special episode this week. We've

preempted the weekly show and there's

only three people we preempt the show

for. President Trump, Jesus, and Jensen.

And uh I'll let you pick which order we

do that. Uh but what an amazing run

you've had and a great event. Uh

>> every industry is here. Every tech

company is here. Every AI company is

here. Incredible. Incredible.

>> Extraordinary. And one of the great

announcements of the past year has been

Grock. When you made the purchase of

Grock, did you realize how insufferable

Cha Chimath would become?

>> I had I had an inkling that that that

>> we're his friends. We have to deal with

him every week.

>> I know it.

>> You had to deal with him for the six

week close.

>> I know it's like two weeks. Two weeks.

>> It's all coming back to me now. It's

it's making me rather uncomfortable. The

the thing is uh many of our strategies

are are presented in in broad daylight

at GTC years in advance of when we do

it. Two and a half years ago, I

introduced the operating system of the

AI factory and it's called Dynamo.

Dynamo, as you know, is a piece of

instrument, a machine that was created

by Seammens to turn essentially water

into electricity. and Dynamo uh powered

the factory of the last industrial

revolution. So I thought it was the

perfect name for the operating system of

the next industrial revolution, the

factory of that. And so inside Dynamo,

the fundamental technology is

disagregated inference.

>> Jason, I I know you're you're super

technical.

>> Absolutely.

>> I know it.

>> I'll let you take this one. Go ahead and

define it for the audience. I don't want

to step on you.

>> Yeah, thank you. I I I know you wanted

to jump in there for a second, but it's

it's this aggregating inference, which

means the the pipeline, the processing

pipeline of inference is extremely

complicated. In fact, it is the most

complicated computing problem today.

Incredible scale, lots of mathematics of

different shapes and sizes. And we came

up came up with the idea that you would

change you would you would disagregate

parts of the processing such that some

of it can run on some GPUs rest of it

can run on different GPUs and that led

to us realizing that maybe even

disagregated computing could make sense

that we could have different

heterogeneous nature of computing that

same sensibility led us to melanox

>> you know today Nvidia's computing is

spread across GPUs, CPUs, switches,

scale up switches, scale out switches,

networking processors, and now we're

going to add Gro to that and we're going

to put the right workload on the right

chips. You know, we just really evolved

from a GPU company to an AI factory

company.

>> I mean, I think that was probably the

biggest takeaway that I had. You're

seeing this fundamental disagregation

where we've gone from a GPU and now you

have this complexion of all these

different options that will eventually

exist. The thing that you guys said on

stage or you said on stage was I I would

like the high value inference people to

take a listen to this and 25% of your

data center space you said should be

allocated to this gro lpu GPU combo

>> grock to about 25% of the ver rubins in

the in the data center. So can you tell

us about how the industry looks at this

idea of now basically creating this next

generation form of disagregated prefill

decode disag?

>> Yeah and take a step back and at the

time that we added this we went from

large language model processing

to agentic processing. Now when you're

running an agent you're accessing

working memory you're accessing

long-term memory. You're using tools.

You're really beating up on storage

really hard. You have agents working

with other agents. Some of the agents

are very large models. Some of them are

smaller models. Some of them are

diffusion models. Some of them are auto

reggressive models. And so there are all

kinds of different types of models

inside this data center. We created Vera

Rubin to be able to run this

extraordinarily diverse workload. My

sense is and so we added what used to be

a one rack company. we now added four

more racks,

>> right?

>> So, Nvidia's TAM, if you will, increased

from what whatever it was to probably

something, call it, you know, 33% 50%

higher. Now, part of that 33% or 50% a

lot of it is going to be storage

processors. It's called Blue Field. Some

of it will be a lot of it, I'm hoping,

will be Grock processors, and some of it

will be CPUs. And they're all and a lot

of it's going to be networking

processors. And so all of this is going

to be running basically the computer of

the AI revolution called agents, right?

>> The operating system of of um modern

modern industry.

>> What about embedded applications? So you

know my daughter's teddy bear at home

wants to talk to her. What goes in

there? Is it a custom ASIC or does there

end up becoming much more kind of a

broader set of TAM with developing tools

that are maybe different for different

use cases at the edge and an embedded

application? We think that there's three

computers in the problem at the at the

largest at the largest scale when you

stick take a step back. There's one

computer that's really about training

the AI model, developing creating the

AI, another computer for evaluating it.

Depending on the type of problem you're

having, like for example, you look

around, there's all kinds of robots and

cars and things like that. You have to

evaluate these robots inside a virtual

gym that represents the physical world.

So it has to be software that obeys the

laws of physics. And that's a second

computer. We call that omniverse. The

third computer is the computer at the

edge, the robotics computer.

>> That robotics computer, one of them

could be self-driving car. Another one's

a robot. Another one could be a teddy

bear. Little tiny one for a teddy bear.

One of the most important ones is one

that we're working on that basically

turns the telecommunications base

stations into part of the AI

infrastructure. So now all of the it's a

$2 trillion industry. All of that in

time will be transformed into an

extension of the AI infrastructure. And

so radios radios will become a edge

devices, factories, warehouses, you name

it. And so so there are three these

three basic computers.

All of them, you know, are going to be

necessary.

>> Jensen, last uh last year, I think you

were ahead of the the rest of the world

in in in saying inference isn't going to

a thousand.

>> Just last year,

>> yes.

Is it is it going to 1 millionx is going

to 1 billionx? Yeah.

>> Right. And I think people at the time

thought it was pretty hyperbolic because

the world was still focused on

pre-scaling, on training. Here we are

now. Inference has exploded. We're

inference constrained. um you announced

an inference factory that I think is

leading edge that's going to be 10x

better in terms of throughput to the

next factory but yet if you if I listen

to what the chatter is out there it's

that your inference factory is going to

cost 40 or 50 billion and the

alternatives the custom AS6 AMD others

are going to cost 25 to 30 billion and

you're going to lose share so why don't

you talk to us what are you seeing how

do you think about share and does it

make sense for all these folks to pay

something that's a 2x X premium to what

others are marketing.

>> The big takeaway, the big idea is that

you should not equate the price of the

factory and the price of the tokens, the

cost of the tokens. It is very likely

that the $50 billion factory, and in

fact, I can prove it that the $50

billion factory will generate for you

the lowest cost tokens. And the reason

for that is because we produce these

tokens at extraordinary efficiency

10 times you know the difference between

50 billion. Now it turns out 20 billion

is just land power and shell. Right.

>> Right.

>> And then on top of that you have storage

anyways networking anyways you got CPUs

anyways you got servers anyways you got

cooling anyways. The difference between

that GPU being 1x price or halfx price

>> is not between 50 billion and 30

billion. Pick your favorite number, but

let's say between 50 billion and 40

billion.

