No music, no walk-on music.

No roaring applause.

I'm just saying that

I'm not used

to coming to work in this way.

This total silence.

I'm just kidding.

There were a lot of Taylor Swift comments

along the way,

so the crowd is ready.

This conference needs humor too.

Is humor allowed here?

Humor is very allowed.

I made investment

banking jokes yesterday Jensen but,

thank you for being here for the last

I think, 25-27 years.

You've been such a great supporter

of this conference.

I think we sometimes become numb

to the scale of the numbers

and the transformation

we're experiencing.

I don't think I'm

the only one in this audience.

I'm getting billions and trillions

confused constantly.

My partner,

Mark Edelstone and I,

27 years ago we sat on a stage

much smaller than this one on the

Morgan Stanley trading floor

and we announced

and introduced Nvidia and you

to the Morgan Stanley sales force

and believe it or not

$48 million IPO,

1998 revenue.

Trailing revenue: $30 million.

Jensen and his team, Colette,

were so generous two years ago,

you hosted our board meeting

in your headquarters.

I think you had just announced

a $30 billion quarter,

in terms of revenue

and then last week

a $46 billion net income quarter,

so we moved from years to quarters,

from millions to billions.

It's really amazing, an unprecedented

scale and growth.

And then you changed our lives.

You changed our lives,

and so I guess my question after that is

what had to come together strategically,

culturally, technically to deliver

that type of hypergrowth at scale?

And the scale is really astounding.

And again, thank you.

That's going to take 37 minutes

and 13 seconds.

Slightly more.

You know, obviously

Nvidia wasn't built overnight.

It's taken us 33 years.

I sort of remember

somehow that when we went public

our price was $13

and I just read here it's $12.

I overstated it, I remembered it

to be much more

optimistically than it was actually.

The company's valuation at the time,

I think was like $300 million.

And,

Mark did such a good job, Mark Edelstone,

did such a good job preparing

all of our investors

that they really only had one question.

It was literally a

one-question IPO roadshow

and the question was

when are you going out of business?

I'm not kidding.

and that exact question

is about as hard to answer

as the one you just gave me.

Well, the answer is

the answer is, as it turns out,

we started the company with the idea

of creating a new computing platform,

a new way of doing computing

and not that the old way was wrong,

it's just that the new way,

a new way is essential

to solve some unique problems

and the type of things that we were

extremely good at are algorithms,

algorithms, because the inner loop

of the software tends to be about 5%

of the code but 99% of the compute time.

And back then, the algorithms

in the world of computers was quite rare

and one of the most important algorithms

was computer graphics.

The simulation of light

and how light travels

through space.

And so,

while computer graphics

was used for things like animation movies,

animation movies of course,

at the time that we were founded,

the cover of

I forget which magazine was,

you know, Jurassic Park was there.

And so it was really,

it was during that time

where computer graphics was becoming

more capable

and we could simulate, you know,

virtual reality with it

and we applied it to creating a new industry,

which did not exist at the time, called

video games.

And so 3D graphics was...

it modernized in my time,

consumerized in my time and,

and the whole video game

industry was created in my time.

And when I say in my time,

meaning it was, it was Nvidia

that pulled it all together.

The reason why we're so beloved

in the video game industry

and we're so deep in it still

is beginning in a lot of ways

we created the modern video game industry.

From the algorithms associated with the

the libraries, you know, the,

in the computer graphics industry,

without RTX there would be nothing today.

Without our contribution of all the algorithms

that goes into all of the game engines,

you wouldn't be able to enjoy

the type of video games

you enjoy today.

So Nvidia has been deep in the world

of algorithms since day one,

33 years ago.

Now,

accelerated computing requires

what is described as a full stack,

meaning the architecture,

the chip design,

the libraries that sit on top of it,

how it's integrated forwardly,

you know, I'm using the...

apparently there's this new idea

called forward deployed engineers

or something like that.

Nvidia's had dev tech engineers 33 years ago.

We deployed them into

the world's video game industries

and video game companies

and game engines,

and we integrate our technology

into their game engine.

Today,

if you look at Epic's Unreal Engine,

Nvidia's technologies

all over it.

And,

you go into every game

developer Nvidia's technologies

all over it.

That's the reason

why all the games

run best on Nvidia.

For good reason.

That's the reason

why Nvidia is the world's

largest game platform.

You probably don't know this,

but there's several hundred million

active GeForce gamers in the world.

