Jensen Huang

Jensen Huang (00:09:13)

So, that was the first step towards programmability. It was our first journey towards moving into the world of computing. The second thing that we did was we created, we put FP32 into our shaders. That FP32 step, IEEE-compatible FP32, was a huge step in the direction of computing. It was the reason why all of the people who were working on stream processors and, you know, other types of data flow processors discovered us. And they said, “Hey, all of a sudden, you know, we might be able to use this GPU that’s incredibly computationally intensive, and it’s now, you know, compliant with IEEE.”

Jensen Huang (00:09:55)

I can take my software that I was writing, you know, previously on CPUs, and I can see about using the GPU for that. And which led us to create, put C on top of FP32, what’s called, we call Cg. The Cg path took us to eventually CUDA. CUDA, step by step by step we… Well, the putting CUDA on GeForce, that was a strategic decision that was very, very hard to do, because it cost the company enormous amounts of our profits, and we couldn’t afford it at the time. But we did it anyway because we wanted to be a computing company. A computing company has a computing architecture. A computing architecture has to be compatible across all of the chips that we build.

Lex Fridman (00:10:42)

Can you take me through that decision? So, putting CUDA on GeForce, could not afford to do? Can you explain that decision? Why boldly choose to do that anyway? Can you explain that decision?

Jensen Huang (00:10:53)

Yeah, excellent. That was… I would say that that was the first strategic decision that is as close to an existential threat.

Lex Fridman (00:11:06)

For people who don’t know, it turned out to be, spoiler alert, one of the most incredibly brilliant decisions ever made by a company. So, CUDA turned out to be an incredible foundation for computation in this AI infrastructure world. So, so- … just setting the context. It turned out to be a good decision.

Jensen Huang (00:11:27)

Yeah, it turned out to have been a good decision. I think the… So, here’s the way it went. So, we invented this thing called CUDA, and it expanded the aperture of applications that we can accelerate with our accelerator. The question is, how do we attract developers to CUDA? Because a computing platform is all about developers. And developers don’t come to a computing platform just because, you know, it could perform something interesting. They come to a computing platform because the install base is large. Because a developer, like anybody else, wants to develop software that reaches a lot of people. So, the install base is, in fact, the single most important part of an architecture. The architecture could attract enormous amounts of criticism.

Jensen Huang (00:12:18)

For example, no architecture has ever attracted more criticism than the x86… you know, as a less than elegant architecture, but yet it is the defining architecture of today. It gives you an example that in fact so many RISC architectures which were beautifully architected, incredibly well-designed by some of the brightest computer scientists in the world, largely failed. And so I’ve given you two examples where one is, you know, one is elegant, the other one’s barely aesthetic, and so yet x86 survived and the reason for-

Lex Fridman (00:12:58)

Install base is everything.

Jensen Huang (00:12:59)

Install base defines an architecture. Not… Everything else is secondary, okay? And so there were other architectures at the time. CUDA came out, OpenCL was here. There were… You know, there’s several other competing architectures. But the thing that… The decision that we made that was good was we said, “Hey, look, ultimately it’s about install base and what is the best way we could get a new computing architecture into the world?” By that timeframe, GeForce had become successful.

Jensen Huang (00:13:29)

We were already selling millions and millions of GeForce GPUs a year, and we said, “You know, we, we ought to put CUDA on GeForce and put it into every single PC whether customers use it or not, and use it as a starting point of cultivating our install base.” Meanwhile, we’ll go and attract developers, and we went to universities and wrote books and taught classes and put CUDA everywhere. And eventually people discover… And at the time, the PC was the primary computing vehicle. There was no cloud, and we could put a supercomputer in the hands of every researcher in school, every scientist, you know, every engineering school, every… or every student in school, and eventually something amazing will happen.

Jensen Huang (00:14:15)

Well, the problem was CUDA increased our cost of that GPU, which is a consumer product, so tremendously, it completely consumed all of the company’s gross profit dollars. And so at the time, the company was probably, you know, worth, I don’t know, at the time, eight… Was it like $8 billion or something? Like six, $7 billion or something like that. After we launched CUDA, I recognized that it was going to add so much cost, but it was something we believed in. You know, our market cap went down to like one and a half billion dollars. And so we were down there for a while and we clawed our way back slowly, but we carried CUDA on GeForce. I always say that NVIDIA is the house that GeForce built, because it was GeForce that took CUDA out to everybody.

Jensen Huang (00:15:10)

Researchers, scientists, they discovered CUDA on GeForce because they were all, you know… Many of ’em were gamers. Many of them built their own PCs anyways. In a university lab, many of them built clusters themselves, you know, using PC components. And, and so that, you know, that’s kind of how we got going.

Lex Fridman (00:15:31)

And then that became the platform and the foundation for the deep learning revolution.

Jensen Huang (00:15:35)

That was also another great, great observation. Yeah.

Lex Fridman (00:15:38)

That existential moment, do you remember… Like, what were those meetings like? What were those discussions like, deciding as a company, risking everything?

Jensen Huang (00:15:48)

Well I had to make it clear to the board what we’re trying to do, and the management team knew our gross margins were gonna get crushed. So you could imagine a world where GeForce would carry the burden of CUDA and none of the gamers would appreciate it and none of the gamers would pay for it. You know, they only pay certain price and it doesn’t matter what your cost is. And so the… You know, we increased our cost by 50% and that consumed… And we were a 35% gross margin company, and so it was a… It was quite a difficult decision to make. But you could imagine that someday this would go into workstations and it would go into supercomputers and in those segments, maybe we can capture more margin.

Lex Fridman (00:16:36)

So you could reason your way into being able to afford this, but it still took… It took a decade.

Lex Fridman (00:16:45)

But that, but that’s more of, like, conversation with the board convincing them, but you psychologically- … as NVIDIA’s continued to make bold bets that predict the future, and in part, especially now, define the future. So I’m almost looking for wisdom about how you’re able to make those decisions, to make leaps- … like that as a company.

Jensen Huang (00:17:14)

Well, first of all, I’m informed by a lot of curiosity. At some point, there’s a reasoning system that convinces me so clearly this outcome will happen. That this will happen. And so I believe it in my mind, and when I believe it in my mind, you know how it is. You manifest a future and that future is so convincing, there’s no way it won’t happen. There’s a lot of suffering in between, but you’ve gotta believe what you believe.

Lex Fridman (00:17:52)

So you, you, you envision the future- … and you essentially, from a sort of engineering perspective, manifest it?

Jensen Huang (00:17:59)

Yeah. And you reason about how to get there. You reason about why it must exist. And you know, I reason… We all reason it here. The management team would reason about it. All the people that I… We spend a lot of time reasoning about it. The thing that… The next part of it is probably a skill thing, which is, you know, oftentimes in leadership the leadership stays quiet or they learn about something, and then they do some manifesto, and it’s a brand-new year, and somehow at the end of the year, next year, we’re gonna have a brand-new plan. Big huge layoff this way, big huge organization change this way, new mission statement… brand new logos, you know, that kind of stuff.

Jensen Huang (00:18:43)

We’ve just never, I never do things that way. When I learn about something and it’s starting to influence how I think, I’ll make it very clear to everybody near me that, you know, this is interesting. This is going to make a difference. This is going to impact that. And I reason about things step by step by step. Oftentimes, I’ve already made up my mind, but I’ll take every possible opportunity—external information, new insights, new discoveries, new engineering revelations, new milestones developed—I’ll take those opportunities and I’ll use it to shape everybody else’s belief system. And I’m doing that literally every single day. I’m doing that with my board, I’m doing that with my management team, I’m doing that with my employees.

Jensen Huang (00:19:33)

I’m trying to shape their belief systems such that when I come the day I say, “Hey, let’s buy Mellanox,” it’s completely obvious to everybody that we absolutely should. On the day that I said, “Hey guys, let’s go all in on deep learning,” and let me tell you why. I’ve already been laying down the bricks to different organizations inside the company. Every organization and everybody, many of the people might have heard everything. Most of the company hears, of course, pieces of it. And on the day that I announce it, everybody’s kind of bought in to many pieces of it.

Lex Fridman (00:20:19)

And in a lot of ways, I like to announce these things, and I imagine that the employees are kind of saying, “You know, Jensen, what took you so long?” And in fact, I’ve been shaping their belief system for some time, and therefore leadership. Sometimes it looks like you’re leading from behind, but you’ve been shaping their, you know, to the point where on the day that I declared it, 100% buy-in. But that’s what you want. You want to bring everybody along. Otherwise, we announce something about deep learning and everybody goes, “What are you talking about?” You know, you announce something about let’s go all in on this thing, and your management team, your board, your employees, your customers, they’re kind of like, “Where’s this coming from?”

Jensen Huang (00:21:02)

You know, this is insane.” And so, so GTC effect, if you go back in time, you look at, look at the keynotes, I’m also shaping the belief system of my partners in the industry and, and I’m using that to shape, you know, the belief system of my own employees. And, and, and so by the time that I announce something, like for example, we just announ- we just announced Grok. We’ve been late… I’ve been talking about the stepping stones for two and a half years. You just go back and go, “Oh my gosh, they’ve been talking about it for two and a half years.” And so I’ve been laying the foundation step by step by step, so when the time comes you announce it, everybody’s saying, “You know, what took you so long?”

Lex Fridman (00:21:44)

But it’s not just inside the company. You’re shaping the landscape, the broader global landscape of innovation. Like, putting those ideas out there, you really are manifesting reality.

Jensen Huang (00:21:53)

We don’t build computers. We actually don’t build clouds. We don’t… As it turns out, we’re a computing platform company. And so nobody can buy anything from us. That’s the weird thing. You know, we vertically design, vertically integrate to design and optimize, but then we open up the entire platform at every single layer to be integrated into other companies’ products and services and clouds and supercomputers and OEM computers, and so the amazing thing is, I can’t do what I do without having convinced them first. And so most of GTC is about manifesting a future that by the time that we… My product is ready, they’re going, “What took you so long?” AI scaling laws

Lex Fridman (00:22:39)

Yeah. So one of the things you’ve been a believer for a long time is scaling laws, broadly defined. So are you still a believer in the scaling laws?

Lex Fridman (00:22:49)

Yeah, yeah. Yeah, we have more scaling laws now.

Lex Fridman (00:22:51)

So I think you’ve outlined four of them with pre-training, post-training, test time, and agentic scaling. What do you think, when you think about the future, deep future and the near-term future, what are the blockers that you’re most concerned about that keep you up at night that you have to overcome in order to keep scaling?

Jensen Huang (00:23:12)

Well, we can go back and reflect on what people thought were blockers. So in the beginning, we were… The pre-training scaling law. You know, people thought, rightfully so, that the amount of data that we have, high-quality data that we have, will limit the intelligence that we achieve. And that scaling law was an important, very important scaling law. The larger the model, the correspondingly more data results in a smarter AI. And so that was pre-training. And Ilya Sutskever said, “We’re out of data,” or something like that. “Pre-training is over,” or something like that. The industry panicked, you know, that this is the end of AI. And of course, that’s obviously not true.

