Jensen Huang: The Mindset That Built NVIDIA

23:51Jensen Huang:I think, well, first of all, I need to understand agents because agents is the new software. And how is this new software processed matters a lot to computer architecture. And the more intimate we are about the nature of agents and how it's different than chatbots, which is how different than maybe inference in the very beginning, however we think about these processing layers, the more intimate we are about the nature of the processing, the better we can design systems. We kind of have to live in the future five to ten years because it takes three or so years just to build a system. It takes a couple of years to ramp it up.

24:34Jensen Huang:And you're dealing, and you would like them to be able to use the computer for 10 years after. So you kind of have to live in the future for a while. And so agentic systems for us, the first principles is just what is the workload? What's the algorithm? How is it going to evolve? Where are the bottlenecks? You know, where are the Amdahl's laws problems? And how does it scale? What happens to concurrency? How do you deal with sandboxes? How do you deal with MCP? How do you deal with working memory, long-term memory? How do you have all these autonomous systems, asynchronous systems working all the time?

25:10Jensen Huang:And so what kind of design architecture makes perfect sense for that? And so we have to go and go discover that. And then, of course, the second thing is I want to use agents ourselves to make NVIDIA go faster. And so we have Boris in the back, and we've got Cloud Code autonomously running in sandboxes all over NVIDIA, and that's really fantastic. And some people use Codex, some people use Cloud Code, some people use Cursor, some people use Cognition. And we let kind of a thousand flowers bloom, let people select the tools they want to use, and then we learn from all of that. And so the second part is just helping the company move faster, use the tools.

25:50Jensen Huang:And the more they use it, the more we're going to learn about how to make it work better in the future. And the last part is discovering the future of solutions, technology for the future. And maybe, you know, when we saw the early versions of Chain of Thought come out of Stanford, this is probably a decade ago at this point, maybe eight years ago, you know, the question is how effective is that going to be in reasoning and how scalable it's going to be. And what is the implication, for example, in computer vision if we can reason from prior knowledge? And then the big breakthrough, of course, just in thinking through that small little domain, you come to realize that maybe we don't need as much data for cars to train a self-driving car, which led us to creating Alpamayo, which is the world's first thinking self-driving car.

26:50Jensen Huang:And with just a million miles or so, a couple million miles, it's an incredibly great self-driving car. And the reason for that is it's kind of like us, right? We don't need that many miles before we could drive fairly well most of our lives. And the reason for that is because we have prior knowledge from our language model, and we can decompose a situation we've never seen before and build it up out of things that we understood and know very well. So that's an example of seeing something and then realizing the impact some time later. When the agentic systems came along, it's very, very clear that obviously a large language model needs memory.

27:35Jensen Huang:It needs prior knowledge. It needs tools. It needs ways to network with other agents. And so that kind of, you know, that once you see some early indicators and you're able to reason about the future, it helps you get a leap into the future. I feel like there's this pattern that I'm starting to see around NVIDIA. You see a problem, there's a new algorithm, there's some new thing happening. And then actually you're right there with open source. I mean, I remember when OpenClaw came out and people said it was unsafe. but you guys came out with a sandboxing sort of toolkit that like surrounds any harness and makes it safe.

28:16Jensen Huang:When I saw OpenClaw my first thought was well first of all I learned about it and then and then I you know without without much imagination you just realized we just designed a modern computer this is the operating system that's going to hold a large language model and and in a lot of ways OpenClaw to me was very Linux moment to me. And now everybody can build their own AI. And I was so excited about that. And we contacted Peter and we said, hey, you know, all of NVIDIA's engineers are your engineers. That's what I told Peter. You got this battleship outside your house. You, you know, break down the problem as you desire and we'll contribute as you wish.

29:00Jensen Huang:Same thing with the Hermes team. I'm so excited about the work that they're doing. I do think that the world needs the ability for everybody to build their own AI. You could, of course, and I encourage everybody to use cloud services as much as possible. Everybody should use ChatGPT and Cloud. Everybody should use that. But if you need to build your own AI because you're a company and you need to build your own domain-specific AIs. Now you have Hermes and you have OpenClaw, you've got all kinds of, you've got Langchain, DeepAgent, you've got all these different ways, right, to build your own AI.

