LIVE: Jensen Huang on Building the Dynamo of the Intelligence Age

0:00Thank you so much, Jensen. So we are in the middle of a massive AI revolution. It is probably bigger and faster than even the Industrial Revolution. And you have called out what's happening right now as the largest infrastructure buildout in human history. At the center of that build-out is the AI factory. And the company enabling all of that is NVIDIA. Can you tell us what is an AI factory and why is it the best investment for any enterprise in the next decade?

0:41Jensen Huang:Okay, so you could understand AI in a particular number of ways. The way that you understand AI probably most is through a chatbot, through a web browser. You're interacting with it. You give it a prompt. It says something back to you. And even those of you who have been using AI for some time, you've seen in the last couple, two, three years, a very significant evolution, improvement in the capabilities of AI. Two years ago, you heard about ChatGPT. and chat GPT basically is a computer software that understands the input you give it it can perceive understand information and it can translate and generate the information into something else okay so you can give it a prompt and you can say here's this PDF I gave you I would like you now to summarize it it went from text to text you could also tell it here's a PDF I gave you I would like you to now generate an image of that story.

1:48Jensen Huang:It goes text to image. You could use go from image to text, meaning you could give it a picture and you can, what's happening inside this picture. It goes text, image to text. Does that make sense? Anything to anything else. And AI in the last, in two years time, two years ago was largely able to do this translation. We call it generation, generative models, generative AI. But the thing that is very big deal inside that word generative AI is in order to do something even more valuable than generation, understanding and generating, is thinking. Well, you can't think if you don't generate words.

2:32Jensen Huang:And so the foundation of generative AI gave us the ability to generate internal thoughts, thinking, reasoning, step-by-step reasoning, problem solving. It also allowed us to do another thing that is now very important, which is generate intelligence to control something else, to generate control to use a tool. Does it make sense? To use a browser, use a spreadsheet, use Photoshop, use PowerPoint, use something, use AutoCAD, use another tool. Now, that tool today is digital, but someday that tool will be mechanical. So if I generate a command to a mechanical system, that would be called robotics.

3:13Jensen Huang:If I generate commands for a machine with steering wheel, that would be called self-driving cars. Does that make sense? Okay. And so two years ago, two years ago, in fact, you saw the foundations, We call it ChatGPT, and everybody said, ah, you know, it's fun, it's silly, or it produced a whole bunch of crazy, hallucinated text. That's all true, but it was the foundational technology that led to all of this. Two years later, we now have agentic systems. Now, that's one view of AI. I just described the view, which is, what can AI do? Right? And so now all of you realize you see it from ChatGPT, you see it from Codex, you see it from Cloud Code.

3:59Jensen Huang:You see that it's now able to not just understand, but it's able to do work. Reason and do work. Now, two years ago, when AI was able to understand you and generate information that was interesting, novel, a little cute, whenever you need a poem written, great way to do it. Right? who doesn't want to write a country song? And so that was two years ago. But now, because it's able to do work, AI is valuable. Valuable meaning it can generate information, it can generate useful work, and it could be paid for it. Because we pay for, we're interested in having friends that are smart. We love people who are know-it-alls, but we don't pay them for it.

4:48Jensen Huang:We pay for people who do work. Does that make sense? All right, which is what happened in the last two years. AI went from having this capability to now Agentech went from not very valuable to now producing useful work. So much useful work that you and I are doing this every day. We're paying AI by the hour, right? And so we might pay them$30 an hour to do the work,$20 an hour to do the work. We're basically paying AI a lot of money today. the fastest growing software business in the history of mankind. Because now it's doing useful work and we can pay them to do it. Now, that's one view of AI, which is what it can do.

5:26Jensen Huang:But one other view of AI that's really important to help reason through what Constantine's saying. So, for example, the reason why some companies, some people are able to build great businesses and could maneuver themselves into the center of very large industries is because when they see this capability, this is very interesting. One interesting thought is, if we're able to do this, what is the implication to this downstream industries? That's an interesting conversation we should have. Okay? So now that AI can do this, what happens to all the industries like healthcare and financial services and life sciences, manufacturing, logistics, transportation, on and on and on?

6:07Jensen Huang:e-tail, advertising, future entertainment, the list of conversations you can have about now that AI can do this, what can it do as a result subsequently? That's an interesting conversation. But you should go upstream, meaning industrially, what does that mean? And so the first thing you realize is this. Go back to first principles. I had told you just now that AI is software and it's being produced by a computer. Now, what happened to the computer that has made it possible to do this? Well, the big idea is about, if you think about the computer as we know it today, really emerged about 64 years ago.

