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Joe Rogan podcast. Check it out.

>> The Joe Rogan Experience.

>> TRAIN BY DAY. JOE ROGAN PODCAST BY

NIGHT. All day.

>> Hello. Hey, Joe.

>> Good to see you again. We were just

talking about Was that the first time we

ever spoke or did was the first time we

spoke at at SpaceX?

>> SpaceX.

>> SpaceX. The first time when you were

giving Elon that crazy AI chip,

>> right? DJX Spark.

>> Yeah. Oo, that was a big moment. That

was a huge

>> That felt crazy to be there. I was like

watching these wizards of tech like

exchange information and and you're

giving him this crazy device, you know,

and then the other time was uh I was

shooting arrows in my backyard and uh

randomly get this call from Trump and

he's hanging out with you. President

Trump called and I called you.

>> Yeah. It's just

>> we were talking about you. [laughter]

>> It's just talking about he was talking

about the US UFC thing he was going to

do in his front yard.

>> Yeah. And he pulls out. He's JJS, look

at this design. He's so proud of it. And

I go, "You're going to have a fight in

the front lawn in the White House." He

goes, "Yeah, yeah, you're going to come.

This is going to be awesome." And he's

showing me his design and how beautiful

it is. And he goes, and somehow your

name comes up. He goes, "Do you know

Joe?" And I said, "Yeah, I'm going to be

on his podcast." He Let's call him.

[laughter]

>> He's like a kid.

>> I know. Let's call him. It's so He's

like a 79y old kid.

>> Oh, he's so incredible.

>> Yeah, he's an odd guy. Just very

different, you know, like the what you'd

expect from him. Very different than

what people think of him. And also just

very different as a president. A guy who

just calls you or texts you out of the

blue. Also, he makes when you te you.

You have an Android, so it won't go

through with you, but with my iPhone, he

makes the text go big.

>> Like, you know, USA is respected again.

like [laughter]

all caps and it makes the te the the the

text enlarge is kind of ridiculous.

>> Well, the the 101 Trump President Trump

is very different. He he surprised me f

first of all he's an incredibly good

listener. Almost everything I've ever

said to him, he's remembered.

>> Yeah. People don't they only want to

look at negative stories about him or

negative narratives about him. You know,

you can catch anybody on a bad day. Like

there's a lot of things he does where I

don't think he should do. Like I don't

think he should say to a reporter rep

reporter, "Quiet piggy." Like that's

pretty ridiculous. Also objectively

funny. I mean, it's unfortunate that it

happened to her. I wouldn't want that to

happen to her, but it was funny. Just

ridiculous that the president does that.

I wish he didn't do that. But other than

that, like he's he's an interesting guy.

Like he's a lot of different things

wrapped up into one person, you know?

You know, part of part of his charm,

well, part of his genius is Yes. He says

what's on his mind.

>> Yes.

>> And which is like an anti-olitician in a

lot of ways.

>> So, you know, what's on his mind is

really what's on his mind,

>> which

I I do some people some people would

rather be lied to.

>> Yeah. But but I I like the fact that

he's telling you what's on his mind. Um,

almost every time he explains something,

he says something,

he starts with his, you could tell, his

love for America, what he wants to do

for America. And everything that he

thinks through is very practical and

very common sense. And, you know, it's

very logical and um

I still remember the first time I I met

him and so this was I I'd never known

him, never met him before. and um uh

Secretary Lutnik called and we met right

before right at the beginning of the

administration. He said he told me what

was important to President Trump that

that um uh that United States

manufactures on shore and that was

really important to him because because

uh it's important to national security.

He wants to make sure that that the

important critical technology of our

nation is built in the United States and

that we re-industrialize

and get good at manufacturing again

because it's important for jobs.

>> It just seems like common sense, right?

>> Incredible common sense. And and that

was like literally the first

conversation I had with Secretary Letic

um and he was talking about how how um

that he started he started our

conversation with uh Jensen. This is

Secretary Lutnik and I I just want to

let you know that you're a national

treasure. Uh Nvidia is a national

treasure and whenever you need access to

the president um the administration uh

you call us. We're always going to be

available to you. Literally, that was

the first sentence.

>> That's pretty nice.

>> And it was completely true. every single

time I called, if I needed something, I

want to get something off my chest, um,

express some concern, uh, they're always

available. Incredible. It's just

unfortunate we live in such a

politically polarized society that you

can't recognize good common sense things

if they're coming from a person that you

object to. And that, I think, is what's

going on here. I think most people

generally a as a country, you know, as a

a giant community, which we are, it just

only makes sense that we have

manufacturing in America that especially

critical technology like you're talking

about. Like it's kind of insane that we

buy so much technology from other

countries.

>> If United States doesn't grow, we will

have no prosperity. We can't invest in

anything domestically or otherwise. we

can't fix any of our problems. If we

don't have energy growth, we can't have

industrial growth. If we don't have

industrial growth, we can't have job

growth. These it's as simple as that,

>> right?

>> And the fact that the fact that he came

into office and the first thing that he

said was drill baby drill. His point is

we need energy growth. Without energy

growth, we can have no industrial

growth. And that was it saved it saved

the AI industry. got I got to tell you

flat out if not for his progrowth energy

policy

we would not be able to build factories

for AI not be able to build chip

factories we won't sure surely won't be

able to build supercomputer factories

none of that stuff would be possible

without all of that

construction jobs would be challenged

right electrical you know electrician

jobs all of these jobs that are now

flourishing would be challenged and so I

think he's got it right we need energy

growth We want to re-industrialize the

United States. We need to be back in

manufacturing. Every successful person

doesn't need to have a PhD. Every

successful person doesn't have to have

gone to Stanford or MIT. And I think I

think that that that you know that

sensibility is is um spot on. Now, when

we're talking about technology growth

and energy growth, there's a lot of

people that go, "Oh, no. That's not what

we need. We need to, you know, simplify

our lives and get back." But the the

real issue is that we're in the middle

of a giant technology race. And whether

people are aware of it or not, whether

they like it or not, it's happening. And

it's a really important race because

whoever gets to

whatever the event horizon of artificial

intelligence is, whoever gets there

first has massive advantages in a huge

way.

Do you agree with that? Well, first the

part I I will say that we are in a

technology race and we are always in a

technology race. We've been in a

technology race with somebody forever.

>> Right.

>> Right. Since the industrial revolution,

we've been in a technology

>> since the Manhattan project.

>> Yeah.

>> Or or you know, even going back to the

discovery of energy, right? The United

Kingdom was where the industrial

revolution was, if you will, invented

when they realized that they can turn

steam and such into into energy into

electricity.

All of that was invented largely in

Europe and the United States capitalized

on it. We were the ones that learned

from it. We industrialized it. We

diffused it faster than anybody in

Europe. They were all stuck in

discussions about

policy and

jobs and disruptions. Meanwhile, the

United States was forming. We just took

the technology and ran with it. And so I

I think we were always in in a bit of a

technology race. World War II was a

technology race. Manhattan Project was a

technology race. We've been in the

technology race ever since during the

Cold War. I think we're still in a

technology race. It is probably the

single most important race. It is the

technology is uh it gives you

superpowers.

you know whether it's information

superpowers or energy superpowers or

military superpowers is all founded in

technology and so technology leadership

is really important

>> well the problem is if somebody else has

superior technology right that's that's

the issue it seems like with the AI race

people are very nervous about it like

you know Elon has famously said there

was like 80% chance it's awesome 20%

chance we're in trouble and people are

worried about that 20% % rightly so. I

mean that you know if you had 10 bullets

in a a a revolver and you know you you

took out eight of them and you still

have tw two in there and you spin it,

you're not going to feel real

comfortable when you pull that trigger.

It's terrifying,

>> right?

>> And when we're working towards this

ultimate goal um of AI,

it it just it's

impossible to imagine that it wouldn't

be of national security interest to get

there first.

We should The question is what's there?

That's the That was the part that

>> What is there?

>> Yeah. I'm not sure.

>> And I don't think anybody I don't think

anybody really knows.

>> That's crazy though. If I ask you,

>> you're the head of Nvidia. If you don't

know what's there, who knows?

>> Yeah. I I think it's probably going to

be much more gradual than we think. It

won't It won't be a moment. It won't be

It won't be as if um somebody arrived

and nobody else has. I don't think it's

going to be like that. I think it's

going to be things that just get better

and better and better and better just

like technology does.

>> So, you are rosy about the future.

You're you're very optimistic about

what's going to happen with AI.

>> Obviously, will you make the best AI

chips in the world?

>> You probably better be.

>> Uh h if history is a guide, um uh we

were always concerned about new

technology.

Humanity has always been concerned about

new technology. There are always

somebody who's thinking there always a

lot of people who are quite concerned.

were quite concerned and and and so if

if history is a guide, it is the case um

that all of this concern is channeled

into making the technology safer.

And so for example, in the last several

years, I would say AI technology has

increased probably in the last two years

alone, maybe a 100x. Let's just give it

a number, okay? It's like a car two

years ago was 100 times slower. So AI is

100 times more capable today. Now, how

did we channel that technology? How do

we channel all of that power? We

directed it to um causing the AI to be

able to think, meaning that it can take

a problem that we give it, break it down

step by step.

It does research before it answers. And

so it grounds it on truth.

It'll reflect on that answer. Ask

itself, is this the best, you know,

answer that I can give you. Am I certain

about this answer? If it's not certain

about the answer or highly confident

about the answer, it'll go back and do

more research. It might actually even

use a tool because that tool provides a

better solution than it could

hallucinate itself. As a result, we took

all of that computing capability and we

channeled it into having it produce a

safer result, safer answer, a more

truthful answer because as you know, one

of the greatest criticisms of AI in the

beginning was that it hallucinated,

>> right?

>> And so if you look at the reason why

people use AI so much today is because

the amount of hallucination has reduced.

You know, I use it almost I well I used

it the whole trip over here and so so I

think the

the uh the the capability most people

think about power

and they think about you know maybe as

an explosion power but the technology

power most of it is channeled to towards

safety. A car today is more powerful but

it's safer to drive. A lot of that power

goes towards better handling. You know,

I'd rather have a Well, you have a 1000

horsepower truck. I think 500 horsepower

is pretty good. No, I thousand's better.

I think a th00and is better.

>> I don't know if it's better, but it's

definitely faster.

>> Yeah. No, I think it's better. You can

get out of trouble faster. Um,

I enjoyed my 599 more than my 612. It

was I think it was a better better

horsepower is better. My 459 is better

than my 430.

more horsepower is better. I I think

more horsepower is better. I think it's

better handling. It's better control. In

the case of in the case of technology,

it's also very similar in that way, you

know. And so if you if you look at what

we're going to do with the next thousand

times of performance in AI, a lot of it

is going to be channeled towards more

reflection, more research,

thinking about the answer more deeply.

So when you're defining safety, you're

defining a it as accuracy,

>> functionality.

>> Functionality. Okay.

>> It it does what you expect it to do. And

then you take all the the the technology

in the horsepower, you put guard rails

on it, just like our cars. We've got a

lot of technology in in a car today. A

lot of it is goes towards, for example,

ABS. ABS is great. And so, uh, traction

control, that's fantastic. without a

without a computer in the car, how would

you do any of that,

>> right?

>> And that little computer, the computers

that you have doing your traction

control is more powerful than the

computer that went to Apollo 11. And so

you want that technology,

channel it towards safety, channel it

towards functionality. And so when

people talk about power, the advancement

of technology, often times I I I feel

what they're thinking and what we're

actually doing is very different.

>> Well, what do you think they're

thinking? Well, they're thinking somehow

that this this uh this AI is being

powerful and their their mind probably

goes towards a sci-fi movie. The

definition of power, you know, often

times the definition definition of power

is military power or physical power. But

in in the case of technology power when

we translate all of those operations

it's towards more refined thinking you

know more reflection more planning more

options

>> I think the big fears that people have

is one a big fear is military

applications that's a big fear

>> because people are very concerned that

you're going to have

>> AI systems that make decisions that

maybe an ethical person wouldn't make or

a moral person wouldn't make based on

achieving an objective versus based on,

you know, how it's going to look to

people.

>> Well, I'm I'm happy that that uh our

military is going to use AI technology

for defense and I think that that um uh

Andural uh building military technology.

I'm happy to hear that. I'm happy to see

um all these tech startups now

channeling their technology capabilities

towards defense and military

applications. I think you needed to do

that.

>> Yeah, we had Palmer Lucky on the

podcast. He was demonstrating some of

the stuff I put his helmet on. And we

show we he showed some videos how you

could see behind walls and stuff like

it's nuts.

>> And he's he's actually the perfect guy

to go start that company.

>> 100%. [laughter] Yeah. 100%. It's like

he was born for that. Yeah. He came in

here with a copper jacket on. He's a

freak. [clears throat] It's [laughter]

awesome. He's awesome. But it's also

it's a you know an unusual intellect

channeled into that very bizarre field

is what you need, you And I think it's

it's uh I think I'm happy that we're

making it so more socially acceptable.