29:41Jensen Huang:And it's, quite frankly, relatively easy because the software's smart, you know? And so AI is smart, and therefore AI must be so smart you could adapt it easily. And so I think that we want to encourage everybody in every company to build their own AIs. And who knows what innovation will come from the fact that it's open source. I feel like all the alpha is in building your own AI. I mean, if someone else is using whatever is off the shelf, but you have a thing that can recursively self-improve. And it is, you know, I mean, people are very flippant about markdown files. They say like, oh, ha ha, it's just text.

30:20But like text is intelligence. And we're in a different... Words are thoughts. Yeah. Yeah, words are thoughts. Yeah. And it turns out you can... Try to think without words. Yeah. So switching gears again, I mean, a lot of people are, anytime you move the cheese, people get a little worried. Intelligence is going to be on tap, which is really awesome. I think it bodes well for everyone in this room. What do you think changes about the economy? What do you think happens in sort of a broader sense?

30:51Jensen Huang:Obviously, what I'm going to say is uneven. There are some, you know, we're going to automate tasks. We're going to automate cognitive tasks. If that task is somebody makes a phone call and sends a bunch of words across the phone to you, and your job is to provide a response. And if all the information is at your fingertip, because you have all the database here, and you should be able to answer that question completely. In that case, that task will be automated away. Ignoring that for a second. Not that we ignored this, but my point is I'm going to answer the question about really the great opportunity.

31:36Jensen Huang:And so many tasks will be automated away. Many jobs, every single job will change, and there'll be a whole bunch of new jobs. And that I think we know. The bottom line is this. The evidence would show that, and it makes perfect sense, that AI and automation is creating jobs everywhere. The narrative about AI destroying jobs is exactly backwards. AI eliminates tasks. AI automates tasks away. But it doesn't necessarily eliminate jobs. And the reason for that is because the job of a person has a purpose. And that purpose has many tasks. Some of those tasks could be automated away. Many of those tasks cannot be.

32:23Jensen Huang:And so the evidence suggests that here we are, we've automated coding, which is a task, but the job of a software engineer appears to be growing, right? The number of software engineer jobs year over year has increased 10%. The task of reading radiology scans has been automated, but the number of radiology jobs has increased some 20 % in the last several years, even though AI has taken over the whole field. And the reason for that is because the backlog of patients is incredibly high. Now doctors and hospitals could admit a lot more patients. In order to admit a lot more patients, you need more nurses, more radiologists.

33:05Jensen Huang:And so the same thing with software. The backlog of ideas, the backlog of ambition and aspiration is so high that if we can automate away the task of programming, we could hire more software engineers to do more things. We could be more ambitious. Same thing just across the board. They said Harvey is going to eliminate all of the paralegal jobs and the number of lawyers will be reduced. Turns out paralegals are growing like crazy. And the reason for that is because the backlog of lawsuits is really high. And now these law firms could get a lot more cases through. In order to do so, you've got to hire more people.

33:46Jensen Huang:And so So this is a classic example of productivity increasing growth. Increasing growth drives more employment. This is the reason why there's more employment today than there was when I first came out of school. So we've been talking a lot about software and agents. Another really exciting thing that NVIDIA is all the way out on the edge on is actually physical robots. You know, how far out? I think in the past you might have even said, as soon as this year, what's the latest thinking on, you know, when can we expect practical robotics? Yeah, the moment that I saw us generating video, that was a great moment for me.

34:29Jensen Huang:The moment that I saw us generating video, I mean, we did the original work on auto progressive GANs, okay? And we did the original work on conditional GANs. Long before the first videos were generated outside that people saw, a couple of years earlier inside our labs, we were driving a simulator completely generated by video and completely generated by neural networks. And so the moment I saw us generating articulation, if I can generate video of a finger moving, if I could generate video of a hand picking up a glass, why can't I cause a robot to do the same? And so the moment I saw that generative AI happening, I realized that robotics articulation was around the corner.

35:20Jensen Huang:And so now the question is, you know, how is the robot going to understand, to generate motions that obey the laws of physics? How does it understand causality? How does it understand, you know, friction, tension? How does it understand the laws of physics? And so it started us down the journey of creating what we call physical AI now. And everybody calls it physical AI. And physical AI, we started working on World Foundation Model, an AI that understands the laws of physics. and how the world works. And we started down the journey of working on robotics. I would say the chat GPT moment of robots happened a couple of years ago already.

36:00Wow.

