At the close of the regular trading session Nvidia's market capitalization was $4.894 Trillion.
If it holds the overnight move—up another $5.37 on top of the regular session's $9.34 gain, the company will have the first $5 Trillion market cap.
From Rev (speech to text wizards), October 28 - synced to the video of the keynote:
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....
....MUCH MORE
Just amazing.