6:46Jensen Huang:IBM System 360 was the biggest announcement of computing, and 64 years ago, IBM was the most valuable company in the world. And they created the modern understanding of computers. Everything that we can describe about a computer was really described in 1964. for 60 for 40 years largely has remained the same and what happened was what happened was that in that form of computing is called pre-recording you write down your story you save it to a file you write a program by hand you save it to a file you take a picture you save it to a file you record music you save it to a file you make a video right now we're in streaming right now but somebody's gonna record it you're gonna save it to a file and when you want to use it later, you retrieve it from the disk drive.

7:32Jensen Huang:Does that make sense? And the retrieval process is done intelligently. So that's why everybody's retrieval of a news story is a little bit different. It's called a recommender system. But basically, computers as we know it today is a retrieval-based system, which is the reason why these buildings are called data centers. They store data. Notice they don't call them computer centers because they're not doing much computing. They just store data that you retrieve based on what you touch on your phone. Well, what happened now, if you look at what I just described, in order for this AI to work, as I described it, every time I say something to it, I have to give it new information.

8:16Jensen Huang:We call it context. I give it a new prompt. That's called a query. Between the context and the query, it will understand it first, reason about it, and it will produce an output based on that context and that query, based on the circumstance. Does that make sense so far? Okay, give me one nod, okay? And so if that is the case, every time you use the AI, the content is produced originally every single time. Everything I'm saying to you right now is being produced in real time. And it's because my explanation is based on the fact that I realize all of you come from 60 different countries, 128 different families.

9:01Jensen Huang:You all have many different backgrounds. Some of you probably came from the computer industry. Most of you probably did not. And so I'm explaining the information to you in a way that is sufficiently deep. But ultimately, my goal is this. So that you know how to make your next investment. That's what I'm leading to. And so I'm going to give you enough sufficient information that you can reason about it for yourself so that when you see something in the next time, you go, that's worth investing in. That's going to be a big industry. That's$100 billion right now, and it looks really big, but that's nothing compared to how big it's going to be.

9:38Jensen Huang:I'm going to give you the intuition to solve that problem. Okay? So here we are. Went from a computer industry that was largely based on retrieval for 60 years, And all of a sudden, one day, it's completely generated in real time. We call it intelligence. This is what I'm doing right now for you. I'm demonstrating intelligence. Contextual awareness. He gave me a prompt. And here comes my answer. Does that make sense? Extreme intelligence.

10:07Jensen Huang:Extremely artificial intelligence. Okay? And so here, what's going on now? I just gave you one word earlier. It's called generative AI. the computer of today has been completely reinvented. It's now generative. And every single letter you see, every single word you see in the future, every video, every image, every ad, every TV commercial, every single time you read a story, a news story, every one of them will be different. What Constantine sees, what I see, what you see will be completely different because it'll be generated for you. because your interest, your context, who you are, for what reason you asked, how you asked is completely different.

10:52Jensen Huang:Does it make sense? And so therefore, therefore, every single pixel that you see, every single sound that you hear in the future, every video you see in the future will be originally generated, not retrieved, which means in the future, we need a lot more generators. And these generators is what we build. that's what we build for a living. These are large computers, and they're generating intelligence. Now the next question is this. Well, how big could it be? How big can it be? And so it turns out, it turns out, the amount of information, the amount of generation of intelligence, we do it for about a billion people in the world today.

11:38Jensen Huang:Now that I've told you that AI has become agentic, meaning that it can actually do work by itself. Well, if it can do work by itself, then one agent can communicate with another agent and say, I have some work to do. Let's team up together and let's do some work. And now you have all these different agents and they're all working together to solve problems inside your company, say. So inside our company, Constantine knows that we're huge users of agentic AI. We have hundreds of thousands of agents probably running around right now that are doing work and they're talking to each other and they're solving problems.

12:13Jensen Huang:They're all guardrailed, all sandboxed, all guardrailed and sandboxed, but they're all working with each other, which means in the future, it is very likely that the internet that we use today for a billion people will likely mostly be several billion, call it 100 billion agents, working around the clock, and they're using the internet and talking to each other. And what are they saying? So for example, there'll be companies Companies working with companies, employees, agents working with other employees, agents. There will be self-driving cars, which are agentic. There will be robots, which are agentic.

12:51Jensen Huang:All the manufacturing systems, every building will be agentic. There will be agents all over the place, and they'll be using the internet. And all of those commands that they generate to each other will be generated. All of the thoughts that they have to understand, all generated. Does that make sense? And so basically the world is going to be this layer of computing that's going to cocoon the earth and it's going to be generating intelligence all the time. Now, I just said something that sounds ridiculous. Except, in fact, it's already happened twice. So 300 years ago, a company in Germany called Siemens produced a machine.

