids, what have you. Um but then you

also have to build your own power and

guess what? We've got shortages of

transformers and turbines and everything

that goes into that. So, it's just,

you know, like you've got to get all

that stuff. It's not this is not a

software problem. It's not just like a

bunch of engineers can't you like work

weekends and that type of stuff like

this is not that that works anyway, but

uh

there are real bottlenecks in this and

these these lead times are not that easy

to compress. And look, we have the best

minds in the world trying to figure out

how to compress them. Uh but

I it's not easy. It's not easy and the

demand is not slowing down. So we're

already behind. The demand is growing,

you know, 10x a year right now. And you

the supply just can't grow that fast.

>> By the way, it is so bad that right now

if we have new companies going for GPUs,

it's often in Mexico or Australia or

another country just because it is so

difficult in the United States. Yeah,

we're creating huge both job and

long-term economic opportunity in other

countries by uh banning data centers

here. I think look the the right answer

would be to set a standard where a data

center contributes

back to the community like that power

gets better, there's no noise, there's

no water issue, um and it's adding jobs

like that ought to be the standard.

Yeah. And then everybody ought to be

just held to that center. And by the

way, like there are there are data

centers that do that now. Like that's

not a you know like a a futuristic dream

or something. Rates energy rates have

gone down like every year they're there.

And the reason is they provide their own

power. They give power to to the state

during the day and then at night they

borrow power from the state when the

state doesn't need it because you're

always the the way power plants work is

you're always generating peak uh

capacity and since a data center has

steady capacity during day and night and

a city goes way up in the day and way

down at night um that's a symbiotic

relationship. zooming out. Uh why do we

think the you know we we've been batting

around the name for for a little bit. Uh

why do we think machine age is a is a

compelling term for for what we're doing

here?

>> Well, listen, let let me let me take it.

So, the first one is I think Ben's

absolutely right. Artificial

intelligence was the wrong word. Like we

shouldn't have called it it's machine

intelligence. Um

>> say more about that. Why is that?

>> Uh because it's not how humans think

necessarily, right? I mean, it is a

cache of how humans thought is a

collection of humans thoughts. But like

to date, we don't know how to take a um

an AI with no knowledge and put it in

out in the world and have it reconstruct

language, right? Like that's not what

we've done, right? We've we've built a

something that can learn off of

everything we've already learned and

then use that in a productive way. And

listen, a a AI is a general term that

goes back 70 years in computer science

formally that applies to many different

things. And of course it's got a lot of

baggage either from science fiction or

from you know Nick Bstrom who wrote

about it or whatever. And so so the

first one is just an acknowledgement

like this really is machine

intelligence. And then you want to

emphasize the machine part of it. I mean

there's kind of this deep irony and this

is from you know the uh the software's

eating the world people that you know

you've really come to a place where you

pour money into something and then

you're limited by the actual machines

below it. And so I think it is a kind of

a nod to like the hardware component is

so significant in this wave and and we

want to acknowledge that.

>> Yeah. I think that's what's going to

create the next breakthroughs in

intelligence is the quality of the

machines underneath. And so that's

that's basically a reason for the name.

>> It's also a cool name. [laughter]

>> That sounds good.

>> Futuristic.

>> Yeah. the um given how much has been

spent on AI infrastructure to date and

how capex intensive these businesses can

be are we past the point where new

companies can break break in at at at

sort of material levels uh you know you

know in why not incumbents like Nvidia

Core etc just take the line share of

these markets

they're all doing well there's no

question about it right but to our

discussion earlier when you're get you

need funly new innovations to keep the

growth both continuing or the pace of

improvement continuing whether it's

tokens per second per dollar or tokens

per watt

uh tokens per rack right um or power you

take any metric if you want to have a

10x on those metrics you got to have new

innovation and new innovation

traditionally comes from brilliant

founders thinking about solving the

problem from first principles in a

different way right and that's what's

needed here for the next jump in

innovation.

>> I mean this is the law of markets,

right? I mean let's assume that the the

existing silicon incumbents are multi-

trillion dollars in market cap which is

absolutely the case.

>> Even

5% of that is a massive private company,

massive private company, right? We're

talking, you know, annual. Um and you

could say, well, but Nvidia could do

that. They could, but why would they if

they're focused on things that are in

the 90% which is also driving the same

amount of growth? You always ask these

questions. We ask these questions during

the cloud days, right? Like why wouldn't

Amazon did this? You would ask these

questions during the Microsoft days. Why

wouldn't Microsoft do this? There's a

very natural law of markets is once you

get to a certain scale, there's

tremendous opportunity for innovation um

at at the margins.