Jensen Huang (00:23:57)

We’re gonna keep on scaling the amount of data that we have to train with. A lot of that data is probably gonna be synthetic, and that also confused people, you know? And what people don’t realize is they’ve kind of forgotten that most of the data that we are training, that we teach each other with, inform each other with, is synthetic. You know, it’s synthetic because it didn’t come out of nature. You created it. I’m consuming it. I modify it, augment it, I regenerate it, somebody else consumes it. And so we’ve now reached a level where AI is able to take ground truth, augment it… Enhance it, synthetically generate an enormous amount of data.

Lex Fridman (00:24:47)

And that part of post-training continues to scale, and so the amount of data that we could use that is human generated will be smaller, and smaller, and smaller. The amount of data that we use to train models is going to continue to scale to the point where we’re no longer limited… Training is no longer limited by… Data is now limited by compute. And the reason for that is most of the data is synthetic. Then the next phase is test time, and I still remember people telling me that, “Inference? Oh, yeah, that’s easy. Pre-training, that’s hard.” These are giant systems that people are talking about. Inference must be easy. And so inference chips are gonna be little tiny chips, and-

Jensen Huang (00:25:32)

… you know, they’re not, they’re not like NVIDIA’s chips. Oh, those are gonna be complicated and expensive, and, you know, we could make… And this is- … in, in the future, inference is gonna be the biggest market, and it’s gonna be easy, and we’re gonna commoditize it. You know, everybody can build their own chips. And, and that was always illogical to me because inference is thinking, and I think thinking is hard. Thinking is way harder than reading.

Jensen Huang (00:25:59)

You know, pre-training is just memorization and generalization, you know, and looking for patterns in relationships. You’re reading and reading, versus thinking, reasoning, solving problems, taking unexplored experiences, new experiences, and breaking it down into… Decomposing it into, you know, solvable pieces that we then go off, either through first principle reasoning, or, you know, through previous examples, prior experiences. You know, or just exploration and search and, you know, trying different things. And that whole process of test time scaling inference, is really about thinking. And it’s about reasoning, it’s about planning, it’s about search, it’s about…

Lex Fridman (00:26:50)

And so how could that possibly be compute light? And we were absolutely right about that. You know, so test time scaling is intensely compute intensive. Then the question is, okay, now we’re at inference and we’re at test time scaling, what’s beyond that? Well, obviously we have now created, you know, one agentic person, and that one agentic person has a large language model that we’ve now developed. But during test time, that agentic system goes off and does research and bangs on databases, and it goes out and, you know, uses tools, and one of the most important things it does is spins off and spawns off a whole bunch of sub-agents. Which means we’re now creating large teams. It’s so much easier to scale NVIDIA by hiring more employees than it is to scale myself.

Lex Fridman (00:27:44)

And so the next scaling law is the agentic scaling law. It’s kind of like multiplying AI. Multiplying AI, we could spin off agents as fast as you want to spin off agents. And so, you know, I… You know, I have four scaling laws. And as we use the agentic systems, they’re gonna create a lot more data, they’re gonna create a lot of experiences. Some of it we’re gonna say, “Wow, this is really good. We ought to memorize this.”

Jensen Huang (00:28:12)

That data set then comes all the way back to pre-training. We memorize and generalize it. We then refine it and fine-tune it back into post-training. Then we enhance it even more with test time, you know, and the agentic systems, you know, put it out to the industry. And so this loop, this cycle, is gonna go on and on and on. It kinda comes down to basically intelligence is gonna scale by one thing, and that’s compute.

Lex Fridman (00:28:41)

But there’s a tricky thing there that you have to anticipate and predict, which is some of these components, it requires different kind of hardware to really do it optimally. So you have to anticipate where the AI innovation’s going to lead. For example, a mixture of-

Jensen Huang (00:28:57)

Perfect.

Lex Fridman (00:28:58)

… experts with sparsity.

Jensen Huang (00:28:59)

Perfect.

Lex Fridman (00:29:00)

With hardware, you can’t just pivot on a week’s notice. You have to anticipate what that’s going to look like. It has some-

Lex Fridman (00:29:06)

So good.

Lex Fridman (00:29:07)

… that’s so scary and difficult to do, right?

Jensen Huang (00:29:09)

For example, these AI model architectures are being invented about once every six months. Right? And system architectures and hardware architectures kind of every three years. And so you need to anticipate what likely is going to happen, you know, two, three years from now. And there’s a couple ways that you could do that. First of all, we could do research internally ourselves, and that’s one of the reasons why we have basic research, we have applied research.

Jensen Huang (00:29:40)

We create our own models. And so we have hands-on life experience right here. This is part of the co-design that I’m talking about. We’re also the only AI company in the world that works with literally every AI company in the world. And to the extent that we can, we try to get a sense of what are the challenges that people are experiencing.

Lex Fridman (00:29:59)

So you’re listening to the whispers across the industry, the AI labs.

Jensen Huang (00:30:02)

That’s right. You got to listen and learn from everybody. And have a… And then the last part is to have an architecture that’s flexible, that can adapt and move with the wind. And one of the benefits of CUDA is that it’s, you know, on the one hand, an incredible accelerator. On the other hand, it’s really flexible. And so that balance, incredible balance between specialization, otherwise we can’t accelerate the CPU, versus generalization, so that we can adapt with changing algorithms, that’s really, really important. That’s the reason why CUDA has been so resilient on the one hand, and yet we continue to enhance it.

Jensen Huang (00:30:44)

We’re at CUDA 13.2, and so we’re evolving the architecture so fast that we can stay with the modern algorithms. For example… When mixtures of experts came out, that’s the reason why we had NVLink 72 instead of NVLink 8. We could now take an entire 4 trillion, 10 trillion parameter model and put it in one computing domain as if it’s running on one GPU. People probably didn’t notice, I said it, but if you look at the architecture of the Grace Blackwell racks, it was completely focused on doing one thing, processing the LLM. All of a sudden, one year later, you’re looking at a Vera Rubin rack. It has storage accelerators. It has this incredible new CPU called Vera. It has Vera Rubin and NVLink 72 to run the LLMs.

Jensen Huang (00:31:46)

It also has this new additional rack called Rock. And so this entire rack system is completely different than the previous one, and it’s got all these new components in it. And the reason for that is because the last one was designed to run MoE large language models, inference. And this one is to run agents and agents bang on tools, and-

Lex Fridman (00:32:10)

Obviously, the design of the system had to have been done before Claude Code, Codex, OpenClaw. So you were anticipating the future, essentially. And that, and that comes from what? From the whispers, from the understanding what all the state-

Lex Fridman (00:32:25)

… of the art is about?

Jensen Huang (00:32:26)

No, it’s easier than that. You just reason about it. First of all, you just reason. No matter, no matter what happens, at some point in order for that large language model to be a digital worker… Let’s just use that metaphor. Let’s say that we want the LLM to be a digital worker. What does that have to do? It has to access ground truth. That’s our file system. It has to be able to do research. It doesn’t know everything. We don’t have… And I don’t wanna wait until this AI becomes, you know, universally smart about everything, past, present, and future before I make it useful. And so therefore, I might as well let it go do research. It’s obvious; if it wants to help me, it’s gotta use my tools.

Lex Fridman (00:33:13)

You know, a lot of people would say, “You know AI is gonna completely destroy software. We don’t need software anymore. We don’t even need tools anymore.” That’s ridiculous. Let’s use the… Let’s use a thought experiment. And you could just sit there, enjoy a glass of whiskey, and think about all these things, and it would become completely obvious. Like, if I were to create the most amazing agent that we can imagine in the next 10 years. Let’s say it’d be a humanoid robot. If that humanoid robot were to be created, is it more likely that the humanoid robot comes into my house and uses the tools that I have to do the work that it needs to do?

Jensen Huang (00:33:54)

Or does this hand turn into a 10-pound hammer in one instance, turn into a scalpel in another instance, and in order to boil water, it beams, you know, microwaves out of its fingers? You know, or is it more likely just to use a microwave, you know? And the first time it goes up to the microwave, it probably doesn’t know how to use it. But that’s okay. It’s connected to the internet. It reads the manual of this microwave, reads it, instantly becomes an expert. And so it uses it. And so I think the… I just described, in fact, almost all of the properties of OpenClaw.

Jensen Huang (00:34:35)

You know, that it’s gonna use tools, that it’s gonna access files, it’s gonna be able to do research. It has an IO subsystem. And when you’re done reasoning through it, reasoning about it in that way, then you say, “Oh, my gosh, the impact to the future of computing is deeply profound.” And the reason for that is, I think we’ve just reinvented the computer. And then now you say, “Okay, when did we reason about that? When did we reason about OpenClaw?” If you take the OpenClaw schematic that I used at GTC, you’ll find it two years ago. Literally, two years ago at GTC, I was talking about agentic systems that exactly reflect OpenClaw today. And, of course, the confluence of many things had to happen.

Jensen Huang (00:35:26)

First of all, we needed Claude and GPT and, you know, all of these models to reach a level of capability. So their innovation and their breakthroughs and their continued advances was really important. And then, of course, somebody had to create an open source project that was sufficiently robust and sufficiently complete and that we can all put to work. And I think OpenClaw did for agentic systems what ChatGPT did for generative systems. And I just think it’s a very big deal.

Lex Fridman (00:36:02)

Yeah, it’s a really special moment. I’m not exactly sure why it captured so much of the world’s attention, but it did, more than Claude Code and Codex and so on.

Jensen Huang (00:36:12)

Because consumers could reach it.

Lex Fridman (00:36:13)

Sure, yeah. But there’s also so much of this is vibes. And Peter, I had a podcast with him, he’s a wonderful human being. So part of it is also the humans that represent the thing.

Jensen Huang (00:36:25)

Yeah, no doubt.

Lex Fridman (00:36:25)

Part of it is memes and the— ‘Cause we’re all trying to figure it out. There’s really serious and complicated security concerns about when you have such powerful technology, how do you hand over your data so they can do useful stuff? But then there’s scary things associated with that. And we, as a civilization, as individual people and as a civilization, are figuring out how to find that right balance.

Jensen Huang (00:36:44)

Yeah, we jumped on it right away and we sent a bunch of security experts this way. And we did this thing called OpenShell. It’s already been integrated into OpenClaw.

Lex Fridman (00:36:55)

And NVIDIA put forward NemoClaw.

Jensen Huang (00:36:58)

Yep, exactly.

Lex Fridman (00:36:59)

They install super easy. It makes sure that it’s secure.