Many of them turned into AI researchers,

is because of GeForce GTX 580 that

you know, that,

Ilya Sutskever and,

Alex Krizhevsky and Jeff Hinton,

it was Jeff that told him to go buy it,

to discover CUDA.

And so,

the first idea about Nvidia

is that we're a full stack company.

The second idea about our company,

and this is,

you know, really old history that

many people might not have been born yet,

but during that time,

the PC architecture was incompatible

with today's

computer graphics capabilities.

And we created some new technology

called Direct Nvidia.

It was a way for applications

to directly communicate with

our APIs.

And, we exposed it to

some very important companies.

It became DirectX .

If you look at the way that we

communicate between us

and the application,

that was completely

revolutionary to bypass

a whole bunch of software

that makes it slow

to make accelerated computing possible.

We introduced the idea of virtualized

frame buffer memory into system memory.

It was initially called AGP,

which then became PCI Express.

Many of the system architecture

had to be reinvented

so that we could accommodate

video games and 3D graphics in a PC.

Well, that same sensibility of both

innovating the full stack

to be integrated into algorithms,

as well as changing

the architecture of systems

so that we could create

new computer systems,

led to that same sensibility

expertise led to DGX-1,

which was the world's first

AI supercomputer that delivered,

you know, by hand to San Francisco

here, and very close by to a company

that eventually became OpenAI.

And so the the fundamental attitude,

if you will, expertise,

how we see the world,

propagated in this way.

It's literally 33 years.

The company's entire

culture is designed to be full stack.

The organization is designed

to be full stack.

The entire system is designed

to create new stacks

and new system architectures

that allow us to do this.

While we started with,

of course,

if you look at Nvidia's graphics cards,

GeForce, it's a technology marvel.

How it's integrated

into the operating system,

how it's integrated

into the system architecture,

completely reinvented

how computers worked before.

Well, we have no trouble with that

with DGX-1.

I've no trouble with that

with the first supercomputing cluster,

which then went to Satya,

for their first supercomputer.

And you might,

you know, people noticed

that Microsoft's first supercomputer

and Nvidia's supercomputer had exactly

the same benchmark, like down to the...

you measure the performance of the system

across all of these GPUs.

That was about 10,000 GPUs or so.

It was exactly the same performance

and the reason for that

is because we designed it

and we delivered to to Azure Cloud,

it was all based on

InfiniBand, was all

based on Ampere 8 - this is the A100,

which became the first computer

that OpenAI used.

And so we're quite comfortable with this

full stack, full system approach.

And without being able to do that,

it is impossible

to stay at the bleeding edge.

It is literally impossible

to keep up with a company

that's building

not just one chip each year,

but we're building

an entire infrastructure each year

because we own the CPU,

we revolutionize

the new way of designing CPUs.

And you'll see

more examples of that.

We revolutionize the way we do CPUs,

revolutionized the way we obviously do

GPUs, connect them together

using this thing called NVLink,

which revolutionized

the way you built computers all together.

Connected together with a new type of

AI Ethernet called Spectrum X.

We connected everything together.

Now we own the entire stack.

We know all the chips inside.

When you own the entire stack

and you own all the chips inside,

you could change it every single year.

If you don't own the entire stack

and you don't own other chips,

it's hard to

innovate every year.

And the reason for that

is because you're connecting

too many cats and dogs,

and there's too much innovation

to pull together once a year

if you can't control it

because it's a full stack problem.

So that's how we got here.

It's amazing.

in the last two years

since you were here last

and our board meeting,

we've sort of gone

from generative AI models to reasoning.

And now agentic and Satya

just finished a panel on the enterprise.

And at the enterprise level,

you know, we're working with Microsoft,

the OpenAI, X AI, Gemini.

The capabilities are extraordinary.

What does it mean around

the size of that enterprise market?

How is it changing?

And how is it going to be adopted?

And how do you sort of

see that playing out, over the years?

Because it's a big,

big topic of the company.

Yeah. Really good.

Literally in the last two years,

we went through three

inflection points in AI.

The first inflection point,

first of all, the technology

sat there in plain sight for months.

GPT-3 sat there in plain sight

for months until somebody wrote

essentially a wrapper around it

and turned it into ChatGPT,

turned it into an API.

Made it available and

easy to use by everybody.

But the first inflection point

was generative. As you mentioned,

the ability to translate,

convert information from one form

to another form,

and auto regressively generate tokens.

