This is an existential issue for us. We could easily stop, but if we stop today, we lose. Functionality, capability, safety, security, they're all at exactly the same place. Priority number one.
It's our third pod back together. In the last 10 days, you've been at the state dinner at the White House. You were at the White House summit where we did the accord on super intelligence, and just yesterday you were there with your parents when the president presented you with the National Medal of Science. You were the only investor at the table. You were the only investor at the table now that we've reflected on it. Well, tell me, being there with your parents yesterday, right, it struck me five out of the six people on stage were all born outside of the United States. How did that feel?
Well, first of all, it's a great honor all by itself, right? And it's a reflection of the contribution that the company has made, right? And obviously, we created, we invented a new computing model. This is probably the most pervasive computing model in the world today. It's the only computing architecture that's in every cloud. It's in every computer company. It's used in every field of science. It's used by gamers. It's used by creators and builders, and it's used by scientists. It powers intelligence, right? I mean, just if you take a step back and you look at what the company has accomplished, you know, we started out building a graphics chip for gamers, and I'm still proud of that, of course. And we now shipped one and a half, 1.67 billion units, right? That's how many generations of, I mean, I raised generations of scientists and kids and designers, you know, and so I think the impact that, and here we are, you know, we're the infrastructure that powers the intelligence of society, and so it's an incredibly proud moment for me to receive the award on behalf of the company.
Now for my parents, you know, I wanted them to go, and I gave them literally, you know, 24 hours lead time, and they don't travel that much, right? They don't travel that much. And my dad didn't even have a suit.
Yeah, so he ran out and bought a suit.
He looked great. They looked great.
Yeah, it was a brand new suit and he showed up. I didn't realize he had a suit. I thought he was just going to wear some shirt. And he went out and bought a suit.
My mom got a new dress, you know, and literally in 24 hours.
Yeah. Oh, I know how it works.
Oh my gosh. It's incredible. And so we got him on a, we chartered a plane for them, got him there, and they were so happy to meet the president. And President Trump walked my mom because she has a hard time walking, and the two of them were walking off stage, and he held her and walked her down the stage, and they were just incredibly, incredibly proud to meet him. And but for them, it's their American dream. You know, my dad, I still remember, I think I was like five years old or something like that, and he had just come back from the United States on a business trip. He was working for an air conditioning company, and he.
And you were living in Taiwan.
We were at the time, we had just moved to Thailand.
Oh no, no, we were just about to move to Thailand. And we were in Taiwan. He had come back and he was in the United States working for a company called Carrier, their air conditioning company. And he was here for training. And of all the places in the world, they landed him in New York.
Okay. And so here you can just imagine a kid in the late 20s, I guess mid-late 20s, and never been outside of Taiwan, goes to the United States, New York City of all places.
And he saw the future of the future, and he came back, you know, mind blown, all the things that he bought at the airport, brought it back. He bought, he brought chocolate. He brought, you know, that cheese that you have in a tube back in the old days. It's kind of like white strange cheese. It comes in a tube.
And he brought back all kinds of strange things. And he says, this is, we should go there someday.
And the first opportunity.
They had, they sent my older brother and I to the United States, and you know, it was his dream.
I don't think many people realize that you came to the United States ahead of your parents.
Right. And they joined a few years later, but it just hit me like a ton of bricks that five out of the six people on that stage yesterday were born outside the United States.
You and I have talked about the need for, you know, with all the other stuff going on, a real strategy and an immigration policy that makes certain that we're still the place that all the great builders in the world want to come build. This is America's superpowers, right?
In a lot of ways, the intellectual property creation capability of our nation is unlike anything else in the world. And the reason for that is we attract the world's best. That's right. You know, we are the place where they want to come and build their lives because of the opportunity it provides, the freedom that it offers, the opportunity that it provides, the spirit of invention and entrepreneurialism, and the can-do attitude. You know, in the United States, everything is possible, right? We start with, of course it's possible, of course we could do that, instead of, you know, I do a lot of travel, and of course you can't do that, of course, you know, in other places, and we take it for granted. But the spirit, the energy that comes around with the culture that we've created here, it just can't be understated. And so I think the flow, the inflow of intellectual property into this country is unlike anything else the world's ever seen.
And we want that. We want all of the world's great minds to come here, bring their great ideas, you know, make them realize them here.
Realize them here and let us, you know, let us keep that flywheel going.