13:34And this machine is a really interesting machine. you go up to this machine you light it on fire and then this incredible invisible force comes out the other end nobody understood what it was we understand now as electricity how many power generators are there in the world we call it a grid power generation cocoons the planet we call it the grid

14:01Jensen Huang:And then of course, 20 some odd years ago, earlier than that, 35 years ago, this networking scheme, networking matrix, fabric was created here in the United States, eventually became the internet. And where is it? It cocoons the world. And so now you have energy, communications, intelligence, and it will cocoon the world. And we'll use it for, you know, it'll just be a commodity and we'll use it all over the place. And so what NVIDIA does for a living is this new machine. The machine that was invented 300 years ago is called the Dynamo. That Dynamo, anything that moves comes in. It could be waterfalls.

14:43Jensen Huang:It could be wind, fire, steam. Transfer it from motion, atoms, to electrons. Atoms to electrons. We then take the electrons into our machine called NVIDIA. Electrons now comes into our machine, comes in this factory, and what comes out are numbers. These numbers, depending on how you combine them, turns into language, math. It can also turn into a new language we've learned, proteins. We learned the language of human biology. we learned the language of the physical world physics climate weather we learned the language of the 3d world robotics self-driving cars we learned the language of all kinds of different forms of intelligence but the point being now these two machines 300 years apart atoms in electrons out electrons in numbers out and those numbers could be rejiggered, reformulated into all kinds of different intelligence.

15:58Jensen Huang:That's what we built. That's what we do for a living. And that's why I call it a factory because it's producing. We call them tokens, but they're just numbers. Tokens. And these tokens are intelligence. That's it. That's what we do. It's not that hard. Brilliant.

16:23Jensen Huang:Now you know what AI is for, and now you know how AI is built, and how big it's going to be. Thank you all for joining today. Thank you. Good job. Excellent question. I really felt that carried it, the prompt. You laid it up. Okay, so this is a massive revolution. And you laid out three transformations. Energy transformation, which touches everyone today. And a lot of the people in the audience are part of these manufacturing and energy producers globally. Telecommunications, which connects all of us. And now intelligence. And in energy, you talked about the generator. Telecommunications, I guess the comparable would be the switch or something along those lines for routing communications globally.

17:13And now in the intelligence revolution, it's the GPU at the core. And the AI factory like the H100 or any of the new systems that bring everything you need under the same hood, Verirubin, what happens?

17:26Jensen Huang:And these factories, so you know, these generators, it's each one of our units, we call it a rack. There's 72 chips inside. We manufacture, call it 8 million of them this year, but 72 of them go into a rack. That rack weighs two tons. It is$4 million, has one and a half million parts, and it's the most expensive piece of equipment in the world. And we manufacture them like, I guess it would be like phones. I mean, we crank them out. and they go into data centers all over the world. We build these machines in volume. They are big devices. This is how you do your weightlifting. I understand. No volume discounts.

18:26Okay, so you laid out this picture of a very exciting world that we are in. We're in the middle of this revolution. You could say decades in, you could say years in. Certainly now in the mainstream of the intelligence revolution. How do we participate? I'm sure everybody here wants to participate in this revolution. Yeah, excellent question. Let's start with big enterprises and let's get to individuals as well. How do people join this movement?

18:56Jensen Huang:And so now I gave you two mental models. I'm going to give you one more mental model. So one mental model, we could talk about this story and hopefully we cover all four phases. I just talked about what AI can do. I talked about how AI is made. And these things are in these factories. These factories are, you know, each gigawatt is about$50 billion. So have you ever seen it's the most expensive factory in the world? But it also that one$50 billion factory also generates$300,$400 billion in intelligence. And so the production value is incredible. Okay, the return on investment is extremely fast.

19:35And so that's the factory part.

19:38Jensen Huang:The part that I'm going to tell you now, and this is very important when you think about investment, is what does the industrial layout look like for AI? And the way to think about the industrial view is think of it as a five-layer cake. Now, I told you on the bottom is energy. The bottom is, remember I said the dynamo, okay? Now we have, of course, different AC generators and things like that, power generation. And so on the lowest layer is energy. This is the single greatest opportunity in several generations for the energy industries to grow. The very first time in probably, I don't know, 100 years since the energy grid in many countries could be invested in.

20:19Jensen Huang:This is the best opportunity to invest in sustainable energy. If you care about sustainable energy, nuclear, air, wind, solar, you name it, whatever form, hydrogen, whatever form. So long as it produces energy, it's going to get funded. And so that tells you something about how great of a time this is because we have a trillion dollars. Just think, this year alone, we're going to put a trillion dollars from the market into this entire five-layer cake. I'm about to describe you. So the first layer is energy. That's the reason why Siemens is doing so well. Mitsubishi is doing fantastically. GE, Vernova.

