Jensen Huang on NVIDIA's Founding Mindset and AI's Frontier

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and went on to invent most of the major breakthroughs in modern computing.

So, I'm going to go to the next stage.

Welcome to Startup School 2026.

Now, let's get started.

Please join me in welcoming you to the stage, the founder and CEO of NVIDIA, Jensen Huang.

Jensen Huang: Hey, Jerry.

Thank you.

Please.

Hey, everybody.

Oh, my God.

This is a surreal moment for me.

Thank you.

Thank you for being here, Jensen.

Jensen Huang: I'm delighted to do it.

It's great to be here.

Jensen Huang: Apparently, if you're here, you are going to make it.

Jensen Huang: So, I'm happy I'm here.

Jensen Huang: 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 Huang: The thing that most people don't believe is that the choice of our technology that we started the company with was absolutely wrong.

Jensen Huang: And so we had started with the idea that we would reinvent 3D graphics.

Well, the company's philosophy and perspective was that Jensen Huang: The general purpose computers, the CPUs were really useful, but if we could augment it with accelerators, we could solve problems that otherwise too hard to solve.

Jensen Huang: And one of the first problems we chose was 3D graphics.

And during that time, 1993, the PC was just rumored to be coming.

Jensen Huang: And 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.

Jensen Huang: And so we thought, you know, what if we could design a system that would fit into the personal computer and it would turn it into a game console.

Jensen Huang: And so we thought we would reinvent the algorithm that would require these large supercomputers and we would fit it into the PC.

Jensen Huang: And we came up with some new algorithms and we were excited about it.

We believed in it.

It was what we reasoned about it in a thoughtful way.

Jensen Huang: And and we went to start the company to go build it.

Well, it turns out the algorithm was exactly wrong.

Jensen Huang: And the technology that founded the company turns out to be exactly wrong.

And so in 1995, we realized that and it was almost too late because by then there were some 3540 other companies that were building 3D graphics for PCs.

Jensen Huang: And and so we realized that it didn't work.

And I went back to the company and we were at the company.

I said, what are we going to do?

It doesn't work.

And we're all talking about it.

Jensen Huang: And I said, look, we we 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.

Jensen Huang: And and then somebody told me, it turns out none of us knew how to do it the right way.

And not only did we choose the wrong technology, we didn't know how to do it the right way.

Jensen Huang: And so so that was a big day for me.

I had a couple of a couple of $60, you know, a couple of hundred dollars in my pocket.

Jensen Huang: And so I went down to Fry's and I bought three textbooks and and the textbooks was about Open GL and how to design Open GL pipelines.

Jensen Huang: I brought it back to the company and gave it to the engineers.

And here we are.

We reinvented computer graphics.

We're the world leader in modern computer graphics.

Jensen Huang: We invented most of the major breakthroughs in the last 25 years.

Jensen Huang: Everybody would have thought that Nvidia is, you know, started out as world leaders in 3D graphics and we learned it from a textbook.

Jensen Huang: And so we actually started the company, raised money and bought textbooks when you think about it.

And so the big lesson is that for me is technology changing all the time.

Jensen Huang: And so long as you're able to confront the reality, so long as you are able to learn the technology itself actually doesn't matter.

Jensen Huang: And so since then Nvidia has been, you know, inventing all kinds of technology since all kinds of technology we've never never really done before.

Jensen Huang: And we approach everything with the same attitude.

You know, this is if it's important to do, we're going to go learn it and how hard can it be.

Jensen Huang: And 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?

Jensen Huang: I mean, backstage we were talking about how, I mean, we were talking with some of the top YC companies and you're saying that each one has expertise in like a domain that you have an, you and Nvidia have an expertise in.

Jensen Huang: And they're all just, I forget what you said.

It was like an algorithmic domain of a sort.

And so it sounds like 3D graphics was merely the first of an algorithmic domain.

Jensen Huang: That's right.

Jensen Huang: And it came from a textbook, but then, you know, anyone could have read that textbook.

Jensen Huang: You created- Jensen Huang: Particle physics, fluid dynamics.

Yeah.

Jensen Huang: But you created the thing that people want, like the end product that people want to pay a lot of money for.

Jensen Huang: The big idea of the company that was spot on is that it is possible to augment the same amount of value.

Jensen Huang: To solve problems that otherwise are too difficult to solve.

Jensen Huang: And, and molecular dynamics is one of them.

Image processing is one of them.

Inverse physics is another one.

Jensen Huang: And so all kinds of different algorithms, of course, deep learning is one of the major ones.

Jensen Huang: And, and in order to create the company that we have today, we realized early on that it's not about building a great chip.

Jensen Huang: It's about accelerating an algorithm domain.

And so 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.

Jensen Huang: It's not so much the technology is not so much the market even those things all matter.

Jensen Huang: And if you have the right technology for the right market at the right time, your life is going to be a lot easier.

Jensen Huang: A high level vision about the future of some important thing, a perspective about it that's somehow unique that you deeply believe in.

And ideally pursuing that vision is hard to do.

