Jensen Huang: The Mindset That Built NVIDIA
Jensen Huang at Startup School 2026 on wrong bets, relentless learning, and resilience.
NVIDIA started with the wrong technology, learned the right one from three textbooks bought at Fry’s, and went on to invent most of the major breakthroughs in modern computing.
At Startup School 2026 at Chase Center, Garry Tan sits down with founder and CEO Jensen Huang to talk about confronting reality, learning your way into new domains, and why resilience — getting through today, today — matters more than anything else.
Timestamps
00:00 — Intro
01:07 — NVIDIA's wrong algorithm
03:14 — Buying textbooks to save the company
05:29 — The real big idea
07:06 — The Sega story
09:37 — The $300M IPO
10:33 — Seeing AlexNet differently
12:44 — Reinventing the full stack
13:30 — How to build a first-principles org
17:01 — Build the car to fit your driving style
19:12 — Frontier algorithms
20:50 — Systems thinking is the new coding
23:26 — Should you own your own AI?
25:43 — How NVIDIA sses agents internally
30:51 — AI and job creation
34:21 — The ChatGPT moment for robots
37:24 — Where physical AI shows up first
39:18 — Why Jensen just joined X
41:01 — What to learn that still matters
44:32 — The mindset you should have - "How hard can it be?"
Transcript
Garry: Please join me in welcoming to the stage the founder and CEO of NVIDIA, Jensen Huang.
Jensen: Hey Garry.
Garry: Please.
Jensen: Hey everybody.
Garry: Oh my God. This is a surreal moment for me. Thank you. Thank you for being here, Jensen.
Jensen: I’m delighted to do it. It’s great to be here. Apparently, if you’re here, you are going to make it. So I’m happy I’m here.
Garry: Oh, Jensen. Well, for the students who only know NVIDIA as a company at the center of AI, what part of the early NVIDIA story do they most need to understand?
Jensen: The thing that most people don’t believe is that the choice of our technology that we started the company with was absolutely wrong. We started with the idea that we would reinvent 3D graphics. The company’s philosophy and perspective was that general purpose computers, the CPUs, were really useful, but if we could augment them with accelerators, we could solve problems that were otherwise too hard to solve. One of the first problems we chose was 3D graphics. During that time, 1993, the PC was just rumored to be coming. Our big idea was that we would turn every single personal computer into a game console because we grew up in the era of game consoles. We thought, what if we could design a system that would fit into the personal computer and turn it into a game console?
We thought we would reinvent the algorithm that would require these large supercomputers and fit it into the PC. We came up with some new algorithms, and we were excited about it. We believed in it. We reasoned about it in a thoughtful way. We went to start the company to go build it. Well, it turns out the algorithm was exactly wrong. The technology that founded the company turns out to be exactly wrong. In 1995, we realized that. It was almost too late because by then there were some 35, 40 other companies that were building 3D graphics for PCs. We realized that it didn’t work. I went back to the company, and we were at the company. I said, “What are we going to do? It doesn’t work.” We were all talking about it.
I said, “Look, we won’t have a company if we don’t confront the fact that this doesn’t work and start working towards the right algorithm.” Then somebody told me, it turns out none of us knew how to do it the right way. Not only did we choose the wrong technology, we didn’t know how to do it the right way. That was a big day for me. I had a couple of $60, a couple of hundred dollars in my pocket. I went down to Fry’s, and I bought three textbooks. The textbooks were about OpenGL and how to design OpenGL pipelines. I brought them back to the company. I gave them to the engineers. And here we are. We reinvented computer graphics. We’re the world leader in modern computer graphics. We invented most of the major breakthroughs in the last 25 years.
Everybody would have thought that NVIDIA started out as world leaders in 3D graphics. We learned it from a textbook. We actually started the company, raised money, and bought textbooks when you think about it. The big lesson for me is technology’s changing all the time. So long as you’re able to confront reality, so long as you are able to learn, the technology itself actually doesn’t matter. Since then, NVIDIA has been inventing all kinds of technology. All kinds of technology we’ve never really done before. We approach everything with the same attitude. If it’s important to do, we’re going to go learn it. How hard can it be? It always turns out to be much, much harder than we expect. But you go into it with the attitude, how hard can it be?