>> That is not a large percentage when the

$50 billion

>> data center is actually 10 times the

throughput,

>> right? Jess, that's the reason why I

said that even for most chips,

>> if you can't keep up with the state of

the technology and the pace that we're

running, even when the chips are free,

it's not cheap enough.

>> Yeah.

>> Can I can I just ask a general strategy

question?

>> Yeah.

>> I mean, you're running the most valuable

company in the world. This thing is

going to do 350 plus billion of revenue

next year, 200 billion of free cash

flow. It's compounding at these crazy

rates. How do you decide what to do?

like how do you actually get the

information? I mean it's famous now

these sort of emails that are people are

meant to send you but how do you really

decide to get an intuition of how to

shape the market where to really double

down where to maybe pull back where to

actually go into a green field how how

does that information get to you how do

you decide these things

>> in a final analysis that's the job of

the CEO yeah

>> and our job is to define the strategy

define the vision define the strategy

we're informed of course by amazing

computer scientists amazing

technologists great people all over the

company but we have to shape that future

well Part of it has to do with is this

something that's insanely hard to do? If

it's not hard to do, we should back away

from it. And the reason for that is if

if it's easy to do, obviously, um,

>> lots of competitors,

>> a lot of competitors. Is this something

that has never been done before that's

insanely hard to do? And that somehow

taps into

>> the special superpowers of our company.

>> And so I have to find this confluence of

things to that meets the standard. And

in the end, we also know that a lot of

pain and suffering is going to go into

it. Yeah.

>> There no great things that are invented

because it was just easy to do and just

like first try, here we are. And so if

it's super hard to do, nobody's ever

done it before, it's very likely that

you're going to have a lot of pain and

suffering and so you better enjoy it. So

can you can you just look at maybe three

or four of the more longtail things you

announced and just talk about the

long-term viability of whether it's the

data centers in space or whether it's

what you're trying to do with ADAS in

autos or you know what you're trying to

do on the biology side just give us a

sense of like how you see some of these

curves inflecting upwards in some of

these longer tail businesses.

>> Excellent. uh physical AI large category

we believe and I just mentioned we have

three computing systems all the software

platforms on top of it physical AI as a

large category

it's technology industry's first

opportunity

to address a $50 trillion industry that

has largely been you know void of

technology until now and so we need to

invent all of the technology necessary

to do that I felt that that was a

10-year journey

We started 10 years ago. We're seeing it

inflecting now. It is a multi-billion

dollar business for us. It's close to10

billion a year now. And so it's a big

business and it's growing exponentially.

And so that's number one. I think in the

case of digital biology, I think we are

literally near the chat GPT moment of

digital biology. We're about to

understand how to represent genes,

proteins, cells. We already know how to

understand chemicals. And so the ability

for us to represent and understand the

dynamics of the building blocks of

biology that's a couple of two three

five years from now in 5 years time I

completely believe that the healthcare

industry where digital biology is going

to inflect and so these are a couple of

the really great ones and you could see

they're all around us

>> agriculture

>> agriculture

>> reflecting now

>> no question yeah

>> Jensen I want to take you from the data

center to the desktop uh the company was

built in large part on hobbyists, video

gamers and and all those graphic cards

in the beginning. And you mentioned in

front of I think 10,000 people here just

clawed open claw clawed code and what a

revolution agents have become and

specifically the hobbyists who are

really where a lot of energy um we see

you know a lot of the innovation breaks

want desktops. You announced one here uh

I believe it's the Dell 6800. Uh this is

a very powerful workstation to run local

models. 750 gigs of RAM. Obviously the

the Mac uh studio sold out everywhere in

my company. We're moving to OpenClaw

everything. Freeberg just got clawed.

You got claw pelled I understand. And

you're obsessed with these.

>> What is this from the streets movement

of creating open-source agents and using

open source on the desktop mean to you?

Great. Where is that going?

>> Yeah. So great. First of all, let's take

a step back. Um in the last two years we

saw basically three inflection points.

The first one was generative chat GPT

brought AI to the common

everybody to our awareness. But the fact

of the matter is the technology sat in

plain sight months before GPT. It wasn't

until chat GPT put a user interface

around it made it easy for us to use

that generative AI took off. Now

generative AI as you know generates

tokens for internal consumption as well

as external consumption. Internal

consumption is thinking which led to

reasoning. 01 and 03

continue that wave of chat GPT grounded

information made AI not only answer

questions but answer questions in a more

grounded way useful.

We started seeing the revenues and the e

the economic model of open AI start to

inflect. Then the third one was only

inside the industry that we saw clock

code the first agentic system that was

very useful really revolutionary stuff

but but claw code was only available for

enterprises. Most people outside never

saw anything about cloud code until open

claw. Open claw basically put into the

po popular consciousness what an AI

agent can do. Mhm.

>> That's the reason why open claw is so

important from a cultural perspective.

Now the second second reason why it's so

important is that open claw is open but

it formulates

it structures a type of computing model

that is basically reinventing computing

all together. It has a memory system. It

scratch is a short-term memory file

system. It has it has it has scales. Did

you say skills or scales?

>> Skills.

>> Oh, skills.

>> They do have scales theoretically. Yeah.

>> Yeah. Skills.

>> So, the first thing first thing it it,

you know, it has resources. It it

manages resources. It's it does

scheduling.

>> Yep.

>> Right. And it cron jobs. It could it

could spawn off agents. It could, you

know, it could decompose a task and and

cause and solve problems as does

scheduling. It has IO subsystems. It

could, you know, input. It has output.

It connect to WhatsApp. And also it has

a API that allows it to run multiple

types of applications called skills.

>> Yeah.

>> These four elements fundamentally define

a computer.

>> Yeah.

>> And therefore what do we have? We have a

personal artificial intelligence

computer for the very first time.

>> Open source.

>> It's open source. It runs literally

everywhere. And so this is now the this

is the op this is basically the

blueprint the operating system of modern

computing.

>> Yeah.

>> And it's going to run literally

everywhere. Now of course one of the

things that we had to help it do is

whenever you have agentic software you

have to make sure that and agentic

software has access to sensitive

information. It execute code. It could

communicate externally. We have to make

sure that all of it has to be governed.

all of it has to be secure and that we

have policies that that gives these

agents two of the three things but not

all three things at the same time

>> and so the governance part of it we

contributed to Peter Peter Steinberger

was here and and so we've got a mountain

of great engineers working with him to

help secure and keep that thing so that

it could protect our privacy protect our

security

>> Jensen that paradigm shift makes some of

the AI legislation that has passed

around the country to regulate AI and a

lot of the proposed legislation

effectively moot, doesn't it? Can you

just comment for a second on how quickly

the paradigm shift kind of obiates a lot

of the models for regulatory oversight

of AI, which is becoming a very hot

topic in politics right now.

>> Well, this is this is the part that that

we just with policy makers, we need to

we need to always get in front of them

and Brad, you do a great job doing this.