Jensen Huang (00:37:03)

We give you two out of three rights. Agentic systems can access sensitive information, it can execute code, and it can communicate externally. We could keep things safe if we gave you two out of those three capabilities at any time, but not all three. And out of those two out of three capabilities, we also give you access control based on whatever rights that you’re given by enterprise. And then we connect it to a policy engine that all these enterprises already have. And so we’re going to try to do our best to help OpenClaw become a better claw. Biggest blockers to AI scaling laws

Lex Fridman (00:37:40)

So you eloquently explained how we have a long history of blockers that we thought were going to be blockers, and we overcame them. But now looking into the future, what do you think might be the blockers now that it’s clear that agents will be everywhere? So it’s obviously we’re going to need compute. So what is going to be the blocker for that scaling?

Jensen Huang (00:37:59)

Power is a concern, but it’s not the only concern. But that’s the reason why we’re pushing so hard on extreme co-design, so that we can improve the tokens per second per watt orders of magnitude every single year. And so in the last 10 years, Moore’s Law would have progressed computing about 100 times in the last 10 years. We progressed and scaled up computing by a million times in the last 10 years. And so we’re gonna keep on doing that through extreme co-design. So energy efficiency, perf per watt, completely affects the revenues of a company. It affects the revenues of a factory. And we’re just going to push that to the limit so that we can keep on driving token costs down as fast as we can.

Jensen Huang (00:38:51)

You know, our computer price is going up, but our token generation effectiveness is going up so much faster that token cost is coming down. It’s just coming down an order of magnitude every year.

Lex Fridman (00:39:04)

So power, that’s an interesting one. So the way to try to get around the power blocker is to try to, with the tokens per second per watt, try to make it more and more efficient. Of course, there’s the question of how do we get more power.

Lex Fridman (00:39:16)

We should also get more power. Supply chain

Lex Fridman (00:39:17)

That’s a really complicated one. You’ve talked about small modular nuclear power plants. There’s all kinds of ideas for energy. How much does it keep you up at night? The bottlenecks in the supply chain of AI, like ASML with EUV lithography machines, TSMC with advanced packaging like CoWoS, and SK Hynix with the high bandwidth memory?

Jensen Huang (00:39:38)

All the time, and we’re working on it all the time. No company in history has ever grown at a scale that we’re growing while accelerating that growth. It’s incredible. And it’s hard for people to even understand this. In the overall world of AI computing, we’re increasing share. And so supply chain, upstream and downstream, are really important to us. I spend a lot of time informing all the CEOs that I work with: what are the dynamics that’s going to cause the growth to continue or even accelerate? It’s part of the reasons why to the entire right-hand side of me were CEOs of practically the entire IT industry upstream and practically the entire infrastructure industry downstream.

Lex Fridman (00:40:32)

And they were all… There were several hundred CEOs. And I don’t think there’s ever been keynotes where several hundred CEOs show up. And part of it is, I’m telling them about our business condition now. I’m telling them about the growth drivers in the very near future and what’s happening. And I’m also describing where are we going to go next so that they could use all of this information and all of the dynamics that are here to inform how they want to invest. And so I inform them that way like I inform my own employees.

Lex Fridman (00:41:06)

And then of course, then I make trips out to them and make sure that, “Hey, listen, I want you to know this quarter, this coming year, this next year, these things are going to happen.” And if you look at the CEOs of the DRAM industry—the number one DRAM in the world was DDR memory for CPUs in data centers. About three years ago, I was able to convince several of the CEOs that even though at the time HBM memory was used quite scarcely, and barely by supercomputers, that this was going to be a mainstream memory for data centers in the future. At first it sounded ridiculous, but several of the CEOs believed me and decided to invest in building HBM memories.

Jensen Huang (00:41:55)

Another memory was rather odd to put into a data center: the low power memories that we use for cell phones. And we wanted them to adapt them for supercomputers in the data center. And they go, “Cell phone memory for supercomputers?” And I explained to them why. Well, look at these two memories, LPDDR5, HBM4. The volumes are so incredible. All three of them had record years in history, and these are 45-year companies. And so, you know, I… That’s part of my job, is to inform and shape, inspire, you know.

Lex Fridman (00:42:36)

So you’re not just manifesting the future and maybe inspiring NVIDIA, the different engineers of the company, you’re manifesting the supply chain of the future. So you’re having conversations with TSMC, with ASML.

Jensen Huang (00:42:50)

Upstream, downstream.

Lex Fridman (00:42:51)

Upstream, downstream. So that’s the thing.

Jensen Huang (00:42:53)

GEV, Caterpillar. Yeah, that’s downstream from us. Yeah, yeah, there you go.

Lex Fridman (00:42:59)

Yeah, the whole thing. I mean, but that’s so… There’s so much incredibly difficult engineering that happens in the entire semiconductor industry, and it just feels scary how intricate the supply chain is, how many components there are, but it works somehow.

Jensen Huang (00:43:18)

Exactly, the deep science. The deep engineering, the incredible manufacturing, and so much of the manufacturing is already robotics, but we have a couple of hundred suppliers that contribute the technology that goes into our 1.3 million component rack. Each rack is 1.3, one and a half million components. There are 200 suppliers across the Vera Rubin rack.

Lex Fridman (00:43:45)

So it’s interesting that you don’t list that as the thing that keeps you up at night in the list of blockers.

Lex Fridman (00:43:49)

But I’m doing, I’m doing all the things necessary to-

Lex Fridman (00:43:52)

Okay.

Jensen Huang (00:43:52)

… yeah, see? I can go to sleep because I checked it off. I said, “Okay,” you know, I go, I can go to sleep and I go, “Well, let’s see, let’s reason about this. What’s important for us?” Because let’s reason about this. Because we changed the system architecture from the original DGX-1 that you remembered to NVLink-72 rack scale computing- … what’s gonna… What does that, what does that mean? What does that mean to software? What does that mean to engineering? What does that mean to how we design and test? And what does that mean to the supply chain? Well, one of the things that it meant was we moved supercomputer integration at the data center into supercomputer manufacturing in the supply chain.

Jensen Huang (00:44:42)

If you’re doing that, you also have to recognize you’re gonna move one… And if your total footprint of whatever data center you’re gonna build, let’s say you would like to have, you know, 50 gigawatts of supercomputers that are running simultaneously, and it takes one week to manufacture that 50 gigawatts of supercomputers, then each week in the supply chain, the supercomputers are gonna need a gigawatt of power. And so we’re gonna need the supply chain to increase the amount of power it has to build and test the supercomputers in the supply chain before I ship it.

Lex Fridman (00:45:25)

Oh.

Jensen Huang (00:45:25)

Well, NVLink-72 literally builds supercomputers in the supply chain and ships ’em two, three tons at a time per rack. It used to be they used to come in parts and we used to assemble ’em inside the data center. But that’s impossible now because NVLink-72 is so dense. And so that’s an example. And I would have to go and fly into the supply chain, go meet my partners saying, “Hey,” I said, “guess what? So here’s what I’m going to do with… This is the way we used to build our DGXs. We’re gonna build them this way. This is gonna be so much better because we’re going to need ’em for inference.” The market for inference is, you know, coming. The inflection point for inference is coming. It’s gonna be a big market.

Lex Fridman (00:46:05)

And so I first explain to them what’s going on, why it’s gonna happen, and then I ask ’em to make several billion dollars of capital investments each. And because they trust me and I’m very respectful of ’em, and I give ’em every opportunity to question me and I spend time to explain things to people and I reason about it. I draw pictures and I reason about it in first principles. And by the time I’m done with them, they know what to do.

Lex Fridman (00:46:35)

So a lot of it is about relationships and building a shared view of the future. But do you worry about certain bottlenecks? I mean, what are the biggest bottlenecks in the supply chain? Are you worried about ASML’s EUV tooling? Are you worried about the packaging, CoWoS packaging of TSMC, about how fast it could scale? Like you said, you’re not only growing incredibly fast, you’re accelerating your growth. So it feels like everybody in the supply chain, and those are certainly bottlenecks, would have to scale up. Are you having conversations with them, like, how can you scale up faster?

Jensen Huang (00:47:12)

All the time.

Lex Fridman (00:47:12)

Do you worry about it?

Jensen Huang (00:47:13)

No.

Lex Fridman (00:47:13)

Okay.

Jensen Huang (00:47:14)

Because I told ’em what I needed. They understood what I need. They told me what they’re gonna go do, and I believe them what they’re going to do.

Lex Fridman (00:47:22)

Interesting. That’s great to hear. So maybe if we can just linger on the power for a little bit. What are your hopes for how to solve the energy problem?

Jensen Huang (00:47:30)

One of the areas, Lex, that I would love us to talk about and just get the message out, you know, our power grid is designed for the worst case condition with some margin. Well, 99% of the time we’re nowhere near the worst case condition because the worst case condition is a few days in the winter, a few days in the summer, and extreme weather. Most of the time we’re nowhere near the worst case condition and we’re probably running around, call it 60% of peak.

Lex Fridman (00:48:08)

And so 99% of the time, our power grid has excess power, and they’re just sitting idle, but they have to be there sitting idle because just in case, when the time comes, hospitals have to be powered and, you know, infrastructure has to be powered and airports have to run and so on and so forth. And so the question that I have is whether we could go and help them understand and create contractual agreements and design computer architecture systems, data centers, such that when they need the maximum power for infrastructure in society, that the data centers would get less.

Lex Fridman (00:48:49)

But that’s in a very rare instance anyways. And during that time, we either have a backup generator for that little part of it, or we just have our computers shift the workload somewhere else, or we have the computers just run slower. You know, we could degrade our performance, reduce our power consumption and provide for a, you know, slightly longer latency response, you know, when somebody asks for, you know, asks for an answer. And so I think that that, that way of using computers, of building data centers, instead of expecting 100% uptime—and these contracts that are really, really quite rigorous, it’s putting a lot of pressure on the grid to be able to… Now, they’re gonna have to increase from their maximum. I just wanna use their excess. It’s just sitting there.

Lex Fridman (00:49:36)

Yeah, that’s not talked about enough. So what’s stopping there? Is it regulation? Is it bureaucracy?

Jensen Huang (00:49:43)

I think it’s a three-way problem. It starts with the end customer. The end customer puts requirements on the data centers that they can never not be available, okay? So that the end customer expects perfection. Now, in order to deliver that perfection, you need a combination of backup generators and your grid power supplier to deliver on perfection. And so everybody’s gotta have six nines. Well, I think first of all, right now, we ought to have everybody understand that when the customer asks for these things, you have somebody in your data center operations team disconnected from the CEO. I bet the CEO doesn’t know this. I’m gonna talk to all the CEOs.

Jensen Huang (00:50:28)

The CEOs are probably not paying any attention to the contracts that are being signed, and so everybody wants to sign the best contract, of course. And they go down to cloud service providers, and the two contract negotiators that are… You, I could just see them now. You know, negotiating these multi-year contracts. Both sides want, you know, the best contract. As a result, the CSPs then have to go down to the utilities, and they expect the nine, the six nines. And so I think, I think the first thing is just make sure that, that all of the customers, the CEOs and the customers realize what they’re asking for. Now, the second thing is we have to build data centers that gracefully degrade.