And the second...

But of course, the problem

with generative AI

is that it's prone to hallucinate.

And the reason for that is because,

not because there's something fundamentally

wrong with the technology,

not because

it didn't learn all the right things,

but because it's not grounded

on contextual information.

It's not grounded

on relevant information.

And so,

the second thing that happened was O1

and reasoning came about, but behind

O1 is also grounding on research,

grounding on truth.

And, the ability to have to combine

generative with semantic,

we call it retrieval augmented generation.

But basically conditional generation.

Conditional generation,

meaning that what you're about

to generate depends on context

and ground truth or whatever

research or whatever it is.

And so the second generation,

introduced reasoning, self-reflection,

the ability to self-correct,

because sometimes

what comes out of your mouth,

you kind of wish

you pull back and you go “oh”, you know,

and so in the case of AI,

it has the ability to do that in real time.

And so,

O1 became much more grounded

and the information that was generated

was more reliable.

So what happened?

What came out as a curiosity

and incredible excitement,

and the tech industry

jumping on to it because we realized

what's about to, what can happen

the next phase of it,

the usefulness of ChatGPT

just skyrocketed.

But the amount of tokens

that it generated was much,

much more than the first generation.

Maybe, you know,

A hundred times more tokens.

The model was maybe ten times larger.

So it's probably something

like a thousand times more compute.

So from from O1 over ChatGPT

call it a thousand times.

And then because it was so useful,

maybe a million times more usage.

Okay.

So the combination of usage,

and its usefulness,

and groundedness, allows us to...

we saw that next phase of growth.

But in the end, what O1 did

was it provided information

essentially, a chatbot that was,

much more, much more factual.

It was informational.

And of course, for many of us,

we use it for research

and we use it all the time,

instead of searching,

you know, our goal is in the search,

our goal is to get answers.

And so, ChatGPT gave us that.

That was kind of the second

inflection.

The inflection that

we're seeing here also sat in plain

sight for quite a long time.

And,

it's basically the ability

for AI to use files, access files

and use tools.

And so now it could reason,

it could think,

it could use tools,

it could solve problems

and it could do search,

it could do planning. And so,

probably the, the biggest

phenomenon that's happening.

And if you're paying attention to it,

I’m sure you are, OpenClaw is

probably the single most important

release of software,

you know, probably ever.

And if you look at OpenClaw,

and the adoption of it, you know,

Linux took

some 30 years to reach this level.

OpenClaw in, what is it,

three weeks, has now surpassed Linux.

It is now the single most downloaded

open source software in history.

And it took three weeks.

If you look at the line

and even in semi log,

this thing is straight up.

It's vertical.

It looks like the,

it looks like the, the Y axis.

I've never seen anything like it.

Okay.

It literally looks like the Y axis.

And so what's happening now?

You could give a problem statement,

“Create”... start with the...

the prompt goes “create.”

You know, the last prompt,

the way you kind of think about it,

the last prompt was,

what is? when is? who is? right?

That's the last prompt.

This prompt goes

“create”, “do”, “build”, ”write”

Does that make sense?

So what's happened?

The last prompt was queries.

This prompt are actions, they're tasks.

Do something for me,

and you describe it as you know

expressively as you like,

as with a lot of intention,

you know,

and let it infer or very specific

and it goes off and it just churns,

It just thinks it goes off.

And it does research and it reads

it reads a manual.

If it has to use a tool,

it's never used it before,

it reads the manual of the tool.

It goes off and studies

what's on the web and it,

you know, applies the tools

and performs the task.

Now, I just said, we went from one,

you know, one generative prompt,

one generative response to now one

that is a thousand times more tokens

and agents,

we call them at the company “claws.”

These “claws” are now consuming, what,

a million times more tokens?

They're running continuously in the background.

We have a whole bunch of “claws”

in the company,

and, they're all continuously running,

doing things for us, writing,

developing tools, developing software.

And so now the question is the implication,

the amount of compute in our company,

that we need is just got skyrocketed.

The amount of compute

every company needs is skyrocketing.

So in that context,

I think over the last few days,

it's come out

certainly at Morgan Stanley as a user,

maximum bullish on tokens,

maximum bullish on doing and creating.

It does require the compute

you just mentioned.

And the question is around the financing

and the CapEx around that to support

that extraordinary large compute.

How does it all get financed, as you see it,

from a sort of top of the ecosystem?

