Jensen Huang, Founder and CEO of NVIDIA

[MUSIC]

Jensen, this is such an honor,

thank you for being here.

>> I'm delighted to be

here, thank you.

>> In honor of your

return to Stanford,

I decided we'd start talking about

the time when you first left.

You joined LSI Logic, and that was

one of the most exciting companies

at the time, you were building

a phenomenal reputation with some

of the biggest names in tech.

And yet you decided to leave to

become a founder,

what motivated you?

>> Chris and Curtis.

Chris and Curtis,

I was an engineer at LSI Logic and

Chris and Curtis were at Sun.

And I was working with some of

the brightest minds in computer

science at the time, of all time,

including Andy Bechtolsheim and

others, building workstations, and

graphics workstations, and so

on and so forth.

And Chris and Curtis said one day

that they'd like to leave Sun, and

they've like me to go figure out

what they're going to go leave for.

And I had a great job, but they

they insisted that I figure out

with them how to build a company.

And so we hung out at Denny's

whenever they dropped by, which is,

by the way, my alma mater,

my first company.

My first job before CEO was

a dishwasher, and

I did that very well.

>> [LAUGH] >> [LAUGH] >> And so

anyways, we got together.

And it was during

the microprocessor revolution,

this is 1993 and

1992 when we were getting together.

The PC revolution was just getting

going, Windows 95, obviously

which is the revolutionary version

of Windows, didn't even come to

the market yet, and

Pentium wasn't even announced yet.

And this is all right before the PC

revolution, and it was pretty clear

that the microprocessor was going

to be very important.

And we thought why don't we build

a company to go solve problems that

a normal computer that is powered

by general purpose computing can't?

And so that became the company's

mission, to go build a computer,

the type of computers and

solve problems that normal

computers can't, and

to this day we're focused on that.

And if you look at all the problems

in the markets that we opened up as

a result,

it's things like computational drug

design, weather simulation,

materials design.

These are all things that we're

really, really proud of.

Robotics, self-driving cars,

autonomous software we call

artificial intelligence.

And then of course we drove

the technology so hard that

eventually the computational cost

went to approximately zero.

And it enabled a whole new way of

developing software,

where the computer wrote

the software itself, artificial

intelligence as we know it today.

And so that was the journey.

>> Yeah.

>> Thank you all for coming.

>> [LAUGH] >> [LAUGH] >> Well these

applications are on all of our

minds today.

But back then the CEO of LSI Logic

convinced his biggest investor, Don

Valentine, to meet with you, he's

obviously the founder of Sequoia.

Now I can see a lot of

founders here edging forward in

anticipation.

But how did you convince the most

sought-after investor in

Silicon Valley to invest in a team

of first-time founders building

a new product for

a market that doesn't even exist?

>> I didn't know how

to write a business plan, and so

I went to a bookstore, and

back then there were bookstores.

And in the business book section,

there was this book and

it was written by somebody I knew,

Gordon Bell.

And this book,

I should go find it again,

but it's a very large book.

And the book says how to write

a business plan, and that was

a highly specific title for

a very niche market, and

it seems like he wrote it for like

14 people and I was one of them.

And so I bought the book, I should

have known right away that that was

a bad idea because Gordon is super,

super smart.

And super smart people have a lot

to say, and I'm pretty sure Gordon

wants to teach me how to write

a business plan completely.

And so I picked up this book,

it's like 450 pages long,

well I never got through it,

not even close.

I flipped through it a few

pages then I go you know what,

by the time I'm done reading this

thing I'll be out of business,

I'll be out of money.

And Lori and I only had about six

months in the bank.

We had already Spencer, and

Madison, and a dog, and so

the five of us had to live off of

whatever money we had in the bank.

And so I didn't have much time,

and so instead of writing

the business plan I just went to

talk to Wilfred Corrigan.

And he called me one day and

said hey, you left the company, you

didn't even tell me what you were

doing, I want you to come back and

explain it to me.

And so I went back and

I explained it to Wilf.

And Wilf at the end of it, he said

I have no idea what you said, and

that's one of the worst elevator

pitches I've ever heard.

>> [LAUGH] >> And

then he picked up the phone, and

he called Don Valentine.

And he called Don, and he says Don,

I'm going to send a kid over,

I want you to give him money, he's

one of the best employees LSI Logic

ever had.

And so the thing I learned is you

can make up a great interview,

you can even have a bad interview.

But you can't run away from

your past, and so have a good past,

try to have a good past.

And in a lot of ways I was serious

when I said I was a good

dishwasher, I was probably Denny's'

best dishwasher.

>> [LAUGH] >> I planned my work, I

was organized, I was mise en place.

And then I washed the living

daylights out of the dishes.

And then they promoted me to

busboy.

I was certain I'm the best

busboy Denny's ever had, I never

left the station empty handed,

I never came back empty handed,

I was very efficient.

And so anyways eventually I

became a CEO, I'm still

working on being a good CEO.

>> You talk about being the best,

you needed to be the best among

89 other companies that were funded

after you to build the same thing.

