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How Jensen Huang Actually Built NVIDIA

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How Jensen Huang Actually Built NVIDIA

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421 segments

0:00

This dishwasher boy built a $5 empire

0:03

from the table of a Denny's diner.

0:05

He pitched the business plan over cheap

0:06

coffee in 1992, and 34 years later, his

0:10

company makes $20

0:11

every single hour.

0:13

Not from the safe bet, but from a gamble

0:15

nobody believed in that crushed entire

0:17

industries and took over the AI space,

0:20

the gaming industry, and even all of

0:22

your app algorithms.

0:23

And just as he started winning, the

0:25

government moved to [music] bring him

0:27

down.

0:29

Jensen Huang was born in Taiwan in 1963.

0:33

When he was nine, [music] his parents

0:35

sent him and his brother to America for

0:36

a better education.

0:38

The plan was simple. Live with relatives

0:40

in Kentucky, study hard, build a future.

0:45

This doesn't look like a school. It

0:47

looks more like somewhere people get

0:48

sent after they've done something very,

0:50

very wrong. That's because

0:52

>> [music]

0:52

>> it kind of is. Welcome to Oneida Baptist

0:54

Institute. You'll learn English,

0:56

discipline, and how not to complain

0:58

while cleaning bathrooms.

1:00

This is not how most tech CEO origin

1:03

stories start, but it didn't break him.

1:05

It taught him something no lecture hall

1:07

could.

1:08

When the situation is terrible, you

1:10

adapt [music]

1:10

or you disappear.

1:12

He eventually made it to Oregon State,

1:14

then Stanford, spent years at chip

1:16

companies,

1:17

AMD, then LSI Logic, studying

1:21

semiconductors with a level of obsession

1:22

that was, frankly, a little alarming.

1:25

By 1992, Jensen had an idea.

1:28

Graphics are going to change computing.

1:31

Games are getting bigger. 3D is getting

1:33

harder. CPUs can't carry this forever.

1:37

Someone is going to build a chip for

1:38

this new world.

1:40

I think it should be us.

1:41

Jensen, I have a mortgage, a stable job,

1:44

and a family that currently believes I

1:46

make responsible decisions.

1:48

Good.

1:49

Then this will be exciting. So, the plan

1:51

is, quit our jobs, start from zero, and

1:54

build a chip for a market that barely

1:56

exists?

1:57

Correct. I hate that this sounds insane

2:00

and also kind of logical.

2:02

They called the company Nvidia.

2:04

The idea was ambitious. Build one

2:06

powerful [music] graphics chip that

2:07

could handle everything. A clean

2:10

universal chip for the future of gaming

2:12

hardware.

2:13

For 2 years they worked. They spent

2:15

every dollar they had. And in 1995,

2:18

Nvidia launched its first product, the

2:20

NV1.

2:22

Jensen, we have a problem. Microsoft

2:25

just [music] published the Direct X

2:26

spec, full triangle based rendering

2:28

pipeline. Holy crap, the NV1 runs

2:31

quadratic mapping?

2:33

Yes.

2:34

We are the only ones going in the

2:35

opposite direction. Games are about to

2:37

be built from tiny flat triangles?

2:40

Our chip is built for curved surfaces.

2:42

We built the wrong chip with tremendous

2:44

confidence. 2 years, every dollar. A

2:47

chip built for a world that no longer

2:48

existed. The NV1 [music] flopped.

2:51

But Nvidia still had a lifeline. Sega

2:54

had contracted them to build the

2:55

graphics processor for the company's

2:57

next generation console,

2:59

the Dreamcast.

3:01

Real money, real chance to start over.

3:04

There was only one problem.

3:06

Sega's chip used the same dead-end

3:08

technology. [music] You're doing the

3:09

Sega math again, aren't you?

3:11

We'll spend the next year building

3:12

something we already know is dead. And

3:14

if we cancel, we lose the only customer

3:17

still paying us.

3:18

Uh, Pretty much.

3:21

I miss normal employment.

3:23

If we're going to die, let's die

3:25

building the right chip.

3:26

>> [music]

3:26

>> So Jensen flew to Japan.

