- In 1993 Jensen co‑founded Nvidia with a mission to create a new kind of parallel computing architecture. - The early years were marked by a lack of market demand; the team spent countless hours playing Sega arcade games to understand 3D graphics. - A pivotal partnership with Sega allowed Nvidia to build a game‑console‑style graphics chip, giving the company its first revenue stream. - When the original graphics approach failed, Jensen convinced Sega to invest $5 million, buying time for a strategic pivot.
From Backyard Arrows to AI Supercomputers: Jensen Huang’s Journey, Nvidia’s Rise, and the Future of Technology

Q: Early Life and Immigration

- Jensen Huang was born in Taiwan, moved to Thailand, and at age 9 was sent to the United States with his brother to live with an uncle in Tacoma, Washington. - He attended a small Baptist school in rural Kentucky, where he experienced poverty, violence, and hard‑working chores that shaped his resilience. - His parents, immigrants with modest jobs, instilled a strong work ethic that later fueled his entrepreneurial drive.
Q: Pivotal Moments: GPUs, CUDA, and AI

- Nvidia introduced the first GPU that moved graphics processing from the CPU to thousands of parallel cores, dramatically increasing performance. - The invention of **CUDA** (Compute Unified Device Architecture) turned GPUs into general‑purpose accelerators, laying the foundation for modern AI workloads. - Jensen’s team built the DGX‑1 supercomputer (≈$300 k) and later the DGX‑Spark, showcasing Nvidia’s AI‑focused hardware. - The breakthrough AlexNet model (2012) trained on two Nvidia GPUs demonstrated that deep learning could scale, cementing Nvidia’s role in the AI boom.
Q: Partnerships with Elon Musk and SpaceX

- Jensen delivered the first DGX‑Spark to Elon Musk’s nonprofit AI lab (later OpenAI) in 2016, marking a historic collaboration. - The same AI chip later powered SpaceX’s autonomous vehicle projects and other advanced research. - These collaborations highlighted Nvidia’s transition from gaming to high‑impact scientific and aerospace applications.
Q: AI, Energy, and Manufacturing Policy

- Jensen praised President Trump’s focus on domestic energy production (“drill, baby, drill”) as essential for AI‑driven industrial growth. - He emphasized that on‑shore manufacturing of critical chips is a matter of national security and job creation. - According to Jensen, without abundant energy, the AI industry could not build the factories needed for next‑generation chips and supercomputers.
PowerfulJRE
Summary Date: 2025-12-04 09:08:28
•
4 min read
From Backyard Arrows to AI Supercomputers: Jensen Huang’s Journey, Nvidia’s Rise, and the Future of Technology

Early Life and Immigration

Jensen Huang was born in Taiwan, moved to Thailand, and at age 9 was sent to the United States with his brother to live with an uncle in Tacoma, Washington.
He attended a small Baptist school in rural Kentucky, where he experienced poverty, violence, and hard‑working chores that shaped his resilience.
His parents, immigrants with modest jobs, instilled a strong work ethic that later fueled his entrepreneurial drive.
The Birth of Nvidia

In 1993 Jensen co‑founded Nvidia with a mission to create a new kind of parallel computing architecture.
The early years were marked by a lack of market demand; the team spent countless hours playing Sega arcade games to understand 3D graphics.
A pivotal partnership with Sega allowed Nvidia to build a game‑console‑style graphics chip, giving the company its first revenue stream.
When the original graphics approach failed, Jensen convinced Sega to invest $5 million, buying time for a strategic pivot.
Pivotal Moments: GPUs, CUDA, and AI

Nvidia introduced the first GPU that moved graphics processing from the CPU to thousands of parallel cores, dramatically increasing performance.
The invention of CUDA (Compute Unified Device Architecture) turned GPUs into general‑purpose accelerators, laying the foundation for modern AI workloads.
Jensen’s team built the DGX‑1 supercomputer (≈$300 k) and later the DGX‑Spark, showcasing Nvidia’s AI‑focused hardware.
The breakthrough AlexNet model (2012) trained on two Nvidia GPUs demonstrated that deep learning could scale, cementing Nvidia’s role in the AI boom.
Partnerships with Elon Musk and SpaceX

Jensen delivered the first DGX‑Spark to Elon Musk’s nonprofit AI lab (later OpenAI) in 2016, marking a historic collaboration.
The same AI chip later powered SpaceX’s autonomous vehicle projects and other advanced research.
These collaborations highlighted Nvidia’s transition from gaming to high‑impact scientific and aerospace applications.
AI, Energy, and Manufacturing Policy

Jensen praised President Trump’s focus on domestic energy production (“drill, baby, drill”) as essential for AI‑driven industrial growth.
He emphasized that on‑shore manufacturing of critical chips is a matter of national security and job creation.
According to Jensen, without abundant energy, the AI industry could not build the factories needed for next‑generation chips and supercomputers.
AI Safety, Cybersecurity, and Quantum Computing

AI safety is achieved by channeling raw compute power into guard‑rails, similar to how modern cars use ABS and traction control.
Nvidia’s AI systems are designed to self‑reflect, research answers, and request external tools when confidence is low, reducing hallucinations.
Cybersecurity is a collaborative ecosystem: companies share breach information, patches, and best practices, making defenses stronger than isolated efforts.
Quantum computing threatens current encryption, but the industry is already developing post‑quantum algorithms to stay ahead.
The Future of Work and Universal Basic Income

Jensen foresees AI permeating every profession, increasing efficiency while displacing routine tasks.
He argues that jobs will evolve; the purpose of a role (e.g., diagnosing disease for radiologists) remains even as AI handles the repetitive parts.
Universal Basic Income may become necessary as AI generates wealth that reduces the need for many people to work for a living.
At the same time, new industries—robot maintenance, AI‑enhanced apparel, and AI‑driven services—will create fresh employment opportunities.
Leadership, Fear, and the Drive to Succeed

Jensen admits he operates under constant anxiety, driven more by fear of failure than by ambition for fame.
He maintains a rigorous routine: early mornings, 6‑7 hours of sleep, and continuous email monitoring to stay alert.
Vulnerability, he says, allows his team to challenge his assumptions, enabling rapid pivots when needed.
The company’s culture attracts top computer scientists who want to work on pure technology rather than advertising or media.
Closing Thoughts

Nvidia’s success is a blend of technical breakthroughs, strategic risk‑taking, and an unwavering belief in first‑principles.
The company’s journey illustrates how a small startup can reshape entire industries—from gaming to AI, from autonomous vehicles to national security.
Jensen’s personal story underscores the American‑dream narrative: immigrant roots, relentless hard work, and the willingness to fail and try again.
Nvidia’s rise shows that relentless innovation, strategic risk‑taking, and a clear vision for technology’s societal role can turn a modest startup into a global powerhouse, while also shaping the future of AI, manufacturing, and the very nature of work.
Full Transcript

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]
Summary

From Backyard Arrows to AI Supercomputers: Jensen Huang’s Journey, Nvidia’s Rise, and the Future of Technology

Early Life and Immigration

The Birth of Nvidia

Pivotal Moments: GPUs, CUDA, and AI

Partnerships with Elon Musk and SpaceX

AI, Energy, and Manufacturing Policy

AI Safety, Cybersecurity, and Quantum Computing

The Future of Work and Universal Basic Income

Leadership, Fear, and the Drive to Succeed

Closing Thoughts

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