And then with six

to nine months of runway left,

you realized that the initial

vision was just not going to work.

How did you decide what to do next

to save the company when the cards

were so stacked against you?

>> Well we started this company for

accelerated computing, and

the question is what is it for,

what's the killer app?

And that came our first great

decision and

this is what Sequoia funded.

The first great

decision was the first killer

app was going to be 3D graphics.

And the technology was going to be

3D graphics, and the application

was going to be video games.

At the time 3D graphics was

impossible to make cheap,

it was million dollar image

generators from Silicon Graphics.

And so it was a million dollars And

it's hard to make cheap.

And the video game market was $0

billion.

So you had this incredible

technology that's hard to

commoditize and commercialize,

and then you have this market that

doesn't exist.

That intersection was

the founding of our company.

And I still remember when,

at the end of my presentation,

Don was still kind of, one of

the things he said to me, which

made a lot of sense back then,

it makes a lot of sense today,

he says, startups don't invest in

startups, or startups don't partner

with startups.

And his point Is that in order for

NVIDIA to succeed,

we needed another startup to

succeed, and that other startup was

Electronic Arts.

And then on the way out,

he reminded me that Electronic

Arts' CTO is 14 years old and had

to be driven to work by his mom.

>> [LAUGH] >> And

he just wanted to remind me that

that's who I'm relying on.

>> [LAUGH] >> [LAUGH] And

then after that he said if you lose

my money, I'll kill you.

And that was- >> [LAUGH]

>> That was kind of my memories of

that first meeting.

But nonetheless,

we created something,

we went on the next several years

to go create the market,

create the gaining market for PCs.

And it took a long time to do so,

we're still doing it today.

We realized that not only do you

have to create the technology and

invent a new way of doing computer

graphics so that what was $1

million is now $300, $400,

$500 that fits in a computer.

And you have to

go create this new market.

So we had to create technology,

create markets.

The idea that a company would

create technology, create markets,

defines NVIDIA today.

Almost everything we do,

we create technology,

we create markets.

That's the reason why people say,

people call it a stack,

an ecosystem, words like that, but

that's basically it.

At the core, for 30 years,

what Nvidia realized we had to do

is in order to create

the conditions by which somebody

could buy our products, we had

to go invent this new market.

And it's the reason why we're early

in autonomous driving.

It was the reason why we're early

in deep learning.

It was a reason why we're early in

just about all these things,

including computational

drug design and discovery,

all these different areas we're

trying to create the market while

we're creating the technology.

And then we got going, and then

Microsoft introduced a standard

called Direct3D, and that spawned

off hundreds of companies.

And we found ourselves,

a couple years later, competing

with just about everybody.

And the thing that we invented,

the company, the technology we

invented 3D graphics with,

consumerized 3D with, turns out to

be incompatible with Direct3D.

So we started this company,

we had this 3D graphics thing,

million dollar thing, we're trying

to make it consumerized, and so

we invented all this technology.

And then shortly after it became

incompatible.

And so we had to reset the company,

or go out of business.

But we didn't know how to build

it the way that Microsoft had

defined it.

And I remember a meeting on

a weekend, and the conversation

was, we now have 89 competitors.

I understand that the way we do

it is not right, but we don't know

how to do it the right way.

And thankfully,

there was another bookstore.

>> [LAUGH] >> And the bookstore

is called Fry's Electronics,

I don't know if it's still here.

And so I think I drove Madison, my

daughter, on the weekend to Fry's,

and it was sitting right there,

the OpenGL manual, which would

defined how Silicon Graphics did

computer graphics.

And so it was it was right there,

it was like $68 a book.

And so I had a couple hundred

dollars, I bought three books,

I took it back to the office and

I said, guys, I found it,

our future, and I handed out,

I had three versions of it.

Handed it out,

had a big nice centerfold.

The centerfold is the OpenGL

pipeline which

is the computer graphics pipeline.

And I handed it to the same

geniuses that I founded the company

with.

And we implemented the OpenGL

pipeline like nobody had ever

implemented the OpenGL pipeline,

and we built something the world

never seen.

And so

a lot of lessons are right there.

That moment in time for our company

gave us so much confidence.

And the reason for that is you can

succeed in doing something,

inventing a future, even if you

were not informed about it at all.

And it's kind of my attitude about

everything now.

When somebody tells me about

something and I've never heard of

it before, or if I've heard of it,

don't understand how it works at

all, my first thought is always,

how hard can it be?

And it's probably just a textbook

away.

You're probably one archive paper

away from figuring this out.

And so

I spent a lot of time reading

archive papers, and it's true.

Now, of course, you can't learn how

somebody else does something and

do it exactly the same way and

hope to have a different outcome.

But you could learn how something

can be done, and then go back to

first principles and ask yourself,

given the conditions today,

given my motivation,

given the instruments, the tools,

given how things have changed,

how would I redo this?

How would I

reinvent this whole thing?

How would I build a car today?

Would I build it incrementally from

1950s and 1900s?

How would I build a computer today?