And how do the factory,

AI factory economics

play out and evolve?

Yeah, so there's a couple of thoughts

that's really important.

Remember,

I appreciate you using the word factory.

You know, several years ago

I described that

these new these data centers,

what people call data

centers, is not for storing data

as in a data center.

They are producing tokens.

And so, a facility, a plant

with the fundamental purpose of

of producing tokens is a factory.

It's an AI factory

and at the time people said, Jensen,

that sounds so grungy.

You know, it's clean and,

but it produces tokens,

and nobody likes to build data centers

because,

you know, who knows what,

what kind of return

you're going to get on a data center.

But everybody loves building factories.

And the reason for that

is because factories make money.

And we now know

for certain

that these factories

directly generate tokens

and these tokens are monetizable.

And the more compute you have,

the more tokens you can produce.

The more tokens you produce,

the greater your top line.

We now know for certain.

We now know for certain that companies'

revenues are directly correlated

to compute.

And we know that for a fact,

It's no different than Mercedes

being factory limited

or any company being factory limited.

And so if they had more compute

in their factories,

they will have higher revenues.

If OpenAI right now

had more compute,

they will have higher revenues.

And so, the first thought is that

compute equals revenues.

Now the big idea of course is

compute equals GDP.

That we also know compute equals

a country's GDP.

And so that's one thought.

The second thought,

the reason why Nvidia is so successful

is because we engineered these systems

full stack end to end,

and they're architected

from the ground up to generate tokens

at incredible effectiveness.

Nvidia's tokens per watt

is an order of magnitude,

an order of magnitude

ahead of the competition.

Alternative. Tokens per watt.

Now, what does that mean?

Remember, your factory has one gigawatt,

and if your tokens per watt

is ten times the alternative,

your revenues are ten times

the alternative.

For the very first time in history,

the computer architecture chosen

in a factory, in a company's factory

must go through CEO review.

No question about it.

That company only has a gigawatt

or 2.3 gigawatts for next year.

If they put the wrong system inside,

it will affect their revenues

the next year.

I promise you that.

And we see it.

And so our architecture

being so advanced now

and pulling further

and further ahead,

you know, those are probably one

of the most exhaustive

benchmarking done,

is by a firm called Semi Analysis.

And they declared,

they declared Nvidia inference king,

inference king. Inference

as tokens per second, tokens per watt.

It's about generating tokens

and tokens per dollar.

When our performance per watt or per

anything is so much ahead

of the competition or the alternative.

Our tokens per dollar is also the best,

which means we're the cheapest tokens

you can produce today

not even close

an order of magnitude better

and so that's the second thought.

The second big idea for AI

is AI is a factory,

because factories are

power limited always.

It doesn't matter

how many plants you have,

each plant is still 100 megawatts or gigawatt

and therefore tokens per watt

is the single most important thing

for the top line of companies.

And they have to make those decisions

very, very carefully.

You know, it's no longer

just about PowerPoint slides.

You're not going to go put $50 billion

down on some of these PowerPoint slides.

So the token demand is extraordinary,

as you just mentioned,

you're seeing it in your numbers.

Right?

I think I mentioned

$46 billion in net income.

But $70 billion.

if you were going to ask me something

about how to fund it,

can I just tell you how to fund it?

First of all,

I just told you.

I just told you,

the reason why you have to build these

factories in the future

is because you either

you just believe

that one, software is important.

And so I hope this audience

believes software is important.

Software runs the world.

First thought.

The second idea is this:

there will be no software in the future

that's not agentic.

Do you guys agree with that?

How could you have software that's dumb.

And so it is absolutely true

that every software company

will become an agentic company.

They're going to simultaneously use,

open models.

Okay.

Open models

meaning the ones that they

download themselves

and they fine tune themselves.

They're also going to use closed models.

The combination of all that, just like we

in all of our companies,

we have employees that we hire,

we have employees that we're grooming.

we have contractors that we bring in.

We have specialists like yourself

that we bring in to the company

just to do our work.

Our job is not to do the job.

Our job is to have the job be done.

That's what every company does.

And so therefore every company

will realize that

these AI models, some of it you rent,

some of it you build.

That's not illogical,

just like biological workers,

you will do that with digital workers.

And so every single software

company in the future

will no longer just rent tools,

but they'll rent also

experts to use the tools.

They'll not just rent tools,

but rent experts that use those tools

because their agents

are going to be extremely good

at using their specialized tools.