You know, I really, it's a delicate system. It's a delicate balance. I get it's a delicate balance, I get. And so we have to do it lawfully. You have to do it rightfully, right? But we surely want to do it.
Yeah, for sure. So, I want to start high level, just kind of where we are. Then I want to dig down a little bit into the safety and regulation and getting this right, where we are in terms of the compute buildout. And then finally, we're going to finish up, think, you know, drilling in a little bit to Nvidia's growth and what's going on there. But you've been really good at calling the future, right? Two years ago, we were sitting here and you said that the market was dramatically underestimating the amount of compute buildout that was going to be required and was going to happen over the course of the next five years. You said it's not going to be a trillion, it's going to be two trillion at a minimum in terms of cumulative compute buildout. And of course, now we're over that. You said before everybody was thinking about it that inference, right, compute was going to 1 billion x because of inference time reasoning and thinking. And then last year, of course, before we saw Opus 4.6, before we saw Claude Code, you said we're entering the age of agents, right, where we're going to unlock a lot of these capabilities. And of course, we saw Opus 4.6 and Claude Code really took off. So, where are we now?
Right, we have Elon and Sam and only three things that I said that came true. There's another 200. Why don't you just keep that? Let's just let that be the podcast. So our investor day in a couple weeks is on, you know, entering the singularity, this acceleration. We have Elon and Sam and Demis all say, hey, we're in the foothills or entering the singularity. We have, you know, model solving Navier-Stokes, as you know. So when you look into your crystal ball, where are we and are we inflecting, are we accelerating, you know, into a different phase of super intelligence?
And so let's unpack that first. First of all, it's very fairly logical why the amount of compute that we're going to need is a billion times more than where we were just recently, so call it last year. And the reason for that is because thinking and problem solving takes a lot more, you know, inferences than repeating something, regurgitating something that you memorized, right? So when you're a ChatGPT, you give it a prompt, most of that knowledge was encoded into the large language model. Unfortunately, of course, it hallucinated, and so the next click was, it was generative, but it was also grounded or conditioned by ground truth.
And so it would have to go out and do a search, do a quick search, comes back with the ground truth, and then from that conditioning augmented with what it already learned pre-training, and it would generate new whatever the answer is. Well, most of the things that we want to really do that are interesting requires you to reason step by step, to come up with several options, plan, and you might decide, let's see how these things all roll out, and then based on the answers of all of those, I reflect on the best answer so far, the best path so far, I might choose one or two or three of them, roll them out, right, okay, prune the rest of it, and then you just keep on doing this. And you know, people, we do this, that's the reason. Why? Why we say great strategists love strategic optionality. You don't want to prune things too fast on the one hand, but you also have to optimize your resources. Well, AIs are doing that. Okay, so they're keeping their strategic optionality, they're rolling things out, they're pruning, they're reflecting, they're pruning, so on and so forth. All of that thinking, all of that exploration just takes a lot more compute. And on top of that, because it's able to now do productive things, we're going to use it for more things. And so the compounding of both, it uses more compute to do its job, but it's doing a good job, and therefore we're going to apply, we're going to hire it to do more jobs.
Okay, and so that all compounds. At this point, I would say that the big observation from last year is that on the one hand we want the models to get better and better, and we still want the models to get better, and the scaling laws of models are going to continue. However, the attachment of a harness, basically the exoskeleton of the large language model, right? You know, kind of the body of the brain that allows it to manage working memory, manage knowledge. How do you go from working memory to short-term to long-term to basically formulating your knowledge graph, which is what we all do? We kind of think about, hey, you know, what did I learn and how does it fit into everything that I know in the past? And you know, when we go to bed, we do compaction, things that don't make sense, we kind of, or don't matter, we kind of eventually forget, but the things that kind of matter somehow it gets reconnected, and so we're doing that kind of offline. And so we got working memory, we have offline knowledge extraction and creation, knowledge graph creation. That whole part of memory is a big deal. The other thing is, of course, tool use. And we've taught these AIs how to interact with the most important tools that humans have ever made. You know, it's no different than if a robot comes into our house, it's more likely you're going to use all the tools that I have in the house than to go off and build it from scratch. And so SPICE is still going to be useful, and Verilog will be useful, and simulators will be useful, and design compilers, and Photoshop, and AutoCAD, and the Siemens tools, all of these different tools that we've been using to run our companies, build our companies, design things, operate things, they're all going to be great tools for the agents to use. But the agents had to learn how to use it, and so these two areas, knowledge and working memory and tool use, huge unlocks in the last year.
