Welcome to Startup School 2026. Now,

let's get started.

Please join me in welcoming to the stage

the founder and CEO of Nvidia, Jensen

Huang.

>> [music]

[music]

>> Hey Jensen.

>> [music]

>> Please.

Everybody.

>> Oh my god.

This is a surreal moment for me. Thank

you. Thank you for being here, Jensen.

>> I'm delighted to do it. It's great to be

here.

>> [cheering]

>> Apparently,

if you're here,

you are going to make it.

So, I'm happy I'm here.

>> [laughter]

>> Oh, Jensen. Uh

Well, for the students who only know

Nvidia at this as a company at the

center of AI,

uh what part of the early Nvidia story

do they most need to understand?

>> The thing that most people don't

don't believe is that

that um

uh the choice of our technology that we

started the company with was absolutely

wrong.

And so, we had started with the idea

that we would reinvent 3D graphics.

Well, the the company's

philosophy and perspective

uh was that

the general purpose computers, the CPUs,

were really useful, but if we could

augment it with uh accelerators, we

could solve problems that otherwise

too hard to solve.

And one of the first problems we chose

was 3D graphics.

And we And during that time 1993 the PC

was just rumored to be coming.

And

and our big idea was that we would turn

every single personal computer into a

game console because we grew up in the

era of game consoles.

And so we thought you know what if we

could could

design a system that would fit into the

personal computer

and it would turn it into a game

console. And so we thought we would

reinvent the algorithm that would

require these large supercomputers and

we would fit it into the PC.

And we came up with some new algorithms.

And we were excited about it. We

believed in it. It was we reasoned about

it

um in a thoughtful way.

And

and we went to start the company to go

build it. Well, it turns out the

algorithm was exactly wrong.

And the technology that founded the

company turns out to be exactly wrong.

And so in 1995

uh we realized that and

it was almost too late because by then

there were some 35 40 other companies

that were building 3D graphics for PCs.

And and so we realized that it didn't

work and I went back to the company and

we were at the company I said what are

we going to do? It doesn't work and we

were all talking about it and I said

look uh we we uh

we won't have a company if we don't

confront the fact that this doesn't work

and start working towards the right

algorithm.

And and then somebody told me it turns

out none of us knew how to do it the

right way.

And not only did we choose the wrong

technology we didn't know how to do it

the right way. And so

so that that was a big day for me. I had

a couple of couple of $60 you know a

couple of $100 in my pocket and so I

went down to Fry's and I bought three

textbooks.

And and the textbooks was about OpenGL

and how to design uh pipelines. I

brought it back to the company and gave

it to the engineers,

and here we are. Uh we reinvented

computer graphics, we're the world

leader in modern computer graphics, we

invented most of the major breakthroughs

in the last 25 years.

Everybody would have thought that Nvidia

is

you know started out as world leaders in

3D graphics, and we learned it from a

textbook.

And so we actually started the company,

raised money,

and bought textbooks.

When you think about it. And so the the

the big lesson is that for me is

technology is changing all the time,

and

so long as you're able to confront the

reality, so long as you are able to

learn, the technology itself actually

doesn't matter.

And so uh since then Nvidia has been,

you know,

inventing all kinds of technology since,

all kinds of technology we've never

never really done before, and we

approach everything with the same

attitude, you know, this is uh if it's

important to do, we're going to go learn

it, and how hard can it be?

And uh it always turns out to be much

much harder than

than uh we expect. Um but you go into it

with the attitude, how hard can it be?

>> I mean, backstage we were uh talking

about how I mean, we were talking with

some of the top YC companies, and you

were saying that each one has an

expertise in like a domain that you have

an you and Nvidia have an expertise in,

and they're all just I I forget what you

said, it was like an algorithmic domain

of a sort. And so it sounds like 3D

graphics was merely the first of an

algorithmic domain.

>> That's right.

>> came from a textbook, but then,

you know, anyone could have read that

textbook. You created

>> Particle physics, fluid dynamics, yeah.

>> But you created the the thing that

people want, like the the end product

that people want to pay a lot of money

for.

>> The big idea of the company that was

spot-on is that it is possible to

augment the CPU to solve problems that

otherwise are too difficult to solve.

And and molecular dynamics is one of

them, image processing is one of them,

inverse physics is another one. And so

all kinds of different algorithms, of

course deep learning is one of the major

ones. And and um

in order to create the company that we

have today, we realized early on

uh that it's not about building a great

chip, it's about accelerating an

algorithm domain.

And so one of the things I've always

believed believed in is what makes great

companies is a unique perspective about

the world that you deeply believe in.

It's not so much the technology, it's

not so much uh the market even. Uh those

things all matter and if you have the

right technology for the right market at

the right time, uh your life is going to

be a lot easier.

