world plays on. It's a GeForce.

This is the chip that's inside.

For nearly 30 years.

Nvidia's chips have been coveted by gamers shaping

what's possible in graphics and dominating the entire

market since it first popularized the term

graphics processing unit with the GeForce 256.

Now its chips are powering something entirely

different.

ChatGPT has started a very intense conversation.

He thinks it's the most revolutionary thing since

the iPhone.

Venture capital interest in AI startups has skyrocketed.

All of us working in this field have been optimistic

that at some point the broader world would

understand the importance of this technology.

And it's it's actually really exciting that that's

starting to happen.

As the engine behind large language models like

ChatGPT, Nvidia is finally reaping rewards for its

investment in AI, even as other chip giants suffer in

the shadow of U.S.-China trade tensions and an ease

in the chip shortage that's weakened demand.

But the California-based chip designer relies on

Taiwan Semiconductor Manufacturing Company to

make nearly all its chips, leaving it vulnerable.

The biggest risk is really kind of U.S.-China relations

and the potential impact to TSMC.

That's, if I'm a shareholder in Nvidia,

that's really the only thing that keeps me up at

night.

This isn't the first time Nvidia has found itself

teetering on the leading edge of an uncertain

emerging market.

It's neared bankruptcy a handful of times in its

history when founder and CEO Jensen Huang bet the

company on impossible seeming ventures.

Every company makes mistakes and I make a lot of them.

And some of them, some of them puts the company in

peril. Especially in the beginning, because we were

small and and we're up against very, very large

companies and we're trying to invent this brand new

technology.

We sat down with Huang at Nvidia's Silicon Valley

headquarters to find out how he pulled off this

latest reinvention and got a behind-the-scenes look at

all the ways it powers far more than just

gaming.

Now one of the world's top ten most valuable companies,

Nvidia is one of the rare Silicon Valley giants that,

30 years in, still has its founder at the helm.

I delivered the first one of these inside an AI

supercomputer to OpenAI when it was first created.

60-year-old Jensen Huang, a Fortune Businessperson of

the Year and one of Time's most influential people in

2021, immigrated to the U.S .

from Taiwan as a kid and studied engineering at

Oregon State and Stanford.

In the early 90s, Huang met fellow engineers Chris

Malachowsky and Curtis Priem at Denny's, where they

talked about dreams of enabling PCs with 3D

graphics, the kind made popular by movies like

Jurassic Park at the time.

If you go back 30 years, at the time, the PC revolution

was just starting and there was quite a bit of debate

about what is the future of computing and how should

software be run.

And there was a large camp and rightfully so, that

believed that CPU or general purpose software was

the best way to go.

And it was the best way to go for a long time.

We felt, however, that there was a class of

applications that wouldn't be possible without

acceleration.

The friends launched Nvidia out of a condo in Fremont,

California, in 1993.

The name was inspired by N .V.

for next version and Invidia, the Latin word for

envy. They hoped to speed up computing so much,

everyone would be green with envy.

At more than 80% of revenue, its primary

business remains GPUs.

Typically sold as cards that plug into a PC's

motherboard, they accelerate - add computing

power - to central processing units, CPUs, from

companies like AMD and Intel.

You know, they were one among tens of GPU makers at

that time. They are the only ones, them and AMD

actually, who really survived because Nvidia

worked very well with the software community.

This is not a chip business.

This is a business of figuring out things end to

end.

But at the start, its future was far from guaranteed.

In the beginning there weren't that many

applications for it, frankly, and we smartly

chose one particular combination that was a home

run. It was computer graphics and we applied it

to video games.

Now Nvidia is known for revolutionizing gaming and

Hollywood with rapid rendering of visual effects.

Nvidia designed its first high performance graphics

chip in 1997.

Designed, not manufactured, because Huang was committed

to making Nvidia a fabless chip company, keeping

capital expenditure way down by outsourcing the

extraordinary expense of making the chips to TSMC.

On behalf of all of us, you're my hero.

Thank you. Nvidia

today wouldn't be here if and nor nor the other

thousand fabless semiconductor companies

wouldn't be here if not for the pioneering work that

TSMC did.

In 1999, after laying off the majority of workers and

nearly going bankrupt to do it, Nvidia released what it

claims was the world's first official GPU, the

GeForce 256.

It was the first programable graphics card

that allowed custom shading and lighting effects.

By 2000, Nvidia was the exclusive graphics provider

for Microsoft's first Xbox.

Microsoft and the Xbox happened at exactly the time

that we invented this thing called the programable

shader, and it defines how computer graphics is done

today.

Nvidia went public in 1999 and its stock stayed largely

flat until demand went through the roof during the

pandemic. In 2006, it released a software toolkit

called CUDA that would eventually propel it to the

center of the AI boom.