So now all of a sudden you could put these large language models encapsulated in their harness or exoskeleton, and you can put them into your company, connect them to the information, the ground truth that you have, connect them to the tools that they can use, and all of a sudden they start doing amazing things. Now the easiest part, frankly, was coding, right? That's kind of the first on-ramp, you know, to useful work. And in a lot of ways, coding is easy because both the customer and the work, the tools that they use, are fairly accustomed to things that has tremendous use but are not extremely easy to use, right? And so in the early days of coding, it wasn't extremely easy to deploy. But software engineers found a way. And so now I think the next click for us is now, and you guys have probably been playing with Astra, OpenAI Astra is really fantastic, and we're using it in all these different places. And so now the next phase of these agents, they're able to do design work and a lot of creative marketing work, and we're using it to take a picture and give it to Astra, it literally recreates it into CAD.
It recreates it into the CAD, and so which is incredible because we need the CAD.
So that we can interact with the CAD and make fine-tuning and adjust, and so Astra could really give us the first initial implementation incredibly productive. Now you take Astra and the latest release and you apply it to all of these different domains. I think that besides software engineering, we're going to see designers and builders and creators and this whole area, scientists, and all of this whole unlocking. Now the challenge, of course, is how do we deploy agents safely and securely into the world's enterprise.
This is exactly the same question again with safety and security, and we'll talk about.
We'll come in on that. What I'm struck by is that all of this occurred this year with OpenAI and Anthropic having, I don't know, two, three gigawatts of compute each. Okay. So relatively small amount of compute relative to what's coming. Okay. And so again, what I'm interested in is the way the economy generally works or people model the economy or they model a company is linear deceleration. They're not very good at modeling acceleration. But here we have this double exponential. We have massive amount of new compute coming on board. So if OpenAI and Anthropic are, I don't know, three to five x-ing their compute next year, does it stand a reason? I mean, we solved Navier-Stokes, we'll look back at this and say that was a tiny amount of compute and intelligence, the worst intelligence we're ever going to have from here forward, solving a millennium problem. So when you look at what's coming online next year, you know, do you expect that we're going to have major breakthroughs in, you know, long-running agentic problems that we can use these swarms of agents on?
Well, Navier-Stokes is really a great example. Remember, OpenAI did not solve Navier-Stokes from nothing.
They solved it from the foundation of a lot of great scientists and a lot of great mathematicians and a lot of great previous work. Earlier I said we're at kind of the tipping point of a lot of these different fields because we're kind of at that point with Navier-Stokes with a great deal of foundational work already been done. If we could just apply the exhaustive exploration of AI to the previous done work, we're going to see one breakthrough after another breakthrough rather quickly. But we do need a lot of compute to do that. And the reason for that is because a large amount of exploration was necessary. You know, if it was simply somebody resolving some fundamental first-principled theory in order to discover the breakthrough, if it didn't require a vast amount of exploration of search space, not space space, but search space, if it didn't require that, then somebody would have found it earlier, right? You know, people are pretty smart. And so I think that there are many problems like this that are just on a precipice and really wants to just have that massive amount of intelligence exploration to get us there.
In the coming years, you know, my expectation is that the cost of intelligence would fall fairly quickly, right? And the reason for that is because obviously we're bringing more compute online. These models are getting smarter. When you're smarter, you could be more efficient, right? And so the combination of these two things, the cost of intelligence will come down, which is of course what they want, right? And this is no different than Moore's law. If you go back thirty-some years, we have three decades of computing cost decreasing by a factor of two every single year, right? And we want intelligence cost to decrease by a factor of two or twenty every single year because the consumption of it is going to more than make up for it.
More than make up for it.
That's right. More than make up for it. And so there's a massive empty space in the world that wants to be filled, empty vacuum of opportunity, and we just got to get the cost down.
I guess what stands out to me about Navier-Stokes is that they had ten thousand agents working together on this. It looks like, to me, that sounds like a hyperscale company basically all pointing agents to work together towards a goal, right? And I think in the early days, right now, it's a lot of parallel search exploration, but I think the goal is that more of these agents can work together to a common goal and that we can solve a lot more. And to your point, I think our math says probably cost about four million dollars of compute to solve this instantiation of Navier-Stokes. The cost to do something like that over the next few years is going to drop by another five x, ten x, right? So the number of things that we're going to be able to do is rapidly expanding. Our ambition is going to go sky high. The original GPT probably cost, let's pick a number, say maybe twenty million dollars to do. If you wanted to train GPT-1 now, it'll probably cost you, what, a thousand, a hundred dollars maybe?