A high-level vision about the future of

some important thing, a perspective

about it that's somehow unique,

that you deeply believe in and ideally

pursuing that that vision is hard to do,

those are kind of good good

combinations. In our case, we realized

that accelerated computing was going to

be important. And accelerated computing

turns out uh to be very important and

our realization is everything to do with

algorithm, not the chip. Uh turns out to

be exactly right.

>> So you've said a lot about I guess the

hardships of a founder. Um

are there a few stories that really jump

out at you? I mean there the people in

this room would love to start a company,

but you know, are they really prepared

for eating glass and you know, possibly

having to shut down the company, like

things going wrong? Like what are some

of the pivotal moments that really jump

out at you?

I think you were just in Japan, right?

And uh

>> Yeah.

>> you were sort of um

honoring uh Sega, was it? So I feel like

that was a really powerful story.

>> The project that led us to realize the

algorithm we chose was wrong was a

partnership with Sega. Sega

uh had contracted us

to build the

uh game console after Saturn

that turned out to have been Dreamcast.

I don't know if Does anybody know what

Dreamcast is?

Okay.

So, we did not build Dreamcast. We were

originally supposed to build Dreamcast.

But because our algorithm and our

technology was fundamentally flawed, I

went to Japan and I told Irimajiri-san,

the CEO at the time, that that the

contract that they gave us was a like

$12 million

contract, um

we will not be able to to fulfill

because the technology doesn't work.

And I told him the reasons why.

And then I advised that

that uh they choose somebody else to do

it.

Uh but then I asked them

uh I told him that I unfortunately still

need the money. And he he asked me,

"Hey,

you know, the conver- You could just

imagine the conversation. So, what

you're telling me is

what I contracted you to to do, uh you

can't do, uh but you would like all the

money

on the contract." And I said, "You got

it. That's exactly right."

>> [laughter]

>> But obviously I was polite. I was I was

humble.

And he realized that that um

I was honest and and and and uh

everything made sense.

I And if he didn't give us the money,

we'd be out of business.

And I think that uh this happens in this

room. You don't invest in companies, you

invest in people. And what Irimajiri uh

recognized was here's, you know,

somebody and a company that uh he

trusted in the first place the contract

and that he believed in

and um that he would love to see, you

know, uh make it make it to the next

day. And so, that $5 million kept us

alive and, you know, gave me enough time

to discover what to do.

>> And then I guess if they held they sold

it for 15 million I heard.

>> Yeah, they sold it the moment we went

public. Uh when Nvidia went public our

valuation was 300 million dollars.

300 million dollars in 1999. That was

real money.

>> I think it's uh north of a trillion

dollars now or so.

>> It's more than a trillion, yeah.

>> Yeah, that's wild.

So,

you're sort of the core, you know, I we

like to say that you're uh you're the

man who controls the spice.

Um

you know, before that, you know, I don't

think anyone could have really predicted

per se um how important uh GPUs and you

know, the technology you built would be

for this AI revolution.

Um

you know, what did you see to I mean,

was it the accelerator and being in the

right place right time or surely there

were a lot of things that led up to that

that allowed you to sort of capture this

position?

>> Yeah.

Uh

I saw AlexNet just like everybody else

saw AlexNet. And and um

I but remember,

our lens of the world, my view of the

world was always looking for algorithms.

And that algorithm

the algorithm could be NAMDI, the

algorithm could be VASP, the algorithm

could be OpenGL.

You know, it could be SQL, some domain

specific language, some algorithm.

And and so my lens of the world was

always looking for some uh problem that

we might be able to help solve. So, when

AlexNet came along, the algorithm was

deep learning. And so, the question is

what is this algorithm and why does it

matter? Why was it so um effective and

what else can it do?

And and if you were to scale algorithms

and scale it beyond that, uh what could

it solve that otherwise you can't solve

today? And and the the breakthrough for

us was realizing that AlexNet was not

AlexNet. That AlexNet was an approach

with deep deep learning that allows you

to learn any function. And so, 15 years

ago, I was telling everybody that, "Hey,

guess what? We just learned the

universal function approximator."

We just discovered the universal

function approximator. We can give it We

could, you know, give it the the answer

for almost any function and it could

learn what the function is.

And for a lot of functions, you don't

have to be precise. And in fact, it's

impossible to be precise. And so, most

of the interesting problems are

imprecise in this way.

And so,

um the day that we realized we have a

universal function approximator, the

question then is what is that what is

that what is that uh do to the computing

stack? What does that happen to

software? What are the industries that

this could impact? So on and so forth.

Um almost right away,

we started working on computer vision.

Almost right away, we started working on

robotics, um self-driving cars because

I

that fundamental capability, you could

imagine solving some imp- important

problems in the area of computer vision

and robotics. And so, so I think I think

the the big breakthrough was simply that

this is much more foundational than

AlexNet. This is a way of doing

software.