It's essentially a computing platform and

programing model that changes how Nvidia GPUs

work, from serial to parallel compute.

Parallel computing is: let me take a task and attack it

all at the same time using much smaller machines.

Right? So it's the difference between having an

army where you have one giant soldier who is able to

do things very well, but one at a time, versus an

army of thousands of soldiers who are able to

take that problem and do it in parallel.

So it's a very different computing approach.

Nvidia's big steps haven't always been in the right

direction. In the early 2010s, it made unsuccessful

moves into smartphones with its Tegra line of

processors.

You know, they quickly realized that the smartphone

market wasn't for them, so they exited right from that

.

In 2020, Nvidia closed a long awaited $7 billion deal

to acquire data center chip company Mellanox.

But just last year, Nvidia had to abandon a $40 billion

bid to acquire Arm, citing significant regulatory

challenges. Arm is a major CPU company known for

licensing its signature Arm architecture to Apple for

iPhones and iPads, Amazon for Kindles and many major

carmakers.

Despite some setbacks, today Nvidia has 26,000

employees, a newly built polygon-themed headquarters

in Santa Clara, California, and billions of chips used

for far more than just graphics.

Think data centers, cloud computing, and most

prominently, AI.

We're in every cloud made by every computer company.

And then all of a sudden one day a new application

that wasn't possible before discovers you.

More than a decade ago, Nvidia's CUDA and GPUs were

the engine behind AlexNet, what many consider AI's Big

Bang moment. It was a new, incredibly accurate neural

network that obliterated the competition during a

prominent image recognition contest in 2012.

Turns out the same parallel processing needed to create

lifelike graphics is also ideal for deep learning,

where a computer learns by itself rather than relying

on a programmer's code.

We had the good wisdom to go put the whole company behind

it. We saw early on, about a decade or so ago, that

this way of doing software could change everything, and

we changed the company from the bottom all the way to

the top and sideways.

Every chip that we made was focused on artificial

intelligence.

Bryan Catanzaro was the first and only employee on

Nvidia's deep learning team six years ago.

Now it's 50 people and growing.

For ten years, Wall Street asked Nvidia, why are you

making this investment and no one's using it?

And they valued it at $0 in our market cap.

And it wasn't until around 2016, ten years after CUDA

came out, that all of a sudden people understood

this is a dramatically different way of writing

computer programs and it has transformational

speedups that then yield breakthrough results in

artificial intelligence.

So what are some real world applications for Nvidia's

AI? Healthcare is one big area.

Think far faster drug discovery and DNA sequencing

that takes hours instead of weeks.

We were able to achieve the Guinness World Record in a

genomic sequencing technique to actually

diagnose these patients and administer one of the

patients in the trial to have a heart transplant.

A 13-year-old boy who's thriving today as a result,

and then also a three-month-old baby that

was having epileptic seizures and to be able to

prescribe an anti-seizure medication.

And then there's art powered by Nvidia AI, like Rafik

Anadol's creations that cover entire buildings.

And when crypto started to boom, Nvidia's GPUs became

the coveted tool for mining the digital currency.

Which is not really a recommended usage, but that

has created, you know, problems because, you know,

crypto mining has been a boom or bust cycle.

So gaming cards go out of stock prices, get bid up and

then when the crypto mining boom collapses, then there's

a big crash on the gaming side.

Although Nvidia did create a simplified GPU made just for

mining, it didn't stop crypto miners from buying up

gaming GPUs, sending prices through the roof.

And although that shortage is over, Nvidia caused major

sticker shock among some gamers last year by pricing

its new 40-series GPUs far higher than the previous

generation. Now there's too much supply and the most

recently reported quarterly gaming revenue was down 46%

from the year before.

But Nvidia still beat expectations in its most

recent earnings report, thanks to the AI boom, as

tech giants like Microsoft and Google fill their data

centers with thousands of Nvidia A100s, the engines

used to train large language models like

ChatGPT.

When we ship them, we don't ship them in packs of one.

We ship them in packs of eight.

With a suggested price of nearly $200,000.

Nvidia's DGX A100 server board has eight Ampere GPUs

that work together to enable things like the

insanely fast and uncannily humanlike responses of

ChatGPT.

I have been trained on a massive dataset of text

which allows me to understand and generate text

on a wide range of topics.

Companies scrambling to compete in generative AI are

publicly boasting about how many Nvidia A100s they have.

Microsoft, for example, trained ChatGPT with 10,000.

It's very easy to use their products and add more

computing capacity.

And once you add that computing capacity,

computing capacity is basically the currency of

the valley right now.

And the next generation up from Ampere, Hopper, has