Garry: Backstage we were talking about how we were talking with some of the top YC companies, and you were saying that each one has an expertise in a domain that you and NVIDIA have an expertise in. They’re all just, I forget what you said. It was like an algorithmic domain of a sort. It sounds like 3D graphics was merely the first of an algorithmic domain.
Jensen: That’s right.
Garry: And it came from a textbook, but then anyone could have read that textbook.
Jensen: Particle physics, fluid dynamics. Yeah.
Garry: But you created the thing that people want, the end product that people want to pay a lot of money for.
Jensen: The big idea of the company that was spot on is that it is possible to augment the CPU to solve problems that otherwise are too difficult to solve. Molecular dynamics is one of them. Image processing is one of them. Inverse physics is another one. All kinds of different algorithms—of course, deep learning is one of the major ones. In order to create the company that we have today, we realized early on that it’s not about building a great chip. It’s about accelerating an algorithm domain. One of the things that I’ve always believed in is what makes great companies is a unique perspective about the world that you deeply believe in. It’s not so much the technology. It’s not so much the market even. Those things all matter. If you have the right technology for the right market at the right time, your life is going to be a lot easier.
A high-level vision about the future of some important thing, a perspective about it that’s somehow unique, that you deeply believe in. Ideally, pursuing that vision is hard to do. Those are kind of good combinations. In our case, we realized that accelerated computing was going to be important. Accelerated computing turns out to be very important. Our realization is everything to do with algorithm, not the chip, turns out to be exactly right.
Garry: So you’ve said a lot about the hardships of a founder. Are there a few stories that really jump out at you? The people in this room would love to start a company, but are they really prepared for eating glass and possibly having to shut down the company? Things going wrong. What are some of the pivotal moments that really jump out at you? I think you were just in Japan, right? And you were honoring Sega, was it? I feel like that was a really powerful story.
Jensen: The project that led us to realize the algorithm we chose was wrong was a partnership with Sega. Sega had contracted us to build the game console after Saturn that turned out to have been Dreamcast. Does anybody know what Dreamcast is? Okay. So we did not build Dreamcast. We were originally supposed to build Dreamcast, but because our algorithm and our technology was fundamentally flawed, I went to Japan, and I told Irimajiri-san, the CEO at the time, that the contract they gave us was a $12 million contract. We would not be able to fulfill it because the technology didn’t work. And I told him the reasons why. Then I advised that they choose somebody else to do it. But then I told him that I unfortunately still needed the money. And he asked me, you could just imagine the conversation. So what you’re telling me is what I contracted you to do, you can’t do, but you would like all the money on the contract.
And I said, you got it. That’s exactly right. But obviously I was polite. I was humble. And he realized that I was honest and everything made sense.
And if he didn’t give us the money, we’d be out of business. I think that this happens in this room. You don’t invest in companies, you invest in people. And what Irimajiri recognized was here’s somebody and a company that he trusted in the first place, the contract, and that he believed in and that he would love to see make it to the next day. And so that $5 million kept us alive and gave me enough time to discover what to do.
Garry: And then I guess if they held. They sold it for 15 million, I heard.
Jensen: Yeah, they sold it the moment we went public. When NVIDIA went public, our valuation was $300 million. $300 million in 1999. That was real money.
Garry: I think it’s north of a trillion dollars now or so.
Jensen: It’s more than true. Yeah.
Garry: Yeah. That’s wild. So you’re the core. We like to say that you’re the man who controls the spice. Before that, I don’t think anyone could have really predicted how important GPUs and the technology you built would be for this AI revolution. What did you see? Was it the accelerator and being in the right place at the right time? Or surely there were a lot of things that led up to that, that allowed you to capture this position.
Jensen: Yeah. I saw AlexNet just like everybody else saw AlexNet. But remember, our lens of the world, my view of the world was always looking for algorithms. The algorithm could be NAMDY, the algorithm could be VASP. The algorithm could be OpenGL. It could be SQL, some domain-specific language, some algorithm. And so my lens of the world was always looking for some problem that we might be able to help solve. So when AlexNet came along, the algorithm was deep learning. And so the question is, what is this algorithm and why does it matter? Why was it so effective? And what else can it do? And if you were to scale algorithms and scale it beyond that, what could it solve that otherwise you can’t solve today? And the breakthrough for us was realizing that AlexNet was not AlexNet. That AlexNet was an approach with deep learning that allows you to learn any function.