Those are kind of good, good combinations.

Jensen Huang: In our case, we realized that accelerated computing was going to be important and accelerated computing turns out to be very important.

And our realization is everything to do with algorithm, not the chip turns out to be exactly right.

Jensen Huang: So you've said a lot about, I guess, the hardships of a founder.

Are there a few stories that really jump out at you?

I mean, the people in this room would love to start a company, but you know, are they really prepared for eating glass and you're possibly having to shut down?

Jensen Huang: The company like things going wrong?

Like what are some of the pivotal moments that really jump out at you?

I think you were just in Japan, right?

Jensen Huang: Yeah.

Jensen Huang: And you're sort of honoring Sega, was it?

So I feel like that was a really powerful story.

Jensen Huang: 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 company.

Jensen Huang: Game console after Saturn that turned out to have been Dreamcast.

I don't know if any, does anybody know what Dreamcast is?

Jensen Huang: Okay.

Jensen Huang: So we did not build Dreamcast.

We were originally supposed to build Dreamcast.

Jensen Huang: But because our algorithm and our technology was fundamentally flawed.

I went to Japan and I told Irimandri-san, the CEO at the time, that the contract that they gave us was like $12 million contract.

Jensen Huang: We will not be able to fulfill because the technology doesn't work.

And I told him the reasons why.

And then I advised that they choose somebody else to do it.

Jensen Huang: But then I asked them, I told him that I unfortunately still need the money.

And he asked me, you know, the conversation, you could just imagine the conversation.

So what you're telling me is what I contract you to do, you can't do, but you would like all the money on the contract.

Jensen Huang: And I said, you got it.

That's exactly right.

But obviously I was polite.

I was, I was humble.

And he realized that, that I was honest and, and, and, and everything made sense.

Jensen Huang: I, and if he didn't give us the money, we'd be out of business.

And I think that this happens in this room, you don't invest in companies, you invest in people.

Jensen Huang: And what your monitoring recognized was here's, you know, 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, you know, make it, make it to the next day.

Jensen Huang: And so that $5 million kept us alive and, you know, gave me enough time to discover what to do.

Jensen Huang: And then I guess if they held, they sold it for 15 million, I heard.

Jensen Huang: Yeah, they sold it the moment we went public.

Jensen Huang: When Nvidia went public, our valuation was $300 million.

Jensen Huang: $300 million in 1999.

Jensen Huang: That was real money.

Jensen Huang: I think it's a north of a trillion dollars now or so.

Jensen Huang: It's more than true.

Jensen Huang: Yeah.

Jensen Huang: Yeah.

Jensen Huang: That's wild.

Jensen Huang: So you're sort of the core, you know, I, we like to say that you're, you're the man who controls the spice.

Jensen Huang: You know, before that, you know, I don't think anyone could have really predicted per se how important GPUs and the technology you built would be for this AI revolution.

Jensen Huang: You know, what did you see to, I mean, was it the accelerator and being in the right place, right time?

Jensen Huang: Or surely there were a lot of things that led up to that that allowed you to sort of capture this position.

Jensen Huang: Yeah, I saw AlexNet just like everybody else saw AlexNet and and.

Jensen Huang: But remember our lens of the world, my view of the world was always looking for algorithms.

Jensen Huang: And that algorithm, the algorithm could be NAMD, the algorithm could be VASC, the algorithm could be OpenGL.

Jensen Huang: You know, it could be SQL, some domain specific language, some algorithm.

Jensen Huang: And and so my lens of the world was always looking for some problem that we might be able to help solve.

Jensen Huang: When AlexNet came along, the algorithm was deep learning.

Jensen Huang: And so the question is, what is this algorithm and why does it matter?

Jensen Huang: Why was it so effective and what else can it do?

Jensen Huang: And and if you were to scale algorithms and scale it beyond that, what could it solve that otherwise you can't solve today?

Jensen Huang: And the the breakthrough for us was realizing that AlexNet was not AlexNet, that AlexNet was an approach.

Jensen Huang: With deep deep learning that allows you to learn any function.

Jensen Huang: And so 15 years ago, I was telling everybody that, hey, guess what?

Jensen Huang: We just learned the universal function approximator.

Jensen Huang: We just discovered the universal function approximator.

Jensen Huang: We can give it we could, you know, give it the the answer for almost any function and it could learn what the function is.

Jensen Huang: And for a lot of functions, you don't have to be precise.

Jensen Huang: And in fact, it's impossible to be precise.

Jensen Huang: And so most of the interesting problems are imprecise in this way.

Jensen Huang: And so the day that we realized we have a universal function approximator, the question then is what does that what does that what does that do to the computing stack?

Jensen Huang: What does that happen to software?

Jensen Huang: What are the industries that this could impact so on and so forth?

Jensen Huang: Almost right away, we started working on computer vision.

Jensen Huang: Almost right away, we started working on robotics, self driving cars, because Jensen Huang: That fundamental capability you could imagine solving some important problems in the area of computer vision and robotics.