36:01Jensen Huang:And the reason for that is, remember when chat GPT first came out, it didn't do anything productive. It didn't do anything useful. But it opened our imagination about what's possible. And I would say a couple of years ago, So, you know, robots walking around that we could do reinforcement learning, fine-tune it for, and ground it in physics. Really happened a couple of years ago. So now what do we need to do? We need to do all the same things that we're doing now for agentic systems. We have to create environments for them to learn in, to eval in, eval against. And so we have to do real to sim to create environments.

36:41Jensen Huang:We have to generate simulators that are based on simulation, grounded physics simulation, as well as generative physics simulations. So Isaac Sim, Cosmos, and all the work that we do in that area is related to simulation. And then the last part is Sim2Real. And so that part has something to do with reinforcement learning, grounding it on physics, grounding it on all the electromechanical systems that robots require. And so, but these three basic systems, I think, builds up the eval, if you will, the post-training of robotics. And I think we're going to see it right around the corner. Amazing. Where does physical AI show up first in a way that's really economically real?

37:29Are you seeing that already?

37:30Jensen Huang:We conjectured that robotics was going to come along and decided that the first application of robotics that has both a large enough market, relatively standardized technology so that we could scale and get the flywheel going, and has real economic value was self-driving cars. And so inside Waymo are chips from NVIDIA. At Tesla, we were in the car. Now we're in the data center. Mercedes, we're in the data center. We're in the car with a software stack. We worked on Alpamayo, and we open-sourced it. And the reason why we open-sourced the self-driving car stack is because you need it for agriculture.

38:15Jensen Huang:You need it for mail delivery. You need it for warehouse AMRs. There's so many different ways that you could apply autonomous navigation, and none of those markets are big enough to be a self-driving car market, and we thought it was sufficiently diverse that we would create the whole stack for it. And so we're working with autonomous vehicles in all kinds of different places. Our robotics business, autonomous vehicle business, basically physical AI business, is probably almost like$10 billion, so it's really, really big already. Likely, this will be one of the largest industries in the world.

38:51Jensen Huang:And it'll take longer than a couple, two, three years. It'll take less than 10. And so this will be our next$100 billion business. Amazing. I want to take a moment. I think this is the exact right crowd to, you know, maybe as an arena, we can welcome Jensen to X. Welcome to X. I think you made your first post. And thank you for your leadership.

39:18Jensen Huang:You know, that shows you how introverted I am. It took me until 2026 to have the first post on X. You know, I'm probably the last human on earth that did it. But what I posted was too important to me and too important to the industry and too important to the world. And so I overcame my shyness and put my first thing out on X. No, thank you for your leadership. I mean, open source, open weights, open source models are incredibly important for, I mean, what all of us in this room want to do. We want to create startups. If not for open source, the mobile cloud industry would have never happened. if not for open, if not for Linux, if not for Kubernetes, if not for all of these platforms, if not for TensorFlow or, more important, PyTorch, and the early versions of a cafe, Torch, I mean, all of the, Theano, remember the early versions of all?

40:21Jensen Huang:Those were all open source. If not for all of that, how would we have modern AI? Well, thank you for your leadership and your voice is incredibly important here. Thank you. Thank you.

40:37Before we go, I feel like we, I just really resonate with your story. I think that everyone here, I mean, would love the wisdom of, you know, your journey coming here. I mean, what should a young person learn now, given all the things that you're seeing, all the algorithms that are going to take hold in society? What should a young person learn now that will still matter based on what you're seeing?

41:00Jensen Huang:Well, some of the things that I saw today and some of the starters I met today was really quite encouraging. And the thing that the big takeaway is, of course, the simple stuff is going to get automated away. And when I say simple stuff, I mean software coding. the idea that you would solve a problem by sitting in front of a computer and you're actually writing code that concept is obviously going to get automated away in my generation when I was growing up we had to do long division for God's sake who has to learn long division and so that got coded away that got automated away and so I think the simple stuff is going to get automated away But the hard problems, the hard sciences, physics, chemistry, biology, computer science, computer engineering, systems thinking, and particularly the domains that are intersecting, those hard problems will never go away.

42:08Jensen Huang:And so AI is just an incredible tool that helps us become even more ambitious, even more impatient about solving these extraordinarily large and incredibly hard problems than before. And so, you know, if you look at my generation, when I first graduated, a chip designer would design a chip with maybe a thousand transistors, and that would be a very large chip. But now, designing a trillion transistor chips is not even, if somebody would have told me, Jensen, our next chip is a trillion transistor, I said, okay. It's not a thing. And the reason for that is because we are so ambitious now that the scale of the problem, the scale of the task is no longer a matter.