And so every single software company -

the IT industry is a couple trillion dollars?

- today they're tool renters in the future,

they will of course have,

they'll rent agents that use those tools,

which means that the software industry

in the future

will be much larger

than the software industry of today.

You pick your favorite software companies,

and I can imagine a much, much larger

future for them.

Cadence is going to be much larger.

Synopsis is going to be much larger.

Siemens is going to be much larger

in the future,

but their business profile will change

because today

they're basically a software

licensing company.

In the future,

they will also rent tokens,

specialized tokens,

which also means

that that $2 trillion industry

today with no token consumption

in the future

will be extraordinary token consumers.

That's where that money

is going to come from.

They're all of those software industry, IT

industry of today,

not the enterprise

companies, the

IT industry alone

is going to shift an enormous...

it is going to consume

enormous amounts of tokens in the clouds.

And they're either open models or...

So that extraordinary token economy

is facing some constraints.

So we've got memory constraints.

We've got power permitting constraints.

I was in Texas with builders.

We have electrician constraints.

How do you see that playing out?

Satya raised it in the last session.

You're closer to it.

And also if it takes a little longer,

is it still okay

or is it really negative if we just

if the if the cycle on

building this extraordinary.

I love constraints. I love constraints.

And the reason for that

is because in a world of constraint,

you have no choice

but to choose the best.

You can't squander your choice

if the data centers,

if the land power and shell is constrained,

you're not going to randomly

put something in there

just to try it out,

you're going to put something

that you know for certain

is going to deliver the tokens per watt

that you know for certain

is going to allow you from the moment you

you secure the capacity,

we're going to be able

to stand up an entire factory for you,

we are the only company in the world

that can come into your company

and help you stand up

an entire AI factory, you know, so

anybody here that needs an AI factory

and you need, you know, I'm happy to help.

You call one person,

and now one person comes in

and next thing you know,

you're in the AI factory business, okay?

And so we have the expertise.

We know the architecture works.

We know there's enormous demand

for the architecture.

You know,

after you're done standing it up

so we can help you get into business.

And so when you're constrained that way,

you have no choice

but to make the best choice,

because your

revenues next

year is directly correlated to it.

And this is one of those questions

now for all the CEOs

that are in the clouds,

that are cloud service

providers or software providers.

If they make poor choices,

this is no different than me

choosing the wrong foundry.

This is no different than me choosing,

you know,

the wrong memory, the wrong anything.

Because I have so little...

everything is so constrained.

If I choose poorly,

my revenues are affected,

everything is affected.

And so they can't choose,

they can't choose poorly.

The second thing is,

you know, Nvidia is,

as you mentioned,

working at such a large scale,

our supply chain,

one of the things

that we do with our money, of course,

is to secure our supply chain.

One of the things that we do with

our capital is to secure our supply chain,

so that

when Satya asked me to help him

stand up a few gigawatts,

the answer is no problem.

And the reason for that is

I got all the memories,

I got all the wafers,

I got all the CoWoS,

I got all the packaging,

I got all the systems,

I've got all of the connectors,

I got all the cables,

you know, everything from copper

to multilayer ceramic capacitors,

everything is secured.

That's one of the reasons why Nvidia's

balance sheet

being strong is so strategic.

A strong balance sheet

today is not only helpful,

it's strategic.

And so you look at the amount of revenues

we're shipping into.

Just look backwards

and look at the amount of

supply chain capacity

we had to go secure

or that they have to believe,

you know, if you set up a factory,

a plant, a DRAM plant,

and I come in and say, you know what,

go ahead and set up the DRAM plant

because I'm going to use it.

That goes a long ways.

You might as well take that to the bank

as many of them have.

And so,

and so I think the,

the fact that everything is scarce

is fantastic for us.

And I think it does create duration,

which I think is extraordinarily

powerful for you.

I think just another layer,

which is the ecosystem.

You're one of the great

you are the greatest cash flow

generating company in history.

And then you've taken that capital

and really created,

it feels like

stability, diversity in the ecosystem.

And so how do you think about that

in both a financial

and a strategic context as you build?

I think both duration and durability

in the entire ecosystem.

Yeah.

You know, when Mark took me public,

I think

it was probably, you know, a little bit

less energetic than I was

delivering it just now.

But I am fairly certain

I said all the same things.

Nvidia has been building.

