This is what hundreds of millions of gamers in the
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
already started to ship.
Some uses for generative AI are real time translation
and instant text-to-image renderings.
But this is also the tech behind eerily convincing and
some say dangerous deepfake videos, text and audio.
Are there any ways that Nvidia is sort of protecting
against some of these bigger fears that people
have or building in safeguards?
Yes, I think the safeguards that we're building as an
industry about how AI is going to be used are
extraordinarily important.
We're trying to find ways of authenticating content so
that we can know if a video was actually created in the
real world or virtually.
Similarly for text and audio.
But being at the center of the generative AI boom
doesn't make Nvidia immune to wider market concerns.
In October, the U.S.
introduced sweeping new rules that banned exports of
leading edge AI chips to China, including Nvidia's
A100. About a quarter of your revenue comes from
mainland China. How do you calm investor fears over the
new export controls?
Well Nvidia's technology is export controlled, it's a
reflection of the importance of the technology
that we make. The first thing that we have to do is
comply with the regulations, and it was a
turbulent, you know, month or so as the company went
upside down to re-engineer all of our products so that
it's compliant with the regulation and yet still be
able to serve the commercial customers that we
have in China. We're able to serve our customers in
China with the regulated parts and delightfully
support them.
But perhaps an even bigger geopolitical risk for Nvidia
is its dependance on TSMC in Taiwan.
There's two issues.
One, will China take over the island of Taiwan at some
point? And two, is there a viable, you know, competitor
to TSMC?
And as of right now, Intel is trying aggressively to to
get there. And you know, their goal is by 2025.
And we will see.
And this is not just an Nvidia risk.
This is a risk for AMD, for Qualcomm, even for Intel.
This is a big reason why the U.S.
passed the Chips Act last summer, which sets aside $52
billion to incentivize chip companies to manufacture on
U.S. soil. Now TSMC is spending $40 billion to
build two chip fabrication plants, fabs, in Arizona.
The fact of the matter is TSMC is a really important
company and the world doesn't have more than one
of them. It is imperative upon ourselves and them for
them to also invest in diversity and redundancy.
And will you be moving any of your manufacturing to
Arizona?
Oh, absolutely. We'll use Arizona.
Yeah.
And then there's the chip shortage.
As it largely comes to a close and supply catches up
with demand, some types of chips are experiencing a
price slump. But for Nvidia, the chatbot boom
means demand for its AI chips continues to grow, at
least for now.
See, the biggest question for them is how do they stay
ahead? Because their customers can be their
competitors also.
Microsoft can try and design these things
internally. Amazon and Google are already designing
these things internally.
Tesla and Apple are designing their own custom
chips, too. But Jensen says competition is a net good.
The amount of power that the world needs in the data
center will grow. And you can see in the recent trends
it's growing very quickly and that's a real issue for
the world.
While AI and ChatGPT have been generating lots of buzz
for Nvidia, it's far from Huang's only focus.
And we take that model and we put it into this computer
and that's a self-driving car.
And we take that computer and we put it into here, and
that's a little robot computer.
Like the kind that's used at Amazon.
That's right. Amazon and others use Nvidia to power
robots in their warehouses and to create digital twins
of the massive spaces and run simulations to optimize
the flow of millions of packages each day.
Driving units like these in Nvidia's robotics lab are
powered by the Tegra chips that were once a flop in
mobile phones. Now they're used to power the world's
biggest e-commerce operations. Nvidia's Tegra
chips were also used in Tesla model 3s from 2016 to
2019. Now Tesla uses its own chips, but Nvidia is
making autonomous driving tech for other carmakers
like Mercedes-Benz.
So we call it Nvidia Drive.
And basically Nvidia D rive's a scalable platform
whether you want to use it for simple ADAS, assisted
driving for your emergency braking warning,
pre-collision warning or just holding the lane for
cruise control, all the way up to a robotaxi where it is
doing everything, driving anywhere in any condition,
any type of weather.
Nvidia is also trying to compete in a totally
different arena, releasing its own data center CPU,
Grace. What do you say to gamers who wish you had kept
focus entirely on the core business of gaming?
Well, if not for all of our work in physics
simulation, if not for all of our research in
artificial intelligence, what we did recently with
GeForce RTX would not have been possible.
Released in 2018, RTX is Nvidia's next big move in
graphics with a new technology called ray
tracing.
For us to take computer graphics and video games to
the next level, we had to reinvent and disrupt
ourselves, basically simulating the pathways of
light and simulate everything with generative
AI. And so we compute one pixel and we
imagine with AI the other seven.
It's really quite amazing.
Imagine a jigsaw puzzle and we gave you one out of eight
pieces and somehow the AI filled in the rest.
Ray tracing is used in nearly 300 games now, like
Cyberpunk 2077, Fortnite and Minecraft.
And Nvidia Geforce GPUs in the cloud allow full-quality
streaming of 1500-plus games to nearly any PC.
It's also part of what enables simulations,
modeling of how objects would behave in real world
situations. Think climate forecasting or autonomous
drive tech that's informed by millions of miles of
virtual roads. It's all part of what Nvidia calls
the Omniverse, what Huang points to as the company's
next big bet.
We have 700-plus customers who are trying it now, from
the car industry to logistics warehouse to wind
turbine plants. And so I'm really excited about the
progress there. And it represents probably the
single greatest container of all of Nvidia's
technology: computer graphics, artificial
intelligence, robotics and physics simulation all into
one. I have great hopes for it.
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