Jensen Huang: And so so I think I think the the big breakthrough was simply that this is much more foundational than Alex net.

Jensen Huang: This is a way of doing software and the implications to the processor middleware, the algorithms, the applications.

Jensen Huang: You know what I now describe as the fiber layer cake that entire industrial stack.

Jensen Huang: I imagine reinventing all all together about 15 years ago, and this is simply about asking questions, reasoning about things to first principles asking, you know, questions like if this then what if if this can get better than so what, you know, asking all of the basic questions about about something that you observe that's really impactful.

Jensen Huang: I mean, one of the things that really jumps out at me is to what degree you go all the way into the weeds papers you you know, talk directly to the principal scientists who are sort of coming up with these things.

Jensen Huang: Do you have any advice for people in the audience?

Jensen Huang: I mean, that's like true founder mode.

Jensen Huang: And then at the same time, you probably you have an organization and you have executives and you have people who say like, here's the graph, we want to stay on this graph.

Jensen Huang: You know, sometimes it ruffles feathers like do you have any advice for people about.

Jensen Huang: An organization and how you navigate that really like how do you build an org that allows you to think in first principles, because if the fortune 500 did that like the fortune 500 will probably look a lot more like Nvidia than not and it doesn't like you have built a very unique company.

Jensen Huang: My state of mind when I'm my state of mind is always starts with curiosity.

Jensen Huang: I have a whole bunch of questions myself.

Jensen Huang: And and of course, like anybody else, I'll see the shortest path to the answer.

Jensen Huang: 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 they're busy doing something and they're pursuing something.

Jensen Huang: And so my first my first inclination is to go discover the answers to my own curiosity.

Jensen Huang: My second is if I find that the information is and that the domain of information or in a particular field could be really important to somebody and could be important to our company.

Jensen Huang: Then my next inclination is how can I learn as much as possible so that I could be of service to the company and share with everybody else.

Jensen Huang: You know, this is no different than than you when you're sharing knowledge.

Jensen Huang: I mean, I watch your podcast and I watch your your videos and I really enjoy them.

Jensen Huang: You're sharing ideas with everybody else.

Jensen Huang: 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.

Jensen Huang: And you want to empower them with some insight.

Jensen Huang: And so that's really where it's coming from.

Jensen Huang: It's not so much a management technique, but a personality technique.

Jensen Huang: You know, I want to empower you.

Jensen Huang: And this is something really important that I just observed.

Jensen Huang: Let me tell you why it's so important.

Jensen Huang: Now part of part of having to be near the ground and be in the weeds of your will is because oftentimes the technology is complicated or it's changing really fast.

Jensen Huang: And especially when it's changing fast like like our world unless you have a tactical sensation of what is actually happening.

Jensen Huang: It could either to you feel like it's just moving way too fast to understand.

Jensen Huang: But if you understand the first principles of it over time, then everything kind of makes sense.

Jensen Huang: You know, it's kind of like surfing, I would imagine.

Jensen Huang: I don't know how to surf, but I can imagine it's kind of like surfing.

Jensen Huang: You get on the wave.

Jensen Huang: To me, it looks like chaos.

Jensen Huang: But to a surfer, you know, somehow they get right.

Jensen Huang: Right, they can read the waves and and they know how to stay on top of it.

Jensen Huang: And so I think being CEO is very similar to that.

Jensen Huang: You know, you have to learn how to serve and in order to learn how to serve you have to understand the waves.

Jensen Huang: 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 actually do it.

Jensen Huang: And so so partly is is to inform myself partly is to try to figure out, you know, what is to try to break down the problem.

Jensen Huang: So that the company can learn it in a way that they can do something about part of it is by inspiring other people.

Jensen Huang: And you know, it's all those basic traits of all the people in this room.

Jensen Huang: You don't have to change your personality or your behavior when you become CEO.

Jensen Huang: It is possible for you to continue to be yourself.

Jensen Huang: And one of the things that I that I learned a long time ago.

Jensen Huang: And and I I had no idea where I saw this.

Jensen Huang: But but you know, the CEO of the founders, you are the you you're building a car that you are going to race.

Jensen Huang: You're going to build an F1 racer, but you're going to build it in a way that you can drive.

Jensen Huang: You should adapt the car to you.

Jensen Huang: You know, somebody I think asked me, you know, Jensen, if you if you don't use conventional management techniques and organizational techniques, you know, what's going to happen when you leave the company?

Jensen Huang: Well, you know, when I die on the job someday, you know, I told them they'll just have to reshape the company for the next CEO.

Jensen Huang: And the reason that wisdom is because we're the F1 drivers, you know, we're the racers and the world is really competitive.

Jensen Huang: And we've got to stay we've got to, you know, we've got to win and we got to achieve our mission.

Jensen Huang: And so whatever it takes to fit the car to you, whatever it takes to fit the organization to you.

Jensen Huang: That's what you got to do.

Jensen Huang: And the next CEO, whatever the personality is, they can figure it out.

Jensen Huang: Amazing.