How would I write software today?

Does that make sense?

And so I go back to first

principles all the time,

even in the company today and

just reset ourselves,

because the world has changed.

And the way we wrote software in

the past was monolithic and

it's designed for supercomputers,

but now it's disaggregated, so

on and so forth.

And how we think about

software today,

how we think about computers today,

just always cause your company,

always cause yourself to go back to

first principles, and it creates

lots and lots of opportunities.

>> Yeah, the way you applied this

technology turns to

be revolutionary.

You get all the momentum that you

need to IPO and then some more,

because you grow your revenue nine

times in the next four years.

But in the middle of all of

this success,

you decide to pivot a little bit

the focus of innovation happening

at NVIDIA based on a phone call you

have with this chemistry professor.

Can you tell us about that phone

call and

how you connected the dots from

what you heard to where you went?

>> I remember at the core of

the company was pioneering a new

way of doing computing.

Computer graphics

was the first application.

But we always knew that there would

be other applications.

And so image processing came,

particle physics came, fluids came,

so on and so forth.

All kinds of interesting things

that we wanted to do.

We made the processor more

programmable so that

we could express more algorithms,

if you will.

And then one day,

we invented programmable shaders,

which made all forms of imaging and

computer graphics programmable,

that was a great breakthrough, so

we invented that.

On On top of that we tried to

look for ways to express more

sophisticated algorithms that could

be computed on our processor which

is very different than a CPU.

And so we created this thing called

CG, and so I think it was 2003 or

so, C for GPUs.

It predated CUDA by about three

years.

The same person who wrote the

textbook that saved the company,

Mark Hilgard, wrote that textbook.

And so CG was super cool,

we wrote textbooks about it.

We started teaching people how to

use it, we developed Tools and

such, and then several

researchers discovered it.

Many of the researchers here,

students here at Stanford,

was using it.

Many of the engineers that then

became engineers at NVIDIA were

playing with it.

A doctor, a couple of doctors at

Mass General picked it up and

used it for CT reconstruction.

So I flew out and saw them and

said, what are you guys doing with

this thing?

And they told me about that.

And then a computational

quantum chemist used it to

express his algorithms.

And so I realized that there's some

evidence that people might want to

use this.

And it gave it gave us gave us

incrementally more confidence

that we ought to go do this.

That this field, this form of

computing could solve problems that

normal computers really can't and

reinforced our belief and

kept us going.

>> Every time you heard something

new, you really savored that

surprise and

that seems to be a theme throughout

your leadership at NVIDIA.

It feels like you make these bets

so far in advance of technology

inflections that when the apple

finally falls from the tree, you're

standing right there in your black

leather jacket waiting to catch it.

>> [LAUGH] >> How do you find

the connection?

>> It always seems like

a diving catch.

>> [LAUGH] >> Always does seem

like a diving catch.

You do things based on core

beliefs.

We deeply believe that we

could create a computer that

solves problems, normal processing

can't do and that there

are limits to what a CPU can do.

There are limits to what general

purpose computing can do, and

then there are interesting problems

that we can go solve.

The question,

the question is always, are those

interesting problems only, or can

they also be interesting markets?

Because if they're not interesting

markets, it's not sustainable.

And NVIDIA went through about

a decade where we were

investing in this future.

And the markets didn't exist.

There was only one market

at the time, was computer graphics.

For 10,

15 years the markets that fuels

NVIDIA today just didn't exist.

And so how do you continue with all

of the people around you.

Our company,

NVIDIA's management team and

all of the amazing engineers.

They're creating this

future with me.

All of your shareholders,

your board of directors,

all your partners, you're

taking everybody with you and

there's no evidence of a market.

That is really, really challenging.

The fact that the technology can

solve problems and the fact that

you have research papers that

are used, that are made possible

because of it are interesting, but

you're always looking for

that market.

But nonetheless,

before a market exists,

you still need early indicators of

future success.

We have this phrase in the company,

there's a phrase called

key performance indicators.

Unfortunately, KPIs are hard to

understand.

I find KPIs hard to understand.

What's a good KPI?

A lot of people, when we look for

KPIs, you go gross margins.

That's not a KPI, that's a result.

You're looking for something that's

an early indicators of future

positive result, okay,

and as early as possible.

And the reason for

that is because you want that early

sign that you're going in the right

direction.

And so we have this phrase that's

called E-O-I-F-S,

early indicators, E-I-O-F-S,

early indicators of future success.

And it helps people because I was

using it all the time to give

the company hope that hey, look,

we solve this problem.

We solve that problem,

we solve this problem.

The markets didn't exist, but

there were important problems and

that's what the company is

about to solve these problems.

We want to be sustainable.

And, therefore, the markets have to

exist at some point.

But you want to decouple the result

from evidence that you're doing

the right thing, okay?

And so that's how you kind of solve

this problem of investing into

something that's very, very far

away and having the conviction to

stay on the road is to find as

early as possible the indicators

that you're doing the right things.

And so start with a core belief.