We had to get in front of them and

inform them about the state of the

technology, what it is, what it is not.

It is not a biological being. It is not

alien. It is not conscious.

Um it is computer software.

>> Yeah. Exactly.

>> And and it is not something that um we

say things like we don't understand it

at all.

>> It is not true. We don't understand at

all. We understand a lot of things about

this technology. and and so so I think

one we have to make sure that we

continue to inform the policy makers and

not affect not allow dumerism and

extremism to affect how policy makers

think and understand about this

technology. However, however, we still

have to recognize is technology is

moving really fast and don't get policy

ahead of the technology too quickly. And

the risk that we we run as a nation, our

greatest source of national security

concern with respect to AI is that other

countries adopt this technology while we

are so angry at it or afraid of it or

somehow paranoid of it that our

industries, our society don't take

advantage of AI. So I'm just mostly

worried about the diffusion of AI here

in United States.

>> Can you just double click if you were in

the seat in the boardroom of anthropic

over that whole scuttlebutt with the

department of war? It sort of builds on

this idea of people didn't know what to

think. It's sort of added to this layer

of either resentment or fear or just

general mistrust that people have

sometimes at the software levels of AI.

What would do you think you would have

told Daario and that team to do maybe

differently to try to change some of

this outcome and some of this

perception?

>> The first thing that I I would I would

say about Anthropic is first of all the

technology is incredible. We are a large

consumer of anthropic technology. Really

admire their focus on security. Really

admires their focus on safety. Um the

the the the culture by which we they

went about it. The the technology

excellence by which they went about it

really fantastic. Um I I would say that

that the the desire to warn people about

the capability of the technology is is

also uh really terrific. We just have to

make sure that we understand that the

world has a spectrum and that that

warning is good, scaring is less good,

>> right?

>> Um and because this technology is too

important to us,

>> right? And and I think that it is fine

to uh predict the future but we need to

be a little bit more circumspect. We

need to have a little bit more humility

that in fact we can't completely predict

the future and the abil and to say

things that that are quite extreme quite

catastrophic that there's no evidence of

it happening um could be more damaging

than people think. And and of course we

are technology leaders. Uh there were

there was a time when nobody listened to

us. Yeah.

>> Um but now because technology is so

important in the social fabric such an

important industry, so important to

national security, our words do matter.

And I think we have to be much more

circumspect. We have to be more

moderate. We have to be more balanced.

We have to be more for more thoughtful.

>> Well, I you know I would nominate you. I

think the industry's got to get

together. 17% popularity of AI in the

United States. I mean, we see what

happened to nuclear, right? We basically

shut down the entire nuclear industry

and now we have a 100 fision reactors

being built in China and zero in the

United States. Um, we hear about

moratoriums on data centers. So, I think

we have to be a lot more proactive about

that. But, but I want to go back to this

agentic explosion that you're seeing

inside your company, the efficiencies,

the productivity gains inside your

company. There's a lot of debate whether

or not we're seeing ROI, right? and you

and I entering into into this year, the

big question was, are the revenues going

to show up? Are the revenues going to

scale like intelligence? And then we had

this kind of Oenheimer moment, a five6

billion month by Anthropic in February.

Um, do you think as you look ahead, you

announced a trillion dollar, you know,

visibility into a trillion dollars of

just Blackwell and Vera Rubin over the

course of the next couple years. When

you see this happening at Anthropic and

Open AI, do you think we're on that

curve now where we're going to see

revenues scale in the way that

intelligence is scaling?

>> When you look around when you I'll

answer this a couple different ways.

When you look around this audience, you

will see that anthropic and open AI is

represented here. But in fact, everybody

99% of everything that is here is all AI

and it's not anthropic and open AI.

>> Right. Right.

>> And the reason for that is because AI is

very diverse.

>> I would say that the second most popular

model as a category is open models.

>> Number one is yeah open open source open

ways open source.

>> Open AI is number one. Open source is

number two. Very distant. Third is

anthropic. And that tells you something

about the scale of all of the AI

companies that are here. And so, so it's

important to recognize recognize that.

Um, let me let me come back and say a

couple things. One, when we went from

generative to reasoning, the amount of

computation we needed was about a

hundred times. Right?

>> When we went from reasoning to agentic,

the computation is probably another 100

times. Now we're looking at in just two

years, computation went up by a fact

10,000x.

Meanwhile,

people pay for information, but people

mostly pay for work.

>> Yes.

>> Talking to a chatbot and getting an

answer is super great,

>> right? helping me do some research.

Unbelievable. But getting work done,

I'll pay fordeed.

>> And so that's where we are. Agentic

systems get work done. They're helping

our software engineers get work done.

And and so then you take that,

>> you got 10,000x more compute.

>> You get probably at this point 100x more

consumption now.

>> Yes.

>> Yeah.

>> And we haven't even started scaling yet.

We are absolutely at a millionx

>> which is I think a great place to talk

about the number of

have 20 30,000 at the company something

like that.

>> We have 43,000 employees. You know, I

would say 38,000

are engineers.

>> The conversation we've had on the pod a

number of times is, "Oh my god, look at

the token usage in our companies. It is

growing massively." And some people are

asking, "Hey, when I join a company, how

many tokens do I get cuz I want to be an

effective employee?" And you postulated,

I believe, during your 2 and 1/2 hour

keynote, pretty long keynote. Well done

that you were spending

>> if it was well done it would be shorter.

I just want

>> you didn't have time to do a time to

write an hour 45.

>> So you guys so you guys know so you guys

know there is no practice

>> and so it's a gripping and ripping

>> rip and rip. Yeah.

>> So so I just want to let you know I was

writing the speech while I was giving

the speech. Okay. So you never know

>> but

>> does that mean if we do back

>> I apologize. back of the envelope math

$75,000 in tokens for each engineer or

something like that. So are you spending

in Nvidia a billion2 billion on tokens

for your engineering team right now?

>> We're trying to Let me give you a

thought experiment. Let's say you have a

software engineer or AI researcher and

you pay them $500,000 a year. We do that

all the time.

>> Okay, this is happening all over the

time. um that $500,000 engineer at the

end of the year I'm going to ask him how

many tok how much did you spend in

tokens and that person said $5,000 I

will go ape something else

>> yes

>> right

>> if that if that $500,000 engineer did

not consume at least $250,000 worth of

tokens I am going to be deeply alarmed

okay and this is no different than one

of our chip designers who says guess

what I'm just going to use paper and

pencil I don't think I'm going to need

any CAD tools.

>> This is a real paradigm shift to start

thinking about these all-star employees.

It almost reminds me of of what we

learned in the NBA when LeBron James

started spending a million dollars a

year just on his health of his body like

and maintaining it.

>> That's right.

>> Here he is at age 41 still playing.

>> It really is, hey, if these are

incredible knowledge workers, why

wouldn't we give them

>> superhuman abilities?