Lex Fridman (00:51:13)

And so if the power, if the utility, if the grid tells us, “Listen, we’re gonna have to back you down to about 80%,” we’re gonna say, “That’s no problem at all.” We’re just gonna move our workload around. We’re gonna make sure that data’s never lost, but we can reduce the computing rate and use less energy. The quality of service degrades a little bit. For the critical workloads, I shift that somewhere else right away so I don’t have that problem, and so, you know, whichever data center still has 100% uptime, and so…

Lex Fridman (00:51:44)

How difficult of an engineering problem is that, that smart, dynamic allocation of power in a data center?

Jensen Huang (00:51:49)

As soon as you could specify, you could engineer it. Beautifully put. So long as it obeys the laws of physics on first principles, I think we’re good.

Lex Fridman (00:51:58)

What was the third thing you were mentioning?

Lex Fridman (00:52:00)

So the second thing is the, the data centers. And the third thing is we need the utilities to also recognize that this is an opportunity- … and instead of saying, “Look, it’s gonna take me five years to increase my grid capability,” if you have, if you’re willing to take power of this level of guarantee, I can make them available for you next month and at this price. And so if utilities also offered more segments of power delivery promises, then I think everybody will figure out what to do with it. Yeah, but there’s just way too much waste in the grid right now. We should go after it. Elon and Colossus

Lex Fridman (00:52:44)

You’ve highly lauded Elon and xAI’s accomplishment in Memphis, in building Colossus supercomputer, probably in record time in just four months. It’s now at 200,000 GPUs and growing very quickly. Is there something that you could speak to the understanding about his approach that’s instructive to, broadly to all the data center creators that enabled that kind of accomplishment? His approach to engineering, his approach to the whole management of construction, everything?

Jensen Huang (00:53:15)

First of all, Elon is deep in so many different topics. Yet he’s also a really good systems thinker. And so he’s able to think through multiple disciplines, and he obviously pushes things, questions everything, where they’re, number one, is it necessary? Number two, does it have to be done this way? And then number three, you know, does it have to take this long? And so he has the ability to question everything to the point where everything is down to its minimal amount that’s necessary, you can’t take anything else out. And yet the necessary capabilities of the product remains, you know? And so he is as minimalist as you could possibly imagine, and he does it at a system scale. I think… I also love the fact that he is represented. He is present at the point of action.

Jensen Huang (00:54:25)

You know, he’ll just go there. If there’s a problem, he’ll just go there and then, “Show me the problem.” You know, when you do all of this in combination, you overcome a lot of previous, “This is just the way we do it.” “You know, I’m waiting for them.” You know, I mean, it’s just, everybody has a lot of excuses. And so, and then the last thing is when you act personally with so much urgency, it causes everybody else to act with urgency, you know? And every supplier has a lot of customers going on. Every supplier has a lot of projects going on, and he makes it his business that he’s the top priority of everybody else’s projects. And so he does that by demonstrating it.

Lex Fridman (00:55:09)

Yeah, I’ve been in a bunch of those meetings. It’s just, it’s fun to watch, ’cause really, not enough people ask the question like, “Okay, so can this be done a lot faster, and how? Why does it have to take this long?”

Lex Fridman (00:55:21)

Yeah, right.

Lex Fridman (00:55:22)

And then in the… That becomes an engineering question often. And yes, I think when you get the ground truth of actually… I remember… One of the times I was hanging out with him, he literally is going through the entire process of how to plug in cables into a rack. He’s working with an engineer on the ground that’s doing that task, and he’s just trying to understand what does that process look like so it can be less error-prone. And just building up that intuition from every single task involved in putting together a data center-

Lex Fridman (00:55:52)

… you start to immediately get a sense at the detailed scale and at the broad systems scale of where the inefficiencies are, and so you can make it more and more and more efficient. Plus you have the big hammer of being able to say, “Let’s do it totally different-“

Jensen Huang (00:56:08)

Yeah. That’s right.

Lex Fridman (00:56:09)

“… and remove all possible blockers.”

Jensen Huang (00:56:10)

That’s right. Jensen’s approach to engineering and leadership

Lex Fridman (00:56:11)

Is there parallels in the NVIDIA Extreme Systems co-design approach that you see in the way Elon approaches systems engineering?

Jensen Huang (00:56:18)

Well, first of all, co-design is an ultimate systems engineering problem. And so we approach the work that we do from that first principle. The other thing that we do and this is a philosophy that, a thought, a state of mind, I guess, a method that I started 30 years ago, and it’s called the speed of light. The speed of light is not just about the speed. The speed of light is my shorthand for what’s the limit of what physics can do. And so every single thing that we do is compared against the speed of light. Memory speed, math speed, power, cost, time, effort, number of people, manufacturing cycle time.

Lex Fridman (00:57:09)

And when you think about latency versus throughput when you think about cost versus throughput, cost versus capacity, all of these things you test against the speed of light to achieve all of these different constraints separately. And then when you consider it together, you know you have to make compromises because a system that achieves extremely low latency versus a cheap, a system that achieves very high throughput are architected fundamentally differently. But you want to know what’s the speed of light of a system that achieves high throughput, what’s the speed of light of a system that achieves low latency? And then when you think about the total system, you can make trade-offs. And so I force everybody to think about what’s the first principles, the limits-

Jensen Huang (00:58:01)

… the physical limits for everything before we do anything. And we test everything against that. And so that’s a good frame of mind. I don’t love the other methods, which is continuous improvement. The problem with continuous improvement, it… First of all, you should engineer something from first principles at the speed, you know, with speed of light thinking. Limit it only by physical limits, and physics limits. And after that, of course you would improve it over time. But I don’t like going into a problem and somebody says, “Hey, you know, it takes 74 days to do this today-” “… Right now. And we can do it for you in 72 days.” You know, I’d rather strip it all back to zero-

Jensen Huang (00:58:52)

… and say, “First of all, explain to me why 74 days in the first place. And l- let’s note, let’s think about what’s possible today. And if I were to- to build it completely from scratch, you know, how long would it take?” Oftentimes, you’d be surprised. It might come to six days. Now, the rest of the six days, the 74, could be very well-reasoned and compromises, and, you know, cost reductions, and all kinds of different things. But at least you know what they are. And then now that you know that six days is possible, then the conversation from 74 to six, surprisingly much more effective.

Lex Fridman (00:59:30)

In such incredibly complex systems that you’re working with, is simplicity sometimes a good heuristic to reach for? I mean, if I can just… I mean, the pod, the Vera Rubin pod that you announced is just incredible. We’re talking about seven chips, seven chip types, five purpose-built rack types, 40 racks, 1.2 quadrillion transistors, nearly 20,000 NVIDIA dies, over 1,100 Rubin GPUs, 60 exaflops, 10 petabytes per second of scale bandwidth. That’s all just one…

Lex Fridman (01:00:03)

That’s just one pod.

Lex Fridman (01:00:04)

That’s just one pod .

Lex Fridman (01:00:06)

Yeah, that’s just one pod.

Lex Fridman (01:00:07)

I mean, in- … so you have the… And then even the NVL72 rack alone is 1.3 million components, 1300 chips, 4,000 pods crammed into a single 19-inch wide rack.

Lex Fridman (01:00:19)

And Lex, we’re probably gonna have to crank out about 200 of these pods a week, just to put it in perspective.

Lex Fridman (01:00:25)

The amount of different components, I suppose simplicity is impossible, but is that a metric that you kind of reach for in trying to design things?

Jensen Huang (01:00:35)

You know, the phrase that I use most often is, we need things to be as complex as necessary, but as simple as possible. And so the question is, is all that complexity there necessary? And we ought to test for that. And we got to challenge that. And then after that, everything else above it, you know, is gratuitous.

Lex Fridman (01:00:56)

But it’s still almost incredible. Semiconductor industry broadly, but what NVIDIA is doing is some of the greatest engineering in history. So these systems are just truly, truly marvels of engineering.

Lex Fridman (01:01:10)

It is the most complex computer the world has ever made.

Lex Fridman (01:01:13)

Yeah, the engineering teams, I mean- … I don’t, it’s not a competition, but I don’t know. If it was like an Olympics of engineering teams, I mean, TSMC does incredible engineering. Like I said, ASML at every scale, but NVIDIA is gonna give them a run for their money. Just incredible, incredible teams.

Jensen Huang (01:01:28)

Well, it’s gold medalists in every single, in every single sport, all assembled right here.

Lex Fridman (01:01:33)

And have to work together. And report directly to you. This is wonderful. You recently traveled to China. So it’s interesting to ask you, China’s been incredibly successful in building up its technology sector. What do you understand about how China’s able to, over the past 10 years, build so many incredible world-class companies, world-class engineering teams, and just this technology ecosystem- … that produces so many incredible products?

Jensen Huang (01:02:05)

A whole bunch of reasons for… Well, first of all, let’s start, let’s start with some facts. 50% of the world’s AI researchers are Chinese, plus or minus, and they’re mostly in China still. We have many of them here, but there’s amazing researchers still in China. They—their tech industry showed up at precisely the right time. At the time of the mobile cloud era, their way of contributing was software, and so this is a country’s incredible science and math really well-educated kids. Their tech industry was created during the era of software. They’re very comfortable with modern software. China is not one giant economic country. It’s got many provinces and cities with mayors all competing with each other.

Jensen Huang (01:03:01)

That’s the reason why there’s so many EV companies. That’s the reason why there’s so many AI companies. That’s the reason why there’s so many—every company you could imagine, they all create some of them. And, and as a result, they have insane competition internally. And, you know, what remains is an incredible company. They also have a social culture where, where it’s family first, friends second, and company third. And so the amount of conversation that goes back and forth between… They’re essentially open source all the time.

Lex Fridman (01:03:47)

So the fact that they contribute more to open source is so sensible because they’re probably, “What are we protecting?” You know, my engineers, their brothers are in that company, their friends are in that company, and they’re all schoolmates. You know, the schoolmate concept. There’s a, you know, one schoolmate, you’re brother for life. And and so they, they, they share knowledge very, very quickly. And so there’s no sense keeping technology hidden. You might as well put it on open source. And so the open source community then amplifies, accelerates the, the innovation process. So you get this rapid, incredible great talent, rapid innovation because of open source and just, you know, the nature of friends, and, and insane competition.

Jensen Huang (01:04:35)

Among the company, what emerges is incredible stuff. And so this is the fastest innovating country in the world today, and this is something that has everything that, everything that I’ve just said is fundamental to just how the kids were grown, the fact that they have excellent education, the fact that they, parents want them to do well in school, the fact that they, their culture is that way. These are, you know, these are just the thing about their country, and they showed up at precisely the time when technology is going through that exponential.

Lex Fridman (01:05:09)

Plus culturally, it’s pretty cool to be an engineer. It connects to all the components that you’re mentioning…

Jensen Huang (01:05:16)

It’s a builder nation.