20:54Jensen Huang:I mean, everybody. The first layer of the cake is energy. The second layer of the cake is chips and computers and networking and switches and silicon photonics. Does that make sense? It's all the computers. The third layer of the cake, we call it infrastructure. Land, power, shell, money, data center operations. Every one of them is scarce supply today. And so that's the next layer, is infrastructure layer. and then the layer that everybody sees that everybody thinks is AI is the model layer. Does it make sense? That's the next layer. It sits on top of the computers, the cloud infrastructure. And this is the greatest opportunity in recent human history that I know that so much market-driven investment is naturally coming into the ecosystem.

21:43Jensen Huang:This is a great time to build. And so now that's the model layer. The model layer is open AI. It's anthropic. But this is the part that you can't overlook. This is very important. So you have two companies that you know of, that you hear about. However, don't forget, AI, as I was explaining earlier, has learned the language. It learns the language and the meaning, the language, the meaning of anything that is structural. So that layer, what's really important is that we hear, we talk about all the language, but don't forget, you can learn anything with structure. And so let me give you an example of something with structure.

22:24Jensen Huang:Today, when I walked into the room, I was expecting a lot of people, and it wasn't unexpected the way you appeared. Now, if some of you were hanging off the ceiling and floating in midair, and some of you, you know, one human but body parts in 17 different places, and I could see through some of you, then it's hard to learn that. Because every time it's different. Okay? Because it's hard to learn quantum things. However, things with structure we can learn. Right? people have eyes and so you can learn these things okay and so 3d I learned the laws of physics I sat down and notice I sat down with confidence I wasn't I wasn't 50 % 53 % of the time I landed safely on the chair the other 47 percent of time I went right through it and so I can't trust it but a hundred percent time do you guys understand and so if if you if things are predictable and are predictable, then there's structure you can learn from it and you can learn the meaning of it, okay?

23:25Jensen Huang:And so we learned the meaning of protein. We're learning the meaning of genes, not just sequencing it, not just CRISPR editing it, but what is the meaning of that gene? What is the meaning of a cell? Why does a cell do what a cell does? What happens in two cells coming together? And so this is no different than, imagine I learned the meaning of a cell the way I learned the meaning of a word. And what happens when I take two words and put them together? These two words activate each other turn into something else of another meaning Okay, and so from a computer's perspective, it doesn't care if it's a cell a protein a word An image a car doesn't make sense.

24:02Jensen Huang:It's just tokens and so we have to figure out as computer scientists we have to figure out how to Represent the world's information in all these different ways so that the computer can understand it Understand it, reason about it, come up with a plan, generate an action, the intelligence loop. Protein's the same way, cell's the same way, the human anatomy's the same way. It must be predictable. It's predictable because tomorrow morning I'm largely the same. It must be predictable, okay? And so we're learning all these different... My point is there are two language models that you guys know about, but AI is a giant industry.

24:42Jensen Huang:The industry of everything else physical is about$80 trillion. It is actually, you know, the most important frontier, the parts that we're not talking about. And then on top of that, this model, this technology then feeds into all the stuff that Constantine gets to see these days, which is all of these startups that are coming up with revolutionary ideas in financial services, in legal, in accounting, in transportation, logistics. Isn't that right? And so that layer above, this last year,$100 billion of venture capital investment, the single largest year of VC investment in the history of humanity.

25:26Jensen Huang:All of that money is going into that fifth layer the top layer, which is the layer that apply applications to enhance human condition. And so there are five layers. When you think about AI and you want to invest in this future, and I promise you this future is going to be gigantic because two years ago, zero, it's approximately, we're about to put$1 trillion in, but that's$1 trillion out of the, you know, we're going to be putting in probably the AI industry, I'm going to guess for a second, probably something along the lines of$20 trillion a year. We're$1 trillion in of a$20 trillion a year ecosystem.

26:08Because the production of intelligence, you just got to ask yourself, how important is

26:13Jensen Huang:intelligence? And who needs it? And how much of it do you want? And so those are kind of the basic questions. And all of that intelligence, whether it's for proteins or cars or robots or language or math, science, whatever it is, has to be generated by these machines. And so this five layer cake is the industrial version. And I think that's a good way to think about where to invest. Hugely so. So you've described what is a multi-trillion dollar opportunity to become part of this revolution. And that includes the hardware and the facilities. If it's$50 billion for a gigawatt and there's 100 plus coming online in the next several years, that is trillions, plus the application layer where that is many, many more trillions, plus, plus, plus.