>> That's exactly

>> where does that go? If we if we

extrapolate out two or three years from

now, what is the efficiency of that

allstar at an Nvidia and what they're

able to accomplish? What do they look

like?

>> Well, first of all, things that that

that um wow, this is too hard. That

thought is gone. Uh this is going to

take a long time. That thought is gone.

Uh we're going to need a lot of people.

That thought is gone. This is no

different than in this in the last

industrial re revolution. Somebody goes,

"Boy, that building really looks heavy."

Nobody says that. Nobody. Wow, that

mountain looks too big. Nobody says

that. Right.

>> Everything that's too big, too heavy,

takes too long,

>> those thought, those ideas are all gone.

>> You're reduced to creativity. That's

right. What can you come up with?

>> Exactly. Which means now the question is

how do you how do you work with these

agents? Well, it's just a new way of

doing computer programming. In the f in

the past, we code. In the future, we're

going we're going to write ideas,

architectures, specifications.

We're going to organize teams. We're

going to give them We're going to help

them define how to evaluate the

definition of good versus bad. What's

the what does it look like when

something is a great outcome? How to

iterate with you, how to brainstorm.

That's really what you're looking for.

And I'm I think that every engineer is

going to have hundred a hundred agents.

>> Back to the PR problem the industry has

right now. You have executives uh like

David Freeberg with Oho who's looking at

literally taking through the use of

technology your technology and AI the

number of calories produced and making

high quality cal calories what is the

factor you think you can bring the cost

down for what impact does this vision

have for what you're doing

>> zero shot genomic modeling and it works

>> and you have that moment and you're like

holy

>> honestly like and and that's after

people are replacing entire enterprise

software stacks in a night. I did

something in 90 minutes I was telling

the guys about replaced a whole software

stack and like a whole bunch of workload

90 minutes on cloud ran this agentic

system built the whole thing deployed it

and we got we were on a Sunday night

>> on a Sunday night 10 p.m. I was done at

11:30. I went to bed.

>> As the CEO, you replaced

>> Yeah. And everyone on my management team

had to do a similar exercise over the

weekend. What we saw on Monday, I was

like, it's over. But the technical

stuff, the science stuff, we did

something in 30 minutes using auto

research, and I'd love your view on auto

research and what that tells us about

how far we still have to go in terms of

efficiency. But using auto research and

a chunk of data, something was published

internally that we said, "Oh my god."

And that would normally be a PhD thesis

that would take seven years. It would be

one of the most celebrated PhD thesis

we've ever seen in this field and it

would be in the journal science and it

was done in 30 minutes on a desktop

computer running on auto research with

all the data we just ingested. We got it

on Friday and we're like, "Hey, let's

try it." Try booted up, went to GitHub,

downloaded Auto Research and ran it. And

you see everyone's face just go like and

then the potential of what this is

unlocking for us is like the kind of

thing that would take seven years and it

happened in 30 minutes and we're

experiencing it in genomics and we're

like this is unbelievable. So I I think

like the acceleration is widening the

aperture for everyone in a way that like

you didn't imagine a few years ago. But

just going back to the auto research

point, can you just comment on what you

think about the fact that this thing got

published with 600 lines of code in a

weekend and the capacity that it has to

run locally and achieve what it can

achieve with all of these diverse data

sets and what that tells us about the

early stages we are in terms of

optimization on algorithms and hardware.

The fundamental reason why Open Claw is

so incredible number one is it's com its

confluence its timing with the

breakthroughs in large language model.

>> Yeah,

>> its timing was perfect. It was

impeccable. Now, in a lot of ways, Peter

wouldn't have come up with it probably

if not for the fact that Claude and GPT

and chat GPT have reached a level that

is really very good,

>> right? It is also a new capability that

allows these models to tool use the

tools that we've created over time web

browsers and Excel spreadsheets and you

know in the case of chip design synopsis

and cadence and uh omniverse and blender

and autodesk and all of these tools are

going to continue to be used. There's

some some people say that that the

enterprise IT software industry is going

to get destroyed. There's it's there's a

let me give you the alternative view.

The enterprise software industry is

limited by butts and seats. It's about

to get a hundred times more agents

banging on those tools. They're going to

be agents banging on SQL. They're going

to be agents bang on vector databases,

agents bang on Blender, agents bang on

Photoshop. And the reason for that is

because those tools are first of all do

a very good job. Second, those tools are

the conduit between us in the final

analysis. When the work is done, it has

to be represented back to me in a way

that I can control.

>> Right?

>> And I know how to control those tools.

And so I need everything to be put back

into synopsis. I want everything to put

back into cadence because that's how I

control it. That's how I've ground

truth.

>> Let me ask you a question about open

source. So we have these closed source

models. They're excellent.

>> We have these openweight models. Many of

the Chinese models are incredible.

Absolutely incredible. Two days ago, you

may not have seen this because you were

busy on stage, but there was a training

run that happened in this crypto project

called Bit Tensor Subnet 3. They managed

to train a 4 billion parameter llama

model totally distributed with a bunch

of people contributing

excess compute, but they were able to do

it statefully and manage a training run,

which I thought was like a pretty crazy

technical accomplishment.

>> Yeah. Because it's like random people

and each person gets a little share.

>> Our our modern version of folding at

home.

>> Exactly. So what what do you think about

the end state of open source? Do you see

this decentralization of architecture as

well and decentralization of compute to

support open weights and a totally open-

source approach to making sure AI is

broadly available to every?

>> I believe we fundamentally need

models as a firstass product proprietary

product as well as models as open

source. These two things are not A or B.

It's A and B. There's no question about

it. And the reason for that is because

models is a technology, not a product.

Model is a technology, not a service.

For the vast majority of consumers, the

horizontal layer, the general

intelligence, I would really, really

love not to go fine-tune my own. I would

really love to keep using chat GPT. I'd

love to use Claude. I love to use

Gemini. I love to you use X. And they

all have their own personalities as you

know, which is kind of depends on my

mood and depends on what problem I'm

trying to solve. you know I might you

know do it on X or I might do it on on

chat GBT and so that that segment of the

of the industry is thriving it's going

to be great however there all these

industries their domain expertise their

specialization has to be channeled has

to be captured in a way that they can

control and that it can only come from

open models the open model industry

we're contributing tremendously to it is

near the frontier

and quite Quite frankly, even if it

reaches the frontier,

I think that products as a service,

worldclass products as as a models as a

product is going to continue to thrive.

>> Every startup we're investing in now is

open- source first and then going to the

proprietary models.

>> Yeah. And the beautiful thing is because

you have a great router you connect it

to by on on first day every single day

you're going to have access to the

world's best model and and then it gives

you time to cost reduce and fine-tune

and specialize and so you're going to

have worldclass capabilities out to

shoot every single time. Let J can I

question?

>> Nobody wants the US to win the global AI

race more than you, right? But a year

ago, the Biden era diffusion rule really

was an anti- American diffusion of AI

around the world. So here we are a year

into the new administration.