And the implications to the processor,

the middleware, the algorithms, the

applications, you know, what I now

describe as the five-layer cake, um that

entire industrial stack, I imagine

reinventing all all together about 15

years ago. And this is simply about

asking questions, reasoning about things

to first principles, uh asking, you

know, questions like, "If this, then

what?" Uh if if this can get better,

then so what? You know, asking all of

the basic questions about about

something that you observe uh that's

really impactful.

>> I mean, one of the things that really

jumps out at me is to what degree you go

all the way into the weeds. You read

papers, you you know, talk directly to

the principal scientists who are sort of

coming up with these things. Do you have

any advice for people in the audience? I

mean, that's like true founder mode. And

then at the same time, you probably you

have an organization and you have

executives and you have people who say

like, here's the graph, we want to stay

on this graph. You know, sometimes it

ruffles feathers. Like, do you have any

advice for people about an organization

and how you

navigate that really? Like, how do you

build an org that allows you to think in

first principles? Cuz if the Fortune 500

did that, like, the Fortune 500 will

probably look a lot more like Nvidia

than not. And it doesn't. Like, you you

have built a very unique company.

>> My state of mind when I'm my state of

mind is always

starts with curiosity.

I have a whole bunch of questions

myself.

And and

of course

like anybody else, I'll seek the

shortest path to the answer.

But often times

the answers from the people that are

near me might not be satisfying and and

I might have other questions and and

maybe they're they're busy doing

something and they're pursuing

something.

And so my first my first

inclination is to go discover the

answers to my own curiosity.

My second is if I find that the

information is in that the domain of

information or you know, particular

field

could be really important to somebody

and could be important to our company,

then my next inclination is how can I

learn as much as possible so that I

could be of service to the company and

share with the everybody else.

You know, this is no different than than

you when you're you're sharing

knowledge. I mean, I watch your podcasts

and I watch your your videos and I

really enjoy them. You're sharing ideas

with everybody else. You know, in a lot

of ways I think a

a CEO is in service of the company, in

service of all the people that are

working there. And you want to empower

them with some insight.

And so that's really where it's coming

from. It's not so much a management

technique, but a personality technique.

You know, I I want to empower you and

this is something really important that

I just observed. Let me tell you why

it's so important.

Now part of part of having to to be near

the ground and be in the weeds if you

will,

is because often times the technology is

complicated or it's changing really

fast.

And especially when it's changing fast

like like our world, um unless you have

a tactile

sensation of what is actually happening,

it could either to you feel like it's

just moving way too fast to understand.

But if you understand the first

principles of it over time, then

everything kind of makes sense.

You know, it's kind of like surfing I

would imagine. I don't know how to surf,

but I can imagine it's kind of like

surfing. You get out on the wave. To me

it looks like chaos, but to a surfer,

you know, somehow they get right? They

can read the waves and and uh they know

how to stay on top of it. And so I think

being CEO is very similar to that. You

know, you have to learn how to surf and

order to learn how to surf you have to

understand the waves. You have to be

able to read the wind and you have to

have good timing and you can't have any

of that unless you try unless you

actually do it. And so so partly is is

to inform myself, partly is to uh try to

figure out, you know, what is try to

break down the problem so that the

company can learn it in a way that they

can do something about. Uh part of it is

about inspiring other people.

And um

you know, it's it's all those those uh

basic traits of all the people in this

room. You don't have to change your

personality or your behavior

uh when you become CEO. It is possible

for you to continue to be yourself.

And one of the things that I that I I

learned a long time ago

um

and and I I have no idea where I saw

this.

Uh

but

but um

you know, the CEO or the founders

you are the you you're building a car

that you are going to race.

You're going to build an F1 racer, but

you're going to build it in a way that

you can drive.

You should adapt the car to you.

You know, somebody I think had asked me

uh you know, Jenson, if you

if you don't use

conventional management techniques and

organizational techniques

you know, what's going to happen when

you leave the company?

Well, you know, when I die on the job

um

someday

uh

you know, I told them they'll just have

to reshape the company for the next CEO.

And the reason that's wisdom is because

we're the F1 drivers.

You know, we're the racers.

And the world is really competitive and

we've got to stay we've got to you know,

we've got to win.

And we've got to achieve our mission.

And so whatever it takes to fit the car

to you

whatever it takes to fit the

organization to you, that's what you got

to do. And the next CEO, whatever the

personality is, they can figure it out.

>> Amazing. I mean, that it does seem like

um

any change you make to the car will just

slow you down and lose you races that

you know, isn't fit to you.

>> Yeah, or we're constantly tweaking the

car to our needs. And I'm that's really

what I'm doing all the time. I'm

constantly tweaking the company,

constantly reshaping business processes

and the way things work so that I can

you know, be more effective for the

company.