42:55Jensen Huang:And so you don't have to worry about how much coding, how many engineers. You don't have to think about those things anymore. You just have to think about what is the problem you have to solve. And so I think that the deep tech stuff, the deep science stuff, understanding the intersection between technology and social issues, understanding market gaps and holes, opportunities, I think all of that still exists. And the better you are at systems thinking so that you could orchestrate millions of agents solving problems autonomously, the better off you are. And so that's why system thinking is going to be so important.

43:37But otherwise, I think the world's going to continue to have a lot of great challenges for us to solve.

43:43Jensen Huang:Go to school the same old way. You know, stay in school. Stay in school.

43:51I guess I usually like to end with, you're looking out on the crowd. There are a lot of people who, I mean, I started this, the opener with like, I honestly look in the crowd and I see people who are not different than us per se. We actually just are technical and love systems. Thank you. Thank you. What advice would you give to this room? And you see yourself in this room, and I'm curious what you would say. If you could send a telegram, a message to the 18 to 22-year-old version of yourself, what would that be?

44:32Jensen Huang:I could tell you exactly how I felt when NVIDIA first founded and the three of us started. The thing I felt at the time is there was so much for me to know and so much for me to learn. And I didn't know it. And I was telling you earlier, at the time, there was no YouTube. There's no YC. Nobody's teaching you how to start a company. And so I went to the bookstore and I bought a book. And the book said, How to Start a Company. Unfortunately, the book was like 500 pages long. And so I figured by the time I read it, I'd be out of business, and Lori and I would be out of money, and so there's no sense reading it.

45:15Jensen Huang:But the thing I remember very vividly is how scared I was to go raise money because I felt that I was about to talk to a bunch of people, and I didn't know how to answer their questions. And it's true. And I barely know how to answer their questions even today. But the thing that I learned is none of that stuff matters, as it turns out. And you're always going to have things that you don't know. And every single day, the world is changing. Technology is changing. Obviously, this is the greatest time in the last 60 years to start a company. The whole industry has changed. It's a complete reset from a technology perspective.

46:02Jensen Huang:The single most important technology in human history, the computer, has been completely reset. And so this is absolutely the single greatest time to start a company. And I'm jealous of all of you and the opportunities you have ahead. I mean, it's going to be incredible. So it's the perfect time on the one hand. On the other hand, the technology is changing so fast. And so the question is, what's the right feeling for you? and eventually, and I told you the story of us, of me buying the other book, the textbook. I think the psychology and the feeling that I have today on all of the new experiences and the new technology and new markets and new dynamics, I look at it and I say, this is important.

46:45Jensen Huang:I've got to go learn it and I've got to go do something about it and I better get to it as fast as I can. And how hard can it be? I always have this feeling, how hard can it be? And truth be told, it is way harder than you think. But you don't want your mind to be there. You want your mind to be, how hard can it be? And let the suffering come to you a little bit at a time. you know don't don't imagine how hard it's going to be and let all of that turn into anxiety and not doing something about it you want to imagine your head how hard can it be you know i got a whole bunch of eight i got a bunch of ai agents helping me anyways and so how hard can it be and then you get going on working on it and so that's probably the the attitude of an entrepreneur you know you have to learn a bunch of stuff along the way you believe in your ability to learn which is, you know, learning is the single greatest superpower.

47:50Jensen Huang:And if you go into it with the attitude, how hard can it be? If anybody can do it, I can do it. And just realize that it will be hard. And you just have to have the resilience to overcome it every single day. You don't have to overcome life in one day. You just have to overcome that morning, that morning. You know, you have to overcome today, today. And so it's not a big deal. Just get through today. Wait till, right? Work towards tomorrow. Keep following your dreams. And the rest of everything, if you stick with it long enough, NVIDIA happens. And so I think the wisdom that I can, if there's anything, is resilience is probably the single most important thing.

48:34Jensen Huang:And if you believe in something, just get going on it and get your mind out of keeping yourself from pursuing it. because of fear or anxiety or lack of confidence or whatever it is. And that you're just going to tell yourself, I'm going to learn my way there. Jensen Wong, everybody. All right, guys. Thank you. Thank you so much. Thank you, guys.