Give us a grade. Where is where are we

in terms of global diffusion and the

rate at which we're spreading US AI

technology around the world? Are we an

A? Are we a B? or we see what what's

working, what's not working.

>> Well, first of all, President Trump

wants American industry to lead. He

wants American technology industry to

lead. He wants American technology

industry to win. He wants us to spread

American technology around the world. He

wants the United States to be the

wealthiest country in the world. He

wants all of that. At the current

moment, as we speak,

Nvidia gave up a 95% market share in the

second largest market in the world, and

we're at 0%.

>> President Trump, That's right. President

Trump wants us to get back in there. And

and uh the first thing is uh to get

license licensed for the companies that

we're going to be able to sell to. We've

got many companies who have requested

for licenses. We've applied for licenses

for them and we've got approved licenses

from sec secretary lutnik. Uh now uh

we've we informed the Chinese companies

and many of them have given us purchase

orders and so we're going to we're going

to we're in the process of cranking up

our supply chain again to go ship. I

think at the highest level Brad um I

think one of the things that we should

acknowledge is this. Our national

security

is diminished when we don't have access

to miniature motors, rare earth

minerals. It's diminished when we don't

control our telecommunications networks.

It's diminished when we can't provide

for sustainable energy for our country.

It is fundamentally diminished. Every

single one of these industries is an

example of what I don't want the AI

industry to be.

>> Right? When we look forward in time and

we say what do we want? What is the what

does it look like when American

technology industry American AI industry

leads the world? We can all acknowledge

that there is no way that AI models is

one universally. It is we can all

acknowledge that that is an outcome that

makes no sense. However, we can all

imagine that the American tech stack

from chips to computing systems to the

platforms are used broadly by the world

where they build their own AI, they use

public AI, they use private AI whatever

and they can build their applications in

their society. I would love that the

American tech stack is 90% of the world.

Yes, I would love that. The alternative

if it looks like solar, rare earth,

magnets, motors, telecommunications, I

consider that a very bad outcome for

national security.

>> Great.

>> Yeah.

>> How much are you monitoring the

situation with the conflicts around the

world right now? And how much does it

worry you Jensen? So, China and Taiwan

and then helium availability coming out

of the Middle East, I understand, can be

a supply chain risk to semiconductor

manufacturing. How much do these

situations worry you? How much are you

spending on them?

>> Well, first of all, I think the in

Middle East, I have we have 6,000

families there.

>> Yeah.

>> Uh we have a lot of Iranians uh at

NVIDIA and their families are still in

Iran. And so so we have we have a lot of

families there. The first thing is is

they're quite anxious. They're quite

concerned, quite scared. Um we're

thinking about them all the time. Uh

we're monitoring and keeping an eye on

them all the time. They have 100% of our

support. Uh I've been asked several

times, are we still considering uh being

in Israel? We are 100% in Israel. We are

100% behind the families there. We are

100% in the Middle East. I was also

asked, you know, given what's happening

in the Middle East, uh is that an area

where we believe that we can expand

artificial intelligence to? Um I believe

that there's a reason we went to war and

I believe at the end of the war, Middle

East will be more stable than before.

And so if we were there, if we're

considering it before, we should

absolutely be considering it after. And

so I'm 100% in on that. With respect to

with with with respect to to Taiwan,

>> we have to do three things. One, we have

to make sure that we re-industrialize

the United States as fast as we can.

>> And whether it's the chip manufacturing

plants, the the computer manufacturing

plants, or the AI factories,

>> how are we doing on that? We're doing

excellent with by by gaining the

strategic support by gaining the

friendship of the supply chain of

Taiwan.

By gaining their friendship by gaining

their support, we were able to build

Arizona and Texas, California at

incredible rates. They're they are

genuinely a strategic partner. Um we we

we really they deserve our support. They

deserve our friendship. They deserve our

generosity and they're doing everything

they can to accelerate the manufacturing

process for us. And so, so I think

that's number one. Number two, we ought

to diversify the manufacturing supply

chain. And whether it's South Korea,

whether it's it's Japan, it's Europe, we

got to we got to diversify the supply

chain, make it more resilient. And

number three, let's be let's let's

demonstrate restraint. And while we're

reducing uh increasing our diversity and

resilience, let's not

press push um

>> unnecessary. We need to be patient.

>> Is helium a problem? A lot of reports,

>> you know, I think helium could be a

problem, but it's also the case that the

supply chain probably has a lot of

buffer in it.

>> These kind of things tend to have a lot

of buffer. Uh but but um you know yeah

>> you've um made massive progress in

self-driving. You made a big

announcement. You've added many more

partners including BYD. There was just a

video of you driving around in a

Mercedes and uh huge announcement uh

with Uber that you're going to have a

number of cars on the road from many

different manufacturers. your bet is I

believe that there's going to be an

Android

type open-source platform that you're

going to play a major part in with

dozens of uh car providers and then

maybe on the other side there could be

an iOS with Tesla or Whimo. What's your

strategy thinking there and how that

chessboard emerges because it feels like

you have a a pretty deep stack and in

some ways you're competing and in other

places you're collaborative. Yeah. Um,

it's taking a step back. We believe that

everything that moves will be autonomous

completely or partly

someday. Number one. Number two, we

don't want to build self-driving cars,

but we want to enable every car company

in the world to build self-driving cars.

And so, we built all three computers,

the training computer, the simulation

computer, the valu evaluation computer,

as well as the car computer. We develop

the world's safest driving operating

system. Uh we also created the world's

first reasoning autonomous vehicle so

that it could decompose complicated

scenarios into simpler scenarios that it

knows how to navigate through just like

us reasoning systems. And so that

reasoning system called Alpommyo has

enabled us to achieve incredible

results.

We

open this we ver we vertical

optimization. We horizontally innovate

and we let everybody decide. Do you want

to buy one computer from us? In the case

of Elon and Tesla, they buy our training

computers. Um, do they want to buy our

training computer and our simulation

computers or do you want to let us uh

work with us to do all three and even

put the car computer in your car. So, we

you know, our attitude is we want to

solve the problem.

We're not the solution provider

and we're delighted however you work

with us. Let me build on this question

because I think it's like it's so

fascinating. You actually do create this

platform. A thousand flowers are

blooming.

>> But it's also true that some of those

flowers want to now go back down in the

stack and try to compete with you a

little bit. Google has TPU, Amazon has

inferentia and tranium. You know,

everybody's sort of spinning up their

own version of I think I can out Nvidia

Nvidia

>> even though they also tend to be huge

customers.

>> How do you navigate that? And yeah, what

do you think happens over time and

>> where do those things play in the

complexion of this kind of vision?

>> Yeah, really great. You know, first of

all, um, we're the only AI company,

we're an AI company. We build foundation

models. We're at the frontier in many

different domains. We build every single

every single layer, every single stack.