Jensen Huang: I mean, doesn't it does seem like any change you make to the car will just slow you down and lose you races that, you know, isn't fit to you.

Jensen Huang: Yeah.

Jensen Huang: 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, you know, be more effective for the company.

Jensen Huang: True founder mode.

Jensen Huang: Yeah, founder mode.

Founder mode could scale for 34 years.

Jensen Huang: That's right.

Jensen Huang: From zero to 5 trillion.

Jensen Huang: No evidence.

Jensen Huang: Nope.

Jensen Huang: I'd love to switch it up.

Jensen Huang: I'd love to switch gears to like what, you know, what are the frontier algorithms that you're most interested in now?

I mean, I love that you're all the way down into the material science, all the way up into the app level.

Jensen Huang: You know, you're the first to speak on stage about Open Claw and now Hermes agent.

I wonder if you can sort of like walk us through a day in the life of like how you think about the different stages.

I mean, going from materials to chips to data centers to even like the app level, like how people are going to work like this sort of this idea of a full stack AI factory.

Jensen Huang: Well, this is one of the things that is probably going to be the most useful skill in the future.

And in fact, just in listening to you talk about technology and your use of it.

Jensen Huang: One of the most important things is systems understanding systems awareness system design system organization, but systems thinking.

And the reason for that is because Jensen Huang: Most of the low level things that has to be done are going to be done agentically anyways, they're going to be automated anyhow.

And so whether it's, you know, in my generation, it's about compiling chips and synthesizing transistors and gates and functional blocks and, and all of that is now synthesized.

Jensen Huang: And so 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.

Jensen Huang: What are the, what are the problems you're trying to solve?

What are the constraints?

Where, you know, where's the input?

Where's the output?

You know, where information coming from?

Jensen Huang: What is the rate of information flowing in and out of the system?

What are the constraints?

You know, and so is a processor is a memory is a networking.

Jensen Huang: You know, so understanding these systems problems at a sufficiently technical level is going to be very helpful to all of the people in this room.

And I don't think that that that way of that fundamental knowledge is ever going to be Jensen Huang: useless.

I think it's going to be more and more useful.

And so I try to understand systems.

Jensen Huang: I the best I can.

One of the things, one of the things that speaking of agents, the fact of the matter is we kind of have course level recursive self improvement already.

Jensen Huang: And the fact that 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.

Jensen Huang: Either compacted or turned into knowledge graphs or so on and so forth.

It's been improved all the time.

Jensen Huang: You know, asynchronously.

And so the agent is getting smarter and smarter every time.

Still the problem is, and this is one of the one of the problems that I think would be helpful for everybody to solve, is how can we have very, very specific fine grain control?

Jensen Huang: You know, if not for rags, if not for conditional inputs, if not for our all of our prompts directly into output was was too coarse.

Jensen Huang: And so 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 and that one word makes a delta difference.

Jensen Huang: Not complete difference, but 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.

Jensen Huang: I think that 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, you know, 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.

And so I think controllability is probably the single biggest breakthrough that we need for agents at every single level.

Jensen Huang: Do you think people will like, I mean, with Hermes or OpenClaw, it feels like that might actually be somewhat existential, like people should control their own personal AGI.

They shouldn't outsource that app and, you know, have it be just in the cloud and someone else's agent that like kind of tells you what to do.

Like you kind of want it to be your own.

Jensen Huang: Yeah.

Jensen Huang: Is that part of the thrust behind Nvidia being so involved?

Jensen Huang: I think, well, first of all, I, I need to understand agents because agents is the new software.

Jensen Huang: And how is this new software processed matters a lot to computer architecture.

And the, the more intimate we are about the nature of agents and how it's different than, than chat bots, which is how different than, than maybe inference in the very beginning.

Jensen Huang: However, we think about these processing layers, the more intimate we are about the nature of the processing, the better we can design systems.

Jensen Huang: We, we kind of have to live in the future five to 10 years because it takes three or so years just to build a system.

Jensen Huang: It takes a couple of years to ramp it up and you're dealing and you would like them to be able to use the computer for 10 years after.

Jensen Huang: And so you kind of have to live in the future for a while.

Jensen Huang: And so agentic systems for us at 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?

Jensen Huang: 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, you know, working memory, long term memory?

Jensen Huang: How do you have all these autonomous systems, asynchronous systems working all the time?

And so what kind of design architecture makes perfect sense for that?

And so we have to go and go discover that.

Jensen Huang: And then of course, the second thing is I want to use agents ourselves to make NVIDIA go faster.

Jensen Huang: And so we have, you know, Boris is in the back and we've got cloud code autonomously running in sandboxes all over NVIDIA.

Jensen Huang: And that's really fantastic.

And some people use codex, some people use cloud code, some people use cursor, some people use cognition.

Jensen Huang: 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.

Jensen Huang: And then we're going to use the tools.

And the more they use it, the more we're going to learn about how to make it work better in the future.