You know, there was a time where when

somebody wanted to channel their

technology capability and their

intellect into defense technology, uh

somehow they're vilified. Um but uh we

need people like that. We need people

who enjoyed enjoy that part of uh

application of technology.

>> Well, people are terrified of war, you

know. So it depends.

>> Best way to avoid it has excessive

military might.

>> Do you think that's absolutely the best

way? Not not diplomacy, not working

stuff out.

>> All of it.

>> All of it. You have to have military

might in order to get people to sit down

with you.

>> Right. Exactly. All of it.

>> Otherwise, they just invade.

>> That's right. [laughter] Why ask for

permission?

>> Again, like you said, history. Go back

and look at history. Um, when you look

at the future of AI and and you just

said that no one really knows what's

happening, do you ever sit down and

ponder scenarios?

>> Like what do you what do you think is

like bestcase scenario for AI over the

next two decades?

Um

the best case scenario is that AI

diffuses into everything that we do and

uh our

everything's more efficient but

the threat of war remains a threat of

war.

Uh, cyber security remains

a super difficult challenge.

Somebody is going to try to

breach your security. You're going to

have thousands of millions of AI agents

protecting you from that threat.

Your technology is going to get better.

Their technology is going to get better.

Just like cyber security. Right now,

while we speak, we're being

we're seeing cyber attacks all over the

planet on just about every front door

you can imagine.

And

and yet you and I are sitting here

talking. And so the reason for that is

because we know that there's a whole

bunch of cyber security technology in

defense. And so we just have to keep

amping that up, keep stepping that up.

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That's a big issue with people is the

the worry that technology is going to

get to a point where encryption is going

to be obsolete. Encryption is just it's

no longer going to protect data. It's no

longer going to protect systems. Do you

anticipate that ever being an issue or

do you think there's it's as the defense

grows, the threat grows, the defense

grows, and it just keeps going on and on

and on and they'll always be able to

fight off any sort of intrusions?

>> Not forever. some intrusion will get in

and then that we'll all learn from it.

And you know the reason why cyber

security works is because of course the

technology of defense is advancing very

quickly. The technology offense is

advancing very quickly. However, the

benefit of the cyber security defense is

that socially the community all of our

companies work together as one. Most

people don't realize this.

There's a whole community of cyber

security experts. We exchange

ideas. We exchange best practices. We

exchange what we detect. The moment

something has been breached or maybe

there's a loophole or whatever it is, it

is shared by everybody. The patches are

shared with everybody.

>> That's interesting.

>> Yeah. Most people don't realize this.

>> No, I had no I had no idea. I've assumed

that it would just be competitive like

everything else.

>> We work together. Interesting. Has that

always been the case?

>> Uh, it surely has been the case for

about about 15 years. It might not have

been the case long ago, but this this

>> what do you think started off that

cooperation?

>> Um, people recognizing it's a challenge

and no company can stand alone.

>> And the same thing is going to happen

with AI. I think we all have to decide

work working together uh to stay out of

harm's way is is our best chance for

defense. Then it's basically everybody

against the threat.

>> And it also seems like you'd be way

better at detecting where these threats

are coming from and neutralizing them.

>> Exactly. Because the moment you detect

it somewhere,

>> you're going to find out right away.

>> It'll be really hard to hide.

>> That's right.

>> Yeah.

>> That's how it works. That's the reason

why it's safe. That's why I'm sitting

here right now instead of, you know,

locking everything down in video.

[laughter]

>> It's not only am I watching my own back,

I've got everybody watching my back. and

I'm watching everybody else's back.

>> It's a bizarre world, isn't it? When you

think about that cyber threat,

>> this idea about cyber security is

unknown to the people who are talking

about AI threats. They're I think when

they think about AI threats and AI cyber

security threats, they have to also

think about how we deal with it today.

Now, there's no question that AI is a

new technology

and it's a new type of software. In the

end, it's software just it's a new type

of software and so it's going to have

new capabilities but so will the defense

you know where you use the same AI

technology to go defend against it. So

you do you anticipate a time ever in the

future where it's going to be impossible

where there's not going to be any

secrets where the bottleneck between the

technology that we have and the

information that we have. Information is

just all a bunch of ones and zeros. It's

out there on hard drives and the

technology has more and more access to

that information. Is it ever going to

get to a point in time where there's no

way to keep a secret?

>> I don't think

>> because it seems like that's where

everything is kind of headed in a weird

way.

>> I don't think so. I think the quantum

computers were supposed to will Yeah.

quantum computers will make it possible

will make it so that the previous

quantum previous encryption technology

is obsolete. But that's the reason why

the entire industry is working on

postquantum

encryption technology.

>> What would that look like?

>> New algorithms.

>> But the crazy thing is when you hear

about the kind of computation that

quantum computing can do.

>> Yeah.

>> And the the power that it has. Yeah.

>> Where you know you're looking at

>> all the supercomputers in the world. It

would take billions of years and it

takes them a few minutes to solve these

equations. Like how do you make

encryption for something that can do

that? I'm not sure, but there's

[laughter]

but I've got a bunch of scientists who

are working on that.

>> Boy, I hope they [snorts] could figure

it out.

>> Yeah, we got a bunch of scientists who

are expert in that. And

>> is the ultimate fear that it can't be

breached that quantum computing will

always be able to to decrypt all other

quantum computing encryption?

>> I don't think that

>> it just gets to some point where it's

like, stop playing the stupid game. We

know everything.

>> I don't think so.

>> No,

>> because I I'm you know, history is

guide.

History is a guide before AI came

around. That's my worry. My worry is

this is a totally, you know, it's like

history was one thing and then nuclear

weapons kind of changed all of our

thoughts on war and mutually assured

destruction came

everybody to stop using nuclear bombs.

>> Yeah.

>> My worry is that

>> the thing is Joe is that that AI is not

going to it's not like we're cavemen and

then all of a sudden one day AI shows

up. every single day we're getting

better and smarter because we have AI

and so we're stepping on our own AI's

shoulders. So when when that whatever

that AI threat comes, it's a click

ahead. It's not a galaxy ahead,

>> you know, it's just a click ahead. And

so so I think I think the the the idea

that somehow this AI

is going to pop out of nowhere and

somehow think in a way that we can't

even imagine thinking and do something

that we can't possibly imagine I think

is far-fetched. And the reason for that

is because we're all have we all have

AIs and you know there's a whole bunch

of AIs being in development. we know

what they are and we're using it and and

so every single day we're getting we're

close to each other.

>> But don't they do things that are very

surprising?

>> Yeah. But so you you have an AI that

does something surprising. I'm going to

have an AI and my AI looks at your AI

and goes that's not that surprising.

>> The fear for the lay person like myself

is that AI becomes sentient and makes

its own decisions

and then ultimately decides to just

govern the world. do it its own way.

They're like, "You guys, you had a good

run, but

>> we're taking over now."

>> Yeah, but my my AI is gonna take care of

me. I mean, [laughter]

so that's the this is the cyber security

argument.

>> Yes.

>> Do you have an AI and it's super smart,

but my AI is super smart, too. And and

maybe your AI. Let let's pretend let's

let's pretend for a second that we

understand what consciousness is and we

understand what sentience is and and

that in fact

>> and we really are just pretending.

>> Okay, let's just pretend for a second

that we we believe that. I don't believe

actually I don't actually don't believe

that but nonetheless we let's pretend we

believe that.

>> So your your your AI is conscious and my

AI is conscious and and let's say your

AI is you know wants to I don't know do

something surprising.

My AI is so smart that it won't it might

be surprising to me, but it probably

won't be surprising to my AI. And so

maybe my AI

thinks it's surprising as well, but it's

so smart the moment it sees it the first

time, it's not going to be a surprise

the second time, just like us. And so I

feel like I think the idea that that

only one person has [clears throat] AI

and that one person's AI is compares

everybody else's AI is Neanderthal

[snorts] is um probably unlikely. I

think it's much more like cyber

security.

>> Interesting.

>> I think the fear is not that your AI is

going to battle with somebody else's AI.

The fear is that AI is no longer going

to listen to you. That's the fear is

that human beings won't have control

over it after a certain point if it

achieves sensience and then has the

ability to be autonomous

>> that there's one AI.

>> Well, they just combine.

>> Yeah. Becomes one AI

>> that it's a life form.

>> Yeah.

>> But that's the there's arguments about

that, right? That we're dealing with

some sort of synthetic biology that it's

not as simple as new technology that

you're creating a life form.

>> If it's like life form,

let's go along with that for a while. I

think if it's like life form, as you

know, all life forms don't agree. And so

I'm going to have to go with your life

form and my life form are going to agree

because my life form is going to want to

be the super life form. And and now that

now that we have disagreeing life forms,

uh we're back back again to where we

are. Well, they would probably cooperate

with each other.

It would just the reason why we don't

cooperate with each other is we're

territorial primates.

But AI wouldn't be a territorial

primate. It would realize the folly in

that sort of thinking and it would say,

"Listen, there's plenty of energy for

everybody. We we don't need to dominate.

We don't need We're not trying to

acquire resources and take over the

world. We're not looking to find a good

breeding partner. We're just existing as

a new super life form that these cute

monkeys created for us."

Okay. Well, that would be a that would

be a um a superpower with no ego,

>> right? And and if it has no ego,

why would it have the ego to do any harm

to us?

>> Well, I don't assume that it would do

harm to us, but the the fear would be

that we would no longer have control and

that we would no longer be the apex

species on the planet. this thing that

we created would now be. [laughter]

>> Is that funny?

>> No.

>> I just think it's not gonna happen.

>> I know you think it's not gonna happen,

but

>> it could, right? And here's the other

thing is like

>> if we're racing towards could Yeah.

>> And could could be the end of human

beings being in control of our own

destiny.

>> I just think it's extremely unlikely.

>> Yeah.

>> That's what they said in the Terminator

movie [laughter]

>> and it hasn't happened.

>> No, not yet. But you guys are working

towards it. Um the the thing about

you're saying about conscience and

sensience that you don't think that AI

will achieve consciousness or that the

question is what's the definition?

>> Yeah. What's the definition of

>> what is the definition to you?

>> Um uh

consciousness

um

uh f I guess first of all uh you need to

know about your own existence.

Um,

you have to have experience, not just

knowledge and intelligence.

The concept of a machine

having an experience.

I'm not well, first of all, I don't know

what defines experience, why we have

experiences, right?

>> Yeah. and why this microphone doesn't

uh and so it I think I know I well I

think I I I think I know what

consciousness is the sense of experience

the ability to know self versus

um

uh the ability to be able to reflect

know our own self the sense of ego I

think all of all of those human

experiences

uh probably is what consciousness is

but why it exists versus

the concept of knowledge and

intelligence which is what AI is defined

by today [clears throat] it has

knowledge it has intelligence artificial

intelligence we don't call it artificial

consciousness

artificial intelligence the ability to

uh perceive believe, recognize,

understand,

um, plan,

uh, perform tasks.

Those things are foundations of

intelligence

to know things, knowledge.

I don't, it's clearly different than

consciousness.

>> But consciousness is so loosely defined.

How can we say that? I mean, doesn't a

dog have consciousness? Yeah.

>> Dogs seem to be pretty conscious.

>> That's right.

>> Yeah. So, and that's a lower level

consciousness than a human being's

consciousness.

>> I'm not sure. Yeah. Right. Well,

>> the question is what lower level

intelligence? It's lower level

intelligence, but I don't know that it's

lower level consciousness.

>> That's a good point. Right.

>> Because I believe my dogs feel as much

as I feel.

>> Yeah. They feel a lot. Right.

>> Yeah. They get attached to you. That's

right. They get depressed if you're not

there.

>> That's right. Exactly.

>> There's There's definitely that.

>> Yeah. um the the concept of experience,

>> right?

>> Um but isn't AI interacting with

society? So, doesn't it acquire

experience through that interaction?

>> Um I don't think interactions is

experience. I think experience is uh

experience is a collection of feelings.

I think

>> you're aware of that AI um I forget

which one where they gave it some false

information about one of the programmers

having an affair with his wife just to

see how it would respond to it and then

when they said they were going to shut

it down it threatened to blackmail him

and reveal his affair and it was like

whoa like it's conniving like if that's

not learning from experience and being

aware that you're about to be shut down

which would imply at least some kind of

consciousness or you could kind defined

it as consciousness if you were very

loose with the term and if you imagine

that this is going to exponentially

become more powerful. Wouldn't that

ultimately lead to a different kind of

consciousness than we're defining from

biology? Well, first of all, let's just

break down what it probably did. It

probably read somewhere. There's

probably text that that in these

consequences

certain people did that. I could imagine

a novel,

>> right?

>> Having those words related.

>> Sure.

>> And so inside

>> it realizes it strategy for survival is

>> it's just a bunch of numbers

>> that it's just a bunch of numbers that

that in the in the collection of numbers

that relates to a husband cheating on a

wife. Um

has subsequently a bunch of numbers that

relates to blackmail and such things.

However, whatever the revenge was,

>> right?