And so 15 years ago, I was telling everybody that, “Hey, guess what? We just learned the universal function approximator. We just discovered the universal function approximator. We could give it the answer for almost any function, and it could learn what the function is. And for a lot of functions, you don’t have to be precise. And in fact, it’s impossible to be precise. And so most of the interesting problems are imprecise in this way. And so the day that we realized we have a universal function approximator, the question then is what does that do to the computing stack? What does that happen to software? What are the industries that this could impact? So on and so forth. Almost right away, we started working on computer vision. Almost right away, we started working on robotics, self-driving cars, because that fundamental capability, you could imagine solving some important problems in the area of computer vision and robotics.
And so I think the big breakthrough was simply that this is much more foundational than AlexNet. This is a way of doing software. And the implications to the processor, the middleware, the algorithms, the applications, what I now describe as the fiber layer cake, that entire industrial stack, I imagined reinventing altogether about 15 years ago. And this is simply about asking questions, reasoning about things to first principles, asking questions like if this, then what? If this can get better, then so what? Asking all of the basic questions about something that you observe that’s really impactful.
Garry: One of the things that really jumps out at me is to what degree you go all the way into the weeds. You read the papers, you talk directly to the principal scientists who are coming up with these things. Do you have any advice for people in the audience? That’s true founder mode. And then at the same time, you have an organization, and you have executives, and you have people who say, “Here’s the graph. We want to stay on this graph.” Sometimes it ruffles feathers. Do you have any advice for people about an organization and how you navigate that? How do you build an org that allows you to think in first principles? Because if the Fortune 500 did that, the Fortune 500 would probably look a lot more like NVIDIA than not. And it doesn’t. You have built a very unique company.
Jensen: My state of mind always starts with curiosity. I have a whole bunch of questions myself. And of course, like anybody else, I’ll seek the shortest path to the answer. But oftentimes the answers from the people that are near me might not be satisfying. And I might have other questions. And maybe they’re busy doing something, and they’re pursuing something. So my first inclination is to go discover the answers to my own curiosity. My second is, if I find that the information and that the domain of information or a particular field could be really important to somebody and could be important to our company, then my next inclination is how can I learn as much as possible so that I can be of service to the company and share with everybody else? This is no different than you when you’re sharing knowledge. I watch your podcasts, and I watch your videos, and I really enjoy them.
You’re sharing ideas with everybody else. In a lot of ways, I think a CEO is in service of the company, in service of all the people that are working there. And you want to empower them with some insight. That’s really where it’s coming from. It’s not so much a management technique, but a personality technique. I want to empower you. And this is something really important that I just observed. Let me tell you why it’s so important. Now, part of having to be near the ground and be in the weeds, if you will, is because oftentimes the technology is complicated or it’s changing really fast. And especially when it’s changing fast like our world. Unless you have a tactile sensation of what is actually happening, it could either, to you, feel like it’s just moving way too fast to understand. But if you understand the first principles of it over time, then everything makes sense.
It’s kind of like surfing, I would imagine. I don’t know how to surf, but I can imagine it’s kind of like surfing. You get out on the wave. To me, it looks like chaos. But to a surfer, somehow they can read the waves, and they know how to stay on top of it. So I think being CEO is very similar to that. You have to learn how to surf. And in order to learn how to surf, you have to understand the waves, and you have to be able to read the wind, and you have to have good timing. And you can’t have any of that unless you try and unless you actually do it. Partly it’s to inform myself. Partly it’s to try to figure out what is, try to break down the problem so that the company can learn it in a way that they can do something about.
Part of it’s about inspiring other people. And it’s all those basic traits of all the people in this room. You don’t have to change your personality or your behavior when you become CEO. It is possible for you to continue to be yourself. One of the things that I learned a long time ago, and I have no idea where I saw this, but the CEO or the founders, you’re building a car that you are going to race. You’re going to build an F1 racer, but you’re going to build it in a way that you can drive. You should adapt the car to you. Somebody asked me, Jensen, if you don’t use conventional management techniques and organizational techniques, what’s going to happen when you leave the company? Well, when I die on the job someday, I told them they’ll just have to reshape the company for the next CEO.