3:29

The chip is a dead end. You should

3:31

cancel our contract and find someone

3:32

else.

3:33

We shouldn't finish the contract. It

3:35

would be a waste of your money.

3:36

>> [music]

3:38

>> There's one more thing. I still need the

3:40

money, the $5 million remaining on our

3:42

contract. Please put it into Nvidia as

3:44

an investment instead. Otherwise, we'll

3:46

vaporize overnight.

3:47

>> [music]

3:48

>> I have nothing to offer you. This money

3:50

will most likely be lost, but I'm asking

3:52

anyway.

3:55

I'll need a few days.

3:56

Jensen [music] bowed, walked out, and

3:58

got on a plane. Nothing to do now but

4:01

sit, and wait, and watch the Pacific

4:03

stretch out beneath him.

4:05

Jensen, you might want to see this.

4:08

He opens [music] it, eyes wide. It's a

4:10

massive 10% offer on the value of

4:13

BetterHelp.

4:15

Just kidding.

4:16

Let's be real for a second. When the

4:18

outcome is completely out of your hands,

4:20

the anxiety Did I make the right call?

4:23

That noise won't switch off. It doesn't

4:25

go away on its own.

4:27

The hardest part for me was noticing the

4:29

pattern.

4:31

I was pushing through instead of

4:33

actually dealing with anything.

4:35

My therapist asked me one question.

4:37

Are you solving something, or are you

4:39

rehearsing a disaster?

4:41

I didn't have an answer. Just having a

4:43

name for what I was doing,

4:45

that single reframe broke the loop.

4:48

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4:52

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5:01

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5:16

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5:19

Every great ship [music] needs

5:20

maintenance. So do you.

5:25

Hello.

5:26

The money is coming.

5:28

The reason the Sega CEO said yes had

5:31

nothing to do with business logic. He

5:33

just liked Jensen.

5:35

That $5 million bought Nvidia 6 months.

5:38

And now they had one last shot to build

5:40

the right chip, or

5:41

>> [music]

5:41

>> go bankrupt.

5:44

They called it the Riva 128,

5:46

triangle-based, [music]

5:47

DirectX compatible, built from scratch.

5:51

Weeks of late nights, bad coffee, and

5:53

absolutely zero plan B. They had exactly

5:56

1 month of payroll [music] left in the

5:58

bank to find out if it worked.

6:01

The chip launched in 1997, and this time

6:04

Nvidia did not miss. Game developers

6:06

could actually use it. Customers

6:08

actually bought it. From the brink of

6:09

bankruptcy to a million units sold in 4

6:13

months.

6:14

Most CEOs would have taken a long

6:16

vacation and slow down a bit.

6:18

He walked back into the office and wrote

6:20

one word on the whiteboard.

6:22

More.

6:24

So, by 1999, Nvidia launched the GeForce

6:27

256 and gave the world a term it didn't

6:29

really have yet, GPU.

6:32

Graphics processing unit.

6:34

It was a genuine leap. A chip that

6:36

handled complex 3D calculations that

6:38

previously required an entirely separate

6:40

processor. For gamers,

6:42

>> [music]

6:43

>> this meant smoother worlds, better

6:45

lighting, and monsters that looked

6:46

slightly less like wet cardboard. Gamers

6:49

loved it. Developers wept [music] with

6:51

joy.

6:52

Revenue exploded. And by the early

6:54

2000s, Nvidia had become the king of PC

6:57

graphics. Every serious gamer, every

7:00

visual effects studio, every game

7:02

developer on the planet. The obvious

7:04

move was simple. Keep making better

7:06

gaming chips, sell them to gamers,

7:08

become rich, buy a very large leather

7:10

jacket museum.

7:11

But Jensen started looking at something

7:13

other people missed.

7:15

A CPU is like one very smart employee

7:18

doing tasks one by one extremely fast.

7:21

Meanwhile, a GPU is like 10,000 interns

7:24

doing [music] tiny calculations at once.

7:26

Are the interns smart?

7:28

No.

7:29

But there are a lot of them, and if your

7:31

problem can be broken into thousands of

7:32

small calculations, physics, chemistry,

7:35

weather, biology, artificial

7:37

intelligence,

7:39

suddenly, the chip built for video games

7:41

[music] starts looking like something

7:42

else, a cheap supercomputer.