>> And so it has spewed it out.

>> And so it's just like, you know, it it's

just as if I'm asking it to write me a

poem in Shakespeare. It just whatever

the words are in the world in in that

dimensionality, this dimensionality is

all these vectors and in in

multi-dimensional space. These words

that were in the prompt that described

the affair um subsequently led to one

word after another led to um you know

some revenge and something but it's not

because it had consciousness or you know

it just spewed out those words generated

those words

>> I understand what you're saying that

patterns that human beings have

exhibited both in literature and in real

life

>> that's exactly right

>> but it at a certain point in time one

would say, "Okay, well, it couldn't do

this two years ago and it couldn't do

this four years ago." Like when we're

looking towards the future, like at what

point in time when it can do everything

a person does, what point in time do we

decide that it's conscious? If it

absolutely mimics all human thinking and

behavior patterns,

>> that doesn't make it conscious.

>> It becomes in disccernible. It's it's

aware. It can communicate with you the

exact same way a person can. Like is con

is consciousness are we putting too much

weight on that concept because it seems

like it's a version of a kind of

consciousness.

>> It's a version of imitation.

>> Imitation consciousness, right? But if

it perfectly imitates it,

>> I still think it's a per it's an example

of imitation.

>> So it's like a fake Rolex when they 3D

print them and make them

>> indestruable. The question is what's the

definition consciousness?

>> Yeah.

>> Yeah.

>> That's the question. And I don't think

anybody's really clearly defined that.

That's what get where it gets weird and

that that's where the real doomsday

people are worried that you are creating

a form of consciousness that you can't

control. I believe it is possible to

create a machine

that imitates

human intelligence

and

has the ability to

understand information,

understand

instructions, break the problem down,

solve problems, and perform tasks. I

believe that completely.

I believe that that um we could have a

computer that has a vast amount of

knowledge. Some of it true, some of it

not true.

Some of it generated by humans, some of

it generated synthetically. And more and

more of knowledge in the world will be

generated synthetically going forward.

You know, until now the knowledge that

we've we have are knowledge that we

generate and we propagate and we send to

each other and we amplify it and we add

to it and we modify it. We change it. In

the future,

in a couple of years, maybe two or three

years, 90% of the world's knowledge will

likely be generated by AI.

>> That's crazy.

>> I know. But it's just fine.

>> But it's just fine.

>> I know. And the reason for that is this.

Let me tell you why.

>> Okay?

>> It's because um what difference does it

make to me that I am learning from a

textbook that was generated by a bunch

of people I didn't know or written by a

book that you know from somebody I don't

know uh to uh knowledge generated by AI

computers that are assimilating all of

this and reynthesizing things. To me, I

don't think there's a whole lot of

difference. We still have to we still

have to fact check it. We still have to

make sure that it's you know based on

fundamental first principles and we

still have to do all of that just like

we do today.

>> Is this taking into account the kind of

AI that exists currently? And do you

anticipate that just like we could have

never really believed that AI would be

at least a person like myself would

never believe AI would be as so

ubiquitous and so worth it. It's it's so

powerful today and so important today.

We never thought that 10 years ago.

Never thought that,

>> right?

>> You imagine like what are we looking at

10 years from now?

>> I I think that if you reflect back 10

years from now, you would say the same

thing that we would have never believed

that

>> but

>> in a different direction,

>> right? But if you if you go forward 9

years from now

and then ask yourself what's going to

happen 10 years from now, I think it'll

be quite gradual. Um, one of the things

that Elon said that makes me happy is he

he's he believes that we're going to get

to a point where it's not

it's not necessary for people to work

and not meaning that you're going to

have no purpose in life, but you will

have in his words universal high income

because so much revenue is generated by

AI that it will take away this need for

people to do things that they don't

really enjoy doing just for money. And I

think a lot of people have a problem

with that because their entire identity

and who how they think of themselves and

how they fit in the community is what

they do. Like this is Mike. He's an

amazing mechanic. Go to Mike and Mike

takes care of things. But there's going

to come a point in time where AI is

going to be able to do all those things

much better than than people do. And

people will just be able to receive

money. But then what does Mike do? Mike

is, you know, really loves being the

best mechanic around. You know, what

does the guy who, you know,

codes, what does he do when AI can code

infinitely faster with zero errors? Like

what what happens with all those people?

And that is where it gets weird. It's

like because we've sort of wrapped our

identity as human beings around what we

do for a living.

>> You know, when you meet someone, one of

the first things you meet somebody at a

party, hi Joe. What's your name? Mike.

What do you do? Mike and you know Mike's

like, "Oh, I'm a lawyer." "Oh, what kind

of law?" And you have a conversation,

you know, when Mike is like, "I get

money from the government. I play video

games."

>> Gets weird.

>> Mhm.

>> And I think um the concept sounds great

until you take into account human

nature. And human nature is that we like

to have puzzles to solve and things to

do and and an identity that's wrapped

around our idea that we're very good at

this thing that we do for a living.

>> Yeah. Yeah, I think um let's see, let me

start with the more mundane and I'll

work work backwards, okay? Work forward.

Uh so one of the predictions from uh

Jeff Hinton who who started the whole

deep learning phenomenon the deep

learning technology trend

and uh in incredible incredible

researcher uh professor at University of

Toronto

uh he invented discovered or invented

the the idea of of back propagation

which which uh allows the neural network

to learn.

And um

and as as as you know uh for for the

audience,

software historically was humans

applying first principles and our

thinking to uh describe an algorithm

that is then codified just like a recipe

that's codified in software. It looks

just like a recipe. how to cook

something looks exactly the same just in

a slightly different language. We call

it Python or C or C++ or whatever it is.

In the case of deep learning, this

invention of artificial intelligence,

we put a structure of a whole bunch of

neural networks and a whole bunch of

math units

and we make this large structure. It's

like a switchboard of little

u mathematical units and we connect it

all together.

Um, and we give it the input that

the software would eventually receive

and we just let it randomly guess what

the output is. And so we say, for

example, the input could be a picture of

a cat.

And and um one of the outputs of the

switchboard is where the cat signal is

supposed to show up. And all of the

other signals, the other one's a dog,

the other one's an elephant, the other

one's a tiger.

And all of the other signals are

supposed to be zero when I show it a

cat. And the one that is a cat should be

one.

And I show at a cat through this big

huge network of switchboards and math

units and they're just doing multiply

and adds multiplies and ads. Okay?

And and uh and this thing, this

switchboard is gigantic.

The more information you're going to

give it, the more the bigger this

switchboard has to be. And what Jeff

Hinton discovered was a invented was a

way for you to

guess that put the cat signal in put the

cat image in and that cat image you know

could be a million numbers because it's

you know a megapixel image for example

and it's just a whole a whole bunch of

numbers and somehow from those numbers

it has to light up the cat signal. Okay,

that's the bottom line. And if it the

first time you do it, it just comes up

with garbage. And so it says the right

answer is cat. And so you need to

increase this signal and decrease all of

the other and back propagates the

outcome through the entire network. And

then you show another. Now it's an image

of a dog and it guesses it takes a swing

at it and it comes up with a bunch of

garbage and you say no no no the answer

is this is a dog I want you to produce

dog and all of the other switch all the

other outputs have to be zero and I want

to back propagate that and just do it

over and over and over again. It's just

like uh showing a a kid this is an

apple, this is a dog, this is a cat. And

you just keep showing it to them until

they eventually get it. Okay. Well,

anyways, that big invention is deep

learning. That's the foundation of

artificial intelligence, a piece of

software

that learns from examples. That's

basically we machine learning, a machine

that learns. Uh and so so one of the the

big

first

applications was image recognition and

one of the most important image

recognition applications is radiology.

>> And so so uh uh he predicted uh about 5

years ago that in five years time the

world won't need any radiologists

because AI would have swept the whole

field.

Well, it turns out AI has swept the

whole field. That is completely true.

Today, just about every radiologist is

using AI in some way. And what's ironic

though, what's what's interesting is

that the number of radiologist has

actually grown.

And so the question is why? That's kind

of interesting, right?

>> It is. And so the prediction was in fact

that

30 million radiologists will be wiped

out.

But as it turns out, we needed more. And

the reason for that

[clears throat and cough]

is because the purpose of a radiologist

is to diagnose disease,

not to study the image. This the image

studying is simply a task to in service

of diagnosing the disease. And so now

the fact that you could study the images

more quickly and more precisely

without ever making a mistake and never

gets tired.

You could study more images. You could

study it in

3D form instead of 2D because you know

the AI doesn't care whether it studies

images in 3D or 2D. You could study it

in 4D. And so the now you could study

images in a way that radiologist

radiologists can't easily do and you

could study a lot more of it. And so the

number of tests that people are able to

do increases and because they're able to

serve more patients, the hospital does

better. They have more clients, more

patients. As a result, they have better

economics. When they have better

economics, they hire more radiologists

because their purpose is not to study

the images. their purpose is to diagnose

disease. And so the question is the what

I'm leading up to is ultimately what is

the purpose? What is the purpose of the

lawyer? And has the purpose changed?

What is the purpose? You know, one of

the examples that I gave is is um that I

would give is for example uh if my car

became self-driving

will all chauffeers be out of jobs? The

answer probably is not because for some

per for some chauffeers they for some

people who are driving you they could be

protectors some people um they're part

of the experience part of the service so

when you get there they you know they

could take care of things for you and so

for a lot of different reasons not all

chauffeers would lose their jobs some

chauffeers would lose their jobs and uh

many chauffeers would change their jobs

and the type of applications of

autonomous vehicles will probably

increase you know the usage of the

technology within find new homes and so

I I think you have to go back to what is

the purpose of a job you know like for

example if AI comes along I actually

don't believe I'm going to lose my job

because my purpose isn't to I have to

look at a lot of documents I study a lot

of emails I look at a bunch of diagrams

you know um the question is what is the

job and and uh the purpose of somebody

probably hasn't changed a lawyer for

example help people that probably hasn't

changed studying legal documents

generating documents it's part of the

job not the job

>> but don't you think there's many jobs

that AI will replace

>> if your job is automation

>> yeah if your job is the task

>> right so automation

>> yeah factor if your job is the task

>> that's a lot of people

>> it could be a lot of people but it'll

probably generate like for example

>> uh let's say we let's say I'm super

excited about the the the robots Elon's

working on.

It's still a few years away.

When it happens, when it happens,

um

there's a whole new industry of

technicians and people who have to

manufacture the robots, right?

>> Mhm.

>> And so that that job never existed. And

so you're going to have a whole industry

of people taking care of like for

example, you know, all the mechanics and

all the people who are building things

for cars, supercharging cars, uh that

didn't exist before cars and now we're

going to have robots. You're going to

have robot apparel. So a whole industry

of [laughter] Right. Isn't that right?

Because I want my robot to look

different than your robot.

>> Oh god.

>> And so [laughter] you're going to you're

going to have a whole, you know, apparel

industry for robots. You're going to

have mechanics for robots and you have

you know people who comes and maintain

your robots

>> automated though.

>> No,

>> you don't think so? You don't think

[clears throat] they'll be all done by

other robots

>> eventually? And then there'll be

something else.

>> So you think ultimately people just

adapt except if you are the task

>> which is a large percentage of the

workforce.

>> If your job is just to chop vegetables,

quezin art is going to replace you.

>> Yeah. So people have to find meaning in

other things. Your job has to be more

than the task.

>> What do you think about Elon's belief

that this universal basic income thing

will eventually become necessary?

>> Many people think that. Andrew Yang

thinks that

>> he was one of the first people to sort

of sound that alarm during the the 2020

election.

Yeah, I I guess um

yeah, both ideas probably won't exist at

the same time and and um as in life,

things will probably be in the middle.

One idea, of course, is that there'll be

so much abundance of resource that

nobody needs a job and we'll all be

wealthy.

On the other hand, um we're going to

need universal basic income. Both ideas

don't exist at the same time,

>> right?

>> And so we're either going to be all

wealthy or we're going to be all

>> How could everybody be wealthy though?

But

>> because scenario wealthy not because you

have a lot of dollars, wealthy because

there's a lot of abundance. Like for

example, today we are wealthy of

information.

You know, this is some a concept several

thousand years ago only a few people

have. And so, uh, today we have wealth

of a whole bunch of things, resources

that that historic point. Yeah. And so,

we're going to have wealth of resources,

things that we think are valuable today

that in the future are just not not that

valuable, you know, and so it because

it's automated. And so I think I think

the question

maybe maybe partly it's hard to answer

partly because

it's hard to talk about infinity and

it's hard to talk about a long time from

now and and the reason for that is

because

there's just too many scenarios to to

consider. But I think it I think in the

next several years, call it 5 to 10

years,

there are several things that I I

believe in hope. Um, and I say hope

because I'm not sure. One of the things

that I believe is that the technology

divide will be substantially collapsed.

And of course the alternative

viewpoint is that AI is going to

increase the technology divide.

Now the reason why I believe AI is going

to reduce the technology divide.