Lex Fridman (01:05:18)

It’s a builder nation.

Jensen Huang (01:05:19)

Yeah, it’s a builder nation. Our country’s leaders, incredible, but they’re mostly lawyers. Their country’s leaders—and because we’re, they’re trying to keep us safe, rule of law governing—their country was built out of poverty. And so most of their leaders are incredible engineers. Some of the brightest minds.

Lex Fridman (01:05:43)

To take a small tangent, because you mentioned open source, I have to go to Perplexity here, who you have been a fan of a long time.

Jensen Huang (01:05:51)

Love it, yeah.

Lex Fridman (01:05:52)

And thank you for releasing open source Nemotron 3 Super, which you can also use inside Perplexity to look stuff up. Now, which is 120 billion parameter open weight MoE model. What’s your vision with open source? So you mentioned China with, with DeepSeek and MiniMax, with all these companies really pushing forward the open source AI movement, and NVIDIA is really leading the way in close to state-of-the-art open source LLMs. What’s your vision there?

Jensen Huang (01:06:28)

First, if we’re gonna be a great AI computing company, we have to understand how AI models are evolving.

Jensen Huang (01:06:36)

One of the things that I love about Nemotron 3 is it’s not just a pure transformer model, it’s transformer and SSMs. And we were early in developing the, the conditional GANs, which, that progressive GANs, which led step-by-step to diffusion. And so the fact that we’re doing basic research in model architecture and in different domains gives us visibility into, you know, what kind of computing systems would do a good job for future models. And so it is part of our extreme co-design strategy. Second, I think we rightfully recognize that on the one hand, we want world-class models as products, and they should be proprietary. On the other hand, we also want AI to diffuse into every industry and every country, every researcher, every student.

Lex Fridman (01:07:37)

And if everything is proprietary, it’s hard to do research and it’s hard to innovate on top of, around, with. And so… Open source is fundamentally necessary for many industries to join the AI revolution. NVIDIA has the scale and we have the motives—not only skills, scale, and motivation—to build and continue to build these AI models for as long as we shall live. And so therefore, we ought to do that. We can open up, we can activate every industry, every researcher, you know, every country to be able to join the AI revolution. There’s the third reason, which is from that, to recognizing that AI is not just language. These AIs will likely use tools and models and sub-agents that were trained on other modalities of information.

Jensen Huang (01:08:39)

Maybe it’s biology or chemistry or you know, laws of physics, or you know, fluids and thermodynamics, and not all of it is in language structure. And so somebody has to go make sure that weather prediction, biology, AI, AI for biology, physical AI, all of that stuff stays, can be pushed to the limits and pushed to the frontier. We don’t build cars, but we wanna make sure every car company has access to great models. We don’t discover drugs, but I wanna make sure that Lilly has the world’s best biology AI systems, so that they can go use it for discovering drugs. And so these three fundamental reasons, both in recognizing that AI is not just language, that AI is really broad, that we wanna engage everybody into the world of AI, and then also co-design of AI.

Lex Fridman (01:09:32)

Well, I have to say, once again, thank you for open sourcing, really truly open sourcing Nemotron 3 and …

Jensen Huang (01:09:39)

Yeah, I appreciate you were saying that. We open sourced the models, we open sourced the weights, we open sourced the data, we open sourced how we created it. Yeah, it’s pretty amazing. TSMC and Taiwan

Lex Fridman (01:09:48)

It’s really incredible. You’re originally from Taiwan and have a close relationship with TSMC. So I have to ask TSMC I think also is a legendary company in terms of the engineering teams, in terms of the incredible engineering work that they do. What do you understand about TSMC culture and their approach that explains how they’re able to achieve this singular unmatched success in everything they’re doing with semiconductors?

Jensen Huang (01:10:19)

You know, first of all, the deepest misunderstanding about TSMC is that their technology is all they have. That somehow they have a really great transistor, and if somebody shows up another transistor, game over. It’s the technology and, of course, you know, I don’t mean just the transistor, the metallization systems, the packaging, the 3D packaging, the silicon photonics, the, you know, all of the technology that they have. That technology is really what makes the company special. Their technology makes the company special.

Lex Fridman (01:10:59)

But their ability to orchestrate the demands, the dynamic demands of hundreds of companies in the world as they’re moving up, shifting out, you know, increasing, decreasing, pushing out, pulling in, changing from customer to customer, wafer starting, wafer stopping, emergency wafer starts, you know, all of this dynamics of the world’s complexity as the world is shape-shifting all the time, and somehow they’re running a factory with high throughput, high yields, really great costs, excellent customer service. They take their promises seriously.

Jensen Huang (01:11:49)

They, when your wafer—because they know that they’re helping you run your company—when the wafers were promised to show up, the wafers show up, you know, so that you could run your company appropriately. And so their system, their manufacturing system is completely miraculous, I would say. Then the second thing is their culture. This culture is simultaneously technology focused on one hand, advancing technology; simultaneously customer service oriented on the other hand. A lot of companies are very customer service oriented, but they’re not very technology excellent. They’re not at the bleeding edge of technology.

Jensen Huang (01:12:27)

There are a lot of companies who are tech, at the bleeding edge of technology, but they’re not the best customer service oriented company. And so it just depends on somehow they’ve, they’ve balanced these two and they’re world-class at both. And then probably the third thing is the technology that I most value in them that they created this, you know, this, this intangible called trust. I trust them to put my company on top of them. That’s a very big deal.

Lex Fridman (01:12:55)

When they trust, I mean, there’s a really close relationship there that you’ve established, and that trust is established based on many years of performance, but there’s human relationships involved there as well.

Jensen Huang (01:13:05)

Three decades, I don’t know how many tens, hundreds of billions of dollars of business we’ve done through them, and we don’t have a contract. That’s pretty great.

Lex Fridman (01:13:15)

Amazing. Okay, there’s this story … … That in 2013, the founders of TSMC, Morris Chang offered you the chance to become TSMC’s chief executive and you said you already had a job. Is this story true?

Jensen Huang (01:13:30)

Story is true. I didn’t, I didn’t dismiss it. But I was deeply honored and, and of course, I knew then as I know now, TSMC is one of the most consequential companies in history. And Morris is one of the highest regarded executives and business and personal friend that I’ve had in my life. And, for him to ask, I was humbled and really honored. But the work that I’m doing here is really important, and I’ve seen, you know, in my mind’s eye, what NVIDIA was going to be and what the impact that we could have. And it was really important work. And it’s my responsibility, you know, my sole responsibility to make this happen. And so I declined it, not because it wasn’t an incredible offer. It’s an unbelievable offer, but I simply couldn’t take it.

Lex Fridman (01:14:38)

I think NVIDIA, both NVIDIA and TSMC are two of the greatest companies in the history of human civilization. And running either one, I’m sure, is an incredibly complicated effort and takes… You have to truly be all in. Everybody at every scale, not just at the CEO level. Everybody is really truly all in-

Jensen Huang (01:14:57)

Yeah. Yeah, no doubt.

Lex Fridman (01:14:59)

… To, to accomplish this kind of complexity.

Lex Fridman (01:15:00)

So now I can help both companies. NVIDIA’s moat

Lex Fridman (01:15:02)

Exactly. So NVIDIA is now the most valuable company in the world. I have to ask, what is the NVIDIA’s biggest moat, as the folks in the tech sector say? The edge you have that protects you from the competition.

Jensen Huang (01:15:20)

Our single most important property as a company is the install base of our computing platform. Our single most important thing today is the install base of CUDA. Now, the reason why 20 years ago, of course, there was no install base. But what makes… And if somebody came up with a GUDA or TUDA, it wouldn’t make any difference at all. And the reason for that is because it’s never been just about the technology. The technology, of course, was incredible, visionary. But it’s the fact that the company was dedicated to it, stuck with it, expanded its reach. It wasn’t three people that made CUDA successful. It was 43,000 people that made CUDA successful.

Lex Fridman (01:16:17)

And the several million developers that believed in us that trusted that we were going to continue to make CUDA 1, 2, 3, 13, that they decided to port and dedicate their software on top of it, their mountain of software on top of it. And so the install base is the number one most important advantage. That install base, when you amplify it with the velocity of our execution at the scale that we’re talking about, no company in history had ever built systems of this complexity, period. And then to build it once a year is impossible. And that velocity combined with the install base, in the developer’s mind, you just go now, take the developer’s mind. From the developer’s perspective, if I support CUDA, tomorrow it’ll be 10 times better. I just have to wait six months on average.

Jensen Huang (01:17:16)

Not only that, if I develop it on CUDA, I reach a few hundred million people, computers. I’m in every cloud, I’m in every computer company, I’m in every single industry, I’m in every single country. So if I create an open source package and I put it on CUDA first, I get these both attributes simultaneously. And not only that, I trust 100% that NVIDIA is going to keep CUDA around and maintain it and improve it and keep optimizing the libraries for as long as they shall live. You could take that to the bank, and that last part, trust. You put all that stuff together, if I were a developer today, I would target CUDA first. I would target CUDA most. And that’s the reason that I think in the final analysis is our first, that’s even our first-

Jensen Huang (01:18:16)

… core advantage. Our second one is our ecosystem. The fact that we vertically integrated this incredibly complex system, but we integrate it horizontally into every single company’s computers. We’re into Google Cloud, we’re into Amazon, we’re in Azure. You know, we’re ramping up AWS like crazy right now. We’re in new companies like CoreWeave and Nscale. We’re in supercomputers at Lilly. We’re in enterprise computers. We’re at the edge in radio base stations. You know, I mean, it’s just crazy. One architecture is in all these different systems. We’re in cars, we’re in robots, we’re in satellites, we’re out in space. And so the fact that you have this one architecture and the ecosystem is so broad, it basically covers every single industry in the world.

Lex Fridman (01:19:03)

Well, how does the CUDA install base evolve into the future with AI factories as a moat? What do you… Do you think it’s possible that NVIDIA of the future is all about the AI factory?

Jensen Huang (01:19:16)

Well, the unit of computing used to be GPU to us. Then it became a computer, then it became a cluster. Now it’s an entire AI factory. When I see a computer, when I see what NVIDIA builds, in the old days, I would, you know, I visualize the chip. And then when I announced the new product, new generation, like, “Ladies and gentlemen, we’re announcing Ampere today,” I’d pick up the chip. That was my mental model- … of what I was building. Today, I wouldn’t… Picking up the chip is kind of still adorable.

Lex Fridman (01:19:47)

But it’s adorable. It’s not my mental model of what I’m doing. My mental model is this giant gigawatt thing that has power generations connected to the grid. It’s got cooling systems and networking of incredible monstrosity, you know. 10,000 people are in there trying to install it, hundreds of networking engineers in there, thousands of engineers behind it trying to power it up. You know, powering up one of those factories, as you know, it’s not somebody going, “It’s on now.” It takes thousands of people to bring it up.

