Skyrocket your IELTS band score by 1-2 points in under a month with our premium plan!
↔
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.
Please play the YouTube video first