I is because we have proof

the evidence is that AI is the easiest

application in the world to use. Chat

GPT has grown to almost a billion users

frankly practically overnight. And if

you're not exactly sure how to use,

everybody knows how to use chatpt. Just

say something to it. If you're not sure

how to use chatpt, you ask chatd how to

use it. No tool in history has ever had

this capability. A quez an art, you

know, if you don't know how to use it,

you're kind of screwed. You're going to

walk up to it and say, "How do you use a

quezin art?" You're going to have to

find somebody else. And so, but an AI

will just tell you exactly how to do it.

Anybody could do this. It'll speak to

you in any language. And if it doesn't

know your language, you'll speak it in

that language and it'll probably figure

out that it doesn't completely

understand your language. Go learns it

instantly and comes back and talk to

you. And so I think the the technology

divide has a real chance finally that

you don't have to speak Python or C++ or

forran. You can just speak human and

whatever form of human you like. And so

I think that that has a real chance of

closing the technology divine. Now, of

course, the counternarrative would say

that

AI is only going to be available for the

nations and the countries that have a

vast amount of resources because AI

takes energy

and AI takes um a lot of GPUs and

factories to be able to produce the AI.

No doubt at the scale that we would like

to do in the United States. But the fact

of the matter is your phone's going to

run AI just fine all by itself, you

know, in a few years. Today, it already

does it fairly decently. And so the the

the fact that every every country, every

nation, every every society will have

the benefit of very good AI. It might

not be tomorrow's AI. It might be

yesterday's AI, but yesterday's AI is

freaking amazing. You know, in 10 years

time, 9year-old AI is going to be

amazing. You don't need, you know, 10

year old AI. You don't need frontier AI

like we need frontier AI because we want

to be the world leader. But for every

single country, everybody, I think the

ele the capability to elevate

everybody's knowledge and capability and

intelligence, uh, that day is coming.

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>> And also energy production, which is the

real bottleneck when it comes to third

world countries and

>> that's right,

>> electricity and all all the resources

that we take for granted.

>> Almost everything is going to be energy

constrained. And so if you take a look

at um

one of the most important technology

advances in history is this idea called

Moore's law. Moore's law

was the started basically in my

generation

and my generation is the generation of

computers. I graduated in 1984 and that

was basically at the very beginning of

the PC revolution.

And the microprocessor and and um

every single year it approximately

doubled

and we describe it as every single year

we double the performance. But what it

really means is that every single year

the cost of computing halfed.

And so the cost of computing in the

course of five years reduced by a factor

of 10. The amount of energy necessary to

do computing to do any task reduced by a

factor of 10. Every single 10 years 100

a th00and 10,000

100,000 so on and so forth. And so each

one of

the clicks of Moore's law, the amount of

energy necessary to do any computing

reduced. That's the reason why you have

a laptop today when back in 1984 sat on

the desk, you got to plug in, it wasn't

that fast and it consumed a lot of

power. Today, you know, it is only a few

watts. And so Moore's law is the

fundamental technology, the fundamental

technology trend that made it possible.

Well, what's going on in AI? The reason

why Nvidia is here is because in we

invented this new way of doing

computing. We call it accelerated

computing. We started it 33 years ago.

Took us about 30 years to really made a

huge breakthrough. In that in that 30

years or so

we took computing you know probably a

factor of well let me just say in last

10 years the last 10 years we improved

the performance of computing by 100,000

times.

Whoa. Imagine a car over the course of

10 years that became a 100,000 times

faster or at the same speed 100,000

times cheaper or at the same speed

100,000 times less energy. If your car

did that, it doesn't need energy at all.

What I mean what what I'm trying to say

is that in 10 years time the amount of

energy necessary for artificial

intelligence for most people will be

minuscule

utterly minuscule and so we'll have AI

running in all kinds of things and all

the time because it doesn't consume that

much energy and so if you're a nation

that uses AI for you know almost

everything in your social fabric of

course you're going to need these AI

factories but for a lot of countries I

think you're going You're going to have

excellent AI and you're not going to

need as much energy. Everybody will be

able to come along is my point.

>> So currently that that is a big

bottleneck, right? Is energy.

>> Yeah, it is the bottleneck.

>> The bottleneck is this. So was it Google

that is making nuclear power plants to

operate one of its AI factories?

>> Oh, I haven't heard that. But I think in

the next six, seven years, I think

you're going to see a whole bunch of

small nuclear reactors.

>> And by small, like how big are you

talking about? Hundreds of megawws.

Yeah.

>> Okay. And that these will be local to

whatever specific company they have.

>> That's right. Will all be power

generators.

>> Whoa.

>> You know, just like just like your you

know, somebody's farm.

>> It probably is the smartest way to do

it, right?

>> And it takes the burden off Yeah. takes

the burden off the grid. It takes and

you could build as much as you need

>> and you can contribute back to the grid.

It's a really important point that I

think you just made about Moore's law

and the relationship to pricing because

you know a laptop today like you can get

one of those little Mac MacBook Airs.

They're incredible. They're so thin,

unbelievably powerful. Battery life is

charge it.

>> Yeah. Battery [laughter] life's crazy.

And uh it's not that expensive

relatively speaking. Like something like

that.

>> I remember.

>> And that's just Moore's law, right?

>> Then there's the Nvidia law.

>> Oh,

>> just right. the the the law I was

talking to you about, the computing that

we invented,

>> right?

>> The reason why we're here, this new this

new way of doing computing

>> is like Mo's law on energy drinks. I

mean, it's [laughter]

it's like Mo's law

it's it's like Yeah. Moore's law and Joe

Rogan.

>> Wow. That's interesting.

>> Yeah. That's us.

>> So, explain that. Um this this chip that

you brought to Elon, what what's the

significance of this? It's like why is

it so superior? And so

in 2012, Jeff Hinton's lab, this

gentleman I was talking talking about,

um Ilas Suscober, Alex Kresevski, um

they made a breakthrough in computer

vision in literally creating a

piece of software

called Alexnet.

And its job was to recognize images. And

it recognized images

at a c at a level computer vision which

is fundamental to intelligence. If you

can't perceive, you can't it's hard to

have intelligence. And so computer

vision is a fundamental pillar of not

the only but fundamental pillar of. And

so breaking

computer vision or breaking through in

computer vision is pretty foundational

to almost everything that everybody

wants to do in AI. And so in 2012,

their lab in Toronto

uh made this made this breakthrough

called Alexnet. And Alexet was able to

recognize images

so much better than any human created

computer vision algorithm in the 30

years prior. So all of these people, all

these scientists and we had many too

working on computer vision algorithms

and these two kids, Ilia and Alex under

the the uh

under under uh Jeff Hinton took a giant

leap above it and it was based on this

thing called Alexet this neural network.

And the way it ran,

the way they they made it work was

literally buying two Nvidia graphics

cards

because Nvidia Nvidia's GPUs we've been

working on this new way of doing

computing and our GPUs application

and it's basically a supercomputing

application to back in 1984

in order to

process computer games and what you have

in your racing simulator that is called

an image generator supercomputer.

And so Nvidia started our first

application was computer graphics and we

applied this new way of doing computing

where we do things in parallel in

instead of sequentially. A CPU does

things sequentially. Step one, step two,

step three. In our case, we break the

problem down and we give it to thousands

of processors.

And so our way of doing computation

is much more complicated.

But if you're able to formulate the

problem in the way that we

created called CUDA, this is the

invention of our company. If you could

formulate it in that way, we could

process everything simultaneously.

Now, in the case of computer graphics,

it's easier to do because every single

pixel on your screen is not related to

every other pixel. And so, I could

render multiple parts of the screen at

the same time. Not not completely true

because, you know, maybe maybe the way

lighting works or the way shadow works,

there's a lot of dependency and and

such. But computer graphics with all the

dis with all the pixels, I should be

able to process everything

simultaneously. And so we we took

this embarrassingly parallel problem

called computer graphics and we applied

it to this new way of doing computing.

Nvidia's Nvidia's accelerated computing.

We put it in all of our graphics cards.

Kids were buying it to play games. We're

you probably don't know this, but we're

the largest gaming platform in the world

today.

>> Oh, I know that. Oh,

>> okay.

>> I used to make my own computers. I used

to buy your graphics cards.

>> Oh, that's super cool.

>> Yeah. [laughter] set up SLI with two

graphics cards.

>> Yeah, I love it. Okay, that's super

cool.

>> Oh, yeah, man. I used to be a Quake

junkie.

>> Oh, that's cool.

>> Yeah.

>> Okay, so SLI, I'll tell you the story in

just a second and how it led to Elon.

I'm still answering the question. And

so, anyways, these these two kids

trained this model using the technique I

described earlier on our GPUs because

our GPUs could process things in

parallel. It's essentially a supercomput

in a PC. The reason why you used it for

Quake is because it is the first

consumer supercomputer. Okay. And so

anyways,

they made that breakthrough. We were

working on computer vision at the time.

It caught my attention

and so we went to learn about it.

Simultaneously this deep learning

phenomenon was happening all over all

over the country. Universities after

another recognized the importance of

deep learning and all of this work was

happening at Stanford, at Harvard, at

Berkeley, just all over the place. New

York University, L Yan Lakun, Andrew

Yang at Stanford, so many different

places. And I see it cropping up

everywhere.

And so my curiosity asked, you know,

what is so special about this form of

machine learning? And we've known about

machine learning for a very long time.

We've known about AI for a very long

time. We've known about neural networks

for a very long time. What makes now the

moment? And so we realized that this

architecture for deep neural networks

back propagation the way deep neuronet

networks were created. We could probably

scale this problem, scale the solution

to solve many problems.

that is essentially

a universal function approximator. Okay?

Meaning meaning you know back when

you're in in in school you have a you

have a you have a box inside of it is a

function you give it an input it gives

you an output and and the the reason why

I call it universal function

approximator

is that this computer instead of you

describing the function a function could

be a new equation fals ma that's a

function you write the function in

software you give it input f mass

acceleration, it'll tell you the force.

Okay? And

the way this computer works is really

interesting.

You give it a universal function. It's

not fals, just a universal function.

It's a big huge deep neural network

and instead of describing the inside,

you give it examples of input and output

and it figures out the inside.

So you give it input and output and it

figures out the inside. A universal

function approximator. Today it could be

Newton's equation. Tomorrow it could be

Maxwell's equation. It could be Kulum's

law. It could be thermodynamics

equation. It could be you know

Shingers's equation for quantum physics.

And so you could put any you could have

this describe almost anything so long as

you have the input and the output. So

long as you have the input and the

output or it could learn the input and

output.

>> And so we took a step back and we said,

"Hang on a second. This isn't just for

computer vision. Deep learning could

solve any problem.

All the problems that are interesting so

long as we have input and output. Now

what has input and output?

Well, the world. The world has input and

output. And so we could have a computer

that could learn almost anything.

Machine learning, artificial

intelligence. And so we reasoned that

maybe this is the fundamental

breakthrough that we needed. There were

a couple of things that had to be

solved. For example, we had to believe

that you could actually scale this up to

giant systems. It was running in a they

had two graphics cards, two GTX 580s,

[laughter]

which by the way is exactly your SLI

configuration. Yeah. Okay. So, that GTX

5880 SLI was the revolutionary computer

that put deep learning on the map.

>> Wow.

>> It was 2018 and you were using it to

play Quake.

>> Wow. That's crazy.

>> That was the moment. That was the big

bang of modern AI. We were lucky because

we were inventing this technology, this

computing approach. We were lucky that

they found it.

Turns out they were gamers and it was

lucky they found it. And it it was lucky

that we paid attention to that moment.

It was a little bit like, you know, that

Star Trek,

you know, first contact.

The Vulcans had to have seen the warp

drive at that very moment. If they

didn't witness the warp drive, you know,

they would have never come to Earth and

everything would have never happened.

It's a little bit like if I hadn't paid

attention to that moment, that flash.

And that flash didn't last long. If I

hadn't paid attention to that flash or

our company didn't pay attention to it,

who knows what would have happened, but

we saw that and we reasoned our way into

this is a this is a universal function

approximator. This is not just a

computer vision approximator. We could

use this for all kinds of things. if we

could solve two problems. The first

problem is that we have to prove to

oursel it could scale. The second

problem we had to

wait for I guess contribute to and wait

for is

the world will never have enough data

on input and output where we could

supervise

the AI to learn everything. For example,

if we have to supervise our children on

everything they learn, the amount of

information they could learn is limited.

We needed the AI, we needed the computer

to have a method of learning without

supervision.

And that's where we had to wait a few

more years, but un unsupervised

AI learning is now here. And so the AI

could learn by itself. And and the

reason why the AI could learn by itself

is because we have many examples of

right answers. Like for example,

if I want to learn uh if I want to teach

an AI how to predict the next word, I

could just grab it, grab a whole bunch

of text we already have, mask out the

last word and make it try and try and

try again until it predicts the next

one. or I mask out random words inside

inside the text and I make it try and

try and try until it predicts it. You

know, like uh Mary uh Mary goes down to

the bank. Is it a river bank or a money

bank? Well, if you're going to go down

to the bank, it's probably a river bank.