Lex Fridman (01:20:22)

So mentally, you’re actually… When you’re thinking about a single unit of compute, you’re like literally, when you go to bed at night, you’re thinking now about a collection of racks, so pods, not individual chips.

Jensen Huang (01:20:33)

Entire infrastructure. And I’m hoping my next click is when I’m thinking about building computers, it’s planetary scale. That’ll be the next click. AI data centers in space

Lex Fridman (01:20:42)

Well, what do you think about the space angle that Elon has talked about, doing compute in space for solving some of the… It makes some of the energy issues in terms of scaling energy easier.

Jensen Huang (01:20:56)

Cooling issues is not easy. Yeah.

Lex Fridman (01:20:58)

Cooling. Well, there’s a large number of engineering complexities involved with that. So what… You know, NVIDIA has also announced that you’re already thinking about that.

Jensen Huang (01:21:09)

Yeah, we’re already there. NVIDIA GPUs are the first GPUs in space. And I didn’t realize it, it was so interesting to… I would have declared it maybe. We’re in space. You know, little, little astronaut suit on one of our GPUs. But we’ve been in space. It’s the right place to do a lot of imaging.

Jensen Huang (01:21:32)

You know, because those satellites have really high resolution imaging systems, and they’re sweeping the Earth, you know, continuously now. And you want, you know, centimeter scale imaging that is done continuously for the world, so that, you know, you’ll basically have real time telemetry of everything. You don’t wanna beam that back down to Earth. It’s just, you know, petabytes and petabytes of data. You gotta just do AI right there at the edge, throw away everything you don’t need, you’ve seen before, didn’t change, and then just keep the stuff that you need. And so AI had to be done at the edge. Obviously we have 24/7 solar, if we put it at the polars. And but, you know, there’s no conduction, no convection.

Lex Fridman (01:22:23)

And so, you know, you’re pretty much just radiation. And but, you know, space is big. I guess, you know, we’re just gonna put big, giant radiators out there.

Lex Fridman (01:22:32)

How crazy of an idea do you think it is? Like is this five years out, 10 years out, 20 years out? So we’re talking about blockers for AI scaling.

Lex Fridman (01:22:41)

You know, I’m just so much more practical. I look for where my next, next bucket of opportunities are first. Meanwhile, I’m cultivating space. And so I send, I send engineers to go work on the problem. We’re starting to… We’re learning a lot about it. How do we deal with radiation? How do we deal with degrading performance? How do we deal with a continuous testing and attestation of defects? And you know, how do we deal with redundancy? And how do we degrade gracefully and things like that? And so we could do a… What about software? How do you think about software and redundancy and performance out in space?

Jensen Huang (01:23:24)

Make it so that the computer never breaks, it just gets slower, you know. And I… So we could start doing a lot of engineering exploration upfront. But in the meantime, my favorite answer is eliminate waste. You know, we’ve got all that idle power, I want to evacuate it as fast as possible.

Lex Fridman (01:23:47)

Yeah. There, there… Yeah, there’s a lot of low-hanging fruit here on Earth- … That we can utilize for the AI scaling. Quick pause. Quick 30-second thank you to our sponsors. Check them out in the description. It really is the best way to support this podcast. Go to lexfridman.com/sponsors. We got Perplexity for curiosity-driven knowledge exploration, Shopify for selling stuff online, LMNT for electrolytes, Fin for customer service AI agents, and Quo for a phone system, like calls, texts, contacts, for your business. Choose wisely, my friends. And now, back to my conversation with Jensen Huang. Do you think NVIDIA may be worth 10 trillion at some point? Let’s, let’s ask it this way. What does the future of the world look like where that’s true? Will NVIDIA be worth $10 trillion?

Jensen Huang (01:24:45)

I think that NVIDIA’s growth is extremely likely, and in my mind, inevitable. And let me explain why. We’re the largest computer company in history. That alone should beg the question, why? And the reason of course… Two reasons. First, two foundational technical reasons. The first reason is that computing went from being a retrieval-based, file retrieval system. Almost everything is a file… We pre-write something, we pre-record something. You know, we draw something, we put it on the web, we put it in a file. And we use a recommender system, some smart filter, to figure out what to retrieve for you. And so we were a pre-recording, human pre-recording, and file retrieving system. That’s what a computer is, largely.

Jensen Huang (01:25:39)

To now, AI computers are contextually aware, which means that it has to process and generate tokens in real time. So we went from a retrieval-based computing system to a generative-based computing system. We’re gonna need a lot more processing in this new world than in the old world. We need a lot of storage in the old world. We need a lot of computation in this new world. And so that’s the first part of it. We fundamentally changed computing and the way how computing is done. The only thing that would cause it to go back……

Jensen Huang (01:26:15)

is if this way of computation, this way of computing generating information that’s contextually relevant, situationally aware, that is grounded on new insight before it generates information, this computation-intensive way of doing computing would only go back if it’s not effective. So if… For the last 10, 15 years while working on deep learning, if at any single moment I would have come to the conclusion that, “You know what? This is not gonna work out. I think this is a dead end.” Or, “It’s not gonna scale, it’s not gonna solve this modality, not gonna be used in this application.” Then, of course, I would feel very differently about it, but I think the last five years has given me more confidence than the previous ten years.

Jensen Huang (01:27:04)

The second idea is computers, because it was a storage system, it was largely a warehouse. We’re now building factories. Warehouses don’t make much money. Factories directly correlates with the company’s revenues. And so, the computer did two things. Not only did it change the way it did it, its purpose in the world changed. It’s no longer a computer, it’s a factory. It’s a factory, it’s used for generation of revenues. We’re now seeing not only is this factory generating products, commodities that people want to consume, we’re seeing that the commodities are so interesting, so valuable to so many different audiences that the tokens are starting to segment, like iPhones. You have free tokens, you have premium tokens, and you have several tokens in the middle.

Lex Fridman (01:28:10)

And so intelligence, as it turns out, you know, it’s a scalable product. There’s extremely high intelligence products, tokens that you could… that are used for specialized things, people be willing to pay. You know, the idea that somebody’s willing to pay $1000 per million tokens is just around the corner. It’s not if, it’s only when. And so, so now we’re seeing that the commodity that this factory makes is actually valuable, and is revenue generating and profit generating. Now the question is how many of these factories does the world need? How many tokens does the world need? And how much is society willing to pay for these tokens? And what would happen to the world’s economy if the productivity were to improve so substantially? What would happen…

Jensen Huang (01:29:08)

Are we, are we gonna discover new drugs, new products, new services? And so when you take these things in combination, I am absolutely certain that the world’s GDP is going to accelerate in growth. I’m absolutely certain the percentage of that GDP that will be used for computation will be 100 times more than the past—mm-hmm—because it’s no longer a storage unit. It’s a product generation unit. And so when you look at it in that context and then you back into what is NVIDIA’s, what does NVIDIA sh—what does NVIDIA do and how much of that new economics, new industry would we have to benefit t—to address, I think we’re gonna be a lot, lot bigger.

Lex Fridman (01:29:58)

And then the rest of it, to me, is: is it possible for NVIDIA to be a, you know, $3 trillion revenue company in the near future? The answer is, of course, yes. And the reason for that is because it’s not limited by any physical limits. There’s nothing that I see that says, you know, gosh $3 trillion is not possible. And as it turns out, NVIDIA’s supply chain is—the burden is shared by 200 companies. And the fact that we scale out on the backs of, with the partnership of this ecosystem, the question is: do we have the energy to do so? And surely we will have the energy to do so. And so all of these things combined, that number is just a number, you know?

Lex Fridman (01:30:51)

And I still remember, NVIDIA was a… the first time we crossed a billion dollars, I was reminded of a CEO who told me, “You know, Jensen, it’s theoretically impossible for a fabless semiconductor company to exceed a billion dollars.” And I won’t bore you with why, but of course it’s illogical and there’s a lot of evidence we’re not. And then somebody told me, “You know, Jensen, you’ll never be more than $25 billion because of some other company.” Somebody told me that, “You’ll never be, you know, because…” And so those aren’t principled, first principled reason thinking. And the simple way to think about that is what is it that we make and how large is the opportunity that we can create?

Jensen Huang (01:31:42)

Now, NVIDIA is not in the market share business. Almost everything that I just talked about don’t exist. That’s the part that’s hard. You know, if NVIDIA was a $10 billion company trying to take NVIDIA’s share, then it’s easy to see for shareholders that, oh, yeah, if they could just take 10% share, they could be this much larger. But it’s hard for people to imagine how large we could be because there’s nobody I could take share from. You know? And so I think that that’s one of the challenges for the world is the imagination of the future. But I got plenty of time, and I’ll keep reasoning about it, and I’ll keep talking about it, and every single GTC will become more and more real.

Jensen Huang (01:32:27)

You know, and then more and more people will talk about it, and one of these days, you know, we’ll get there. But I’m 100% we’ll get there.

Lex Fridman (01:32:34)

Yeah, this view of you know, token factories essentially, this token per second per watt, and every token having value. Like it’s an actual thing that brings value, and it brings different kinds of value, different amounts of value to different people with value. That’s the actual product—it really could be loosely thought of as the token. And so you have a bunch of token factories. And then it’s very easy, first principles, to imagine a future, given all the potential things that AI can solve, that you’re going to need an exponential number more of token factories.

Jensen Huang (01:33:05)

Yeah. And what’s really interesting, the reason why I was so excited about it, the iPhone of tokens arrived.

Lex Fridman (01:33:11)

What do you call it? Wait, are you saying OpenClaw’s iPhone?

Jensen Huang (01:33:13)

Yeah.

Lex Fridman (01:33:14)

That’s interesting.

Jensen Huang (01:33:15)

Agents.

Lex Fridman (01:33:16)

Yeah, agents. True.

Jensen Huang (01:33:18)

Agents in general. The iPhone of tokens arrived. It is the fastest-growing application in history. It went straight up. Went straight up.

Lex Fridman (01:33:26)

That says something.

Jensen Huang (01:33:27)

Yep, there’s no question OpenClaw is the iPhone of tokens.

Lex Fridman (01:33:31)

Is there something truly, as you know, something truly special happening from about December, where people have really woke up to the power of Claude Code of Codex, of OpenClaw? I mean, I’m embarrassed to admit that on the way here in the airport, I’ve… It’s the first time I’ve done this in public. I was programming, quote unquote, by talking to my laptop.

Jensen Huang (01:33:59)

Yeah, exactly.

Lex Fridman (01:33:59)

And I was embarrassed because I was pretending like I’m talking to a human colleague. I’m not sure how I feel about the future where everybody- … is walking around talking to their AI, but it’s such an efficient way to get stuff done.