Um, we're the only AI company in the

world that works with every AI company

in the world. They never show me what

they're building and I always show them

exactly what I'm building.

>> Right.

>> Yeah. And so so the confidence comes

from this one. Uh we are delighted to

compete on what is the best technology

and to the extent that to the extent

that we can continue to run fast I

believe that buying from Nvidia still is

one of the most economic things they

could do and that's just incredible

confidence there. Number one. Number two

we're the only architecture that could

be in every cloud and that gives us some

fundamental advantages. where the only

architecture you could take from a cloud

and put into onrem in the car in any

region

>> in space.

>> That's right. In space. And so there's a

whole whole part of our market about 40%

of our of our business most people don't

realize this 40% of our business unless

you have the CUDA stack unless you can

build an entire AI factory you have the

customers don't know what to do with

you. They're not trying to build chips.

They're not trying to buy chips. They're

trying to build AI infrastructure. And

so they want you to come in with the

full stack. And we've got the whole

stack. And so surprisingly, Nvidia is

gaining market share. If you look at

where we are today, we're gaining share.

>> Do you think what happens is these guys

try and they realize, oh my god, it's

too much. And then they come back. Is

that why the share grows?

>> Well, we're gaining share for several

reasons. One, um, our velocity has gone.

We help people realize it's not about

building the chip, it's about building

the system.

>> And that system is really hard to build.

uh and and so their their their business

with us is increasing. In the case of

AWS, I think they just announced, I

think it was yesterday, that they're

going to buy a a million chips uh in the

next couple years. I mean, that's a lot

of chips from from AWS. And that's on

top of all the chips they've already

bought. And so, we're delighted to do

that. But number one, we're gaining

share this last couple years because we

now have Anthropic coming to Nvidia.

Meta SL is coming to Nvidia. And the

growth of open models is incredible. And

that's all on Nvidia. And so we're

growing in share because of the number

of models. We're also growing in share

because out all of these companies are

outside of the cloud and they're growing

regionally in enterprise in industries

at the edge and that entire segment of

growth is you know really hard to do if

it's just building an as

>> Brad

>> related to that um and not to get in the

weeds on the numbers but analysts don't

seem to believe right so if you look at

the consensus forecast you said compute

could 1 millionx right and Yet they have

you growing next year at 30%, the year

after that at 20%. And in 2029, which is

supposed to be a monster year at 7%.

Right? So if you just if you take your

TAM and you apply their growth numbers,

it suggests that your share will

plummet. Do you see anything in your

future order book that would make that

correct?

>> Yeah. First of all, they just don't

understand the scale and the breadth of

AI.

>> Yes.

>> Yeah.

>> Yeah. I think that's true. Most people

think that AI is in the top five

hyperscalers,

>> right? That's right. There's also an

orthodoxy around these law of large

numbers where,

>> you know, they have to go back to their

investment banking risk committee and

show some model.

>> They're not going to believe in their

minds that 5 trillion goes to 15

trillion. They're like go to it can go

to seven or they can have a 10 trillion

company.

>> It's all just CIA stuff that I think

>> it's never happened before. So you can't

say it will

>> and and because because you have to

redefine what it is that you do. There

was somebody who made an observation

recently that Nvidia

Jensen, how can you be larger than Intel

in servers and the reason for that is

because the CPU market of the entire

data center was about $25 billion a

year,

>> right?

>> We do $25 billion a year as you guys

know in a very in the time that we were

sitting here.

>> And so obviously obviously

That was a joke.

>> No, it's but it's

>> all in podcast.

>> Don't worry. Everything on this show is

rough. Don't worry about it. It's all in

here. Anyways, that was not guidance.

But anyhow, anyhow, it the the point is

how big you can be

>> depends on what is it that you make,

>> right?

>> Nvidia is not making chips. Number one,

making chips does not help you solve the

AI infrastructure problem anymore. It's

too complicated. Number three, most

people think that AI is narrowly in the

things that they talk about and hear and

see.

>> It's AI is much open AI is incredible.

They're going to be enormous. Anthropic

is incredible. They're going to be

enormous. But AI is going to be much

much bigger than that.

>> Tell us

>> and we addressed that segment.

>> Tell us about data centers in space for

a second.

>> Yeah.

>> Um

>> we're already in space. How should the

layman think about what that business is

versus when you hear about these big

data center buildouts that's happening

in in on the ground?

>> Well, we should definitely work on the

ground first because we're already here

and number one. Number two, we should

prepare to be out in space and obviously

there's a lot of energy in space. Um the

challenge of course is that cooling

you can't take advantage of conduction

and convection and so you can only use

radiation and radiation requires very

large surfaces and so now that's not an

impossible thing to solve and there's a

lot of lot of space in space. Um but

nonetheless

the expense is still quite there is is

there uh we're going to go explore it.

We're already there. We're already

radiation hardened. Uh we have we have

uh uh uh CUDA in satellites around the

world. Um they're doing imaging, image

processing, AI imaging and um and that

kind of stuff ought to be done in space

instead of sending all the data back

here and do imaging down here. We ought

to just do imaging out in space. And so

there's a lot of things that we ought to

done do do in space. And in the

meantime, uh we're going to explore what

is the architecture of data centers look

like uh in space. And it'll take it'll

take years. It's okay. We got I got

plenty of time. I wanted to um double

click on healthcare. I know you've got a

big effort there. We're all of a certain

age where we're thinking about lifespan,

health span. I mean, we all look great.

I think

>> some better than others.

>> I think some better than others. I don't

know what your secret is, Jensen.

>> Pretty good these these

>> I mean what's what are you taking what's

off the menu? You got to talk to me when

we're backstage. I want to know in the

green room what you got going on.

>> Squat squats and push-ups and sits.

>> Perfect. Okay. Um but

>> that works. what you know in terms of

the buildout in healthcare

where is that going and what kind of

progress are we making? I was just using

Claude to do some analysis and saying

like where are all these billing codes?

We spend twice as much money in the US.

We get seem to get half as much. It

seemed like uh 15 to 25% of the dollar

spent were on these first GP visits. And

I think we all know like chat GBT and a

large language model does a better job

more consistently today at a first

visit. So what has to happen there to

kind of break through all that

regulation and have AI have a true

impact on the health care system?

>> There's several several areas that we're

involved in in um in healthcare. One is

uh AI

uh physics uh and and that's or AI

biology using AI to understand represent

predict biology behavior biological

behavior and so that's one that's very

important in drug discovery. There's

second which is AI agents and that's

where the assistance and helping

diagnosis and things like that. Open

evidence is a really good example.

Hypocratic is a really good example.

Love working with those companies. Um I

really think that this is an area uh

where agentic technology is going to

revolutionize how we interact with

doctors and how do we interact for

healthcare. The third part that we're in

involved in is physical AI. The first

one is AI physics using AI to predict

physics. The second one is physical AI.