>> True founder mode.

>> Yeah, founder mode. Founder mode could

scale for 34 years.

>> That's right.

>> From zero to 5 trillion.

No evidence. No

>> [applause]

>> I'd love to switch gears to like what

you know, what are the what are the

frontier algorithms that you're most

interested in now? I mean Um, I love

that you're all the way down into the

material science, all the way up into

the app level.

Uh, you know, you're the first to speak

on stage about Open Claw and now Hermes

agent. Um,

I wonder if you can sort of like walk us

through a day in the life of like how

you think about the different stages. I

mean, going from materials to chips to

data centers to even like the app level,

like how people are going to work. Like

there's sort of this idea of a full

stack AI factory.

>> Well,

this is one of the things that that is

probably going to be the most useful

skill in the future. And in fact, just

in listening to you talk about about

technology and and you your use of it,

you know, one of the most important

things is systems understanding.

Systems awareness, system design, system

organization.

Um,

but systems thinking.

And the reason for that is because

most of the low-level things that that

has to be done are going to be done

agentically anyways. They're going to be

automated anyhow. And so, whether it's,

you know, in my generation it's about

compiling chips and synthesizing

transistors and gates and functional

blocks and

and all of that is now synthesized. And

so, most of our designers are systems

designers.

In the case of software,

uh, most software is going to be done

agentically anyhow. So, you have to be

much more

able to think abstractly about systems.

What are the what are the the problems

you're trying to solve? What are the

constraints? Where, you know, where's

where's the input? Where's the output?

You know, where information coming from?

Um, what is the rate of of uh,

information flowing in and out of the

system? Uh, what are the constraints?

Um, you know, and so

is it processor? Is it memory? Is it

networking? Uh,

you know, and so under

these

uh systems problems at a sufficiently

technical level is going to be very

helpful to all of the people in this

room. And I don't think that that that

way of that fundamental knowledge is

ever going to be useless. I think it's

going to be more and more useful. And so

I

I try to understand systems um

I

the best I can. One of the things

One of the things that speaking of

agent,

the fact of the matter is we we kind of

have coarse level

uh recursive self-improvement already.

And the fact that every time you use it,

it improves the markdown files. Uh every

time you use it, it updates its

uh long-term memory. And the long-term

memory is being processed either either

compacted or turned into knowledge

graphs or, you know, so on and so forth.

Uh it's being improved all the time.

Uh

you know, asynchronously. And so

the agent's getting smarter smarter

every time. Still, the problem is and

this is one of the one of the problems

that I think it'd be helpful for

everybody to solve is how can we have

very very specific fine-grained control?

You know, if not for rags, if not for

conditional inputs, if not for our all

of our prompts

um directly into output was was too

coarse.

And so the fact that we can condition,

the fact that we can control the agents

um all the way down to eventually uh

when it comes up with a plan, I change

one word in a plan file, and that one

word makes

a delta difference.

Not complete difference, but specific

difference. Um maybe it's one pixel,

maybe it's one triangle, maybe it's one

component in a CAD file, maybe one

layer, one via, one connection.

And then it regenerates everything else.

I think that that level of control and

that level of collaboration with agents

will be game changing. We don't need the

the agents to be 100% accurate, 100%

high quality in order for us to use it.

It could, you know, literally be 80% and

then we help it the rest of the way, or

it could be 99% we help it the rest of

the way. And so I I think

controllability is probably the single

biggest breakthrough

that we need for agents at every single

level.

>> Do you think people will like I mean,

with Hermes or Open Claw, it feels like

that might actually be somewhat

existential. Like people should control

their own personal AGI. Like they

shouldn't outsource that app and, you

know, have it be just in the cloud and

someone else's agent that like kind of

tells you what to do. Like you kind of

want it to be your own.

>> Yeah. Is that part of the thrust behind

Nvidia being so involved in

>> I think well, first of all, I I need to

understand agents because agents is the

new software. And how is this new

software processed matters a lot

>> to computer architecture.

>> And the the more intimate we are about

um the nature of agents and how it's

different than than um

uh chatbots, which is how different than

than um maybe inference in the very

beginning. However, we think about these

processing layers, the more intimate we

are about the nature of the processing,

the better we can design systems.

We we kind of have to live in the future

5 to 10 years because it takes three or

so years just to build a system, takes a

couple years to ramp it up, and you're

dealing and you would like them to be

able to use the computer for 10 years

after. And so you kind of have to live

in the future for a while. And so

agentic systems for us at the first

principles is just what is the workload,

what's the algorithm, how is it going to

evolve, where are the bottlenecks, you

know, where are the Amdahl's law's

problems, and um

how How it scale, uh what happens to

concurrency? How do you deal with

sandboxes?