Unless something changes your mind,

you continue to believe in it and

look for

early indicators of future success.

>> What are some of those early

indicators that have been used by

Product Teams at NVIDIA?

>> All kinds.

I saw a paper,

long before I saw the paper I met

some people that needed my help on

this thing called deep learning.

At that time I didn't even know

what deep learning was.

And they needed us to create

a domain specific language so that

all of their algorithms could be

expressed easily on our processors.

And we created this thing called

cuDNN, and it's essentially the SQL

in storage computing.

This is neural network computing.

And we created a language, if you

will, domain-specific language for

them, kind of like the OpenGL of

deep learning.

And so they needed us to do that so

that they could express

their mathematics.

And they didn't understand CUDA,

but they understood their

deep learning.

And so we created this thing in

the middle for them.

And the reason why we

did it was because even

though there were zero,

these researchers had no money, and

this is kind of one of the great

skills of our company, that

you're willing to do something,

even though the financial returns

are completely non-existent or

may be very, very far out even if

you believed in it.

We ask ourselves,

is this worthy work to do?

Does this advance a field of

science somewhere that matters?

Notice, this is something that I've

been talking about since

the very beginning of time.

We find inspiration not from

the size of a market, but

from the importance of the work.

Because the importance of

the work is the early

indicators of a future market.

And nobody has to do a business

case on it.

Nobody has to show me a P&L,

nobody has to show me a financial

forecast.

The only question is,

is this important work?

And if we didn't do it,

would it happen without us?

Now, if we didn't do something, and

something could happen without us,

it gives me tremendous joy,

actually.

And the reason for that is,

could you imagine,

the world got better.

You didn't have to lift a finger.

That's the definition of Ultimate

laziness, and in a lot of ways,

you want that habit, and

the reason for that is this.

You want the company to be lazy

about doing things that other

people always do, can do.

If somebody else can do it,

let them do it.

We should go select the things that

if we didn't do it,

the world would fall apart.

You have to convince yourself

of that.

That if I don't do this,

it won't get done.

And if that work is hard and

that work is impactful and

important, then it gives you

a sense of purpose.

Does it make sense?

And so our company has been

selecting these projects.

Deep learning was just one of them,

and the first indicator of

the success of that was this fuzzy

cat that Andrew Ng came up with.

And then Alex Kruszewski detected

cats, not all the time, but

successfully enough that it was,

this might take us somewhere.

And we reasoned about the structure

of deep learning, and

we're computer scientists and

we understand how things work, and

so we convinced ourselves this

could change everything.

And anyhow, but that's an example.

>> So these selections that you've

made, they've paid huge dividends,

both literally and figuratively.

But you've had to steer the company

through some very challenging

times, like when it lost 80% of its

market cap amid the financial

crisis because what Wall Street

didn't believe in your bet on ML.

In times like these,

how do you steer the company and

keep the employees motivated at

the task at hand?

>> My reaction during that time is

the same reaction I had about

this week.

Earlier today,

you asked me about this week.

My pulse was exactly the same.

This week is no different than last

week or the week before that.

And so the opposite of that, when

you drop 80%, don't get me wrong,

when your share price drops 80%,

it's a little embarrassing, okay?

And you just want to wear a t-shirt

that says, it wasn't my fault.

>> [LAUGH] >> But

even more than that, you just don't

want to get out of your bed,

you don't want to leave the house,

all of that is true,

all of that is true.

But then you go back to just doing

your job, woke up at the same time,

prioritize my day in the same way.

I go back to what do I believe,

you gotta always gut-check back to

the core.

What do you believe?

What are the most important things?

And just check them off.

Sometimes it's helpful,

the family loves me, okay, check,

double-check right?

And so you just gotta check it off

and you go back to your core, and

then go back to work, and

then every conversations go back to

the core.

Keep the company focused

back on the core.

Do you believe in it,

did something change?

The stock price changed, but

did something else change?

Did physics change,

did gravity change?

Did all of the things that we

assumed that we believed that led

to our decision,

did any of those things change?

Because if those things change,

you've gotta change everything.

But if none of those things change,

you change nothing, keep on going,

that`s how you do it.

>> In speaking with your employees,

they say that you- >> I try to

avoid the public.

>> [LAUGH] In speaking with

your employees,

they've said that your leadership-

>> Including the employees.

>> [LAUGH] >> I'm just kidding.

>> [LAUGH] No, the lead leaders

have to be seen, unfortunately,

that's the hard part.

I was an electrical engineering

student and I was quite young when

I went to school.

When I went to college,

I was still 16 years old and so

I was young when I did everything.

And so I was a bit of an introvert,

I'm shy,

I don't enjoy public speaking.

I'm delighted to be here, I'm not

suggesting- >> [LAUGH] >> But it's

not something that I do naturally.

And so when things are challenging,

it's not easy to be in front of

precisely the people that you

care most about.

And the reason for

that is because could you

imagine a company meeting,

our stock prices dropped by 80%?

And the most important thing I have

to do as the CEO is this,

to come and face you, explain it.