AI that understand the properties of the

laws of physics and that's used for a uh

robotic surgery huge amounts of

activities there. Every single

instrument whether it's ultrasound or

you know CT or whatever instrument we

interact with in a hospital in the

future will be agentic.

>> Yeah.

>> You know open claw in a safe version

will be inside every single instrument.

And so in a lot of ways that instrument

is going to be interacting with patients

and nurses and doctors in a very unique

way. so much investment in AI weapons.

It would be wonderful to see some

investment in AI EMTs and paramedics and

saving lives, not just taking them,

which I think is a great segue into

robotics. You've got dozens of partners.

We have this very weird

>> I I don't know want to call a lost

decade or 20 years of Boston Dynamics.

Google bought a bunch of companies. They

then wound up selling them and spinning

them out where people just thought

robotics is just not ready for prime

time. And now here we have the world's

greatest entrepreneur at this time. Uh

tied with you, uh Elon Musk doing well,

that was a good save, I hope. Optimus,

uh pretty impressive. And then other

companies in China. How how close is

that to actually being in our lives

where we might see a chef, a robotic

chef, a robotic nurse, a robotic

housekeeper, you know, this humanoid

factor actually working in the real

world knowing what you know with those

partners and the fidelity, especially in

China where they seem to be doing as

good a job as we're doing here or maybe

better.

>> Um,

we invented the industry largely.

America invented. We c you could argue

we got into it too soon.

>> Yeah.

>> And and we got exhausted. We got tired

um about five years before the enabling

technology appeared.

>> The brain.

>> Yeah. Yeah. And we we just got tired of

it just a little too soon. Okay. That's

number one. But it's here now. Now the

question is how much longer? From the

point of high functioning existence

proof, high functioning exist existence

proof to reasonable products

technology never takes more than a

couple two three cycles. And so a couple

two three cycles basically be somewhere

around three years to 5 years. That's

it. 3 years to 5 years we're going to

have robots all over the place. Uh I

think I think um uh China is is uh

formidable and the reason for that is

because their micro electronics, their

uh motors, their rare earth, their

magnets, which is foundational to

robotics, they are the world's best. And

so in a lot of ways, our robotics

industry relies deeply on their

ecosystem and their supply chain. Um and

uh and and they're, you know, obviously

moving very quickly. Uh we're going to,

you know, our robotics industry will

have to rely a lot on it. the world's

robotics industry will have to rely on a

lot on it. And so so I think um you're

gonna see some fast fast movements here

>> ultimately one for one. Elon seems to

think we're going to have one robot for

every human. 7 billion for 7 billion, 8

billion for 8 billion.

>> Well, I'm hoping more. Yeah, I'm hoping

more. Yeah. Uh well, first of all,

there's a whole bunch of robots that are

going to be in factories working around

the clock. There's going to be a whole

bunch of fac that that don't move. They

move a little bit. Uh almost everything

will be robotic. What does the world

look like?

>> Sorry, let me I think like this is one

of the robotics for me is one of the

pieces that I think unlocks uh economic

mobility opportunities for every

individual. Everyone now like when

everyone got a car, they could now go

and do a lot of different jobs. When

everyone gets a robot, their robot can

do a lot of work for them. They can

stand up an Etsy store, a Shopify store.

They can create anything they want with

their robot. They could do things that

they independently cannot do. I think

the robot is going to end up being the

greatest unlock for prosperity for more

people on Earth than we've ever seen

with any technology before.

>> Yeah, no doubt. I mean, just a simp the

simple math at the moment is we're

millions of people short in labor today.

Right. Yeah.

>> Right. We're we're we're actually really

desperate in need of robotics and so

that all of these companies could grow

more if they had more labor. I mean,

we're we're number one. Some of the

things that you mentioned are super fun.

I mean, because of robots, we'll have

virtual presence. Uh, you know, I'll be

able to go into the robot of my house

and virtually operate it. I'm on a

business trip,

>> right?

>> Walk around the house.

>> Yeah. Walk the dog.

>> Yeah. Walk the dog.

>> Break the leaves.

>> Yeah. Exactly. Freak out the dog.

>> Maybe not quite that, but just, you

know, just, you know, wander around and

just see what's going on in the house.

You know, chat with the dogs, chat with

the kids. Yeah.

>> Yeah.

And time travel is also we're going to

be able to travel at the speed of light,

you know, and so, you know, clearly

we're going to send our robots ahead of

us.

>> Yeah.

>> Not going to send myself. I'm going to

send a robot, you know.

>> Check it out.

>> Yeah. Yeah. And then I'm going to upload

my AI.

>> Well, it's inevitable. It unlocks the

moon and it unlocks Mars as um targets

for for colonization, which gives us

>> infinite resources. Getting back from

the moon is effectively zero energy cost

to move material back because you can

use solar and accelerate. So you could

have factories that make everything the

world needs on the moon and the robots

are going to be the unlock for enabling.

>> That's right. Distance no longer

matters.

>> Distance doesn't matter. Yeah.

>> The more the more revenue we get out of

models and agents, the more we can

invest in building the infrastructure

which then unlocks more capabilities on

models and agents. Dario on Dwaresh's

podcast recently said by 2728 we'll have

hundreds of billions of dollars of

revenue out of the model companies and

the agent companies. and he forecasts a

trillion dollars by 2030, right? This is

non-infrastructure AI revenue. Um,

>> I think he I think he's he's being very

conservative. I believe Dario and

Anthropic is going to do way better than

that.

>> Wow.

>> Way better than that.

>> Wow. So, from 30 billion to a trillion.

>> Yeah. and not and and the reason for

that is the one part that he hasn't

considered is that I believe every

single enterprise software company will

also be a reseller

value added reseller of anthropic code

anthropics tokens value added reseller

open AI that's right and they're going

to that that that part of their

>> get this logarithmic expansion

>> yes

>> their go to market is going to expand

tremendously this year

>> what do you think in that world is the

moat what's left over. I mean you have

some moes that are frankly I think as

this scales almost insurmountable the

best one that nobody talks about is

probably CUDA which is just like an

incredible strategic advantage. But in

the future if a model can be used to

create something incredible then the

next spin of a model can be used to

maybe disrupt it. Sort of in your mind

what do you think for these companies

that are building at that application

layer? What's their moat? like how do

they differentiate themselves?

>> Deep specialization.

Deep specialization. I believe that um

these models they're going to have

general general models that are

connected into the software company's

agentic system,

>> right?

>> Many of those models are cloud models

and proprietary models, but many of

those models are specialized

sub aents that they've trained on their

own.

>> Right. All right. So, the call to arms

for you for entrepreneurs is look,

>> know your vertical.

>> That's right.

>> Know it as deep and as better than

everybody else.

>> That's right.

>> And then wait for these tools because

they're catching up to you and now you

can imbue it with your knowledge.