How do you deal with MCP? How do you

deal with

you know, working memory, long-term

memory? How do you have all these

autonomous systems, asynchronous systems

working all the time?

And so, what kind of design architecture

makes perfect sense for that? And so, we

have to go and go discover that.

And then, of course, the second thing is

I want to use agents ourselves to make

NVIDIA go faster. And so, we have, you

know, voices in the back

and we've got cloud code autonomously

running in sandboxes all over NVIDIA,

and that's really fantastic.

And some people use code codex, some

people use cloud code, some people use

cursor, some people use cognition. And

and we we let kind of a a thousand

flowers bloom, let people select the

tools they want to use, and then we

learn from from all of that. And so, the

second part is just helping the company

move faster.

Use the tools, and the more they use it,

the more you're going to learn about

how to make it work better in the

future. And then the last part is is

discovering the future of of um

solutions technology for the future. And

maybe you know, when we when we saw

when we saw the early versions of of

chain of thought come out of Stanford,

it was probably a decade ago at this

point, maybe eight years ago.

You know, the question is

is

how how effective is that going to be in

reasoning, and how scalable is going to

be?

And what is the implication, for

example, in computer vision,

if we can reason

from prior knowledge.

And and then the big breakthrough, of

course, is just in in thinking through

that small little domain, you come to

realize that maybe we don't need as much

data for cars to train a self-driving

car.

Which led us to creating Alpaca My which

is the world's first thinking

self-driving car.

And with just a million miles or so, a

couple million miles, it's an incredibly

great self-driving car. And the reason

for that is it's kind of like us, right?

We don't need that many miles before

uh we could drive fairly well most of

our lives. And the reason for that is

because we have prior knowledge from our

language model, and we can decompose um

a situation we've never seen before, uh

and um I and build it up uh out of

things that we understood and know very

well. And so So, that that's an example

of seeing something and then realizing

the impacts on sometime later. Uh when

the agentic systems came along, uh it's

very very clear

that obviously a large language models

uh needs memory, it needs prior

knowledge, it needs tools, it needs ways

to network with other agents. And so,

that kind of, you know, that once you

see some early indicators, uh and you're

able to reason about the future, uh

helps you get a leap, you know, into

into the future.

>> I I I feel like there's this pattern

that I'm starting to see around Nvidia.

It's like you see a problem, there's a

new algorithm, there's some new thing

happening, and then actually you're

right there with open source. I mean, I

remember when OpenCL came out and people

said it was unsafe, but you guys came

out with uh sandboxing sort of uh

toolkit that like surrounds any harness

and makes it safe. And so,

>> When I saw OpenCL, my first thought was

Well, first of all, I I I learned about

it. And then and then um

you know, without without much

imagination, you just realized we just

designed the modern computer. This is

the operating system that's going to

hold a large language model.

And and um

uh in a lot of ways, OpenCL to me was

very Linux moment to me.

>> Yeah.

>> And now everybody can build their own

AI. And I was so excited about that. And

we contacted Peter, and um we said,

"Hey, you know, all of Nvidia's

engineers are your engineers.

That's what I told Peter.

You got this battleship outside your

house. You you

you know, break down the problem as you

desire and we'll contribute as as you

wish.

Same thing with the the the Hermes team.

You know, and I'm so excited about the

work that they're doing.

I do think that the world needs

the ability for everybody to build their

own AI.

And you could you could of course

and I encourage everybody to to use

cloud services as much as possible.

Everybody should use chat GPT and Claude

and right, everybody should use that.

And but if you if you need to build your

own AI because you're a company and and

you need to build your own domain

specific AIs. Now you have Hermes and

you have open Claude, you've got all

kinds of you got LangChain, deep agent,

you got all these different ways, right?

To build your own AI. And it's it's

quite frankly relatively easy because

the software is smart.

You know, and so AI smart and therefore

AI must be so smart you could adapt it

easily. And so I I think that that we

want we want to encourage everybody and

every company to build their own AIs.

And and and who knows what innovation

will come from the fact that it's open

source.

>> I feel like all the alpha is in building

your own AI. I mean, if someone else is

using whatever is off the shelf, but

you're you have a thing that can

recursively self-improve and it is, you

know, I mean, the mech people are very

flippant about market markdown files.

They say like, oh haha, it's just text,

but like text is intelligence.

And we're in a different

>> Words are thoughts.

>> Yeah.

>> Yeah, words are thoughts.

>> Yeah, and it turns out you can

>> Try to try to think without words.

>> Yeah, that's right.

>> [laughter]

>> So switching gears again, I mean, a lot

of people are

anytime you move the cheese, people get

a little worried.

Intelligence is going to be on tap,

which is really awesome. I think it

bodes well for everyone in this room.

