NVIDIA CEO Jensen Huang gives the keynote address at GTC 2025. Read the transcript here.
Speaker 1 (00:00):
(Upbeat music).
Blue (28:17):
This is how intelligence is made, a new kind of factory generator of
tokens, the building blocks of AI. Tokens have opened a new frontier,
the first step into an extraordinary world where endless possibilities
are born. Tokens transform images into scientific data charting alien
atmospheres and guiding the explorers of tomorrow. They turn raw data
into foresight, so next time we'll be ready. Tokens decode the laws of
physics to get us there faster and take us further. Tokens see disease
before it takes hold. They help us unravel the language of life and
learn what makes us tick. Tokens connect the dots so we can protect our
most noble creatures. They turn potential into plenty. And help us
harvest our bounty. Tokens don't just teach robots how to move, but to
bring joy, to lend us a hand, and put life within reach. Together we
take the next great leap to bravely go where no one has gone before. And
here is where it all begins.
Speaker 2 (31:24):
Welcome to the stage NVIDIA founder and CEO, Jensen Huang.
Jensen Huang (31:30):
Welcome to GTC. What an amazing year. We wanted to do this at NVIDIA
so through the magic of artificial intelligence, we're going to bring
you
Jensen Huang (32:00):
…to NVIDIA's headquarters. I think I'm bringing you to NVIDIA's
headquarters. What do you think? [inaudible 00:32:15] This is where we
work. This is where we work. What an amazing year it was, and we have a
lot of incredible things to talk about, and I just want you to know that
I'm up here without a net. There are no scripts, there's no
teleprompter, and I've got a lot of things to cover. So, let's get
started. First of all, I want to thank all of the sponsors, all the
amazing people who are part of this conference. Just about every single
industry is represented. Healthcare is here, transportation, retail,
gosh, the computer industry, everybody in the computer industry is here,
and so it's really, really terrific to see all of you, and thank you
for sponsoring it. GTC started with GeForce. It all started with
GeForce, and today [inaudible 00:33:07] I have here a GeForce 5090, and
5090, unbelievably 25 years later, 25 years after we started working on
GeForce, GeForce is sold out all over the world.
(33:23)
This
is the 5090, the Blackwell generation, and comparing it to the 4090,
look how it's 30% smaller in volume, it's 30% better at dissipating
energy, and incredible performance. Hard to even compare, and the reason
for that is because of artificial intelligence. GeForce brought CUDA to
the world. CUDA enabled AI, and AI has now come back to revolutionize
computer graphics. What you're looking at is real-time computer
graphics, 100% path traced for every pixel that's rendered. Artificial
intelligence predicts the other 15. Think about this for a second, for
every pixel that we mathematically rendered, artificial intelligence
inferred the other 15, and it has to do so, with so much precision that
the image looks right, and it's temporally accurate, meaning that from
frame to frame to frame going forward, or backwards, because it's
computer graphics, it has to stay temporally stable. Incredible.
Artificial intelligence has made extraordinary progress. It has only
been 10 years. Now, we've been talking about AI for a little longer than
that, but AI really came into the world's consciousness about a decade
ago.
(34:49)
Started
with perception AI, computer vision, speech recognition, then
generative AI. The last five years, we've largely focused on generative
AI, teaching an AI how to translate from one modality to another,
another modality, text to image, image to text, text to video, amino
acids to proteins, properties to chemicals, all kinds of different ways
that we can use AI to generate content. Generative AI fundamentally
changed how computing is done. From a retrieval computing model, we now
have a generative computing model, whereas almost everything that we did
in the past was about creating content in advance, storing multiple
versions of it, and fetching whatever version we think is appropriate at
the moment of use. Now, AI understands the context, understands what
we're asking, understands the meaning of our request, and generates what
it knows. If it needs, it'll retrieve information, augments its
understanding, and generate answer for us. Rather than retrieving data,
it now generates answers. Fundamentally changed how computing is done.
Every single layer of computing has been transformed. The last several
years, the last couple, two, three years, major breakthrough happened.
(36:20)
Fundamental
advance in artificial intelligence. We call it agentic AI. Agentic AI
basically means that you have an AI that has agency. It can perceive,
and understand the context of the circumstance. It can reason, very
importantly, it can reason about how to answer, or how to solve a
problem, and it can plan an action, it can plan, and take action. It can
use tools, because it now understands multimodality information, it can
go to a website, and look at the format of the website, words, and
videos, maybe even play a video, learns from what it learns from that
website, understands it, and come back, and use that information, use
that newfound knowledge to do its job. Agentic AI. At the foundation of
agentic AI, of course, something that's very new, reasoning. And then of
course the next wave is already happening. We're going to talk a lot
about that today. Robotics, which has been enabled by physical AI, AI
that understands the physical world. It understands things like
friction, and inertia, cause, and effect. Object permanence. When
[inaudible 00:37:38] doesn't mean it's disappear from this universe,
it's still there, just not seeable.
(37:43)
And
so that ability to understand the physical world, the three-dimensional
world, is what's going to enable a new era of AI we called physical AI,
and it's going to enable robotics. Each one of these phases, each one
of these waves opens up new market opportunities for all of us. It
brings more, and new partners to GTC. As a result, GTC is now
jam-packed. The only way to hold more people at GTC is we're going to
have to grow San Jose, and we're working on it. We've got a lot of land
to work with. We've got to grow San Jose. So that we can make GTC… Just
know as I'm standing here, I wish all of you could see what I see, and
we're in the middle of a stadium, and last year was the first year back
that we did this live, and it was like a rock concert, and it was
described, GTC was described as the Woodstock of AI, and this year it's
described as the Super Bowl of AI. The only difference is everybody wins
at this Super Bowl. Everybody's a winner. And so, every single year,
more people come, because AI is able to solve more interesting problems
for more industries, and more companies, and this year we're going to
talk a lot about agentic AI, and physical AI. At its core, what enables
each wave, and each phase of AI, three fundamental matters are involved.
The first is how do you solve the data problem? And the reason why
that's important is because AI is a data-driven computer science
approach. It needs data to learn from. It needs digital experience to
learn from. To learn knowledge, and to gain digital experience. How do
you solve the data problem? The second is, how do you solve the training
problem without human in the loop? The reason why human in the loop is
fundamentally challenging is because we only have so much time, and we
would like an AI to be able to learn at super human rates, at super
real-time rates, and to be able to learn at a scale that no humans can
keep up with.
(40:18)
And
so the second question is, how do you train the model? And the third is
how do you scale? How do you create? How do you find an algorithm
whereby the more resource you provide, whatever the resource is, the
smarter the AI becomes. The scaling law? Well, this last year, this is
where almost the entire world got it wrong. The computation requirement,
the scaling law of AI is more resilient, and in fact, hyper
accelerated. The amount of computation we need at this point as a result
of agentic AI as a result of reasoning, is easily 100 times more than
we thought we needed this time last year, and let's reason about why
that's true. The first part is let's just go from what the AI can do.
Let me work backwards. Agentic AI, as I mentioned at this foundation is
reasoning. We now have AIs that can reason, which is fundamentally about
breaking a problem down step by step. Maybe it approaches a problem in a
few different ways, and selects the best answer....
Just amazing.