Remember, accelerated

computing requires

that I build an ecosystem.

You can't just take code and C-compile it

and it works.

There's no such thing

as a universal

accelerated computing system.

Accelerated computing is,

by definition, proprietary.

There is nothing about our architecture

that is compatible with somebody else's.

It's just not.

The instruction set is different.

The architecture is different.

The micro architecture is different.

Everything is different.

And so we hide it underneath,

you know, these things

in such a way that that makes

it makes you feel like.

And because of Nvidia

we accelerate everything

from data processing,

molecular dynamics, fluid dynamics,

particle systems,

you know, biology, chemicals, you know,

all the way to deep learning, right?

Robotics,

you know, long sequence, spatial 3D,

you name it, right.

It sounds like a five-layer cake,

sounds like a five-layer cake.

It's a five-layer cake, right? Exactly.

But because we've been working on it

so long,

it looks like everything's accelerated.

But it's not true.

It's because I did it one

at a time, one domain at a time,

that all of the important

domains in the world

are now fully accelerated.

And so the thing that we do

on the supply chain side,

our balance sheet is incredibly valuable

because it provides security

for our customers.

On the upstream side,

I’m cultivating new ecosystems

for the future.

All these AI natives

that I'm investing in,

the companies are partnering with,

these are expanding,

extending the CUDA ecosystem.

100% of everything that we do

is on top of CUDA.

Every investment that we've made

is on top of CUDA.

So recently

there was a question about,

are we going to invest

$100 billion in OpenAI?

We just... just for everybody's

update.

We finalized our agreement.

We're going to invest

$30 billion in OpenAI.

I think the opportunity to invest $100

billion in OpenAI is probably

not in the cards.

And the reason for that is

because they're going to go public.

And so I'm fairly

sure that if we provide the capacity

they need,

which the compute capacity

they need, which

we're ramping up hard to go do,

the revenues will more than follow

and they're going to go public

towards the end of the year.

And so

this might be the last time

we'll have the opportunity

to invest in a consequential,

you know, in a company like this.

And speaking of that,

one of the things that I wanted

to make sure I told you guys at this time

and something new

that you probably haven't internalized,

you see all the news,

you probably have internalized,

some of the

the really great work

that we did last year,

the last year and a half or so,

last year or so,

we expanded,

we expanded,

OpenAI's capacity

from Azure

to OCI

to now AWS.

We expanded

OpenAI's reach of capacity to AWS.

We're ramping AWS like mad.

We're ramping them as hard as we can

so that OpenAI has access

to even more capacity.

But the amount of capacity

that we're going to bring online

for them, you know, supporting,

supporting their revenues,

their quality of revenues are so good.

We just need a lot more capacity

for them.

So I think that

this is something

that is somewhat new.

And of course,

the third thing that happened

is a brand new

AI lab flashed into the world.

Isn't that right?

I don't know, we just mentioned them.

A brand new lab came into the world,

and they're in a need

a few million GPUs, and that's MSL.

And so the MSL is a net

new on top of Meta.

So we've we've worked with Meta

a long time.

MSL is a net new on top of Meta.

And so,

our demand profile, went from

being incredibly high

to higher than that.

Speaking of,

speaking more than that,

there's Waymos everywhere.

I want to walk my new dog with the

my new robot, physical AI

could be the next place.

How does that take TAM and tokens

to a whole other level at Nvidia?

Yeah, that's really great.

That's really great.

AI is all the stuff

that we're doing inside the building.

But obviously,

obviously, ultimately

the largest industries

are outside the building.

And that AI needs to be, needs

to have,

physical awareness,

physical understanding,

you know, causality.

You push a bottle,

it falls over and understands

gravity, understands collision,

you know, understands inertia.

Understand those two things, okay.

And, understand,

for example, object permanence.

Yeah.

I take this and I put it behind my chair

in your mind, you can't see it,

but you realize it hasn't disappeared.

Okay, so object permanence thing,

things like that, that affects,

physical behavior and

physical intelligence,

you know, fairly importantly.

And so,

you probably also don't know this,

that Nvidia

is the frontier of physical AI.

Cosmos is the most downloaded physical

AI model in the world.

Nvidia is also the frontier

of autonomous AI.

Two versions.

Autonomous vehicle called Alpamayo.

Look it up. Number one downloaded,

and then the next one, Gr00t.

Human or robotics physical AI.

We are at the frontier

on all three of those.