And partly, you're not sure why,

partly, you're not sure how long,

how bad,

you just don't know these things.

But you still gotta explain it,

face all these people,

and you know what they're thinking.

Some of them are probably thinking

we're doomed.

Some people are probably thinking

you're an idiot, and

some people are probably thinking

something else.

And so, there are a lot of

things that people are thinking and

you know that they're thinking

those things, but you still have to

get in front of them and

do the hard work.

>> You may be thinking of those

things, but yet, not a single

person of your leadership team left

during times like this.

And in fact- >> They're

unemployable.

>> [LAUGH] >> That's what I keep

reminding them.

>> [LAUGH] >> I'm just kidding,

I'm surrounded by geniuses.

I'm surrounded by geniuses, yeah,

other geniuses, unbelievable.

Nvidia is well known to have

singularly the best management team

on the planet.

This is the deepest technology

management team the world's

ever seen.

I'm surrounded by a whole bunch

of them, and they're just geniuses.

Business teams, marketing teams,

sales teams, just incredible.

Engineering teams, research teams,

unbelievable.

>> Your employees say that your

leadership style is very engaged,

you have 50 direct reports.

You encourage people across all

parts of the organization to send

you the top five things on their

mind, and you constantly remind

people that no task is beneath you.

Can you tell us why you

purposefully designed such a flat

organization, and how should we be

thinking about our organizations

that we design in the future?

>> To me, no task is beneath me

because, remember, I used to be

a dishwasher, and I mean that, and

I used to clean toilets.

I mean, I cleaned a lot of toilets,

I've have cleaned more toilets than

all of you combined.

>> [LAUGH] >> And some of them,

you just can't unsee.

>> [LAUGH]

>> I don't know what to tell you,

that's life, and so you can't show

me a task that's beneath me.

Now, I'm not doing it only because

of whether it's beneath me or

not beneath me.

If you send me something and

you want my input on it and

I can be of service to you, and

in my review of it, share with you

how I reasoned through it,

I've made a contribution to you.

I've made it possible for you to

see how I reason through something,

and by reasoning, as you know,

how someone reasons

through something empowers you.

You go, my gosh, that's how you

reason through something like this.

It's not as complicated

as it seems.

This is how you reason through

something that's super ambiguous.

This is how you reason through

something that's incalculable.

This is how you reason through

something that seems to be very

scary, this is how you see,

do you understand?

And so, I show people how to reason

through things all the time,

strategy things,

how to Forecast something,

how to break a problem down.

And you're empowering people all

over the place.

And so that's how I see it.

If you send me something you want

me to help review it,

I'll do my best.

And I'll show you how I would

do it.

In the process of doing that,

of course I learned a lot from you.

Is that right?

You gave me a seed of a lot of

information, I learned a lot.

And so

I feel rewarded by the process.

It does take a lot of energy

sometimes because in order to add

value to somebody and they're

incredibly smart as a starting

point, and I'm surrounded by

incredibly smart people, you have

to at least get to their plane.

You have to get into their

headspace.

And that's really hard.

That's really hard.

And that takes just an enormous

amount of emotional and

intellectual energy.

And so I feel exhausted after I

work on things like that.

I'm surrounded by a lot of

great people.

A CEO should have the most

direct reports by definition

because the people that

report to the CEO requires

the least amount of management.

It makes no sense to me that

CEOs have so

few people reporting to them.

Except for

one fact that I know to be true.

The knowledge, the information of

a CEO is supposedly so valuable,

so secretive.

You can only share it

with two other people, or three.

And their information is so

invaluable so

incredibly secretive, that they can

only share it with a couple more.

Well, I don't believe in

a culture, an environment, where

the information that you possess

is the reason why you have power.

I would like us all to contribute

to the company and our position in

the company should have something

to do with our ability to reason

through complicated things, lead

other people to achieve greatness,

inspire, empower other people,

support other people.

Those are the reasons why

the management team exists in

service of all of the other people

that work in the company,

to create the conditions by which

all of these amazing people,

volunteer to come work for you

instead of all of the amazing high

tech companies around the world.

They elected, they volunteered to

work for you, and so

you should create the conditions

by which they could do their

life's work, which is my mission.

You've probably heard it,

I've said that pretty clearly and

I believe that.

What my job is is very simply to

create the conditions by which you

could do your life's work.

And so how do I do that?

What does that condition look like?

What that condition should result

in a great deal of empowerment,

you can only be empowered

if you understand the circumstance.

Isn't that right?

You have to understand the context

of the situation you're in,

in order for you to come up with

great ideas.

And so I have to create

a circumstance where you understand

the context, which means you have

to be informed.

And the best way to be informed is

for there to be as little layers of

information mutilation, right?

Between us.

And so that's the reason why it's

very often that I'm reasoning

through things,

like in an audience like this.

I say, first of all,

this is the beginning facts.

These are the data that we have,

this is how I would reason

through it.

These are some of the assumptions,

these are some of the unknowns,

these are some of the knowns.

And so you reason through it, and

now you've created an organization

that's highly empowered.