Um,

what do you think changes about the

economy? What do you think, you know,

happens in sort of a broader sense?

>> Uh, obviously, what I'm going to say is

uneven. Uh, there are some uh, you know,

we're going to automate tasks.

We're going to automate cognitive tasks.

If that task is uh, somebody makes a

phone call and and sends a bunch of

words,

you know, across the phone to you and

your job is to provide a response. And

and um, if all the information is at

your fingertip because you you have all

the database here and you should be able

to to answer that question completely.

Uh, in that case, that task will be

automated away. Okay? Ignoring that for

a second. Not that Not that you Not that

we we ignore this, but my my point is

I'm going to answer the question about

about really the great opportunity. And

so, um, many tasks will be automated

away. Um, many jobs Every single job

will be will change and there'll be a

whole bunch of new jobs and that that

that I think we know. Um, the bottom

line is this.

The evidence would show that and it

makes perfect sense that AI and

automation is creating jobs everywhere.

The narrative about AI destroying jobs

is exactly backwards. AI eliminate

tasks.

AI automates tasks away.

But it doesn't necessary

doesn't necessarily eliminate jobs. And

the reason for that is because the the

job of a person has a purpose and that

purpose has many tasks. Some of those

tasks could be automated away. Many of

those tasks cannot be.

And so, the evidence suggests that here

we are, we've automated coding, which is

a task,

but the job of a software engineer

appears to be growing, right? The number

of software engineer jobs year over year

has increased 10%.

The task of reading radiology scans

has been automated, but the number of

radiology jobs has increased some 20% in

the last several years,

even though AI's taken over the whole

field. And the reason for that

is because the backlog of patients

is incredibly high. Now doctors and

hospitals could admit a lot more

patients. In order to admit a lot more

patients, you need more nurses, more

radiologists.

And so, the same thing with software. We

hit the backlog of ideas, the backlog of

ambition and aspiration

is so high that if we can automate away

the task of programming, we could hire

more software engineers to do more

things. We could be more ambitious.

Same thing all you know, just across the

board.

Uh they said Harvey is going to

eliminate all of the paralegal jobs, and

the number of lawyers will be

reduced. Turns out paralegals are

growing like crazy. And the reason for

that is because the backlog of lawsuits

is really high, and now these law firms

could get a lot more cases through.

And in order to do so, you got to hire

more people. And so, this is a classic

classic example of productivity

increasing growth. Increasing growth

drives more employment. This is the

reason why there's more employment today

than there was when I first came out of

school.

>> So, we've been talking a lot about

software and agents. Um

another really exciting thing that

Nvidia is all the way out on the edge on

is actually physical robots. Um

you know, how far out? I think in the

past you might have even said um

this as soon as this year. What's the

latest thinking on, you know, when can

we expect practical robotics?

>> Yeah, the moment that I saw as

generating video,

that was

that was a great moment for me. The

moment that I and I start I saw as

generating video, I mean, we did the

original work on

um auto

uh progressive GANs, okay? And we did

the original work on uh conditional

GANs. Um long before the first videos

were generated outside that people saw,

um a couple of years earlier inside our

labs, we were driving a a uh simulator

completely generated by video. And

computer completely generated by neural

networks. And so, the moment I saw

us generating articulation,

if I can generate video of a finger

moving, if I could generate video of a

hand picking up a glass, why can't I

cause a robot to do the same?

And so, the moment I saw that generative

AI happening, I realized that robotics

articulation was around the corner.

And so, now the question is, you know,

how's the robot going to understand

uh

uh

to generate motions that obey the laws

of physics? How's it How does it

understand causality? Um how does it

understand, you know, friction, tension?

How does it understand the laws of

physics? And so, it started us down the

journey of creating what we call

physical AI now. And everybody calls it

physical AI. And physical AI, uh we

started working on world foundation

model,

um an AI that understands the laws of

physics and the how the world works. And

um uh we started down the the journey of

of uh working on robotics. I would say

the chat GPT moment of robots happened a

couple of years ago already.

>> Wow.

>> And and the reason for that is remember

when chat GPT first came out,

it didn't do anything productive.

It didn't do anything useful, but it

opened our imagination about what's

possible. And I would say a couple of

years ago, you know, robots walking

around that we could do reinforcement

learning, fine-tune it for and ground it

in physics, uh really happened a couple

of years ago.

So, now what what do we need to do? We

need to do all the same things that

we're doing now for agentic systems.

We have to create environments for them

to learn in, to eval in, eval against.

And so, we have to do real to sim to

create environments.