7:45

Jensen looked at this and thought, "What

7:47

if the same chip that rendered

7:48

explosions in video games could help

7:50

simulate molecules, predict weather,

7:53

model fluids, and maybe one day train

7:55

machines to think?"

7:57

So, in 2006, Nvidia launched CUDA,

8:00

Compute Unified Device Architecture, a

8:03

platform that let scientists,

8:04

researchers, and engineers use Nvidia

8:07

GPUs [music] for general-purpose

8:08

computing.

8:10

And then, he committed roughly $500

8:12

million to build it.

8:14

Jensen,

8:16

the gaming business is printing money.

8:18

Walk me through who CUDA is actually

8:20

for.

8:21

Researchers, scientists, people running

8:23

fluid dynamics simulations, climate

8:26

models, drug discovery?

8:28

So, not our customers.

8:30

Not yet.

8:32

I'm starting to hate that phrase. This

8:34

is our CUDA revenue after 2 years. Do

8:36

you see a number?

8:37

Not today.

8:39

Jensen, this is not a business. We are

8:42

subsidizing a hobby for PhD students.

8:45

PhD students are where the future

8:47

starts. For the first time, they can use

8:49

a gaming chip like a supercomputer.

8:51

We don't know what they'll build with

8:53

it. But when they build [music]

8:54

something important, it will run on us.

8:56

If they build nothing,

8:58

we still have the best gaming cards in

9:00

the world and a $500 million science

9:03

project. We've survived worse.

9:06

>> [music]

9:06

>> Six years passed. The gaming business

9:08

kept printing money. CUDA kept printing

9:11

almost nothing.

9:12

And Jensen kept waiting for the problem

9:14

big enough to prove him right.

9:16

Then, in Toronto, someone found it.

9:20

It's 2012. Every year, the world's top

9:23

AI researchers compete in a challenge

9:24

[music] called ImageNet. Tens of

9:27

millions of images, and your software

9:29

has to identify what's in them.

9:31

You see the Toronto submission? Some PhD

9:34

student [music] who running a a network

9:35

on gaming cards. We've been hand-coding

9:37

classifiers for a decade. Gaming cards

9:40

aren't going to crack this.

9:41

Ladies and gentlemen, thank you for

9:43

waiting.

9:44

The ImageNet [music] 2012 results are

9:46

final. Second place, University of Tokyo

9:50

with a 26.2% error rate. And this year's

9:53

winner,

9:54

University of Toronto, AlexNet, 15.3%.

9:59

I'm sorry, 15?

10:00

That's not a win. That's a murder scene.

10:03

For years, researchers have been trying

10:05

to teach computers how to see by

10:07

manually designing the features they

10:09

should look for.

10:10

Edges, textures, shapes, patterns.

10:14

AlexNet took a different path. Give the

10:16

neural network enough data, enough

10:18

layers, and enough computing power,

10:21

and let it learn the features itself.

10:24

Training a neural network requires

10:26

millions of small calculations happening

10:28

at the same time.

10:29

Exactly the kind of work that Nvidia had

10:32

been quietly perfecting for 6 years.

10:35

The $500 million bet

10:36

>> [music]

10:36

>> had finally found its customer.

10:38

Suddenly, every major AI lab started

10:40

paying attention. Stanford, MIT, Google,

10:45

Meta, Microsoft.

10:47

They weren't just buying graphics cards

10:49

[music] anymore.

10:50

They were buying computing power for

10:51

intelligence itself.

10:53

And Nvidia had the hardware, the

10:55

software, and the scale to deliver it.

10:58

Great news for Nvidia, until in 2022,

11:00

[music]

11:01

Washington made a phone call.

11:05

Before Washington called, Jensen had

11:07

already tried to fix Nvidia's biggest

11:08

weakness. Because Nvidia designed the

11:10

chips,

11:12

but it didn't make them.

11:13

That job belonged mostly to TSMC in

11:16

Taiwan.

11:17

And he didn't own the underlying

11:19

architecture, the fundamental

11:20

instruction set that tells processors

11:22

how to think.