NVIDIA is 30,000 people,

we're the smallest large company in

the world.

We're a tiny little company, but

every employee is so empowered and

they're making smart decisions on

my behalf every single day.

And the reason for that is because

they understand my condition,

I'm very transparent with people.

And I believe that I can trust you

with the information.

Oftentimes the information is hard

to hear, and

the situations are complicated, but

I trust that you can handle it.

A lot of people hear me say,

you're adults here,

you can handle this.

Sometimes they're not

really adults.

They just graduated.

I'm just kidding.

[LAUGH] I know that when I first

graduated, I was barely an adult.

But I was fortunate that I was

trusted with important information.

So I want to do that.

I want to create the conditions for

people to do that.

>> I have I do want to now address

the topic that is

on everybody's mind, AI.

Last week, you said that generative

AI and accelerated computing

have hit the tipping point.

So as this technology becomes

more mainstream,

what are the applications that you

personally are most excited about?

>> Well, you have to go back to

first principles and ask yourself,

what is generative AI?

What happened?

What happened was we now have

the ability to have software that

can understand something.

First of all,

we digitized everything.

That was, for example,

gene sequencing,

you digitize genes.

But what does it mean?

That sequence of genes,

what does it mean?

We've digitized amino acids.

But what does it mean?

And so we now have the ability,

we digitize words,

we digitize sounds,

we digitize images and videos,

we digitize a lot of things.

But what does it mean?

We now have the ability through

stunning a lot of data.

And from the patterns and

relationships we we now understand

what they mean.

Not only do we understand what they

mean, we can translate between them

because we learn about the meaning

of these things in the same world.

We didn't learn about them

separately.

So we we learned about speech and

words and paragraphs and

vocabulary in the same context.

And so we found correlations

between them and they're all

registered, if you will,

registered to each other.

And so now, not only do we

understand the modality,

the meaning of each modality,

we can understand how

to translate between them.

And so for obvious things, you

could caption the video to text,

that's captioning, text to images,

mid-journey text-to-text ChatGPT

amazing things.

And so

we now know that we understand

meaning and we can translate.

The translation of something is

generation of information, and

all of a sudden, you have to take

a step back and ask yourself, what

is the implication in every single

layer of everything that we do?

And so I'm exercising

in front of you.

I'm reasoning in front of you the

same thing I did a 15 years ago,

when I first saw AlexNet some 13,

14 years ago, I guess,

how I reasoned through it.

What did I see?

How interesting?

What can it do?

Very cool.

But then, most importantly,

what does it mean?

What does it mean?

What does it mean to

every single layer of computing?

Because we're in the world of

computing.

And so what it means is that that

the way that we process information

fundamentally will be different

in the future.

That's when a video builds,

chips and systems.

The way we write software will be

fundamentally different in

the future.

The type of software will be

able to write in the future will be

different, new applications.

And then also,

the processing of those

applications will be different.

What was historically

a retrieval-based model

where information was pre-recorded,

if you will, almost.

We wrote the text, pre-recorded,

and we retrieved it based on some

recommender system algorithm.

In the future,

some seed of information will be,

Be the starting point.

We call them prompts,

as you guys know, and

then we generate the rest of it.

And so the future of computing will

be highly generated.

Well, let me give you an example of

what's happening.

For example, we're having

a conversation right now,

very little of the information

I'm conveying to you is retrieved,

most of it is generated.

It's called intelligence.

And so in the future,

we're going to have a lot more

generative, our computers will

perform in that way.

It's going to be highly generative,

instead of highly retrieval-based.

Then you go back and

you're going to ask yourself, now,

for entrepreneurs,

you've gotta ask yourself,

what industries will be disrupted,

therefore?

Will we think

about networking the same way?

Will we think about storage the

same way? Would we be abusive of

Internet traffic as we are today?

Probably not, notice we're having

a conversation right now, and I'm

to get in my car every question.

So we don't have to be as abusive

of transformation, information,

transporting, as we used to.

What's going to be more?

What's going to be less?

What kind of applications, etc?

So you can go through the entire

industrial spread and ask yourself,

what's going to get disrupted?

What's going to get big different?

What's going to get nude, so on so

forth?

And that reasoning

starts from what is happening.

What is generative AI?

Foundationally, what is happening?

Go back to first principles with

all things.

There was something I was going to

tell you about organization,

you asked the question and

I forgot to answer it.

The way you create an organization,

by the way, someday, don't worry

about how other companies or

charts look,

you start from first principles.

Remember what

an organization is designed to do.

The organizations of the past,

where there's a king, CEO, and then

you have all the royal subjects,

the royal court, and

then eased out.

And then you

keep working your way down,

eventually, there are employees.

But the reason why it was designed

that way is because they wanted

the employees to have as little

information as possible,

because their fundamental purpose

of the soldiers is to die in

the field of battle.

To die without asking questions,

you guys know this.

I only have 30,000 employees,

I would like none of them to die.

[LAUGH] I would like them to

question everything.

Does it make sense?