>> Yeah, there's a funny uh quote from our

partner Alex Rmpelle. He had this

startup called Trial Pay and he was

trying to sell it to or sell the it his

services to to Meta and uh then Facebook

and Dan Rose who was the head of corp

dev at the time said Alex that's great

you're it sounds like you can collect a

lot of silver bricks but I'm like I have

so many gold bricks I can't even pick

them all up so the last thing I'm doing

is looking at a silver brick and I think

Nvidia is in that position

>> 100% yeah we were talking about as

relates to the model providers that if

you're, you know, in the sweet spot of

of what open airropic can can do, you

know, one of their main sort of interest

areas, that might be a tough place to

be, but anything outside of those maybe,

you know, three to five areas might uh,

you know, as as markets expand, they

fragment, right? And it happens all the

time. And remember in the early days of

Ford, there was the 1913, there was the

Rouge River plant. Literally this, you

know, this was like made cars like in

went like water, coal, and rubber trees

and out came cars.

>> By the way, he bought a whole

>> rubber

plantation in the Amazon jungle,

>> right? And and there's a great book

called Ford Landia where so because he

wanted to like own like the complete

vertical thing where he created this

city called Ford Landia in the Amazon

jungle um which had like was all

Americanized band stands and ice cream

and all this kind of stuff. And it

actually worked for a while until uh he

made people like show up to things on

time and then they were like screw this

get the out of here. [laughter]

So, so now if you look at the car

industry, of course, there's multiple

levels of supplier and there's a bunch

of companies and this always happens.

So, you know, as markets expand, they

fragment. There's a lot of and then and

then once that growth slows down, they

tend to consolidate. The consolidation

can either be acquisition or it can be

like new challengers rise up and that

that is the, you know, everlasting cycle

of private markets.

>> Yeah. the use cases are are multiplying

and there's no way like if you're the

biggest company you can get to the

biggest use cases but there's so many

use cases and all as Martin was saying

very valuable use cases that it's just

very hard to get to in a great way

>> yeah even inference used to be one

simple architecture right and it no

longer is like it's so complex now so

it's inevitable that you can optimize

things in a different way

>> by the way here's a very interesting

thing like people don't um uh often

don't understand that like the margins

kind of fell out of the standard way of

doing the technology with software,

right? Like it wasn't really a

technology problem. Like once you got

the business working, you tended to have

pretty good margins because that's just

kind of how software works. certainly

when you shipped it but even as a

service uh and that's not necessarily

the case with AIS we may actually be

entering an era where the optimization

in the hardware is absolutely meaningful

to the upside of the business in a way

that we haven't seen in the past so

there's a lot of opportunity here let's

get deeper in talking about the types of

companies we'll be investing in um maybe

we could start by either illustrating

the the the subsectors or if we can talk

about a few um or a couple investments

that we've I know there's some that

haven't been announced yet, but Robert,

do you want to take us down?

>> Yeah, I mean the sub sectors as we've

been talking all is every one of these

categories, right? The obvious ones are

um uh computer chips, but these days

it's not enough to build a chip. You

need to build a full system, right? And

then therefore, what goes into the

system? There's potentially memory

innovation. There is potentially

networking innovation. There's

potentially power chips and so on and so

forth. So each one of these categories

are categories where you can see public

company style companies emerging and

those are all things that we are looking

into. Um and then once you put it all

together there's a layer of software

around it to automate all of these

things to manage these fleets and so on

and so forth. So that is another

important area. So these things keep

building on each other but every one of

these categories is important.

>> Talk about what's different about these

kinds of companies from from the usual

company. I mean one thing you can tell

from the companies we announced is their

first rounds have been massive. You know

hundreds of millions. Is it a different

kind of founder or what else is

different as we think about just the

practice of you know building and

investing these kinds of businesses

relative to our traditional software?

>> Well I I think the big thing is you hit

on one of the big things which is a lot

of money goes in um before they get to a

product. Um and that's just kind of the

nature of it. Now that's true on big

models too, but I I would say

that's a little more of a known path. Uh

whereas this has got a little more risk

and a little more money than uh than

some of the other things that we've

done. But

>> um and you know, look, a lot of a lot of

the chip founders um are here from the

past.

>> You know, like the guys who know how to

make memory, [snorts] they're not young.

Yeah, [laughter]

you know, so it's uh you know that part

is different too, but it's kind of

exciting, you know.

>> Yeah. The other thing about these

founders,

they have all got to be

systems founders. So what I mean by that

is you can't just be a researcher or a

great computer scientist, right? You got

to be able to architect and design the

chip or the system, whatever it is. Then

you got to think about how is this thing

going to actually get manufactured,

right? who's going to be supplying this

and a whole bunch of these downstream

things which normally if you're building

software you don't have to think about

all of these things. So the to really

the best founders um

and of course Jensen is the Michael

Jordan of this right they think the

entire ecosystem right from the get- go

before they start designing the chain

right because of the nature of the

bottlenecks and all these things that

have to come together. So that's that's

a big characteristic that is different.