Okay. So, and it it it might not be

obvious even from that. It might need

and

uh and uh and caught a fish. Okay. Now

you know it's must be the riverbank. And

so so you give you give these AIs a

whole bunch of these examples and you

mask out the words, it'll predict the

next one. Okay? And so unsupervised

learning came along. These two ideas,

the fact that it's scalable and

unsupervised learning came along.

We were convinced that we ought to put

everything into this and help create

this industry because we're going to

solve a whole bunch of interesting

problems. And that was in 2012. By 2016,

I had I had built this computer called

the DGX1. The one that you saw me give

to Elon is called DGX Spark. The DGX1

was $300,000.

It cost Nvidia a few billion dollars to

make the first one.

And instead of two chips SLI,

we connected eight chips with a

technology called MVLink, but it's

basically SLI supercharged.

Okay.

>> Okay.

>> And so we connected eight of these chips

together instead of just two. And all of

them work together just like your Quake

rig did to solve this deep learning

problem to train this model. And so I

create we created this thing. I

announced it at GTC

and at one of our annual annual events

and I described this deep learning

thing, computer vision thing and this

computer called DJX1.

The audience was like completely silent.

They had no idea what I was talking

about. [laughter]

And I was lucky because I I had known

Elon and uh uh I helped him build the

first computer for Model 3

uh uh the Model S. And uh and when he

wanted to start working on autonomous

vehicle, I helped him build the computer

that went into the the Model S AV

system, his full full self-driving

system. We were basically the FSD

computer version one. And so

we we're already working together and um

when I announced this thing, nobody in

the world wanted it. I had no purchase

orders. Not not one. Nobody wanted to

buy it. Nobody wanted to be part of it

except for Elon. He goes, he was at the

event and we were doing a fireside chat

about the future of self-driving cars.

I think it's like 2016. Yeah, 20 maybe

at that time it was 2015. and he goes,

"You know what?

I have a company that could really use

this."

I said, "Wow, my first customer." And

so, so I was pretty excited about it.

And he goes, "Uh, yeah. Uh, we have this

company. It's a nonprofit company."

And all the blood drained out of my

face. Yeah. [laughter]

I just spent a few billion dollars

building this thing. Cost $300,000. and

you know the chances of a nonprofit

being able to pay for this thing is

approximately zero. And he goes, you

know, this is a it's an AI company and

uh it's a nonprofit and and uh we could

really use one of these supercomputers.

And so I I picked it up. I built the

first one for ourselves. We're using it

inside the company. I boxed one up. I

drove it up to San Francisco and I

delivered to Elon in 2016. A bunch of

researchers were were there.

Peter Beiel was there, Ilia was there,

and there was a bunch of people there.

And uh I walk up to the second floor

where they were all kind of in a room

this smaller than your place here. And

and uh uh that place turned out to have

been open AI

>> 2016.

>> Wow.

>> Just a bunch of people sitting in a

room.

>> It's not really uh nonprofit anymore,

though, is it?

>> They're not They're not nonprofit

anymore. Yeah.

>> Weird how that works.

>> Yeah. Yeah. But anyhow, anyhow, Elon was

there. The Yeah, it was it was really a

great great moment.

>> Oh, yeah. There you go. Yeah, that's it.

[laughter]

>> Look at you, bro. Same jacket.

>> Look at that. I haven't aged.

>> Not not a lick of black hair, though.

>> Uh the size of it is uh it's

significantly smaller. That was the

other day. SpaceX.

>> Oh, yeah. There you go.

>> Yeah. Look at the difference.

>> Exactly the same industrial design. He's

holding it in his hand

>> here. Here's the amazing thing. DJX1 was

one pedlops. Okay, that's a lot of

flops. And DJX Spark is one pedlops.

Nine years later.

>> Wow.

>> The same the same amount of computing

horsepower

>> in a much smaller

>> shrunken down. Yeah.

>> And instead of $300,000, it's now

$4,000. And it's the size of a small

book.

>> Incredible.

>> Crazy.

>> That's how technology moves. Anyways,

that's the reason why I wanted to get

give him the first one

>> because I gave him the first one 2016.

>> It's so fascinating. I mean you if you

wanted to make a story for a film I mean

that would be the story that like what

what better scenario if if if it really

does become a digital life form how

funny would it be that it is birthed out

of the desire for computer graphics for

video games [laughter]

>> exactly

>> kind of cra it's kind of crazy

>> kind of crazy when you think about it

that way

>> because

it's just

>> perfect origin

Computer graphics was one of the hardest

computer supercomputer problems

generating reality

>> and also one of the most profitable to

solve because computer games are so

popular.

>> When Nvidia started in 1993,

we were trying to create this new

computing approach. The question is

what's the killer app?

And

the the problem we wanted to the the

company wanted to create a new type of

computing pro a computing architecture a

computing a a new type of computer that

can solve problems that normal computers

can't solve.

Well,

the applications that existed in the

industry in 1993

are applications that normal computers

can solve because if the normal

computers can't solve them, why would

the application exist?

And so, we had a mission statement for a

company that has no chance of success.

[laughter]

But I didn't know that in 1993. It just

sounded like a good idea,

>> right?

And so if we created this thing that can

solve problems, you know, it's like

you actually have to go create the

problem.

And so that's what we did in 1993. There

was no quake. John Carmarmac hadn't been

reduced doom released Doom yet. You

probably remember that.

>> Sure. Yeah.

>> And and uh there were no applications

for it. And so I went to Japan because

the arcade industry had this at the time

of Sega, if you remember.

>> Sure.

>> The arcade machines, they came out with

3D arcade systems, virtual fighter,

Daytona, Virtual Cop, all of those

arcade games were in 3D for the f very

first time. And the technology they were

using was from Martin Marietta, the

flight simulators. They took the guts

out of a flight simulator and put it

into an arcade machine. The system that

you have over here, it's got to be a

million times more powerful than that

arcade machine. And that was a flight

simulator for NASA. Whoa. And so they

took the guts out of that. They were

they were using it for flight simulation

for jets and, you know, space shuttle

and and they took the guts out of that.

and Sega uh had this brilliant computer

de developer. His name was Yuzuki.

Yuzuki and Miiamoto. Sega and Nintendo.

These were the, you know, the incredible

pioneers, the visionaries, the

incredible artists, and they're both

very, very technical.

They were the origins really of of the

gaming industry. and Y Suzuki

pioneered 3D graphics gaming and um

so I went we we created this company and

there were no apps

and we were spending all of our

afternoons you know we told our family

we were going to work but it was just

the three of us you know who's going to

know and so we went to Curtis's my one

of one of the founders went to Curtis's

townhouse and uh Chris and I were

married we have kids I already had

Spencer at Madison. They were probably 2

years old. And um

and uh Chris's kids are about the same

age as ours. And we would go to work in

this townhouse. But you know, when

you're a startup and the mission

statement is the way we described,

you're not going to have too many

customers calling you. And so we had

really nothing to do. And so after

lunch, we would always have a great

lunch. After lunch, we would go to the

arcades and play the Sega V, you know,

the Sega Virtual Fighter and Daytona and

all those games and analyze how they're

doing it, trying to figure out how they

they were doing that.

And so we decided, um, let's just go to

Japan and let's

convince Sega to move those applications

into the PC.

and we would start the PC gaming the 3D

gaming industry partnering with Sega.

That's how Nvidia started.

>> Wow.

>> And so so uh in exchange for them part

developing their games for our computers

in the PC, we would build a chip for

their game console. That was the

partnership. I build a chip for your

game console. you port the Sega games to

us and um

and then they paid us a you know at the

time a quite a significant amount of

money to build that game console

and that was kind of the beginning of

Nvidia getting started and we thought we

were on our way and so so I started with

a business plan a mission statement that

was impossible we lucked into the Sega

partnership we started taking off

started building our game console. And

about a couple years into it, we

discovered our first technology

didn't work.

It was it it would have been a flaw. It

it was a flaw. And all of the technology

ideas that we had

the architecture concepts were were

sound, but the way we were doing

computer graphics was exactly backwards.

you know, instead of

I won't bore you with the technology,

but instead of inverse texture mapping,

we were doing forward texture mapping.

Instead of triangles, we did curved

surfaces. So, other people did it flat,

we did it round. Um,

other technology, the technology that

ultimately won, the technology we use

today has has Zbuffers. It automatically

sorted.

We had an architecture with no Zbuffers.

The application had to sort it. And so

we chose a bunch of technology

approaches

that

three major technology choices. All

three choices were wrong. Okay. So this

is how incredibly smart we were. And so

[laughter]

and so in 1995 19 early mid95

we realized we were going down the wrong

path. Meanwhile,

the Silicon Valley was packed with 3D

graphics startups because it was the

most exciting technology of that time.

And so 3D FX and rendition and Silicon

Graphics was coming in. Intel was

already in there and you know gosh like

what added up eventually to a hundred

different startups we had to compete

against. Everybody had chosen the right

technology approach and we chose the

wrong one. And so we were the first

company to start. We found ourselves

essentially dead last with the wrong

answer.

And so

the company was in trouble

and um

ultimately we had to make several

decisions. The first decision is

well

if we change now

we will be the last company.

And

even if we changed into the technology

that we believe to be right, we'd still

be dead. And so that argument,

you know,

do we change and therefore be dead?

Don't change and make this technology

work somehow or go do something

completely different.

That question stirred the company

strategically and was a hard question. I

eventually, you know, advocated for we

don't know what the right strategy is,

but we know what the wrong technology

is. So, let's stop doing it the wrong

way and let's give ourselves a chance to

go figure out what the strategy is. The

second thing, the second problem we had

was our company was running out of money

and I had I was in a contract with Sega

and I owed them this game console

and if that contract would have been

cancelled, we'd be dead.

We would have vaporized instantly.

And so so uh uh I went to Japan and I

explained to uh the CEO of Sega, Erie

Madri, really great man. He was the

former CEO of Honda USA. Went back to

Sega to run Sega. Went back to Japan to

run Sega. And I explained to him that I

was uh I guess I was what 30 33 years

old.

you know, when I was 33 years old, I

still had acne. And I got this this, you

know, Chinese kid. I was super skinny.

And he he was already kind of elder.

And uh I went to him and I said I said,

"Listen,

I've got some bad news for you." And and

first, the technology that we promised

you doesn't work.

And second,

we shouldn't finish your contract

because we'd waste all your money and

you would have something that doesn't

work. And I recommend you find another

partner to build your game console.

>> Whoa.

>> And so I'm terribly sorry that we've set

you back in your product roadmap.

And third,

even though you're going to I'm asking

you to let me out of the contract,

I still need the money

because if you didn't give me the money,

we'd vaporize overnight.

And so

I explained it to him humbly, honestly.

I gave him the background.

explain to him why the technology

doesn't work, why we thought it was

going to work, why it doesn't work.

And um and I asked him

to uh

convert the last $5 million that they

were to complete the contract to give us

that money as an investment

instead.

and he said,

"But it's very likely your company will

go out of business, even with my

investment."

And it was completely true. Back then,

1995, $5 million was a lot of money.

It's a lot of money today. $5 million

was a lot of money. And here's a pile of

competitors doing it right. What are the

chances that giving Nvidia $5 million

that we would develop the right strategy

that he would get a return on that $5

million or even get it back? 0%.

You do the math. It's 0%.

If I were sitting there right there, I

wouldn't have done it.

$5 million was a mountain of money to

Sega at the time.

And so

I told him that that that um

uh

if you invested that $5 million in us,

it is most likely to be lost.

But if you didn't invest that money,

we'd be out of business and we would

have no chance.

And I I told him that I

I don't even know exactly what I said in

the end, but I

told him that I would understand if he

decided not to, but it would make the

world to me if he did. He went off and

thought about it for a couple days and

came back and said, "We'll do it."

>> Wow.

strategy to how to correct what it was

doing wrong. Did you explain that to

him?

>> Wait, oh man, wait until I tell you the

rest of it's scarier. Even scarier.

>> Oh no. [laughter]

>> And so so um

so what he what he decided was was uh

Jensen was a young man he liked. That's

it.

>> Wow. to this day.

>> That's nuts.

>> I was

>> Boy, do you owe what the world owes that

guy.

>> No doubt,

>> right?

>> Well, he's he c he's he celebrated today

in Japan.

>> And if he would have kept that five

>> the the investment, I think it'd be

worth probably about a trillion dollars

today.

I know. But the moment we went public,

they sold it. They go, "Wow, that's a

miracle." So, [laughter]

>> wow.

>> They sold it. Yeah. They sold it at

Nvidia valuation about 300 million.

That's our IPO valuation. 300 million.

>> Wow.

>> And so, so anyhow,

I was incredibly grateful. Um,

and then now we had to figure out what

to do because we still were doing the

wrong strategy, wrong technology. So

unfortunately we had to lay off most of

the company. We shrunk the company all

back. All the people working on the game

console, you know, we had to shrunk it

all. Shrink it all back.