Uh we have to do uh

uh we have to generate simulators that

are based on simulation, grounded

physics simulation, as well as

generative uh physics simulations. And

so, uh Isaac Sim, uh Cosmos, and all the

work that we do in that area is related

to simulation. And then the last part is

sim to real. And so, uh that part is has

something to do with reinforcement

learning, um uh grounding it on physics,

uh grounding it on grounding it on all

on um all the electromechanical

uh systems that that robots require. And

so, but these three basic system, I I

think builds up uh the eval, if you

will, the the the the post-training of

um of robotics. And I I think we're

we're going to see it right around the

corner.

>> Amazing. Where does physical AI show up

first in a way that's really

economically real? Are you seeing that

already?

>> We conjectured that uh

that robotics was going to come along

and decided that the first application

of robotics that has both a large enough

market,

um relatively standardized

technology so that we could scale and

get the flywheel going,

um and has real economic value was uh

self-driving cars. And so, uh inside

Waymo, uh our chips from Nvidia. Uh at

at Tesla, we were in the car. Uh now

we're in the data center. Um uh

Mercedes, we're in the data center,

we're in the car with a software stack.

Uh we uh uh worked on Alpaca Myo, and we

open-sourced it. And the reason why we

open-sourced the self-driving car stack

is because you need it for agriculture,

you need it for mail delivery, you need

it for warehouse AMRs. There's so many

different ways that you could apply um,

uh, autonomous

navigation

uh, and none of those markets are big

enough to be a self-driving car market

and we thought it was

sufficiently diverse that we would

create the whole stack for it. And so

we're working with autonomous vehicles

in all kinds of different places.

Our robotics business, autonomous

vehicle business, basically physical AI

business is probably almost like $10

billion. So it's really, really big

already.

Um, likely this will be one of the

largest industries in the world and um,

uh, it'll take longer than a couple two,

three years. It'll take less than 10.

And so this will this will be our next

$100 billion business.

>> Amazing.

Um, I want to take a moment. Uh, I think

this is the exact right crowd to uh,

you know, maybe as a arena we can

welcome Jensen to X.

Welcome to X. I mean, you made your

first post uh, and thank you for your

leadership.

>> [applause]

>> You know, that's that just that shows

you how introverted I am.

It took me until 2026 to have the first

post on X.

You know, it's I'm probably the last

human on Earth that that did it.

Uh, but but uh, what I posted was too

important to me and too important to the

to the industry and too important to the

world. And so so uh, I I over overcame

my um, my shyness and and put my first

thing out on X.

>> No, thank you for your leadership. I

mean, open source, open weights, open

source models are incredibly important

for

I mean, what all of us in this room want

to do. Like we want to create products.

>> If not for open source, the mobile cloud

industry would have never happened.

If not for open, if not for Linux, if

not for Kubernetes, if not for all of

these, you know, platform, if not for

uh, TensorFlow or more important, uh,

PyTorch,

right? The and the early versions of a

cafe, right? Torch. I mean, all of the

Theano. Remember the early versions of

all Those were all open source. If not

for all of that, how would we have

modern AI?

>> Well, thank you for your leadership and

your voice is incredibly important here.

Thank you.

>> [applause]

[applause]

>> Before we go, I feel like we I just

really resonate with your story. I think

that everyone here, but I mean, would

love the wisdom of,

you know, your journey coming here. I

mean, what should a young person learn

now, given all the things that you're

seeing, all the algorithms that are

going to take hold in society? Um

what should a young person learn now

that will still matter, based on what

you're seeing?

>> Well, some of the things that I saw

today

and some of the starters I met today was

really really quite quite encouraging

and

and and the thing that that um

the big takeaway is, of course,

the simple stuff is going to get

automated away.

And when I say simple stuff, I mean,

software, you know, coding.

Uh

the idea that you would you would do a

you would solve a problem by sitting in

front of a computer and you're you're

actually writing, you know, writing

code, that concept is obviously going to

get automated away.

Um you know,

in my generation, when I was when I was

growing up, we had to do long division.

I mean, for God's sakes, who has to

learn long division, you know? And so,

that got coded away, that got automated

away. And so, I think the simple stuff

is going to get automated away, but the

hard problems, the hard sciences, um

physics, chemistry, biology, uh you

know, computer science, uh computer

engineering, systems thinking,

uh you know, all and and particularly

the domains that are intersecting,

uh those hard problems will never go

away. And so, AI is just an incredible

tool that helps us become even more

ambitious.

Even more um impatient about solving

these extraordinarily large and

incredibly hard problems

uh than before. And so, you know, if you

if you look at my generation,

when I first graduated,

a chip designer would design a chip with

maybe a thousand transistors, and that

would be a very large chip.

You know, now

designing a trillion transistor chips is

not even, you know, if somebody would

have told me, "Jensen, our next chip is

a trillion transistor." I said, "Okay."

You know, it's not a thing.

And the reason for that is because we

are so ambitious now,

the

the

the scale of the problem, the scale of

the task is no longer a matter.