>> That's right. The sooner you connect

your agent,

>> the sooner you connect your agent with

customers,

>> that flywheel is going to cause your

agent to get

>> it very much is an inversion of what we

do today because today we build a piece

of software and we say what generalizes

>> and then let's try to sell it as broadly

as possible and then sell the

customization around it

>> and we in fact in fact exactly right we

we create a horizontal but notice there

are all these gsis and all of these

consultants who are specialists Yes.

>> Who then take your horizontal platform

and specializes it into

>> Exactly.

>> And that's arguably a five or six time

bigger industry is the customization.

>> It is absolutely the whole very much is

>> that's right. So I think that these

platform companies have an opportunity

to become that specialist to become that

vertical.

>> Yeah. Domain expert.

>> You know, I just want to give you your

flowers. I think it was three years ago

you said you're not going to lose your

job to AI. You're going to lose your job

to somebody using AI. And here we are.

The entire conversation has revolved

around this concept of agents making

people superhuman and the business

opportunity expanding and

entrepreneurship expanding. You actually

saw it pretty clearly. Yeah.

>> Have you changed your view?

>> I do. I'm not I'm not doomer. I do I do

have doomer.

>> No I you can hold space for I think two

ideas. One is there are going to be a

lot

>> that's spiral Jake we call it.

>> No there you can

>> but that's just because he doesn't hang

out with me enough. Well, we I mean we

a little bit. Be careful.

>> We don't talk about it.

>> He will show your breakfast. HE'LL

FOLLOW YOU AROUND.

>> I'm not asking for it. He'll follow you

around. I'm not asking for it.

>> You can come with me and Tucker. We ski

in Japan every January. Love it. Me and

Tucker go road trip. Um there is going

to be job displacement. And then the

question becomes,

>> you know, do those people have the

fortitude, the resolve to then go

embrace these,

>> you know, technologies? We're we're

going to see 100% of driving go away by

humans. That's just it's that's a

beautiful thing in the lives saved, but

we have to recognize that's 15 million

people in the United States, 10 to 15

million who are employed in that way.

And and so that is going to happen. Yes,

>> I I think I think that jobs will change.

For example, um there are many

chauffeers today uh who drives the car.

I believe that though many of those

chauffeers will actually be in the car

sitting behind the drive the steering

wheel while the car is driving by

itself. And the reason for that is

because remember what a chauffeur does

in the end. These chauffeers they're

helping you they're your assistants.

They're helping you with your luggage.

They're helping you. I mean, they're

helping you with a lot of things and and

so I wouldn't be surprised actually if

the chauffeers of the future becomes

your mobility assistant and they are

helping you do on a whole bunch of other

stuff to the hotel.

>> Yeah. And the car is driving by itself.

>> The autopilot in planes created a lot

more pilots and didn't take any of the

pilots out of the cockpit even though

the autopilot is flying the plane 90% of

the time. And by the way, while that car

is driving itself, that chauffeur is

going to be doing a bunch of other work

on his phone and he's going to be

>> arranging, for example, coordinating a

bunch of things for you, getting, you

know,

>> it's all the pie just grows in a way

that

>> one of the things that that that

yes, every job will be will be

transformed. Um, some jobs will be

eliminated. However, we also know that

many many jobs will be recre will be

created. The one thing that I will say

to young people who are coming out of

school who are concerned who are anxious

about AI be the expert of using AI

>> how much look we all want our employees

to be expert at using AI and it's not

not

>> not trivial not trivial and so knowing

how to specify not to overprescribe

leaving enough room for the AI to

innovate and create while we guide it to

the outcome we want. it. All of that

requires artistry.

>> You had you had this great advice to

when you were at Stanford, I think it

was, which is I wish to you pain and

suffering. Do you remember that?

>> Yeah.

>> Fantastic.

>> What's your advice to young people

around what they should be studying? So,

if they're sort of about to leave high

school because now those are the kids

that are at this like really native,

they haven't made a decision about

college, what to study, if at all go to

college. How do you guide those kids?

What would you tell them? I I still

believe that deep science, deep math, um

language skills, you know, as you know,

language is the programming language of

AI, the ultimate program.

>> And so, as it turns out, it it could be

that the English major could be the most

successful. Yeah.

>> And and so so I think I think um I I

would just advise whatever whatever

education you get, just make sure that

you're deeply deeply expert in using

AIs. One of the things that I wanted to

say with respect to jobs and I want

everybody to hear it that in fact at the

beginning of the deep learning

revolution, one of the the finest

computer scientists in the world deeply

deeply I deeply uh deeply uh um respect

uh predicted that computer vision will

completely eliminate radiologists

and and that the one at the one field he

advises everybody to not go into is

radiology. 10 years later, his

prediction was at 100% right. Computer

vision has been integrated into all of

the radiology technologies and radiology

platforms in the world 100%. The

surprising outcome is the number of

radiologists actually went up and the

demand for radiologists is skyrocketed.

The reason for that is because

everybody's job

has a purpose and its task. The task

that you do is studying the scans,

>> but your purpose is to diag helping the

doctors, helping the patient diagnose

disease.

>> And so what's surprising is because the

scans are now being done so quickly,

>> they could do more scans, improving

healthcare.

>> Yes.

>> But doing more scans more quickly allows

patients to

>> be

onboarded a lot more quick, treated a

lot more quickly. And as it turns out,

because hospitals enjoy making money,

too.

>> Yeah.

>> Right.

>> They're doing more scans.

>> They're treating more customers.

The revenues go up. And guess what?

Perfect. And and a country that grows

faster, productivity increases. A

wealthier country can put more teachers

in the classroom, not less teachers in

the classroom. That's right. You just

give every one of those teachers a

personalized curriculum for every

student in the room. It makes them all

bionic and leads to a lot more. Every

single student will be assisted by AI,

but every single student will need great

teachers.

>> Yeah. Yeah. Amazing. Uh Jensen,

congratulations. I know your success and

really this is an incredibly positive,

uplifting discussion. We really

appreciate you taking the time for us.

He is the steward we need.

>> You are you are you need to be more

vocal. I'm being very vocal about the

positive side of it. I think there's too

much dumerism is

>> but I also think it takes the humility

to have this level of success and be

humble about we're making software guys.

Yeah.

>> And I think that that's actually really

healthy for people to hear. We have done

this before. We have invented categories

and industries before.

>> We don't need to go to this

>> scaremongering place. It does nothing.

>> And we get to choose, right? We have

autonomy and and agency. We get to pick

how to

>> we do this. Okay, everybody. We'll see

you next time on the All-In interview.

Okay.

>> Well done, brother.

>> Thanks, man.

>> Good job.

>> Thank you, sir. That was awesome.

>> Good. Good.

>> Appreciate you. You guys are awesome.

>> Look at this. Look at this big crowd

behind you guys,

>> man. I think they're here for you.