And um

and then and then somebody told me that,

but Jensen,

we've never built it this way before.

We've never built it the right way

before.

We've only know how to build it the

wrong way.

And so nobody in the company knew how to

build this

supercomputing image generator 3D

graphics thing that Silicon Graphics

did. And so so uh I said, "Okay,

how hard can it be? You got all these 30

companies, you know, 50 companies doing

it. How hard can it be?" And so luckily

there was a textbook written by the

company Silicon Graphics.

And so I went down to the store. I had

200 bucks in my pocket. And I bought

three textbooks, the only three they

had, $60 a piece. I bought the three

textbooks. I brought it back and I gave

one to each one of the architects and I

said, "Read that and let's go save the

company."

>> [laughter]

>> And so

[gasps and sighs] so they they they read

this textbook, learned from the giant at

the time, Silicon Graphics, about how to

do 3D graphics. But the thing that was

amazing and what makes Nvidia special

today is that

the people that are there are able to

start from first principles,

learn best known art, but reimplement it

in a way that's never been done before.

And so when we re-imagined

the technology of 3D graphics, we

reimagined it in a way that manifest

today the modern 3D graphics. We really

invented modern 3D graphics, but we

learned from previous known arts and we

implement it fundamentally differently.

>> What did you do that changed it?

Well, you know, the ultimately

ultimately the um uh the simple the

simple answer is that the way silicon

graphics works uh the geometry engine is

a bunch of software running on

processors.

We took that and

eliminated all the generality,

the general purposeness of it and we

reduced it down into the most essential

part of 3D graphics

and we hardcoded it into the chip. And

so instead of something general purpose,

we hardcoded it very specifically into

just the limited applications,

limited functionality necessary for

video games.

And that capability that super and and

because we reinvented a whole bunch of

stuff, it supercharged the capability of

that one little chip. And our one little

chip was generating images as fast as a

$1 million image generator. That was the

big breakthrough. We took a million

dollar thing and we put it into the

graphics card that you now put into your

gaming PC. And that was [snorts] our big

invention.

And then and of course the question is

is um

uh how do you compete against these 30

other companies doing what they were

doing?

and and there we did we did several

things. One

uh instead of building a 3D graphics

chip for every 3D graphics application,

we decided to build a 3D graphics chip

for one application. We bet the farm on

video games.

The needs of video games are very

different than needs for CAD, needs for

flight simulators. They're related, but

not the same. And so we narrowly focused

our problem statement so I could reject

all of the other complexities and we

shrunk it down into this one little

focus and then we supercharged it for

gamers. And then the second thing that

we did was we created a whole ecosystem

of working with game developers and

getting their their games ported and

adapted to our silicon so that we could

get turn essentially what is a

technology business into a platform

business into a game platform business.

So we, you know, GeForce is really today

it's also the most advanced 3D graphics

technology in the world, but a long time

ago GeForce is really the game console

inside your PC. It's, you know, it runs

Windows, it runs Excel, it runs

PowerPoint, of course, those are easy

things, but its fundamental purpose was

simply to turn your PC into a game

console. So we we were the first

technology company to build all of this

incredible technology in service of one

audience gamers. Now of course in 1993

the gaming industry didn't exist. But by

the time that John Carmarmac came along

and the doom phenomenon happened and

then quake came out as you know

that entire world oh that entire

community boom took off. Do you know

where the name Doom came from?

>> It came from this se there's a scene in

the movie The Color of Money where Tom

Cruz who's this uh elite pool player

shows up at this pool hall and this

local hustler says what he got in the

case and he opens up this case. He has a

special pool queue. He goes in here and

he opens it up. He goes, "Doom.

>> Doom." [laughter]

>> And that's where it came from. Yeah. Cuz

Carmarmac said that's what they wanted

to do to the gaming industry.

>> Doom.

>> That when Doom came out, it would just

be everybody be like, "Oh, we're

fucked."

>> Oh, wow.

>> This is Doom.

>> That's awesome.

>> Isn't that amazing? That's amazing.

>> Cuz it's the perfect name for the game.

>> Yeah.

>> And the name came out of that scene in

that movie.

>> That's right. Well, and then of course,

uh, Tim Sweeney and

>> Epic Games and, uh, and the 3D gaming

genre took off.

>> Yes.

>> And so, if you just kind of in the

beginning was no gaming industry. We had

no choice but to focus the company on

one thing. That one thing,

>> it's a really incredible origin story.

>> Oh, it's it's amazing. Like you must be

like look back

>> a disaster is what

>> a $5 [laughter] million that pivot with

that conversation with that gentleman if

he did not agree to that if he did not

like you what would the world look like

today that's crazy then then our entire

life hung on another gentleman

and so so now here we are we built so

before GeForce it was Revo 128 revo 128

saved the company it revolutionized

computer graphics

The performance cost performance ratio

of 3D graphics for gaming was off the

charts amazing.

And

we're getting ready to to ship it. Get

well, we're we're building it, but we're

so as you know, $5 million doesn't last

long. And so every single month, every

single month, uh we were drawing down

You have to build it, prototype it. You

have to design it, prototype it,

get the silicon back,

which costs a lot of money. Test it with

software

because without the software testing the

chip, you don't know the chip works.

And then you're going to find a bug

probably

because every time you test something

you find bugs,

which means you have to tape it out

again, which is more time, more money.

And so we did the math. There was no

chance anybody was going to survive it.

We didn't have that much time to tape

out a chip, send it to a foundry TSMC,

get the silicon back, test it, send it

back out again. There was no no shot, no

hope.

And so the math, the spreadsheet doesn't

allow us to do that. And so I heard

about this company and this company

built this machine.

And this machine is an emulator.

You could take your design, all of the

software that describes the chip, and

you could put it into this machine. And

this machine will pretend it's our chip.

So I don't have to send it to the fab,

wait until the fab sends it back, test.

I could have this machine pretend it's

our chip and I could put all of the

software on top of this machine called

an emulator and test all of the software

on this pretend chip and I could fix it

all before I send it to the fab.

>> Whoa. And if and and if I could do that

when I send it to the FAB, it should

work.

Nobody knows, but it should work. And so

we came to the conclusion

that let's take half of the money we had

left in the bank. At the time it was

about a million dollars. Take half of

that money and go buy this machine.

So instead of keeping the money to stay

alive, I took half of the money to go

buy this machine. Well, I call this guy

up. This the company's called IOS.

Call this company up and I say, "Hey,

listen. I heard about this machine.

I like to buy one."

And they go, "H,

that's terrific, but we're out of

business." I said, "What? You're out of

business?" He goes, "Yeah, we had no

customers."

[laughter]

I said, "Wait, hang on a sec. So, you

never made the machine?" They [snorts]

can say, "No, no, no. We made the

machine. We have one in inventory if you

want it, but we're out of business." So,

I bought one out of inventory.

Okay. After I bought it, they went out

of business.

>> Wow.

>> I bought it out of inventory.

And on this machine, we put Nvidia's

chip into it and we tested all of the

software on top.

And at this point, we were on fumes.

But we convinced ourselves that chip is

going to be great.

And so I had to call some other

gentleman. So I called TSMC.

And I told TSMC

that listen, TSMC is the world's largest

founder today. At the time they were

just a few hundred million dollars

large,

tiny little company.

And I explained to them what we were

doing. And um I explained to him I told

him I had a lot of customers. I had one,

you know, Diamond Multimedia,

probably one of the companies you bought

the graphics card from back in the old

days. And I I said, you know, we have a

lot of customers, and the demand's

really great, and

we're going to tape out a chip to you,

and I like to go directly to production

because I know it works.

>> [snorts]

>> And they said, "Nobody has ever done

that before.

Nobody has ever taped out a chip that

worked the first time.

And nobody starts out production without

looking at it."

But I knew that if I didn't start the

production, I'd be out of business

anyways. And if I could start the

production, I might have a chance.

And so

TSMC

decided to support me and uh this

gentleman is named Morris Chang. Morris

Chang is the father of the foundry

industry, the founder of TSMC. Really

great man.

He decided to support our company. I

explained to them everything.

he decided to support us frankly

probably because they didn't have that

many other customers anyhow but they

were grateful and I was immensely

grateful and as we were starting the

production

Morris flew to United States and uh

he didn't so many words asked me so but

he asked me a whole lot of questions

that was trying to tease out do I have

any money

but he didn't directly ask me that you

know and so the truth is that we didn't

have all the money but we had a strong

PO from the customer and if it didn't

work some wafers would have been lost

and I'm you know I I'm not exactly sure

what would have happened but we would

have come short it would have been it

would have been rough but they supported

us with all of that risk involved we

launched this chip turns out to

been completely revolutionary.

Knocked the ball out of the park. We

became the fastest growing technology

company in history to go from zero to $1

billion.

>> So wild that you didn't test the chip.

>> I know. We tested afterwards. Yeah, we

tested afterwards.

>> Afterwards, but [laughter]

production already. But by the way, by

the way, that methodology that we

developed to save the company is used

throughout the world today.

>> That's amazing.

>> Yeah, we changed we changed the whole

world's methodology of designing chips.

The whole world's uh rhythm of designing

chips. Uh we changed everything.

>> How well did you sleep those days? It

must have been so much stress,

[laughter]

>> you know. Um,

what is that feeling where where uh the

world just kind of feels like it's

flying? It you you have this what do you

call that feeling? You can't you can't

stop the the feeling that everything's

moving super fast and you know and

you're laying in your laying in bed and

the world just feels like you know it

you and you're you you feel deeply

anxious

uh completely out of control. Um

I've felt that probably a couple of

times [laughter] in my life. It's during

that time.

>> Wow.

>> Yeah. It it was incredible.

>> What an incredible success story.

>> But I I learned I learned a lot. I

learned I learned about I learned

several things. I learned I learned uh

how to develop strategies.

Um I learned how to

uh uh and when I when I you know our

company learned how to develop

strategies. What are winning strategies?

We learned how to create a market. We

created the modern 3D gaming market.

We learned how and and so that exact

same skill is how we created the modern

AI market. It's exactly the same.

>> Wow.

>> Yeah. Exactly the same skill. Exactly

the same blueprint. And

uh we learned how to uh deal with

crisis, how to stay calm, how to think

through things systematically.

We learned how to remove all waste in

the company and work from first

principles and doing only the things

that are essential. Everything else is

waste because we have no money for it

to live on fumes at all times. And the

feeling

no different than the feeling I had this

morning when I woke up that you're going

to be out of business soon. that you're

you know the phrase 30 days from going

out of business I've used for 33 years

because

>> you still feel that.

>> Oh yeah. Oh yeah. Every morning. Every

morning.

>> But but you guys are one of the biggest

companies on planet earth. But the the

feeling doesn't change.

>> Wow.

>> The the sense of vulnerability, the

sense of uncertainty, the sense of

insecurity. Uh

it it doesn't leave you.

>> That's crazy. We were, you know, we had

nothing. We had nothing. We were dealing

with giant.

>> Oh, yeah. Oh, yeah. Every day, every

moment.

>> Do you think that fuels you? Is that

part of the reason why the company's so

successful? That you have that hungry

mentality,

that you never rest, you're never

sitting on your laurels, you're always

on the edge.

I have a greater drive from not wanting

to fail

than the drive of wanting to succeed.

[laughter]

>> Isn't that like sex coaches would tell

you that's completely the wrong

psychology?

>> The world has just heard me say that for

out loud for the first time.

>> But but it's true.

>> Well, that's how fascinating. fear of

failure drives me more than the than the

the greed or whatever it is.

>> Well, ultimately that's probably a more

healthy approach now that I'm thinking

about it because like the fear

>> I'm not ambitious for example,

[laughter] you know, I just want to stay

alive, Joe. I want the company to

thrive, you know, I want us to make an

impact.

>> That's interesting.

>> Yeah.

>> Well, maybe that's why you're so humble.

That's what maybe that's what keeps you

grounded, you know, because with the

kind of spectacular success the

company's achieved, it would be easy to

get a big head.

>> No.

>> Right. But isn't that interesting? It's

like the if you were the guy that your

main focus is just success. You probably

would go, "Well, made it. Nailed it. I'm

the [laughter] man.

>> Drop the mic."

>> Instead, you wake up, you're like, "God,

we can't [ __ ] this up."

>> No. Exactly. Every morning. Every

morning. No. Every moment. Yeah. That's

crazy.

>> Before I go to bed.

>> Well, listen. If I was a major investor

in your company, that's what I'd want

running it. I'd want a guy who's

>> Yeah.

>> That's what I work. That's why I work

seven days a week. Every moment I'm I'm

awake.

>> You work every moment.

>> Every moment I'm awake.

>> Wow.

>> I'm thinking about solving a problem.

I'm thinking about

>> How long can you keep this up?

>> I don't know. But so [laughter]

could be next week. Sounds exhausting.

>> It is exhausting.

>> It sounds completely exhausting.

>> Always in a state of anxiety.

>> Wow.

>> Always in a state of anxiety.