Jensen Huang: And then 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, Jensen Huang: probably a decade ago at this point, maybe eight years ago, you know, the question is, is how, how effective is that going to be in reasoning and how scalable is going to be?

Jensen Huang: And what is the implication, for example, in computer vision, if we can reason from prior knowledge?

Jensen Huang: And, 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, Jensen Huang: which led us to creating Alpa Mayo, which is the world's first thinking self-driving car.

Jensen Huang: And with just a million miles or so, a couple million miles, it's an incredibly great self-driving car.

Jensen Huang: And the reason for that is, it's kind of like us, right?

Jensen Huang: We don't need that many miles before we could drive fairly well most of our lives.

Jensen Huang: 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, Jensen Huang: and build it up out of things that we understood and know very well.

Jensen Huang: And so that's an example of seeing something and then realizing the impact some, some time later.

Jensen Huang: When the agentic systems came along, it's very, very clear that obviously a large language models needs memory.

Jensen Huang: It needs prior knowledge.

Jensen Huang: It needs tools.

Jensen Huang: It needs ways to network with other agents.

Jensen Huang: And so that kind of, you know, that once you see some early indicators, Jensen Huang: and you're able to reason about the future helps you get a leap, you know, into into the future.

Jensen Huang: I feel like there's this pattern that I'm starting to see around NVIDIA.

Jensen Huang: So you see a problem.

Jensen Huang: There's a new algorithm.

Jensen Huang: There's some new thing happening.

Jensen Huang: And then actually you're right there with open source.

Jensen Huang: I mean, I remember when open clock came out and people said it was unsafe, Jensen Huang: but you guys came out with a sandboxing sort of toolkit that like surrounds any harness and makes it safe.

Jensen Huang: When I saw open claw, my first thought was, well, first of all, I learned about it.

Jensen Huang: And then and then, you know, without without much imagination, you just realized we just designed the modern computer.

Jensen Huang: This is the operating system that's going to hold a large language model.

Jensen Huang: And and in a lot of ways, open claw to me was very Linux moment to me.

Jensen Huang: Yeah.

Jensen Huang: And now everybody can build their own AI.

Jensen Huang: And I was so excited about that.

Jensen Huang: And we contacted Peter and we said, hey, you know, all of the videos engineers are your engineers.

Jensen Huang: That's what I told Peter, you've got this battleship outside your house.

Jensen Huang: You, you, you know, break down the problem as you desire and we'll contribute as you wish.

Jensen Huang: Same thing with the the Hermes team, you know, and I'm so excited about the work that they're doing.

Jensen Huang: I do think that the world needs the ability for everybody to build their own AI.

Jensen Huang: And you could you could, of course, and I encourage everybody to to use cloud services as much as possible.

Jensen Huang: Everybody should use chat GPT and cloud and right everybody should use that.

Jensen Huang: And but if you if you need to build your own AI because your company and and you need to build your own domain specific AIs.

Jensen Huang: Now you have Hermes and you have open cloud, you've got all kinds of you got Lang chain deep agent.

Jensen Huang: You got all these different ways right to build your own AI.

Jensen Huang: And it's it's quite frankly relatively easy because the software smart, you know, and so AI smart and therefore AI must be so smart.

Jensen Huang: You could adapt it easily.

Jensen Huang: And so I I think that that we want we want to encourage everybody and every company to build their own AI's and and and and who knows what innovation will come from the fact that it's open source.

Jensen Huang: I feel like all the alpha is in building your own AI.

Jensen Huang: I mean, if someone else is using whatever is off the shelf, but you're you have a thing that can recursively self improve and it is, you know, I mean, the mechanic people are very flippant about marketing markdown files.

Jensen Huang: They say like, oh, haha, it's just text, but like text is intelligence and we're in a different words or thoughts.

Jensen Huang: Yeah, yeah, words or thoughts.

Jensen Huang: Yeah, and it turns out you try to try to think without words.

Jensen Huang: Yeah, that's right.

Jensen Huang: So switching gears again.

Jensen Huang: I mean, a lot of people are anytime you move the cheese people get a little worried.

Jensen Huang: Intelligence is going to be on tap, which is really awesome.

Jensen Huang: I think it bodes well for everyone in this room.

Jensen Huang: What do you think changes about the economy?

Jensen Huang: What do you think, you know, happens in sort of a broader sense?

Jensen Huang: Obviously, what I'm going to say is uneven.

Jensen Huang: There are some, you know, we're going to automate tasks.

Jensen Huang: We're going to automate cognitive tasks.

Jensen Huang: If that task is somebody makes a phone call and sends a bunch of words, you know, across the phone to you, and your job is to provide a response.

Jensen Huang: And if all the information is at your fingertips, because you have all the database here, and you should be able to answer that question completely.

Jensen Huang: In that case, that task will be automated away.

Jensen Huang: Okay.

Jensen Huang: Ignoring that for a second.

Jensen Huang: Not that we ignore this, but my point is I'm going to answer the question about really the great opportunity.

Jensen Huang: And so many tasks will be automated away.