Lex Fridman (01:34:13)

And it’s more likely that your AI is bothering you all the time. And the reason for that is because it’s getting stuff done so fast. It’s reporting back to you, “I got that done.” “You know, what do you want me to do next?” You know, it… That’s the part that I think most people don’t realize is the person who’s gonna be chatting with them, texting them most, is their, is their claws or lobster. Leadership under pressure

Lex Fridman (01:34:37)

What an incredible future. I read that you attribute a lot of your success to your ability to work harder than anyone and withstand more suffering than anyone. So we can list many of the things that entails. I mean, dealing with failure, the cost and engineering problems we’ve talked about. The human problems, uncertainty, responsibility, exhaustion, embarrassment, the near-death company moments that you’ve mentioned but also the pressure. Now, as the CEO of this company that economies and nations strategize around, plan their financial allocations around, plan their AI infrastructure around, how do you deal with this much pressure? What gives you strength, given how many nations and peoples depend on you?

Jensen Huang (01:35:38)

I’m conscious about the fact that NVIDIA’s success is very important to the United States. We generate enormous amounts of tax revenues. We established technology leadership for our nation. Technology leadership is important for national security. National security not just in one aspect of national security, all aspects of national security. When our country’s more prosperous, we could do a better job with domestic policies and helping social benefits. Because we’re generating so much re-industrialization in the United States, we’re creating mountains of jobs. We’re helping shift how we build things back to the United States in so many different plants, chips, computers, and of course, these AI factories. I’m completely aware that, that…

Lex Fridman (01:36:35)

And I have the benefit, and this is a real gift with mainstream investors, teachers, policemen who have somehow, for whatever reason, invested in NVIDIA or because they watched Jim Cramer, bought some stock and now are millionaires.

Lex Fridman (01:36:57)

And I am completely aware of that circumstance. I’m aware of the circumstance that NVIDIA is central to a very large network of ecosystem partners behind us and downstream from us. And so the way I deal with that is exactly what I just did. I reason about what is… what is it that we’re doing? What is it causing? What’s the impact that has on other people benefit, you know, positively or even through great burden, for example, to supply chain? And the question is therefore, what are you gonna do about it? In almost everything that I feel, I break it down, I reason about, “Okay, what’s the circumstance? What has changed? What’s hard? And what am I gonna do about it?” And I’m…

Jensen Huang (01:37:56)

I break it down, decompose the problem, and the decomposition of these circumstances turns it into manageable things that I can do. And the only thing that after that I could do is, “Did you do it? Did you either do it or did you get somebody else to do it? And if you didn’t do it, you reasoned that you need to do it, and you didn’t do it, and you didn’t get anybody else to do it, then stop crying about it.”… you know? And so, and so-

Lex Fridman (01:38:27)

so I’m fairly tough on myself. And, but I also break things down so that I don’t panic. I can go to sleep because I’ve made the list of things that needed to be done, and I’ve made sure that everything that could put our company in harm’s way, could put my partners in harm’s way, put our industry in harm’s way, I’ve told somebody. Everything that I feel could put anybody in harm’s way, I’ve told someone. And I’ve told that someone who could do something about it. And so I’ve gotten it off my chest or I’m doing something about it. And so after that, Lex, what else can you do?

Lex Fridman (01:39:10)

So given all the insane, intense amount of suffering on the journey of building up NVIDIA, have you hit low points psychologically?

Jensen Huang (01:39:22)

Oh, yeah. Oh, yeah. Sure. All the time. All the time.

Lex Fridman (01:39:27)

And there-

Jensen Huang (01:39:27)

All the time

Lex Fridman (01:39:27)

… you just break down the problem into pieces? See what you could do about it?

Lex Fridman (01:39:33)

And part of it, Lex, part of it is forgetting. One of the most important attributes of AI learning, as you know, is, right? Systematic forgetting. You need to know when to forget some things. You can’t memorize everything. You can’t keep everything and, you know, you don’t want to carry everything. One of the things that I do very quickly is decompose the problem, I reason about the problem, and I share the load with it. When I say I tell everybody, I’m essentially sharing that burden.

Jensen Huang (01:40:04)

As quickly as possible. Whatever worries me, tell somebody else. Don’t just keep it. You know, don’t freak them out. Decompose the problem into smaller parts and get people to, and inspire them to be able to go do something about it. But part of it is just forgetting. You know, like, a lot of it is you gotta be tough on yourself. You know, just come on, stop crying about it. Let’s get going. You know? And then you get out of bed. And then the other part is you’re attracted to the next shiny light, the next future, the next opportunity, the next, “Okay, that’s behind us. What’s next?” It’s a lot, I think, you know, you watch this with great athletes. They just worry about the next point. The last point is behind them. The embarrassment, the, you know- … the setback.

Jensen Huang (01:40:56)

You know, and because I do so much of my job publicly, you know? Lex, you do a fair amount of your job publicly too. And so I do a lot of my job publicly. And so you know, I say a lot of things that seem sensible at the time or funny at the time, mostly it’s just because it’s funny to me at the time. And then, you know, you reflect on it, it’s less funny, but…

Lex Fridman (01:41:20)

Yeah. No, trust me, I know. But you basically allow yourself to be pulled by the light of the future. Forget the past and just keep-

Jensen Huang (01:41:27)

That’s right.

Lex Fridman (01:41:28)

… keep working towards that. I mean, you did say, there’s this kind of famous thing you said that if you knew how hard it would be to build NVIDIA it turned out to be—what is it? A million times more hard than you anticipated—that you wouldn’t do it.

Jensen Huang (01:41:46)

Yeah, right.

Lex Fridman (01:41:47)

But isn’t… You know, when I hear that, that’s probably true about everything worth doing, right?

Lex Fridman (01:41:53)

Exactly. That is, by the way, what I was trying to explain, is that there’s an incredible superpower of having the mind of a child. You know? And I say to myself oftentimes when I look at something, and almost everything my first thought is, “How hard can it be?” You know? And so you get yourself into that mode, how hard could it be? And nobody’s ever done it. It looks gigantic. It’s gonna cost hundreds of billions of dollars. It’s gonna take, you know, all this… And you just go, “Yeah, but how hard could it be?” You know? How hard could it be?

Lex Fridman (01:42:37)

And so, you gotta get yourself into that state of mind. You don’t wanna actually over-simulate everything and all the setbacks and all the trials and tribulations and all the disappointments. You don’t wanna simulate all that in advance. You don’t wanna know that. You wanna go into a new experience thinking it’s gonna be perfect, it’s gonna be great, it’s gonna be incredibly fun. And then while you’re there, you know, you need to have endurance, you need to have grit, so that when the setbacks actually happen, and those setbacks are gonna surprise you, the disappointments are gonna surprise you, the embarrassments are gonna surprise you, the humiliations are gonna surprise you.

Jensen Huang (01:43:17)

You just can’t let… Now you just gotta turn on the other bit, which is just forget about it. Move on, keep moving. And to the extent that my assumptions about the future and why the future is gonna manifest, so long as those assumptions and that input doesn’t change or didn’t change materially, then I should expect that the output won’t change. And so my simulated output of the future is still gonna happen. And if it’s still gonna happen, I’m still gonna go after it.

Jensen Huang (01:43:54)

I believe it’s gonna, you know, and so there’s a combination of two or three human characteristics: the ability to go into an experience fresh-minded, the ability to forget the setbacks, the ability to believe in yourself, you know, to believe what you believe and stay true to that belief. But you’re constantly reevaluating.

Jensen Huang (01:44:20)

This combination of three, four, five things I think is really important for resilience. And, you know, I’m fortunate that whatever life experiences led to this, I’ve got kind of those four, five things. You know, I’m always curious, always learning. I’m always learning from everybody, you know? I’m always asking my… And because I’m humble about everything, I’m always thinking, “Gosh, they did that so nicely. They did that so wonderfully.” You know, I wonder what they’re thinking through. How do they… So I’m simulating everybody. In a lot of ways, you know, I’m emulating almost everybody I watch, right? You’re empathetic towards everything that they do that you’re observing and respect. And so you’re constantly learning and, you know.

Lex Fridman (01:45:10)

You’re now one of the wealthiest people on Earth. One of the most successful humans on Earth. Is it harder to be humble and to be able to… Do you feel the effect of money and power and fame in making it harder for you to sort of be wrong in your own head? Enough to hear out an opinion of somebody else when they disagree with you and learn from them? Those kinds of things.

Jensen Huang (01:45:41)

Surprisingly, no. And I would actually go the other way. Because I do so much of my work publicly, when I’m wrong, pretty much everybody sees it.

Lex Fridman (01:45:53)

You get humbled. Fair enough.

Lex Fridman (01:45:55)

And when I’m wrong—when I’m wrong or it didn’t turn out that way or, you know, I mean, most of the things that I say outside I’m fairly certain about. And the reason for that is because it’s gonna impact somebody else and I want to be quite concerned about that and quite circumspect about that. For stuff that I’m reasoning about inside a meeting, you know, a lot of things could turn out differently. And so, but it doesn’t ever stop me from reasoning. The way that I manage and lead, I’m constantly reasoning in front of people. And even when I’m talking to you, you can kind of see me reasoning through things. And I want to make sure that you understand what I’m saying not because I told you-

Jensen Huang (01:46:40)

… because I’m so humble about what I’m about to tell you. I kind of show you the steps that I got there. And then you can decide whether you believe what I said in the end. And so I’m doing that all day long in meetings. With all of my employees, I’m constantly reasoning through, “Let me tell you how I see it.” And then I reason through it. It gives everybody the opportunity to intercept and say, “I disagree with that part.” The nice thing about reasoning through things and letting people interact with it is that they don’t have to disagree with your outcome. They can disagree with your reasoning steps. And they could pull me in different directions, and then we can reason forward. And so we’re kind of, you know, a collective path searching method. And it’s really fantastic.

Lex Fridman (01:47:29)

Yeah, you have this way about you of … When you’re explaining stuff, I can feel you actually reasoning on the spot about it with a constant open-mindedness where you could … I could feel like I could steer your thinking. And that’s a—that’s really beautiful that you’ve been able to maintain that after so many years of success, and pain. I think sometimes pain closes you down a bit. And I think to maintain-

Lex Fridman (01:47:57)

Yeah. Tolerance for embarrassment, I think is…

Lex Fridman (01:47:59)

Yes, that’s… The tolerance… I mean, that’s a real thing. Is many years of embarrassing yourself. Even those meetings knowing that there’s people around you where you declared one idea and it was shown that that idea was wrong- … and be able to admit that and to grow from that. That’s not—that’s very difficult on a human level.

Jensen Huang (01:48:17)

Yeah. Well, you know. They knew I was—they knew that recently my first job was cleaning toilets, so. Video games

Lex Fridman (01:48:25)

I’m glad you maintained that same spirit of Denny’s, the work. I mean, that was beautiful. Your whole journey starting from Denny’s is a beautiful one. Let me ask you about video games. So I’m a big gaming fan. So I have to say thank you to NVIDIA for many years of incredible graphics.