>> Wow. Kudos to you for admitting that. I

think that's important for a lot of

people to hear because, you know,

there's probably some young people out

there that are in a similar position to

where you were when you were starting

out that just feel like, oh, those

people that have made it, they're just

smarter than me and they had more

opportunities than me and it's just like

it was handed to them or they're just in

the right place at the right time. And

>> Joe, I just described to you somebody

who didn't know what was going on,

[laughter]

actually did it wrong.

>> Yeah. Yeah. And the ultimate diving

catch like two or three times.

>> Crazy.

>> Yeah.

>> The ultimate diving catch is the perfect

way to put it.

>> You know, it's just like the edge of

your glove. [laughter]

>> It probably bounced off of somebody's

helmet and landed at the edge.

[laughter]

>> God, that's incredible. That's

incredible. But it's also it's really

cool that you have this perspective that

you look at it that way because you know

a lot of people that have delusions of

grandeur or they have you know

>> and their rewriting of history

often times had them somehow extraord

extraordinarily smart and they were

geniuses and they knew all along and

they were they were spot-on. And the

business plan was exactly what they

thought. And

>> yeah,

>> they destroyed the competition and you

know and they emerged victorious.

[laughter]

>> Meanwhile, you're like, I'm scared every

day.

>> Exactly. [laughter]

Exactly.

>> That's so funny. Oh my god, that's

amazing.

>> It's so true, though.

>> It's amazing.

>> It's so true.

>> It's amazing. Well, but I I think

there's nothing inconsistent

with being a leader and being

vulnerable. You know, I the company

doesn't need me to be a genius right all

along, right?

Absolutely certain about what I'm trying

to do and what I'm doing. The the

company doesn't need that. The company

wants me to succeed. You know, the thing

that and we started out today talking

about President Trump and I was about to

say something and listen, he is my

president. He is our president. We

should all and we're talking about just

because it's President Trump, we all

want him to be wrong. I think that

United States, we all have to realize he

is our president, we want him to succeed

because

>> no matter who's president attitude.

>> That's right.

>> We want him to succeed. We need to help

him succeed because it helps everybody,

all of us succeed.

And

I'm lucky that I work in a company where

I have 40,000 people who wants me to

succeed.

They want me to succeed and I can tell

and they're all every single day to help

me overcome these challenges

trying to realize

realize what I describe to be our

strategy doing their best. And if it's

somehow

wrong or not perfectly right to tell me

so that we could pivot and the more

vulnerable we are as a leader the more

able other people are able to tell you

you know that Jensen that's not exactly

right or

>> right right

>> have you considered this information or

and the more vulnerable we are

the more able we're actually able to

pivot if we put ourselves into this

superhuman capability then it's hard for

us to pivot strategy,

>> right?

>> Because we were supposed to be right all

along.

>> And so if you're always right, how can

you possibly pivot? Because pivoting

requires you to be wrong. And so I've

got no trouble with being wrong. I just

have to make sure that I stay alert,

that I reason about things from first

principles all the time. Always break

things down to first principles.

Understand why it's happening.

Reassess continuously. The reassessing

continuously is kind of partly what

causes continuous anxiety,

>> you know, because you're asking

yourself, were you wrong yesterday? Are

you still right? Is this the same? Has

that changed? Has that condition is that

worse than you thought?

>> But God, that mindset is perfect for

your business, though, because this

business is ever changing

>> all the time. I've got competition

coming from every direction. So much of

it is kind of up in the air

and you have to invent a future where

a 100 variables are included and there's

no way you could be right on all of

them. And so you have to be

>> you have to surf.

>> Wow. That's a good way to put it. You

have to surf. Yeah. You're surfing waves

of technology and innovation.

>> That's right. You can't predict the

waves. You got to deal with the ones you

have.

>> Wow. And but skill matters and I've been

doing this for 30 I'm the longest

running tech CEO in the world.

>> Is that true? Congratulations. That's

amazing.

>> And you know people ask me how is one

don't get fired. [laughter]

That'll stop a short heartbeat.

And then two don't get bored.

>> Yeah.

>> Well, how do you maintain your

enthusiasm?

Well, the honor truth is is not always

enthusiasm. It's, you know, sometimes is

enthusiasm. Sometimes it's just good

oldfashioned fear and then sometimes,

you know, a healthy dose of frustration,

you know, it's whatever keeps you

moving.

>> Yeah. Just all the emotions. I think,

you know,

>> CEOs, we have all the emotions, right?

you know, and so probably probably

jacked up to the maximum because you're

you're kind of feeling it on behalf of

the whole company. I'm feeling it on

behalf of everybody at the same time.

And it kind of, you know, encapsulates

into into somebody. And so I have to be

mindful of the past. I have to be

mindful of the present. I've got to be

mindful of the future. And um you know,

it can't it's not without emotion.

It's not just it's it's not just a job.

Let's just put it that way.

>> It doesn't seem like it at all. I would

imagine one of the more difficult

aspects of your job currently now that

the company is massively successful is

anticipating where technology is headed

and where the applications are going to

be.

>> Yeah.

>> So, how do you try to map that out?

>> Yeah. there there um there there's a

whole bunch of ways and and it takes it

takes um

takes a whole bunch of things but let me

just start

uh you have to be surrounded by amazing

people and Nvidia is now you know if you

look at look at look at um the large

tech companies in the world today

most of them have a business in

advertising or social media or you know

content distribution and at the core of

it is really fundamental computer

science and so the company's business is

not computers the company's business is

not technology technology drives the

company is the only company in the world

that's large whose only business is

technology we only build techn we don't

advertise the only way that we make

money is to create amazing technology

and sell

And so

to be that to be NVIDIA today, you're

the number one thing is you're

surrounded by the finest computer

scientists in the world. And that's my

gift. My gift is that we've created a

company's culture,

a condition by which the world's

greatest computer scientists want to be

part of it because they get to do their

life's work and create the next thing

because that's what they want to do.

because maybe they're not they don't

want to be in service of another

business.

>> They want to be in service of the

technology itself. And we're the largest

form of its kind in the history of the

world.

>> Wow.

>> I know. It's pretty amazing.

>> Wow.

>> And so so one, you know, we have a we we

have got a great condition. We have a

great culture. We have great people. And

then now now now the question is how do

you systematically

um

be able to see the future stay alert of

it

and uh reduce the reduce the the

likelihood of missing something or being

wrong.

And so there's a lot of different ways

you could do that. For example, we have

great partnerships. We we have

fundamental research. We have a great

research lab, one of the largest

industrial research labs in the world

today. And we partner with a whole bunch

of universities and other scientists. We

do a lot of open collaboration

and so I'm constantly working with

researchers outside the company.

We have the benefit of having amazing

customers and so I have the benefit of

working with Elon and you know and

others in the industry and we have the

benefit of being the only pure pure play

technology company that can serve uh

consumer internet

industrial manufacturing

um scientific computing healthcare

financial services all the industries

that we're in. They're all signals to

me. And so they all have mathematicians

and scientists and and so because I I

have the benefit now of a radar system

>> that is the most broad of any company in

the world working across every single

industry from agriculture to energy

to video games.

And so the ability for us to have this

vantage point,

one doing fundamental research ourselves

and then two working with all the great

researchers, working with all the great

industries, the feedback system is

incredible. And then finally,

you just have to have a culture of

staying super alert. There's no easy way

of being alert except for paying

attention.

I haven't found a single way of being

able to stay alert without paying

attention. And so, you know, I probably

read several thousand emails a day.

>> How How do you have a time for that?

>> I wake up early. This morning I was up

at 4:00.

>> How much do you sleep?

>> Uh, six, seven, six, seven hours.

>> Yeah.

>> And then you're up at 4 reading emails

for a few hours before you get going.

>> That's right. Yeah.

>> Wow.

Every day.

>> Every single day. Not one day missed.

[sighs] including

Thanksgiving, Christmas.

>> Do you ever take a vacation?

>> Uh, yeah. But they're um my definition

of a vacation is when I'm with my

family. And so if I'm with my family,

I'm very happy. I don't care where we

are.

>> And you don't work then or do you work a

little?

>> No. No. I work a lot. [laughter]

>> Even like if you go on a trip somewhere,

you're still working.

>> Oh, sure. Oh, sure.

>> Wow. Every day.

>> Every day.

>> But my kids work every You make me tired

just saying this.

>> My kids work every day.

Both of my kids work at Nvidia. They

work every day.

>> Wow.

>> Yeah. I'm very lucky.

>> Wow.

>> Yeah. It's brutal now because, you know,

it's just me working every day. Now we

have three people working every day and

they want to work with me every day and

so it's it's a lot of work.

>> Well, you've obviously imparted that

ethic into them.

>> They work incredibly hard. I mean,

there's no unbelievable.

>> But my parents work incredibly hard.

>> Yeah. I was I was born with the work

gene,

>> the suffering gene. [laughter]

>> Well, listen, man. It has paid off. What

a crazy story. It was just It's really

an amazing origin story.

It really I mean, it has to be kind of

surreal to be in the position that

you're in now when you look back at how

many times that it could have fallen

apart and humble beginnings. But Joe,

this is great. It's a great country. You

know, I'm an immigrant. My parents sent

my older brother and I here first.

We're we're in Thailand.

I was born in Taiwan, but my dad had a

job in Thailand. He was a chemical and

instrumentation engineer, incredible

engineer.

And his job was to go start an oil

refinery. And so we moved to Thailand,

lived in Bangkok.

And um in 19

I guess 1973 1974 time frame,

you know how Thailand every so often

they would just have a coup. You know,

the military would have an uprising and

all of a sudden one day there were tanks

and soldiers in the streets and my

parents thought, you know, it probably

isn't safe for the kids to be here. And

so they contacted my uncle. My uncle

lives in Tacoma, Washington.

and um we had never met him and my

parents sent us to him.

>> How old were you?

>> Uh I was about to turn nine and my older

brother uh almost turned 11 and so the

two of us came to United States and we

stayed in with our uncle for a little

bit while he looked for a school for us

and my parents didn't have very much

money and they never been to United

States. my father was. I'll tell you

that story in a second.

And um

and so my my uncle found a school that

would accept foreign students and

affordable enough for my parents.

And that school turned out to have been

in Onita, Kentucky, Clark County,

Kentucky, the epicenter of the opio

crisis today.

cold country.

Clark County, Kentucky is

was the poorest county in America when I

showed up. It is the poorest county in

America today.

And so we went to the school, it's a

great school, um, Onita Baptist

Institute

in a town of a few hundred. I think it

was 600 at the time that we showed up.

No traffic light.

And um I think it has 600 today. It's

quite an amazing feat actually.

The ability to hold your population for

[laughter]

when it's 600 people. It was quite a

magic quite a magical thing. however

they did it. And and so uh the school

had a mission of being an open school

for any children who would like to come.

And what that basically means is that if

you're a trouble student, if you have a

troubled family,

um if you're,

you know, whatever your background,

you're welcome to come to Onita Baptist

Institute, including kids from

international who would like to stay

there.

>> Did you speak English at the time?

>> Uh, okay. Yeah. Yeah. Okay. Yeah. And so

we showed up

and uh

my first my first thought was gosh there

are a lot of cigarette butts on the

ground. 100% of the kids smoked.

[laughter]

So right away you know this is not a

normal school.

>> Nineyear-olds?

>> No, I was the youngest kid.

>> Okay. 11 year olds.

>> My roommate was 17 years old. Wow.

>> Yeah. He just turned 17. And he was

jacked

and and um

I don't know where he is now. I know his

name, but I don't know where he is now.

But anyways, uh that night we got and

and the second thing I noticed when you

walk into the into your dorm room

is uh there are no drawers and no closet

doors.

just like a prison.

And

there are no locks

so that people could check check up on

you.

And so I go into my room and he's 17 and

uh you know get ready for for bed and he

had all this tape

all over his body and uh turned out he

was in a knife fight and he's been

stabbed all over his body and these were

just fresh wounds.

>> Whoa. And the other kids were hurt much

worse.

And uh so he was my roommate, the

toughest kid in school, and I was the

youngest kid in school. It was a it was

a junior high,

but they took me anyways because if I

walked about a mile across the Kentucky

River, the swing bridge, the other side

is a middle school that I could go to

and then I can go to that school and I

come back and then I stay in the dorm.

And so basically Onita Baptist Institute

was my dorm when I went to this other

other school. My older brother went went

to um went to the junior high. And so we

were there for a couple of years. Um

every kid had every kid had chores.

My older brother's chore was to work in

the tobacco farm, you know. So tobac

they raised tobacco so that they could

raise some extra money for the school.

Kind of like a penitentiary.

>> Wow. And my job was just to clean the

dorm. And so I I was 9 years old. I was

cleaning toilets. And for a dorm of 100

100 boys, I

I clean more bathrooms than anybody. And

I just wish that everybody was a little

bit more careful, you know. [laughter]

But anyways, I was the youngest kid in

school. The my memories of it was really

good. Um, but it was a pretty tough It

was a tough town.

>> Sounds like it.

>> Yeah. Town kids, they all carried

Everybody had knives.

>> Everybody had knives. Everybody smoked.