And so the way you organize

in the past and the way you

organize today is very different.

Second, the question is what is in

Nvidia build?

An organization is designed so

that we could build whatever it is

we build better.

And so we all build

different things,

why are we organized the same way?

Why would this organizational

machinery be exactly the same

irrespective of what you build?

It doesn't make any sense.

You build computers,

you're organize this way.

You build health care services,

you're built exactly the same way.

It makes no sense whatsoever.

And so you got to go back to first

principles, just ask yourself,

what kind of machinery?

What is the input?

What is the output?

What are the properties of this

environment?

What is the forest that this animal

has to live in?

What is characteristics?

Is it stable?

Most of the time, you're trying to

squeeze out the last drop of water.

Or is it changing all the time,

being attacked by everybody?

You're the CEO, your job is to

architect this company.

That's my first job,

to create the conditions by which

you can do your life's work, and

the architecture has to be right.

And so you have to go back to first

principles and

think about those things.

And I was fortunate that when I was

29 years old, I had the benefit of

taking a step back and

asking myself, how would I build

this company for the future,

and what would it look like?

And what's the operating system,

which is called culture?

What kind of behavior do we

encourage enhance?

And what do we discourage and

not enhance, so on and so forth,

anyways?

>> I want to save time for

audience questions, but

this year's theme for View from

the Top is Redefining Tomorrow.

And one question we've asked all of

our guests is, Jensen,

as the co-founder and

CEO of Nvidia,

if you were to close your eyes and

magically change one thing about

tomorrow, what would it be?

>> Were we supposed to think about

this in advance?

>> [LAUGH] >> I'm going to

give you a horrible answer.

I don't know that it's one thing.

Look, there are a lot of things we

don't control.

There are a lot

of things we don't control.

Your job is to make

a unique contribution,

live a life of purpose.

To do something that nobody else in

the world would do or can do to

make a unique contribution.

So that in the event that after

you're done, everybody says,

the world was better because you

were here.

And so I think that to me,

I live my life kind of like this,

I go forward in time and

I look backwards.

So you asked me a question that's

exactly from a computer vision pose

perspective, exactly the opposite

of how I think.

I never looked forward from where

I am, I go forward in time and

look backwards.

And the reason for

that is as easier.

I would look backwards and

kind of read my history,

we did this and we did that way,

and we broke that prom down.

Does that make sense?

And so it's a little bit like how

you guys solve problems.

You figure out what is the end

result that you're looking for, and

you work backwards to achieve it.

And so I imagine Nvidia making

a unique contribution to advancing

the future of computing,

which is the single most important

instrument of all humanity.

Now, it's not about our

self-importance but

this is just what we're good at,

and it's incredibly hard to do.

And we believe we can make

an absolute unique contribution.

It's taken us 31 years to be here,

and we're still just beginning our

journey.

And so this is insanely hard to do.

And when I look backwards,

I believe we're going to be

remembered as a company that

kind of changed everything.

Not because we went out and

changed everything through all

the things that we said, but

because we did this one thing that

was insanely hard to do.

That we're incredibly good at

doing, that we love doing,

we did for a long time.

>> I'm part of the GSB LEAD,

I graduated in 2023.

So my question is, how do you see

your company in the next decade?

What challenges do you see your

company would face and

how you are positioned for that?

>> First of all,

can I just tell you what was going

on through my head?

As you say, what challenges,

the list that flew by my head-

>> [LAUGH] >> Was so

large that I was trying to

figure out what to select.

>> [LAUGH] >> Now, the honest truth

is that when you ask that question,

most of the challenges

that showed up for

me were technical challenges.

And the reason for that is

because that was my morning.

If you were chosen yesterday,

it might have been market creation

challenges.

There are some markets that, gosh,

I just desperately would love

to create.

Can't we just do it already?

But we can't do it alone.

Nvidia's a technology platform

company, we're here in service of

a whole bunch of other companies so

that they could realize,

if you will, Our hopes and

dreams through them.

And so, some of the things

that I would love, I would love for

the world of biology to be at

a point where it's kind of like the

world of chip design 40 years ago.

Computer aided in design, EDA,

that entire industry,

really made possible for us today.

And I believe we're going to make

possible for them tomorrow,

computer-aided drug design,

because we are are able to now

represent genes, and proteins, and

even cells now.

Very, very close to be able to

represent and understand the

meaning of a cell, a combination of

a whole bunch of genes.

What does a cell mean?

It's kind of like,

what does that paragraph mean?

Well, if we can understand a cell

like we can understand a paragraph,

imagine what we could do.

And so, I'm anxious for

that to happen,

kind of excited about that.

There's some that I'm just

excited about that I know we're

around the corner on.

For example,

human-oriented robotics.

They're very, very close,

around the corner.

And the reason for that is because

if you can tokenize and understand

speech, why can't you tokenize and

understand manipulation?

And so these kind of computer

science techniques,

once you figure something out,

you ask yourself, well,

if I do that, why can't I do that?

And so I'm excited about those kind

of things.

And so that challenge is kind

of a happy challenge.