11:24

That belonged to a British company

11:25

called ARM.

11:27

A company whose technology sat inside

11:29

virtually every smartphone on Earth.

11:31

Apple, Samsung, Qualcomm, your pocket.

11:35

Their DNA, not Jensen's.

11:38

So, for all of Nvidia's power, [music]

11:39

Jensen was still renting the building.

11:42

He owned the furniture.

11:43

So, in 2020,

11:44

>> [music]

11:44

>> Jensen made his move. Nvidia offered $40

11:47

to buy ARM.

11:49

Own the GPU, own the software, own the

11:52

architecture. Simple.

11:54

Unless you were literally everyone else

11:55

in tech.

11:57

Nvidia cannot own ARM. Agreed. Agreed.

11:59

Also agreed.

12:01

Wow, I hate how united we are. If Jensen

12:04

owns ARM, then every chip we build runs

12:06

through our most dangerous competitor.

12:08

Phones,

12:09

>> [music]

12:09

>> servers, AI chips, everything. So, what

12:12

do we do?

12:13

We complain to every regulator with an

12:15

email address.

12:17

And they did. The FTC sued. The European

12:20

Commission investigated. The UK blocked

12:22

it on national security grounds.

12:24

18 months later, Nvidia walked away.

12:27

Then Washington called. Mr. Huang,

12:30

effective immediately, advanced AI

12:32

chips, the H100,

12:34

>> [music]

12:34

>> can no longer be exported to China.

12:37

How much revenue are we talking about?

12:39

That sounds like a you problem.

12:41

Just like that, some of Nvidia's biggest

12:43

customers were cut off.

12:45

Alibaba, Baidu, ByteDance, China's AI

12:49

companies,

12:50

gone.

12:51

So, Nvidia did what Nvidia always did.

12:53

It engineered around the problem.

12:56

They built a weaker version, the H800,

12:58

designed to fit inside the rules.

13:01

Legal enough to ship. Powerful enough

13:03

[music] to sell.

13:04

For about 5 minutes, because by late

13:06

2023, Washington tightened the rules

13:08

again.

13:09

The H800 was restricted to

13:11

>> [music]

13:12

>> The message was clear. Nvidia was now a

13:14

strategic weapon in America's technology

13:16

war with China.

13:18

Jensen wasn't just a CEO anymore. He was

13:21

a piece on someone else's chessboard,

13:23

moved by people he couldn't negotiate

13:24

with.

13:25

But while politics got messier, demand

13:27

got insane. OpenAI, Google, [music]

13:30

Anthropic, xAI, Microsoft. Everyone

13:34

building large AI models needed the same

13:36

thing, more Nvidia chips, more data

13:39

centers, more power, more cooling, more

13:42

everything.

13:44

Blackwell, Nvidia's next [music]

13:45

generation chip, was being ordered

13:47

before factories could even finish

13:48

making it.

13:50

By October 2025, Nvidia [music] crossed

13:52

$5 trillion in market value.

13:54

The most valuable company on Earth,

13:56

ahead of Google, ahead of Apple.

13:59

Not bad for a company that once had 30

14:01

days of cash left and one very wrong

14:03

chip.

14:04

None of it was supposed to work.

14:06

Every single [music] bet looked insane

14:08

from the outside, and every single one

14:10

paid off.

14:11

As what he himself [music] said,

14:13

"My will to survive exceeds everybody

14:15

else's will to kill me."

Interactive Summary

Jensen Huang's journey began with a humble background, including being sent to a disciplinary school, before he founded Nvidia in 1992. Despite an initial product failure (the NV1) and near-bankruptcy, a risky negotiation with Sega secured crucial funding, leading to the success of the Riva 128 and the invention of the GPU. Huang made a controversial, long-term bet by investing $500 million in CUDA to enable general-purpose computing with GPUs, a gamble that paid off spectacularly with the rise of AI and the ImageNet breakthrough in 2012. Facing regulatory challenges like the failed ARM acquisition and US government export restrictions to China, Nvidia continued to innovate, becoming the world's most valuable company by October 2025, a testament to Huang's relentless will to survive and his foresight in risky ventures.

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