Jensen Huang: Many jobs, every single job will change and there'll be a whole bunch of new jobs.

Jensen Huang: And that I think we know.

Jensen Huang: The bottom line is this.

Jensen Huang: The evidence would show that, and it makes perfect sense, that AI and automation is creating jobs everywhere.

Jensen Huang: The narrative about AI destroying jobs is exactly backwards.

Jensen Huang: AI eliminate tasks.

Jensen Huang: AI automates tasks away.

Jensen Huang: But it doesn't necessarily eliminate jobs.

Jensen Huang: And the reason for that is because the job of a person has a purpose, and that purpose has many tasks.

Jensen Huang: Some of those tasks could be automated away.

Jensen Huang: Many of those tasks cannot be.

Jensen 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.

Jensen Huang: Right, the number of software engineer jobs year over year has increased 10%.

Jensen Huang: 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.

Jensen Huang: And the reason for that is because the backlog of patients is incredibly high.

Jensen Huang: Now doctors and hospitals could admit a lot more patients in order to admit a lot more patients.

Jensen Huang: You need more nurses, more radiologists.

Jensen Huang: And so the same thing with software, with the backlog of ideas, the backlog of ambition and aspiration is so high that if we can automate away the task of programming, Jensen Huang: We could hire more software engineers to do more things.

Jensen Huang: We could be more ambitious.

Jensen Huang: Same thing, you know, just across the board.

Jensen Huang: They said Harvey is going to eliminate all of the paralegal jobs and the number of lawyers will be reduced.

Jensen Huang: Turns out paralegals are growing like crazy.

Jensen Huang: And the reason for that is because the backlog of lawsuits is really high.

Jensen Huang: And now these law firms could get a lot more cases through.

Jensen Huang: In order to do so, you've got to hire more people.

Jensen Huang: And so this is a classic example of productivity increasing growth.

Jensen Huang: Increasing growth, increasing growth drives more employment.

Jensen Huang: Just the reason why there's more employment today than there was when I first came out of school.

Jensen Huang: So we've been talking a lot about software and agents.

Jensen Huang: Another really exciting thing that NVIDIA is all the way out on the edge on is actually physical robots.

Jensen Huang: You know how far out I think in the past you might have even said this as soon as this year.

Jensen Huang: What's the latest thinking on, you know, when can we expect practical robotics?

Jensen Huang: Yeah, the moment that I saw us generating video, that was a great moment for me.

Jensen Huang: The moment that I saw us generating video, I mean we did the original work on auto progressive GANs.

Jensen Huang: Okay, and we did the original work on conditional GANs.

Jensen Huang: Long before the first videos were generated outside that people saw, a couple of years earlier inside our labs, Jensen Huang: We were driving a simulator completely generated by video and completely generated by neural networks.

Jensen Huang: 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, Jensen Huang: How can I cause a robot to do the same?

Jensen Huang: And so the moment I saw that generative AI happening, I realized that robotics articulation was around the corner.

Jensen Huang: And so now the question is, you know, how is the robot going to understand, Jensen Huang: To generate motions that obey the laws of physics?

Jensen Huang: How does it understand causality?

Jensen Huang: How does it understand, you know, friction, tension?

Jensen Huang: How does it understand the laws of physics?

Jensen Huang: And so it started us down the journey of creating what we call physical AI now.

Jensen Huang: And everybody calls it physical AI.

Jensen Huang: And physical AI, we started working on world foundation model, an AI that understands the laws of physics and how the world works.

Jensen Huang: And we started down the journey of working on robotics.

Jensen Huang: I would say the chat GPT moment of robots happened a couple of years ago already.

Jensen Huang: Wow.

Jensen Huang: And the reason for that is remember when chat GPT first came out, Jensen Huang: it didn't do anything productive.

Jensen Huang: It didn't do anything useful, but it opened our imagination about what's possible.

Jensen Huang: And I would say a couple of years ago, you know, robots walking around that we could do, Jensen Huang: reinforcement learning, fine tune it for and ground it in physics.

Jensen Huang: It really happened a couple of years ago.

Jensen Huang: So now what do we need to do?

Jensen Huang: We need to do all the same things that we're doing now for agentic systems.

Jensen Huang: We have to create environments for them to learn in, to eval in, eval against.

Jensen Huang: And so we have to do real to sim to create environments.

Jensen Huang: We have to do, we have to generate simulators that are based on simulation, grounded physics simulation, Jensen Huang: as well as generative physics simulations.

Jensen Huang: And so Isaac Sim, Cosmos and all the work that we do in that area is related to simulation.

Jensen Huang: And then the last part is sim to real.

Jensen Huang: 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.

Jensen Huang: And so, but these three basic system, I think builds up the eval, if you will, the post training of robotics.

Jensen Huang: And I think we're going to see it right around the corner.

Jensen Huang: Amazing.

Jensen Huang: Where does physical AI show up first in a way that's really economically real?

Jensen Huang: Are you seeing that already?