Some of the other challenges of

course are industrial, and

geopolitical and

they're social, but

you've heard all that stuff before.

These are all true,

the social issues in the world, the

geopolitical issues in the world.

Why can't we just get along with

things in the world?

Why do we have to say those kind of

things in the world?

Why do we have to say those things

and amplify them in the world?

Why do we have to judge people so

much in the world?

All those things,

you guys all know that.

I don't have to say those

things over again.

>> My name's Jose, I'm a class with

the 2023 from the GSB.

My question is, are you worried at

all about the pace at which we're

developing AI?

And do you believe that any sort of

regulation might be needed?

Thank you.

>> Yeah, the answer is yes and no.

The greatest breakthrough in modern

AI, of course deep learning, and

it enabled great progress.

But another incredible breakthrough

is something that humans know and

we practice all the time.

And we just invented it for

language models called grounding,

reinforcement learning human

feedback.

I provide reinforcement learning

human feedback every day.

That's my job.

And for the parents in the room,

you're providing reinforcement

learning human feedback all

the time, okay?

Now, we just figured out how to

do that at a systematic level for

artificial intelligence.

There are a whole bunch of other

technology necessary to guard rail,

fine tune, ground.

For example,

how do I generate tokens that obey

the laws of physics?

Right now, things are floating in

space and doing things, and they

don't obey the laws of physics.

That requires technology.

Guard railing requires technology,

fine tuning requires technology,

alignment requires technology,

safety requires technology.

The reason why planes are so safe

is because all of the autopilot

systems are are surrounded by

diversity and redundancy, and

all kinds of different functional

safety, and active safety systems

that were invented.

I need all of that to be invented

much, much faster.

You also know that the border

between security and artificial

intelligence cybersecurity and

artificial intelligence is

going to become blurry and blurry.

We need technology to advance very,

very quickly in the area of

cybersecurity, in order to protect

this from artificial intelligence.

And so

in a lot of ways we need technology

to go faster, a lot faster, okay?

Regulation, there's two types of

regulation.

There's social regulation,

I don't know what to do about that.

But there's product and services

regulation, we know exactly what

to do about that, okay?

So, the FAA, the FDA, the NHTSA,

you name it, all the Fs and

all the Ns, and all the FCCs,

they all have regulations for

products and services that have

particular use cases.

Bar exams and doctors, and so on,

and so forth.

You all have qualification exams,

you all have standards that you

have to reach,

you all have to continuously be

certified accountants, and so

on and so forth.

Whether it's a product or

a service, there are lots and

lots of regulations.

Please do not add a super

regulation that cuts across of it.

The regulator who is regulating

accounting should not be the

regulator that regulates a doctor.

I love accountants, but if I

ever need a open heart surgery,

the fact that they can close books

is interesting but not sufficient.

>> [LAUGH] >> And so

I would like all of those fields

that already have products and

services, to also enhance their

regulations in context of AI, okay?

But I left out this one very big

one, which is the social

implication of AI.

And how do you deal with that?

I don't have great answers for

that but

enough people are talking about it.

But it's important to subdivide all

of this into chunks,

does it make sense?

So that we don't become super

hyper-focused on this one thing,

at the expense of a whole bunch of

routine things that we could

have done.

And as a result, people are getting

killed by cars and planes, and

it doesn't make any sense.

We should make sure that we do

the right things there, okay?

Very practical things.

May I take one more question?

>> Well, we have some rapid fire

questions for you as view

from the [INAUDIBLE] division.

>> Okay. >> [LAUGH] >> I was trying

to avoid that.

>> [LAUGH] >> Okay, all right,

fire away.

>> Okay.

>> Fire away.

>> Well, your first job was

at Denny's, they now have a booth

dedicated to you.

What was your fondest

memory of working there?

>> My second job was AMD,

by the way.

>> [LAUGH] >> Is there a booth

dedicated to me there?

>> [LAUGH] >> I'm just kidding.

>> [LAUGH] >> I loved my job there,

I did, I loved it,

it was a great company, yeah.

>> Yeah, if there were a worldwide

shortage of black leather jackets,

what would we be seeing you

wearing?

>> [LAUGH] >> No, I've got a large

reservoir of black jackets.

>> [LAUGH] >> I'll be the only

person who is not concerned.

>> [LAUGH] You spoke a lot about

textbooks, if you had to write one,

what would it be called?

>> I wouldn't write one.

>> [LAUGH] >> You're asking me

a hypothetical question that has no

possibility of-

>> [LAUGH] >> [LAUGH] That's fair.

And finally, if you could share

one parting piece of advice to

broadcast across Stanford,

what would it be?

>> It's not a word,

but, Have a core

belief, Gut-check it every day.

Pursue it with all your might,

pursue it for a very long time.

Surround yourself with people you

love, and take them on that ride.

So, that's the story of Nvidia.

>> Jenson,

this last hour has been a treat,

thank you for spending [INAUDIBLE].

>> Thank you very much.

>> [APPLAUSE]

[MUSIC]