And the reason that’s wisdom is because we’re the F1 drivers. We’re the racers. And the world is really competitive, and we’ve got to stay, we’ve got to win. And we have to achieve our mission. So whatever it takes to fit the car to you, whatever it takes to fit the organization to you, that’s what you ought to do. And the next CEO, whatever the personality is, they can figure it out.
Garry: Amazing. It does seem like any change you make to the car that isn’t fit to you will just slow you down and lose you races.
Jensen: Yeah. Or we’re constantly tweaking the car to our needs. And that’s really what I’m doing all the time. I’m constantly tweaking the company, constantly reshaping business processes and the way things work so that I can be more effective for the company. True founder mode. Yeah. Founder mode. Founder mode could scale for 34 years.
Garry: That’s right.
Jensen: From zero to five trillion. No evidence. Nope.
Garry: I’d love to switch gears to what are the frontier algorithms that you’re most interested in now? I love that you’re all the way down into material science, all the way up into the app level. You’re the first to speak on stage about OpenClaw and now Hermes agent. I wonder if you can walk us through a day in the life of how you think about the different stages. Going from materials to chips to data centers to even the app level, how people are going to work. There’s this idea of a full stack AI factory. Well,
Jensen: This is one of the things that is probably going to be the most useful skills in the future. And in fact, just listening to you talk about technology and your use of it, one of the most important things is systems understanding. Systems awareness, system design, system organization, but systems thinking. The reason for that is because most of the low-level things that have to be done are going to be done agentically anyway. They’re going to be automated anyhow. Whether it’s in my generation, it’s about compiling chips and synthesizing transistors and gates and functional blocks. All of that is now synthesized. Most of our designers are systems designers. In the case of software, most software is going to be done agentically anyhow. So you have to be much more able to think abstractly about systems. What are the problems you’re trying to solve?
What are the constraints? Where’s the input? Where’s the output? Where is information coming from? What is the rate of information flowing in and out of the system? What are the constraints? Is it a processor, is it memory, is it networking? Understanding these systems problems at a sufficiently technical level is going to be very helpful to all of the people in this room. I don’t think that fundamental knowledge is ever going to be useless. I think it’s going to be more and more useful. I try to understand systems the best I can. Speaking of agents, the fact of the matter is we already have course-level recursive self-improvement. Every time you use it, it improves the markdown files. Every time you use it, it updates its long-term memory, and the long-term memory is being processed, either compacted or turned into knowledge graphs, and so on and so forth.
It’s being improved all the time asynchronously. The agent is getting smarter and smarter every time. Still, the problem is, and this is one of the problems that I think could be helpful for everybody to solve, how can we have very, very specific, fine-grained control? If not for RAGs, if not for conditional inputs, if not for all of our prompts, the direct output was too coarse. The fact that we can condition, the fact that we can control the agents all the way down to eventually, when it comes up with a plan, I change one word in a plan file. That one word makes a delta difference. Not a complete difference, but a specific difference. Maybe it’s one pixel, maybe it’s one triangle, maybe it’s one component in a CAD file. Maybe one layer, one via, one connection, and then it regenerates everything else.
I think that level of control and that level of collaboration with agents will be game-changing. We don’t need the agents to be 100% accurate, 100% high quality in order for us to use it. It could literally be 80%, and then we help it the rest of the way. Or it could be 99%, we help it the rest of the way. I think controllability is probably the single biggest breakthrough that we need for agents at every single level.
Garry: Do you think people will— with Hermes or OpenClaw, it feels like that might actually be somewhat existential. People should control their own personal AGI. They shouldn’t outsource that and have it be just in the cloud and someone else’s agent that tells you what to do. You want it to be your own. Is that part of the thrust behind NVIDIA being so involved?
Jensen: I think, well, first of all, I need to understand agents because agents are the new software. How this new software is processed matters a lot to computer architecture. The more intimate we are about the nature of agents and how it’s different than chatbots, which is 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 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. You would like them to be able to use the computer for ten years after. So you have to live in the future for a while. Agentic systems for us, the first principles are just: what is the workload?