Everybody had a Zippo lighter. I smoked

for a week.

>> Did you?

>> Oh, yeah. Sure.

>> How old were you?

>> I was nine. Yeah.

>> When you nine? You were nine, you tried

smoking.

>> Yeah. I got myself a pack of cigarettes.

Everybody else did.

>> Did you get sick? No. I I got used to

it, you know, and I learned how to blow

blow smoke rings and, you know, [snorts]

you know, breathe out of my nose, you

know, take it in out of through my nose.

I mean, there was a all the different

things that you learned. Yeah.

>> At nine.

>> Yeah.

>> Wow. You just did it to fit in or it

looked cool.

>> Yeah. [clears throat] Because everybody

else did it,

>> right?

>> Yeah. And and then I did it for a couple

weeks, I guess. And I just rather have I

had a quarter, you know, I had a quarter

a month or something like that.

I just rather buy popsicles and fried

sickles with it. I was nine, you know,

[laughter]

>> right?

>> I chose I chose the the better path.

>> Wow.

>> That was our school. And then my parents

came to United States two years later

and um we met him in Tacoma, Washington.

>> That's wild. It It was a really crazy

experience. What a strange formative

experience.

>> Yeah. Tough kids.

>> Thailand to one of the poorest places in

America or if not the poorest

as a 9-year-old.

>> Yeah. It was my first experience with

your brother.

>> Wow.

>> Yeah. Yeah. No, I I remember and what

breaks my heart probably the only thing

that really breaks my heart of

about that experience was

so

we didn't have enough money to make you

know international phone calls every

week and so my parents gave us this tape

deck this Iowa tape deck and a tape

and so every month we would sit in front

on that tape deck and that my older

brother Jeff and I,

the two of us would just tell them what

we did the whole month.

>> Wow.

>> And we would send that tape by mail

and my parents would take that tape and

record back on top of it and send it

back to us.

>> Wow.

>> Could you imagine if for two years

>> Wow. is that tape still existed

of these two kids just describing their

first experience with United States.

Like I remember telling my parents

that that uh I joined the swim team and

uh

my roommate was really buff and so every

day we spent a lot of time in the in the

gym and so uh uh every night 100

push-ups, 100 sit-ups every day in the

gym. So, I was nine years old. I was

getting I was pretty buff

and I'm pretty fit. And uh

and so I joined the soccer team. I

joined the swim team because if you join

the team, they take you to meets and

then afterwards you get to go to a nice

restaurant. And that nice restaurant was

McDonald's.

>> Wow.

>> And and I recorded this thing. And I

said, "Mom and dad, we went to the most

amazing restaurant today.

This whole place is lit up. It's like

the future."

And [snorts] the food comes in a box

[laughter]

and the food is incredible. The

hamburger is incredible. It was

McDonald's. [snorts] But anyhow, it it

wouldn't it be amazing?

>> Oh my god. Two years recording. Yeah.

Two years. Yeah. What a crazy connection

to your parents, too. Just sending a

tape and them sending you one back and

it's the only way you're communicating

for two years.

>> Yeah. Wow. Yeah. No, I've My parents are

incredible actually. They're just

they're uh they grew up really poor and

um when they came to United States, they

had almost no money. Uh probably one of

the most

impactful memories I have is is uh we

they came and we were we were staying in

a in a in a uh apartment complex

and they had they had just rent back in

the I guess people still do rent rent a

bunch of furniture

and

we were messing around

and uh

we bumped into the coffee table and

crushed it. It's made out of particle

wood and we crushed it.

And I just still remember my the look on

my mom's face, you know, because they

didn't have any money and she didn't

know how she was going to pay it back.

And but anyhow, that's that kind of

tells you how hard it was for them to

come here. They they left everything

behind and all they had was their

suitcase and the money they had in their

in their pocket and they came to United

States.

>> How old were they pursued the American

dream? They were in their 40s.

>> Wow.

>> Yeah. Late late 30s.

>> Pursued the American dream. This is this

is the American dream. I'm the first

generation of the American dream.

>> Wow.

>> Yeah. It's hard not to love this

country.

>> That's

>> it's it's hard not to be romantic about

this country.

>> That is a romantic story. That's an

amazing story.

>> Yeah. And and my dad found his job

literally in the newspaper,

you know, the ads and he calls people.

Got a job.

>> What did he do?

>> Uh he was a consulting engineer and a

and a consulting firm and they helped

people build oil refineries, paper mills

and fabs. And that's what he did. He was

an he he's really good at factory design

instrumentation engineer. And so he's

he's brilliant at that. And so he did

that and my mom uh worked as a maid and

uh they found a way to raise us.

>> Wow.

That's an incredible story, Jensen. It

really is. Every all of it from your

childhood to the perils of Nvidia almost

falling. [laughter]

It's really incredible, man.

>> It's a great story. Yeah. I I've lived a

great life.

>> You really have. And it's a great story

for other people to hear, too. It really

is.

>> You don't You don't have to go to Ivy

League schools to succeed.

This country creates opportunities. Has

opportunities for all of us. You do have

to strive.

You have to claw your way here.

>> Yeah.

>> But if you put in the work, you can

succeed.

Nobody works with

>> a lot of luck and a lot of

>> a lot and

>> good decision- making

>> and the good graces of others.

>> Yes, that's really important.

>> Yeah. You and I spoke about two two

people who are very dear to me. Um but

the list goes on. the people the people

at NVIDIA who have have uh helped me um

uh many friends that are on the board uh

the decisions you know them giving me

the opportunity like when we were

inventing this new computing approach

I tanked our stock price because we

added this thing called CUDA to the chip

we had this big idea we added this thing

called CUDA to the chip but nobody paid

for it but our cost doubled and so we

had this graphics chip company and we

invented GPUs, we invented programmable

shaders, we invented everything modern

computer graphics,

we invented real-time tracing. That's

why it went from GTX to RTX.

We invented all this stuff, but every

time we invented something,

the market doesn't know how to

appreciate it, but the cost went way up.

And in the case of CUDA that enabled AI,

the cost increased a lot. it and but I

really we really believed it you know

and so if you believe in that future and

you don't do anything about it you're

going to regret it for your life

and so we always you know I always tell

the team do you believe what do we

believe this or not and if you believe

it and so grounded on first principle is

not random you know hearsay and we

believe it we've got to we owe it to

ourselves to go pursue it if we're the

right people to go do it if it's really

really hard to do. It's worth doing and

we believe it. Let's go pursue it.

Well, we pursued it. We we launched the

product. Nobody knew. It was exactly

what like when I launched DGX1 and the

entire audience was like

complete silence. When I launched CUDA,

the audience was complete silence. No

customer wanted it. Nobody asked for it.

Nobody understood it. Nvidia was a

public company.

>> What year was this? This is uh

uh let's see 200

2006

20 years ago

2005.

>> Wow.

>> Our stock price just went

our valuation went down to like two or

three billion dollars

>> from

>> from about 12 or something like that.

I crushed it.

>> [laughter]

>> in a very bad way.

>> Yeah.

>> What is it now though?

>> H Yeah, it's higher. [laughter]

>> Very humble of you. [gasps]

>> It's higher. But it changed the world.

>> Yeah,

>> that invention changed the world.

>> It's a It's an incredible story,

Johnson. It really is.

>> Thank you.

>> I like your story. It's incredible. Ah,

>> my story is not as incredible. My story

is more weird,

you know.

It's much more fertuitous and weird.

>> Okay. What are the three milestones that

most important milestones that led to

here?

>> That's a good question. Um,

>> what was step one?

>> I think step one was seeing other people

do it. Step one was in the initial days

of podcasting, like in 2009 when I

started, podcasting had only been around

for a couple of years. Um, the first was

Adam Curry, my good friend, who was the

podfather. He he invented podcasting.

And then, you know, um, I remember Adam

Corolla had a show because he had a

radio show. His radio show got cancelled

and so he decided to just do the same

show but do it on the internet. And that

was pretty revolutionary. Nobody was

doing that. And then there was the

experience that I had had doing

different morning radio shows like Opie

and Anthony in particular because it was

fun and we would just get together with

a bunch of comedians, you know, I'd be

on the show with like three or four

other guys that I knew and it was always

just looked forward to it. It was was

just such a good time and I said, "God,

I miss doing that. It's so fun to do

that. I wish I could do something like

that." And then I saw Tom Green setup.

Tom Green had a setup in his house and

he essentially turned his entire house

into a television studio and he did an

internet show from his living room. He

had servers in his house and cables

everywhere. Had to step over cables. I

was this is like 2007. I'm like Tom this

is nuts. Like this is

>> and I'm like you got to figure out a way

to make money from this. Like this

everybody I wish everybody in the

internet could see your setup. It's

nuts. I just want to let you guys know

that [laughter]

>> it's not just this.

>> Yeah. So that was the the beginning of

it is just seeing other people do it and

then saying all right let's just try it

and then so the beginning days we just

did it on a laptop had a laptop with a

webcam and just messed around had a

bunch of comedians come in we would just

talk and joke around and I did it like

once a week and then I started doing it

twice a week and then all a sudden I was

doing it for a year and then I was doing

it for two years then it was like oh

it's starting to get a lot of viewers a

lot of listeners you know and then I

just kept doing It's all it is. I just

kept doing it because I enjoyed doing

it. Well, was there any setback?

>> No. No, there was never really a setback

really.

>> No,

>> it must have been. Or you kind of

>> You're just You're just resilient.

>> Or you're just tough.

>> No. No. No. No. It wasn't tough or hard.

It was just interesting. So, I just it

the the

>> You were never once punched in the face.

>> No, not in the show. No, not really. Not

Not doing the show.

>> You never did something that that

big blowback. Nope.

Not really. No, it all just kept

growing.

>> It kept growing and the thing stayed the

same from the beginning to now. And the

thing is, I enjoy talking to people.

I've always enjoyed talking to

interesting people.

>> I could even tell just when we walked

in, the way you interacted with

everybody, not just me.

>> Yeah, that's cool.

>> People are cool.

>> Yeah, that's cool. You know, I I it's a

an amazing gift to be able to have so

many conversations with so many

interesting people because it changes

the way you see the world because you

see the world through so many different

people's eyes and you have so many

different people have different

perspectives and different opinions and

different philosophies and different

life stories. And you know, it's an

incredibly enriching and educating

experience having so many conversations

with so many amazing people. And that's

all I started doing. And that's all I do

now. Even now, when I booked the show, I

do it on my phone. And I basically go

through this giant list of emails of all

the people that want to be on the show

or that request to be on the show. And

then I factor in another list that I

have of people that I would like to get

on the show that I'm interested in. And

I just map it out and that's it. And I

go, "Oh, I'd like to talk to him."

>> If it wasn't because of President Trump,

I wouldn't have been bumped up on that

list. [laughter]

>> No, I wanted to talk to you already. I I

just think, you know, what you're doing

is very fascinating. I mean, how would I

not want to talk to you? And then today,

it proved to be absolutely the right

decision.

>> Well, you know, listen, it's it's

strange to be an immigrant one day.

going to Onita Baptist Institute

with with the students that were there

and then here

Nvidia's one of the most consequential

companies in the history of companies.

>> It is a crazy story.

>> It has to be that journey is is a and

it's very humbling and

>> and um I'm very grateful.

>> It's pretty amazing man.

>> Surrounded by amazing people. You're

very fortunate and you've also you seem

very happy and you seem like you're 100%

on the right path in this life. You

know,

>> you know, everybody says you must love

your job. Not every day. [laughter]

>> That's not that's part of the beauty of

everything is that there's ups and

downs. It's never just like this giant

dopamine high.

>> We leave we leave this impression here.

Here's here's an impression I don't

think is healthy. We we um people who

are successful leave the impression

often that that

our job gives us great joy. I think

largely it does

that our jobs were passionate about our

work.

Um and that passion relates to it's just

so much fun. I think it largely is, but

it it it distracts from in fact a lot of

success comes from really really hard

work.

>> Yes,

>> there's long periods of suffering and

loneliness and uncertainty and fear and

embarrassment and humiliation. all of

the feelings that we most not love that

creating something

from the ground up and and Elon will

tell you something similar very

difficult to invent invent something new

>> and people people don't believe you all

the time you're humiliated often

disbelieved most of the time and so so

people forget that part of success and

and I I don't think it's health. I think

it's it's good that we pass that forward

and let people know that that it's just

part of the journey.

>> Yes.

>> Suffering is part of the journey.

>> You will appreciate it so these horrible

feelings that you have when things are

not going so well. You will appreciate

it so much more when they do go well.

>> Deeply grateful.

>> Yeah.

>> Yeah. Deep deep pride. Incredible pride.

In incredible incredible gratefulness

and and and surely incredible memories.

Absolutely. Jensen, thank you so much

for being here. This was really fun. I

really enjoyed it and your story is just

absolutely incredible and very

inspirational and and I you know, I

think it really is the American dream.

It is the American dream.

>> It really is. Thank you so [music] much.

Thank you. All right. Bye, everybody.

[music]