Jensen Huang: We conjectured that robotics was going to come along and decided that the first application of robotics, Jensen Huang: That has both a large enough market, Jensen Huang: Relatively standardized technology so that we could scale and get the flywheel going, Jensen Huang: And has real economic value was self-driving cars.

Jensen Huang: So inside Waymo are chips from Nvidia.

Jensen Huang: At Tesla, we were in the car.

Jensen Huang: Now we're in the data center.

Jensen Huang: Mercedes, we're in the data center.

Jensen Huang: We're in the car with the software stack.

Jensen Huang: We worked on Alpa Mayo and we open sourced it.

Jensen Huang: And the reason why we open source the self-driving car stack is because you need it for agriculture.

Jensen Huang: You need it for mail delivery.

Jensen Huang: You need it for warehouse AMRs.

Jensen Huang: There's so many different ways that you could apply Jensen Huang: Autonomous navigation.

Jensen Huang: And none of those markets are big enough to be a self-driving car market.

Jensen Huang: And we thought it was sufficiently diverse that we would create the whole stack for it.

Jensen Huang: And so we're working with autonomous vehicles in all kinds of different places.

Jensen Huang: Our robotics business, autonomous vehicle business, basically physical AI business, Jensen Huang: is probably almost like $10 billion.

Jensen Huang: So it's really, really big already.

Jensen Huang: Likely, this will be one of the largest industries in the world.

Jensen Huang: And it'll take longer than a couple, two, three years.

Jensen Huang: It'll take less than 10.

Jensen Huang: And so this will be our next $100 billion business.

Jensen Huang: Amazing.

Jensen Huang: I want to take a moment.

Jensen Huang: I think this is the exact right crowd to, you know, maybe as an arena, we can welcome Jensen to X.

Jensen Huang: Welcome to X.

Jensen Huang: Oh, no.

Jensen Huang: I think you made your first post.

Jensen Huang: And thank you for your leadership.

Jensen Huang: You know, that shows you how introverted I am.

Jensen Huang: It took me until 2026 to have the first post on X.

Jensen Huang: You know, it's I'm probably the last human on earth that did it.

Jensen Huang: But what I posted was too important to me and too important to the industry and too important to the world.

Jensen Huang: And so I overcame my shyness and put my first thing out on X.

Jensen Huang: No, thank you for your leadership.

Jensen Huang: 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.

Jensen Huang: We want to create startups.

Jensen Huang: Yeah.

Jensen Huang: If not for open source, the mobile cloud industry would have never happened.

Jensen Huang: If not for open, if not for Linux, if not for Kubernetes, if not for all of these, you know, platform, if not for TensorFlow or more important, PyTorch.

Jensen Huang: Right, the and the early versions of a cafe, right, Torch.

Jensen Huang: I mean, all of the, Ciano, remember the early versions of all those were all open source.

Jensen Huang: If not for all of that, how would we have modern AI?

Jensen Huang: Well, thank you for your leadership and your voice is incredibly important here.

Jensen Huang: Thank you.

Jensen Huang: Yeah, thank you.

Jensen Huang: Before we go, I feel like we, I just really resonate with your story.

Jensen Huang: I think that everyone here, I mean, would love the wisdom of, you know, your journey coming here.

Jensen Huang: 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?

Jensen Huang: What should a young person learn now that will still matter based on what you're seeing?

Jensen Huang: Well, some of the things that I saw today and some of the starters I met today was really, really quite, quite encouraging.

Jensen Huang: And the thing that, the big takeaway is, of course, the simple stuff is going to get automated away.

Jensen Huang: And when I say simple stuff, I mean software coding.

Jensen Huang: The idea that you would solve a problem by sitting in front of a computer and you're actually writing, you know, writing code, that concept is obviously going to get automated away.

Jensen Huang: You know, in my generation, when I was growing up, we had to do long division.

Jensen Huang: I mean, for God's sake, who has to learn long division?

Jensen Huang: You know?

Jensen Huang: And so that got coded away.

Jensen Huang: That got automated away.

Jensen Huang: And so I think the simple stuff is going to get automated away.

Jensen Huang: But the hard problems, the hard sciences, physics, chemistry, biology, you know, computer science, computer engineering, systems thinking, you know, all.

Jensen Huang: And particularly the domains that are intersecting, those hard problems will never go away.

Jensen 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.

Jensen Huang: 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.

Jensen Huang: You know, now designing a trillion transistor chips is not even, you know, if somebody would have told me, Jensen, our next chip is a trillion transistor, I said, okay.

Jensen Huang: You know, it's not a thing.

Jensen Huang: 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.

Jensen Huang: And so you don't have to worry about, about, you know, how much coding, how many engineers, you don't have to think about those things anymore.

Jensen Huang: You just have to think about what is the problem you have to solve.

Jensen Huang: And so I think that the deep, deep tech stuff, the deep science stuff, understanding, understanding the intersection between technology and social issues, understanding market, market gaps and, and holes, opportunities, I think all of that still exists.

Jensen Huang: And, and the better you are at systems thinking so that you could orchestrate millions of agents solving problems autonomously, the better off you are.