What’s the algorithm? How is it going to evolve? Where are the bottlenecks? Where are the Amdahl’s Law problems? 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? What kind of design architecture makes perfect sense for that? We have to go and discover that. The second thing is I want to use agents ourselves to make NVIDIA go faster. We have Boris in the back and we’ve got Claude Code autonomously running in sandboxes all over NVIDIA. That’s really fantastic. Some people use Codex, some people use Claude Code, some people use Cursor, some people use Cognition. We let a thousand flowers bloom, let people select the tools they want to use.
And then we learn from all of that. The second part is just helping the company move faster, use the tools. The more they use it, the more we’re going to learn about how to make it work better in the future. The last part is discovering the future of solutions, technology for the future. Maybe 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. The question is how effective is that going to be in reasoning and how scalable is it going to be? What is the implication, for example, in computer vision, if we can reason from prior knowledge? 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.
And with just a million miles or so, a couple million miles, it’s an incredibly great self-driving car. The reason for that is it’s kind of like us. We don’t need that many miles before we can drive fairly well most of our lives. 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. That’s an example of seeing something and then realizing the impact sometime later. When the Agentis systems came along, it’s very, very clear that obviously a large language model needs memory. It needs prior knowledge. It needs tools. It needs ways to network with other agents. Once you see some early indicators and you’re able to reason about the future, it helps you get a leap into the future.
Garry: I feel like there’s this pattern that I’m starting to see around NVIDIA where 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 remember when OpenClaw came out, and people said it was unsafe, but you guys came out with a sandboxing toolkit that surrounds any harness and makes it safe.
Jensen: When I saw OpenClaw, my first thought was, well, first of all, I learned about it. And then without much imagination, you just realize we just designed a modern computer. This is the operating system that’s going to hold a large language model. In a lot of ways, OpenClaw to me was a very Linux moment to me. Now everybody can build their own AI. I was so excited about that. We contacted Peter, and we said, “Hey, all of NVIDIA’s engineers are your engineers.” That’s what I told Peter. “You’ve got this battleship outside your house. You break down the problem as you desire and we’ll contribute as you wish. 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. I encourage everybody to use cloud services as much as possible.
Everybody should use ChatGPT and Claude. 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 LangChain, DeepAgent, you’ve got all these different ways to build your own AI. Quite frankly, it’s relatively easy because the software’s smart. AI is smart, and therefore AI must be so smart, you could adapt it easily. I think that we want to encourage everybody and every company to build their own AIs. Who knows, that’s where innovation will come from, the fact that it’s open source.
Garry: I feel like all the alpha is in building your own AI. If someone else is using whatever is off the shelf, but you have a thing that can recursively self-improve. People are very flippant about markdown files. They say, “Oh, ha ha. It’s just text.” But text is intelligence, and we’re in a
Jensen: Different—Words are thoughts.
Garry: Yeah. Yeah.
Jensen: Words are thoughts. Yeah.
Garry: And it turns out you can—
Jensen: Try to think without words. Yeah, that’s
Garry: Right. So switching gears again, 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 a broader sense?
Jensen: Obviously, what I’m going to say is uneven. 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. If all the information is at your fingertips because you have all the database here, 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. So many tasks will be automated away. Many jobs, every single job will change, and there’ll be a whole bunch of new jobs. 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. 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. 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. 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. The reason for that is because the backlog of patients is incredibly high. Now doctors and hospitals can admit a lot more patients.
In order to admit a lot more patients, you need more nurses, more radiologists. 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. The reason for that is because the backlog of lawsuits is really high. Now these law firms can get a lot more cases through. In order to do so, you have to hire more people. 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.
Garry: 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. How far out? I think in the past you might have even said as soon as this year. What’s the latest thinking on when can we expect practical robotics?
Jensen: Yeah. The moment that I saw us generating video, that was a great moment for me. I saw us generating video. We did the original work on autoprogressive GANs. 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. 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? The moment I saw that generative AI happening, I realized that robotics articulation was around the corner. Now the question is, 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 friction, tension? How does it understand the laws of physics? That started us down the journey of creating what we call physical AI now, and everybody calls it physical AI. Physical AI—we started working on world foundation model and AI that understands the laws of physics and how the world works. We started down the journey of working on robotics. I would say the ChatGPT moment of robots happened a couple of years ago already. The reason for that is, remember when ChatGPT first came out, it didn’t do anything productive. It didn’t do anything useful, but it opened our imagination about what’s possible. I would say a couple of years ago, 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. We have to do real-to-sim to create environments.