We're also at the frontier

of digital biology AI.

Look up La-Proteina

incredibly successful,

La-Proteina for digital biology.

There's a whole bunch of other models

Gr00t, N2, is now the number one

most downloaded

human robotics model in the world.

And so we are at the frontier

of physical AI.

Physics, laws of physics, multi physics.

Earth-2.

We're at the frontier of

physical AI, that is physical AI and AI physics

and so this whole area of physical AI

Nvidia defines the frontier.

It is completely open.

We open it because we want to enable

every company,

new or old industry to be able

to take advantage of this capability.

And we've got the whole stack

and the necessary computers for you to

advance the AI for your own use,

as well as deploy it, inside a robot,

inside a plant, at the edge,

at a radio tower,

deploy it everywhere.

This is the next frontier.

In two years time,

we're going to be

largely done talking about agentic AI,

because we're all going to be using it.

In two years time,

if you invite me back again...

Every year. Every year Jensen.

We're going to be talking

about all these new companies.

Of course, we announced

a very important one,

a co-innovation lab with Lilly.

There'll be others, but,

you know this in order to set up

Lilly's AI factory,

unless you are,

unless you have the capabilities

of Nvidia and the full software stack

and the capabilities

of all the model

and the expertise

in that digital biology domain,

how would you even do it?

And so,

the things that we are building

in the next couple of years, you'll see,

really come to the fore.

And, we're going to

be talking about physical AI for,

you know, starting

next couple of 2 or 3 years

and for a decade.

So the speed of innovation

and the pace that you're operating in

is truly extraordinary.

So at the beginning of the week,

my partner Joe Moore made Nvidia

his number one pick. -Is that right?

It's his number one pick. Thank you.

Thank you, thank you.

Good timing Joe.

33 years later.

How do you think about the stock?

Do you think about the stock?

Do you have perspectives on it?

You're so extraordinarily important

and busy around

driving all this innovation

for, in essence,

everything that's going on

with 3500 attendees

and we had $40 trillion of market cap here.

How do you think about that?

Well, you know,

of course I care about the stock.

I care about shareholders, I care about

I care about our employees.

I care about all of you.

And you might be referring to,

we just had the best earnings

in the history of earnings.

Is that what you were saying?

I mean, somebody actually told me

that this might be the single best print

in the history of humanity.

And I said it must be only

you know, recorded humanity.

I'm sure somebody had better returns.

But anyways,

we had a very good quarter.

Listen,

you can't hold the stock back.

You can't hold it back.

And the reason for that is very simple.

Compute equals revenues for companies.

In the future,

every single company

will need compute for revenues.

I'll just make that prediction for now.

Every single company

will need compute for revenues.

And the reason for that

is because compute translates

to intelligence, which translates

to your digital workforce,

which translates to your revenues.

I'm certain compute equals revenues.

I'm certain also that compute equals GDP.

Therefore every country will have it

because not one country in the future

will say, guess what?

You know,

we're going to opt out on own

intelligence.

We've got...

I don't know what we got,

but we don't need intelligence.

That's the one thing we don't need. Okay.

And so if you need intelligence

you're going to need digital.

You need AI, you going to need compute.

And so compute equals GDP.

I know that for certain.

I also know that we're at the beginning

of this journey.

And I see crystal clearly

exactly how it's going to get funded.

We know for a fact that all the CSPs

took all of their CapEx

and they converted it to

generative agentic systems, AI systems,

because it helps search,

because it helps shopping,

because it helps ads,

because it helps social,

because it helps

literally every single internet

service in the world

has been reinvented into generative AI.

So they could take 100%,

the entire internet industry

could take 100% of their CapEx

and make it AI because it's better,

we've proven it to be better.

Meta has proven to be better.

Google has proven to be better.

AWS has proven to be better.

And so you can now take your CapEx

and convert to this.

Number two,

I just said the entire software

industry will be token driven,

the entire software industry.

You pick your favorite software company,

and I can show you

exactly how they're going

to be token driven.

And that token,

you take your favorite,

you know, software company, their token,

will be either produced by themselves,

which needs compute,

or they could be resold

and that needs compute.

And so what that says

for the first time is the entire

IT industry

will have to be fueled by compute.

That's exactly where all this is going to

come from, trillions of dollars of it.

And we're at the beginning of that.

So that's my prediction.

Thank you, Jensen,

for making history at this conference

27 years.

Thank you.