It's like a hundred bucks.
Yeah. Okay. And so you went from twenty million dollars, let's say ten million dollars, to a hundred bucks. And we did that in ten years.
And so I fully expect, meanwhile, our ambition got so large that the models that we're training now, three trillion, ten trillion, thirty trillion, we're talking about gigantic models now. And we want it to be, and so our ambition for the capabilities of technology has gone up. Meanwhile, our use of it has gone up between ten years ago to now probably, I don't know, a million times. And so I don't think this trend is going to stop. I think there's a fairly insatiable demand for intelligence. And the smarter you are, the more intelligent you are, the more ideas of applying intelligence you have. You know, it's over the more I've learned in life, the more I realize there's more to do. And that's because every time you learn something, it opens a new door. Every time you learn, it opens a new door. And every time you open a new door, you see more opportunity. You see more challenges. You see more problems. And so that's kind of the, that's the future, right? You know, people say we're going to have more time. I completely disagree. I think for most people, this intelligence is going to unlock more opportunities. We're just going to, and they're going to want to pursue it because they're ambitious. Yes. You know, give me an example of a family, a father, a son, you know, a husband, a wife, a mother who doesn't want it, who don't have ambition for their families to have more. It's just they just don't have the opportunity, and these things are going to open up opportunity.
If you go back to 1930, Keynes talked, he made a prediction about the industrial revolution, and he said, you know, in a hundred years the economy will have eight x'd, but the work week will go to fifteen hours a week. He was right. The economy did eight x, right? We had massive abundance and productivity gains from it.
Yeah, except we're working eighty hours.
But we didn't go to a fifteen-hour week. And what he got wrong was the human spirit, right? He didn't realize that work is purpose. Discovery is purpose, right? It's deeply encoded in, I think, our DNA. And I'm working harder than I ever have, having more fun than I ever have. Not because I have to, like my grandparents did, right? I'm doing it because I want to. And so I agree with you on that. I'm fascinated with this idea. You know, the singularity postulates that, you know, we've kind of now have all these agents that can work together on long-running tasks. They can stay on task. I love your.
Keep each other on task, working as a team. Hey, you're off.
That's right. I love your idea that, you know, we build on top of all the things that have come, right? And so if I think about full self-driving as an example, we talked about it for a decade and we really didn't get there, right? But all of a sudden, like you go on Twitter, everybody experiencing Tesla FSD, experiencing Waymo, we're here. Somebody asked me, how was the drive here this morning? I said, I don't know, because I got in my car and I hit FSD and I didn't pay any attention until we pulled into the parking garage, right? I was working the whole way here. But if I think about that in the fields of science, the breakthroughs that are going to come in the fields of pharma and genetics, right? We've been laying down the tracks for thirty years, we're going to break through on a lot of massive cures for the world. Of course, ChatGPT is already helping parents plan their day and solve their kids' health issue and helping them get better educated. Talk to us a little bit though as to why that's having such a hard time breaking through. Jensen, we have all of these things that are out there improving lives and that are right around the corner. And so that's kind of curious to me as to why that's not breaking through.
I guess, let me take a shot at it. Yeah. And, you know, we'll ultimately not really know. I've spoken to a lot of people. They told me what they thought. I mean, one of the things, of course, is AI or super intelligence is a soft matter.
You can't look, you can't see it. And so you got the whole world talking about this technology, but you can't see it. You can't touch it. And even though they're already benefiting from it, when they do search, they're already benefiting from it. When they go shopping, they're benefiting from it. When they're using Netflix, they're benefiting from it. You know, you go to Walmart, you're benefiting from it. The fact that FedEx is getting stuff to us, you know, it's all leveraging super intelligence and accelerated compute today, but we don't do a very good job telling that story often. So life is, they're benefiting from it, it's just invisible, right? So if we shut off the data centers and shut off super intelligence for a weekend, I think everybody would notice.
Right. I know you had a question about robotics in particular, though.