Jensen Huang: And so that's why system thinking is going to be so important.

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

Jensen Huang: Go to school the same old way.

Jensen Huang: You know, stay in school.

Jensen Huang: Stay in school.

Jensen Huang: I guess I usually like to end with you looking out on the crowd.

Jensen Huang: 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.

Jensen Huang: You know, we actually just are technical and like love systems.

Jensen Huang: Thank you.

Jensen Huang: Thank you.

Jensen Huang: What advice would you give to this room of, you know, and you see yourself in this room.

Jensen Huang: And like, 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.

Jensen Huang: What would that be?

Jensen Huang: I could tell you exactly how I felt when I first when Nvidia first founded and and the three of us started.

Jensen Huang: The thing I felt at the time is there was so much for me to know and so much for me to learn.

Jensen Huang: And I didn't know it and I was telling you earlier that at the time there was there were no YouTube there's you know, no YC nobody's teaching you how to start a company.

Jensen Huang: And so I went to the bookstore and I bought a book and the book said how to start a company.

Jensen Huang: Unfortunately, the book was like 500 pages long.

Jensen Huang: And and so I you know I figured by the time I read it, you know, I'd be out of business and Lord Laurie and I be out of money.

Jensen Huang: And so there's no sense reading it.

Jensen Huang: But the thing that the thing I remember very, very vividly is that 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.

Jensen Huang: And and it's true and I barely know how to answer their questions even today.

Jensen Huang: But the thing that I learned is none of that stuff matters.

Jensen Huang: As it turns out.

Jensen Huang: And and you're always going to have things that you don't know.

Jensen Huang: And every single day, the world is changing technology changing.

Jensen Huang: Obviously, this is the greatest time in the last 60 years to start a company.

Jensen Huang: The whole industry has changed.

Jensen Huang: It's a complete reset from a technology perspective, the single most important technology in human history, Jensen Huang: The computer has been completely reset.

Jensen Huang: And so this is absolutely the single greatest time to start a company and I'm jealous of all of you.

Jensen Huang: And and and the opportunities you have ahead.

Jensen Huang: I mean, it's going to be incredible.

Jensen Huang: So it's the perfect time on the one hand.

Jensen Huang: On the other hand, the technology is changing so fast.

Jensen Huang: And so the question is, what's the right feeling for you?

Jensen Huang: And eventually, and I told you the story of us of me buying the other book, the textbook.

Jensen Huang: I think the psychology and the feeling that I have today on all of the new experiences and a new technology and new markets and new dynamics.

Jensen Huang: I look at it and I say, this is important.

Jensen 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.

Jensen Huang: And how hard can it be?

Jensen Huang: I always have this feeling, how hard can it be?

Jensen Huang: And truth be told, it is way harder than you think.

Jensen Huang: And but you don't want your mind to be to be there.

Jensen Huang: You want your mind to be how hard can it be?

Jensen Huang: And let the suffering come to you a little bit at a time.

Jensen Huang: You know, don't imagine how hard it's going to be and let all of that turn into anxiety and not doing something about it.

Jensen Huang: You want to imagine your head, how hard can it be?

Jensen Huang: You know, I've got a whole bunch of AI agents helping me anyways.

Jensen Huang: And so how hard can it be?

Jensen Huang: And then you get going on working on it.

Jensen Huang: And so that's probably the attitude of an entrepreneur.

Jensen Huang: You know you have to learn a bunch of stuff along the way.

Jensen Huang: You believe in your ability to learn, which is, you know, learning is the single greatest superpower.

Jensen Huang: And if you go into it with the attitude, how hard can it be?

Jensen Huang: If anybody can do it, I can do it.

Jensen Huang: And just realize that it will be hard and you just have to have the resilience to overcome it every single day.

Jensen Huang: You don't have to overcome life in one day.

Jensen Huang: You just have to overcome that morning, that morning.

Jensen Huang: You know, you have to overcome today today.

Jensen Huang: And so it's not a big deal.

Jensen Huang: Just get through today.

Jensen Huang: Wait till, right?

Jensen Huang: Work towards tomorrow.

Jensen Huang: Keep following your dreams.

Jensen Huang: And the rest of everything, if you stick with it long enough, you know, NVIDIA happens.

Jensen Huang: And so, you know, I think that the wisdom that I can, if there's anything, is resilience is probably Jensen Huang: the single most important thing.

Jensen Huang: And if you believe in something, just get going on it and get your mind, you know, out of Jensen Huang: out of keeping yourself from pursuing it because, you know, fear or anxiety or lack of confidence or whatever it is.

Jensen Huang: And that you're just going to tell yourself, I'm going to learn my way there.

Jensen Huang: Jensen Huang: Jensen Huang, everybody.

Jensen Huang: All right, guys.

Jensen Huang: Thank you.

Jensen Huang: Thank you so much.

Jensen Huang: Yes.

Jensen Huang: Thank you guys.

Jensen Huang: Thank you.

Jensen Huang: Thank you guys.