We have to generate simulators that are based on simulation, grounded physics simulation, as well as generative physics simulations. Isaac Sim, Cosmos, and all the work that we do in that area is related to simulation. The last part is sim-to-real. That part has something to do with reinforcement learning, grounding it on physics, grounding on all the electromechanical systems that robots require. These three basic systems, I think, build up the eval, if you will, the post-training of robotics. I think we’re going to see it right around the corner.
Garry: Amazing. Where does physical AI show up first in a way that’s really economically real? Are you seeing that already?
Jensen: 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. Inside Waymo, our 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. We’re the software stack. We worked on Alpimayo and we open-sourced it. The reason why we open-sourced the self-driving car stack is because you need it for agriculture, you need it for mail delivery, you need it for warehouse AMRs. There are so many different ways that you could apply autonomous navigation. None of those markets are big enough to be a self-driving car market. 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 $10 billion. So it’s really, really big already. Likely this will be one of the largest industries in the world, and it’ll take longer than two or three years. It’ll take less than 10. And so this will be our next hundred billion dollar business.
Garry: Amazing. I want to take a moment. I think this is the exact right crowd to—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.
Jensen: That shows you how introverted I am. It took me until 2026 to have the first post on X. 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.
Garry: Thank you for your leadership. Open weights, open source models are incredibly important for what all of us in this room want to do. We want to create
Jensen: 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 Caffe, right? Torch. Theano. Remember the early versions of all? Those were all open source. If not for all of that, how would we have modern AI?
Garry: Well, thank you for your leadership, and your voice is incredibly important here. Thank you. Before we go, I really resonate with your story. I think that everyone here would love the wisdom of your journey coming here. 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?
Jensen: Well, some of the things that I saw today and some of the starters I met today were really, really quite encouraging. And 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 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.
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. 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. Now, designing a trillion transistor chip is not even—if somebody would have told me, Jensen, our next chip is a trillion transistors, I’d say, okay. It’s not a thing. And the reason for that is because we are so ambitious now.
The scale of the problem, the scale of the task is no longer a matter. 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 can orchestrate millions of agents solving problems autonomously, the better off you are. And so that’s why systems thinking is going to be so important. But otherwise, I think the world’s going to continue to have a lot of great challenges for us to solve. Go to school the same old way. Stay in school.
Stay
Garry: In school. I guess I usually like to end with you looking out on the crowd. There are a lot of people who—I started the opener with, 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
Jensen: You.
Garry: Thank
Jensen: You.
Garry: What advice would you give to this room? And do you see yourself in this room? 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?
Jensen: I could tell you exactly how I felt when NVIDIA was 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. 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’d be out of money. And so there’s no sense reading it. But the thing I remember very, 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. 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. You’re always going to have things that you don’t know. Every single day, the world’s changing, technology’s 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. The single most important technology in human history, the computer, has been completely reset. So this is absolutely the single greatest time to start a company. I’m jealous of all of you and the opportunities you have ahead. It’s going to be incredible. So it’s the perfect time on the one hand. On the other hand, the technology’s changing so fast. So the question is, what’s the right feeling for you?
And eventually, I told you the story 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. 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? Let the suffering come to you a little bit at a time. 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 in your head, how hard can it be? I’ve got a bunch of AI agents helping me anyways. So how hard can it be? Then you get going on working on it. That’s probably 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 the single greatest superpower. 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. 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. You have to overcome today. So it’s not a big deal. Just get through today. Work towards tomorrow. Keep following your dreams. The rest of everything, if you stick with it long enough, NVIDIA happens. I think that the wisdom that I can, if there’s anything, is resilience is probably the single most important thing. 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. You’re just going to tell yourself, I’m going to learn my way there.
Garry: Jensen Huang, everybody.
Jensen: All right guys, thank you.
Garry: Thank you so much.
Jensen: Thank you, guys.