>> There there's two environmental factors

that are important too. The first one is

the labs are so desperate that they will

engage with startups. And so like we

actually have quite a bit of signal

early on because they're you labs are

inking deals with companies before they

actually have hardware available. And

that's a big big shift than you know 5

years ago, right? Like you just didn't

go and you know sell your kind of janky

hardware thing to Google or whatever. So

that that's a shift. The second one is

the the capital availability has

loosened up a lot. I think there's

general consensus that that you know it

is the time that to reshape this stuff

and so follow on rounds there's a lot of

capital available which you know of

course you want to be investing into

areas where there's capital available

and so the atmospherics are also just

different. Patrick Carlson, you know,

remarked a few years ago said, "Hey, it

feels like there's less younger founders

today, you know, in the way that you

know, Zuck, you know, in college

building next Facebook or or Gates, um,

you know, in the same way with Microsoft

and of course, you know, the Michael

Trolls of the world, there there's

still, you know, some young founders

building iconic companies, but it does

seem, you know, to your point that

there's more older founders building

these these these companies or or less

20-year-olds. I'm I'm curious if you

resonates and why. Well, I think it's

Ragu's point that if

if you're building something that has

like a very complicated supply chain,

has to manufacture things um and is

technically complicated that you know

some experience helps uh and you know if

you look at Elon or Travis Kalanick

their companies when they were young

were software companies. It wasn't until

they got like a lot even those guys the

best guys um needed some experience in

building a companies building technology

and so forth to kind of graduate to the

much more kind of complicated or

I would say elaborate domains you know

there's just much more there many more

moving parts in these things and so look

when you're learning how to build a

company it's hard enough if you

completely understand the product. If

you don't completely understand the

product and have to learn it while you

build the company, um that's just such a

steep learning curve for a brand new

entrepreneur. So, I think that what

we're seeing is you see Michael on the

one hand, um who is a very young guy,

brilliant, but what he built was kind of

a pure

software AI thing.

>> Yeah. And then on the other end, you

have like an Elon or a Travis who can

who's got enough experience. I think

Michael could probably do that, you

know, 10 years from now, but today that

would have been hard.

>> And it's important to remember like it's

been defocused by the entire industry

and academia for the last 20 years,

right? It just there just hasn't been

the same opportunity. Like it's been

there, but like it's never been a growth

area. The growth areas have been, you

know, software, um, networking, things

like that. And so I also think we just

have a posity of people coming out of

the universities or having experience at

large companies that have have done

this. I there just not that many.

>> Like you don't go intern and like build

a chip. So but a lot of that's changing

now. like listen we're going to create a

whole generation of of you know founders

that come from these new companies that

will know how to do this and you know

they'll be hired in much more junior and

like I would say actually one of the

greatest legacies of Elon towards this

is is of course he's created these great

companies but the amount of

entrepreneurs that have come out of

SpaceX that that have are changing the

entire industrial complex maybe even

greater um legacy than than the

companies themselves and I think we're

going to see the same thing uh for for

computer science and hardware

>> y as a matter of All of our investments

was started by uh two founders in their

20ies. But if you go watch one of their

offices, you see the experienced people

as well. So it's ideal combination here.

>> Yeah.

>> Yeah. Yeah. It doesn't necessarily have

to be the founder with experience, but

that founder better be able to tap into

that experience with it.

>> Yeah. Yeah. Well, and then be able to

work with them and and and

they have to be good and and all these

kinds of things. It's complicated.

Speaking of experience, this is a a big

new fund we're we're launching and

there's no new GPS. Um yeah, we're sort

of collecting. It's because you guys

have a lot of experience and the rest of

the group, you know, in in this field

that it's been kind of latent and uh

dormant. Yeah. Well, it's kind of funny.

I think we almost had to be warned

against it almost just because like our

backgrounds are from is hard. And I

think the reason that we needed a

reminder is because all of us have spent

so much in our careers existence and

hardware. We're kind of drawn to that.

And so listen, we've been clearly

invested

um in harbor over other years, right?

We're in SpaceX, we're in Andrew and

all. These are very early checks. We're

in astronomics, we're in Whimo, you

know, so we even even early on we did a

number of those investments. Um but like

you know this is because it's so much in

our DNA and so I don't think this is

necessarily need to increase the team

competencies just bonus.

>> If this fund does what we think it will

do,

>> how do we see the world changing or

looking like in 5 to 10 years? Well, you

know, hopefully uh America wins in the

infrastructure game. Um and we have lots

of like super eco-friendly efficient

data centers out there and lots and lots

an abundance of chips and abundance of

memory and abundance of power. Uh and

you know that would be awesome. Uh, and

I think we look we we, you know, it goes

back to like we really think uh America

is a special place and um we're

important not only to everybody here but

anybody in the world who wants to kind

of make a contribution and do something

bigger than themselves that it's kind of

the best place to come with nothing and

do something profound. So we'd like to

keep that going and I I think that

doesn't continue to go if we lose our

lead in technology. Think I think we'll

be in another era and it'll be another

country and maybe they have a different

set of values around that.

>> Thanks. That's a wrap.

>> Great for seeing me fun. Martine Beni.

Thank you. Thank you.

>> Thank you. Thanks. Yeah.