Yeah, I guess you've said, notably said a few times this year that the ChatGPT moment for robotics is here, and I think you've also said that it could be your next hundred billion dollar market. So I guess what are you seeing now in the inflection point in this year? Yeah.
That this is around the corner.
I understated the hundred billion dollars because we're already, the first six months of this year, we did ten billion dollars in physical AI. Physical AI is basically, you know, AI that's somehow interacting with the physical world. And it could be photon-counting CT machines. It could be ultrasounds. It could be new imaging radars. Next generation of 5G. Not only does it understand radio waves, it's going to understand signals of all kinds. It's a sensing network. Of course, robotics, self-driving cars, manufacturing systems, visual inspections, geospatial satellites, imaging the world and reconstructing it into 3D. And so there's basically, if you look at the entire electromagnetic spectrum, there's AI stuff going on from visible to invisible spectrums. Okay. And so we're doing work across that entire thing. And as an AI, there's the perception part of it, there's the reasoning part of it, and then there's the planning and actuation part of it. When you finally come to planning and actuation, then that's self-driving cars and robotics and things like that, you know, in warehouse delivery vehicles, pick-and-place robots that you don't have to program as precisely because it learns by itself, you know, all the way to people talking about consumer robots, you know, coming into your house, you know, making your bed and things. And so, or healthcare robots, hospital robots, lots of really good examples there. You know, precisely delivering exactly your prescription to the patient. Having robotic care literally twenty-four seven, every single second to keep patients safe. And so just lots and lots of great examples, entire surgical rooms that are going to be completely robotic, right? And so lots of great technology being deployed. And so if you look at this whole thing, we're the world's largest physical AI company. We did ten billion dollars in the first six months. We did ten billion dollars in the last year.
And that was our first ten billion dollar year. The year before that we did five.
Okay. And so it went five, ten. Let's say we did twenty.
So maybe the hundred was a little understated. And I think the reasoning part of it is everything that matters to us in the final analysis touches the physical world, and we live in the physical world. And so I think the market opportunity for bringing software, computing, intelligence into an industry that largely historically was mechanical, electrical, that's a big opportunity. I mean, look at obviously what Elon has done to the automotive industry. Turned it into essentially a computing industry, a technology industry. And so I think every car company is going to be a technology company.
Every shipping and logistics company is going to be a technology company. I'm super excited for FedEx. They're obviously going to be an AI company. They're obviously going to be a technology company. Walmart obviously going to be a technology company, an AI company. So super excited for them. Yeah, the entire telecommunications industry will be reinvented again into a technology industry. They were a technology industry a long time ago, then became a services industry. They're going to be a technology industry again. And I'm delighted to work with AT&T and Verizon and Europe around the world as they reinvent themselves. And so you take a look at every one of these industries that touch the physical world, and there's a great idea for reinventing them with AI. You know, I was just in Washington.
So weird, you know, people can put us in chips.
You know, and so we're actually in warehouse and logistics.
Every industry on the planet. And so we're in life sciences, you know, and so all these entertainment. I mean, obviously for a long time Nvidia was really vastly an entertainment company, you know, and so each one of these verticals, Nvidia has a massive stack that's inside of it, and we're very deep in the ecosystem, and we have excellent partnerships there. And so, you know, one of these days, you know, people might not categorize us as a chip company anymore.
The playbook would be to take the automotive, I guess, playbook and insert it into each one of these industries.
Yeah, that's the final contribution.
But in each one of these, it takes three computers to build a physical AI platform. And so you need the computer to create the brain. That's the training system. But very importantly, and this is where you're going to see the labs also do this, they need to build a digital twin of the internet so that they don't have to put their AI agents out into the internet to learn how to use it. And so just like we created Omniverse as the digital twin of warehouses and buildings and cities and the physical world, we put our robot into the digital twin. The digital twin learns how to be a robot, and then we deploy it into the last computer, which is the computer that's inside, you know, for example, a robot or a car or surgical instrument or whatever it is. And so it takes three robots, three computers, and three complete stacks, and we build complete stacks for each one of them. We're the only company in the world that does that. And then if you're a company that wants to build it end to end, you could use two or three or, you know, one of our computers. We don't care. And so each one of our computers has its own software stack. We do the same across all of our verticals.
But of course these capabilities cut both ways, which is why, you know, we hear a lot of people talking about safety. You've been highly critical of the doomer narrative. You can only get there if you build the technology responsibly.
And so the first thing is build the technology responsibly.