And so, you don't have to worry about

about, you know,

how much coding, how many engineers. You

don't have to You don't have to think

about those things anymore. You just

have to think about what is the what is

the problem you have to solve. And so, I

think that the deep deep tech stuff, the

deep science stuff, uh understanding

understanding the intersection between

technology and social issues, um

understanding market market gaps and and

holes, uh opportunities, I think all of

that still exists.

Um and and the better you are at systems

thinking so that you could orchestrate

millions of agents solving problems

autonomously, the better off you are.

And so, that's why system thinking is

going to be so important. But uh

otherwise, I think the world's going to

continue to have a lot of great

challenges for us to solve. Go to school

the same old way.

You know, stay in school.

>> Stay in school.

>> [applause]

>> I guess um I usually like to end with um

you're looking out on the crowd. There

are a lot of people who

uh I mean,

I started this uh the opener with like I

honestly look in the crowd and I see

people who are not different than us per

se, You know, we actually just

are technical and like love systems.

How you know

>> Thank you. Thank you.

>> What advice would you give to this room

of, you know,

And you you see yourself in this in this

room and like I'm curious what you would

say. If you could send a

telegram, a message to the 18 to

22-year-old version of yourself, what

would that be?

>> I could tell you exactly how I felt when

I first when Nvidia founded and and the

three of us started. Um

The the thing I felt at the time is

there was so much for me to know and so

much for me to learn.

And I didn't know it. And I was telling

you earlier there at the time there was

there were no YouTube, there's you know,

no YC, nobody's teaching you how to

start a company. And so I went to the

bookstore and I bought a book and the

book said, "How to start a company?"

Uh unfortunately, the book was like 500

pages long.

And and so I you know, I figured by the

time I read it, you know, I'd be out of

business. And Lor- Lori and I be out of

money. And so there's no sense reading

it. Um but the thing that the thing I

remember very very vividly is that how

scared I was uh to go raise money

because I felt that I was about to talk

to a bunch of people and I didn't know

how to answer their questions. And um

and it's true. And I barely know how to

answer their questions even today. Uh

but the thing that I learned is

um none of that stuff matters.

As it turns out.

And and you're always going to have

things that you don't know.

And every single day the world's

changing, technology changing.

Obviously, this is the greatest time in

the last 60 years to start a company.

The whole industry has changed. It's a

complete reset from a technology

perspective. The single most important

technology in human history, the

computer, has been completely reset. And

so, this is absolutely the single

greatest time to start a company. And

I'm I'm I'm jealous of all of you.

I and and

and the opportunities you have ahead. I

mean, it's going to be incredible. So,

it's the perfect time on the one hand.

On the other hand, the technology is

changing so fast.

And so, the question is, what's the

right feeling for you? And eventually,

and I told you the story of us of me

buying the other book, the textbook.

I think

the psychology and the feeling that I

have today

on all of the new experiences and the

new technology and new markets and new

dynamics,

I look at it and I say, this is

important. I've got to go learn it.

And I've got to go do something about

it. And I better get to it as fast as I

can.

And how hard can it be?

I always had this feeling, how hard can

it be?

And

truth be told,

it is way harder than you think.

And but you you don't want your mind to

be to be there. You want your mind to

be, how hard can it be?

And let the suffering come to you

a little bit at a time.

You know, don't

don't imagine how hard it's going to be

and let all of that turn into anxiety

and not doing something about it.

You want to imagine your head, how hard

can it be? You know, I've got a whole

bunch of I've got a bunch of AI agents

helping me anyways.

And so, how hard can it be? And then you

get going on working on it. And so,

that's probably the the attitude of an

entrepreneur. You you know you have to

learn a bunch of stuff along the way.

You believe in your ability to learn.

Which is, you know, learning is the

single greatest superpower. And if you

go into it with the attitude, how hard

can it be? If anybody can do it, I can

do it. And just realize that it will be

hard and you just have to have the

resilience to overcome it every single

day. You don't have to overcome life in

one day. You just have to overcome that

morning. That morning, you know, you

have to overcome today today. And so

it's not a big deal. Just get through

today. Wait till right? Work towards

tomorrow. Keep following your dreams.

And the rest of everything if you stick

if you stick with it long enough,

uh you know, Nvidia happens.

And so, you know, I think that the

wisdom

that I can

if it there's anything is resilience is

probably the single most important

thing.

And if you believe in something, just

get going on it and get your mind

you know, out of out of keeping your

yourself from pursuing it

because of you know, fear or anxiety or

lack of confidence or whatever it is.

And then you're just going to tell

yourself I'm going to learn my way

there.

>> Jensen Huang everybody.

>> All right, guys. Thank you.

>> Thank you so much. Yes, it was

>> Thank you guys.