Jensen Huang (01:48:47)

By the way, GeForce is our still, to this day- … our number one marketing strategy. Right. People learn about NVIDIA while they’re in their teenage years. And then they go to college and they know who NVIDIA is and in the beginning it’s just, you know, playing Call of Duty, Fortnite. And then later they’re using CUDA, and then later they’re using NVIDIA and, you know, Blender and Dassault and Autodesk.

Lex Fridman (01:49:16)

Yeah. I mean, I should say I mentioned to a friend that I’m talking with you. He said, “Oh, they make great gaming GPUs.”

Lex Fridman (01:49:25)

Yeah, exactly.

Lex Fridman (01:49:26)

It’s like-

Lex Fridman (01:49:26)

Exactly.

Lex Fridman (01:49:27)

You know, there’s more to it, but, yeah, people really love it. It really brought a lot of joy to a lot of people. The hardware really brings these worlds to life. There was some controversy around this with DLSS 5. Can you explain to me the drama around this? I guess people, the gamers online were concerned that it makes games look like AI slop. What do you think of this drama?

Jensen Huang (01:49:56)

Yeah. I think their perspective makes sense and I could see where they’re coming from, because I don’t love AI slop myself. You know, all of the AI-generated content increasingly looks similar and they’re all beautiful, and so I’m empathetic towards what they’re thinking. That’s just not what DLSS 5 is trying to do. I showed several examples of it. But DLSS 5 is 3D-conditioned, 3D-guided. It’s ground truth structure data guided. And so the artist determined the geometry. We are completely truthful to the geometry maintained in every single frame. It’s conditioned by the textures, the artistry of the artist. And so every single frame, it enhances but it doesn’t change anything.

Jensen Huang (01:50:55)

Now, the question is about enhancing. DLSS 5 also lets, because the system is open, you could train your own models to determine, and you could even in the future prompt it. You know, I want it to be a toon shader. I want it to look like this kind of, so you can give it even an example. And it would generate in the style of that, all consistent with the artistry, the style, the intent of the artist. And so all of that is done for the artist, so that they can create something that is more beautiful but still in the style that they want. I think that they got the impression that the games are gonna come out the way the games are, shipped the way they do, and then we’re gonna post-process it. That’s not what DLSS is intended to do.

Lex Fridman (01:51:50)

DLSS is integrated with the artist, and so it’s about giving the artist the tool of AI, the tool of generative AI. They could decide not to use it, you know?

Lex Fridman (01:52:01)

I think people are very sensitive to human faces. And we’re now living in this moment, which I think is a, is a beautiful one, which is people are sensitive to AI slop. It puts a mirror to ourselves to help us realize that what we seek is imperfections. What we seek is sometimes not perfect graphics. It helps us understand what we find compelling in the worlds we create. And that’s beautiful. And as long as it’s tools that help us create those worlds-

Lex Fridman (01:52:28)

Yeah, that’s right.

Lex Fridman (01:52:28)

… it’s wonderful.

Jensen Huang (01:52:29)

That’s right. Yet, yet another tool, and they want the generative models to generate the opposite of photo real. Yeah, it’ll do that too. And so it’s just yet another tool. I think the gamers might also appreciate that in the last couple of years, we introduced skin shaders to the game developers. And many of those games have skin shaders that include subsurface scattering that make skin look more skin-like. And so the industries, you know, game developers are looking for more and more tools to express their art. And so this is just yet one more tool, and they get to decide what to use.

Lex Fridman (01:53:16)

Ridiculous question. What do you think is the greatest or most influential game ever made? Maybe from NVIDIA’s perspective?

Lex Fridman (01:53:24)

Doom.

Lex Fridman (01:53:25)

Doom, unquestionably. That was the start of the 3D.

Lex Fridman (01:53:28)

I would say Doom, from an art, the intersection of the cultural implication as well as the industry, turning a PC into a gaming device. That was a very important moment. Now, of course, flight simulation companies were before it. And but they just didn’t have the popularity that Doom did to have made the industry turn the PC from an office automation tool into a personal computer for families and gamers and things like that. And so Doom was really impactful there. From an actual game technology perspective, I would say Virtua Fighter. And so we’re great friends with both of them, you know?

Lex Fridman (01:54:07)

And then there’s games more recently—I mean, Cyberpunk 2077, really nice GPU-accelerated graphics. Like-

Lex Fridman (01:54:16)

Fully ray traced.

Lex Fridman (01:54:17)

Fully ray traced. Also, I like, I personally, I’m a huge fan of Skyrim, Elder Scrolls, and the, you know, it’s, it’s been released a long, long time ago, but people release mods and-

Lex Fridman (01:54:29)

We love mods.

Lex Fridman (01:54:30)

… they create these inc- I mean, it’s like a different game and it just allows me to replay the game over and over. It makes you realize that you can re-experience in a totally new way the world you already love. So-

Lex Fridman (01:54:45)

That’s right.

Lex Fridman (01:54:45)

… I do that all the time. One of my favorite things is just walk across Skyrim.

Lex Fridman (01:54:48)

We created this thing called RTX Mod. Yeah, it’s a modding tool.

Lex Fridman (01:54:53)

Awesome.

Lex Fridman (01:54:53)

It allows the community to inject the latest technology into an old game.

Lex Fridman (01:55:00)

Of course, like what makes a great video game is not just graphics, it’s also story and character development, but- AGI timeline

Lex Fridman (01:55:06)

That’s right

Lex Fridman (01:55:06)

… beautiful graphics can add to the immersion. The feeling like it’s another place you’re transported to. Ah, what you said, I think accurately, that the AGI timeline question rests on your definition of AGI. So let’s, let me ask you about possible timelines here. Let’s, this ridiculous definition perhaps of what AGI is, but an AI system that’s able to essentially do your job. So, run, no, start, grow, and run a successful technology company that’s worth-

Lex Fridman (01:55:52)

A good one or a one?

Lex Fridman (01:55:54)

No. It has to be worth more than a billion, more than a billion dollars. So, you know, you know how hard it is to do all those components. So, how far are we away from that? So, we’re talking about Open-Claude that does all the incredibly complex stuff that are required to, first of all, innovate, to find customers, to sell to them, to manage, to build a team of some agents, some humans, all that kind of stuff. Is this five, 10, 15, 20 years away?

Lex Fridman (01:56:31)

I think it’s now. I think we’ve achieved AGI.

Lex Fridman (01:56:35)

Do you think you could have a company run by an AI system like this?

Jensen Huang (01:56:37)

Possible, and the reason for that is this. You said a billion, and you didn’t say forever. And so for example… It is not out of the question that a Claude was able to create a web service, some interesting little app that all of a sudden, you know, a few billion people used for 50 cents, and then it went out of business again shortly after. Now, we saw a whole bunch of those type of companies during the internet era, and most of those websites were not anything more sophisticated than what Open-Claude could generate today.

Lex Fridman (01:57:20)

Interesting. Achieve virality and monetize that virality.

Jensen Huang (01:57:23)

Yeah. It’s just that I don’t know what it is, but I couldn’t have predicted any of those companies at the time either, you know? And – Future of programming

Lex Fridman (01:57:30)

You’re gonna get a lot of people excited with that statement.

Lex Fridman (01:57:32)

Yeah, no. Yeah.

Lex Fridman (01:57:33)

It’s like, what do you mean? I can just launch an agent and make a lot of money.

Jensen Huang (01:57:38)

Well, by the way, it’s happening right now, right? You know that when you go to China you’re gonna see, you’re gonna see a whole bunch of people teaching their, getting their Claudes to try to go out and look for jobs and, you know, do work, make money. And I’m not, I’m not actually… I wouldn’t be surprised if some social thing happened or somebody created a digital influencer, super, super cute, or some social application that, you know, feeds your little Tamagotchi or something like that, and it become out of the blue an instant success. A lot of people use it for a couple of months and it kind of dies away. Now, the odds of 100,000 of those agents building NVIDIA is zero percent.

Lex Fridman (01:58:28)

And then, and then the one part that I will, I won’t do and I wanna make sure we all do, is to recognize that people are really worried about their jobs. And I just want to remind them that the purpose of your job and the tasks and tools that you use to do your job are related, not the same. I’ve been doing my job for 33 years. I’m the longest running tech CEO in the world, 34 years. And the tools that I’ve used to do my job has changed continuously in the last 34 years, and sometimes quite dramatically, you know, over the course of a couple, two, three years. And the one story that I really wanna make sure that everybody hears is the story that the first job that computer scientists said, AI researchers said was gonna go away was radiology.

Lex Fridman (01:59:25)

Because computer vision was going to achieve superhuman levels, and it did. CV… Computer vision was superhuman in 2019, 20, maybe maybe a little bit later, 2020?

Jensen Huang (01:59:39)

Okay? And so it’s been a long time since computer vision has been superhuman. And so the prediction was radiologists would go away because studying radiology scans was a thing of the past. AI will do that. Well, they were absolutely right. Computer vision is completely superhuman. Every radiology platform and package today is driven by AI, and yet the number of radiologists grew. And so the question is why? And we now have a shortage of radiologists in the world. And so, one, the alarmist warning went too far and it scared people from doing this profession that is so important to society. And so it did harm. Now, why was it wrong? The reason why is because the purpose of a radiologist, the purpose is to diagnose disease and help patients and doctors diagnose disease.

Lex Fridman (02:00:38)

And because we’re able to study scans so much faster now, you could study more scans, you could diagnose better, you could in-patient faster, you can see people more. The hospitals are making more money. You have more patients in the hospital. You need more radiologists. I mean, the amazing thing is, it’s so obvious this was gonna happen. The number of software engineers at NVIDIA is gonna grow, not decline. And the reason for that is because the purpose of a software engineer and the task of a software engineer coding are related, not the same. I wanted my software engineers to solve problems. I didn’t care how many lines of code they wrote, you know? But their job, their purpose of their job didn’t change.

Jensen Huang (02:01:25)

Solving problems, working as a team, diagnosing problems, evaluating the result, looking for new problems to solve, innovation, connecting dots. You know, none of that stuff is gonna go away.

Lex Fridman (02:01:39)

Do you think it’s possible that… Let’s even take coding. Do you think the number of programmers in the world might increase, not decrease?

Jensen Huang (02:01:45)

Yes. And the reason for that is this. What is the definition of coding? I believe it is… The definition of coding, as of today, is simply specifying, specification, and maybe if you want to be rather directive, you could even give it an architecture of the software that you wanted to write. So the question is, how many people could do that? Describe a specification for a computer to go… telling the computer what to go build. How many people? I think we just went from 30 million to probably 1 billion. And so every carpenter in the future will be a coder, except a carpenter with AI is also an architect. They’ve just increased the value that they could deliver to the customer. Their artistry just elevated tremendously.

Jensen Huang (02:02:43)

I believe that every accountant is, you know, also your financial analyst, also your financial advisor. So, all of these professions have just been elevated… and if I were a carpenter, I see AI, I would just completely go berserk. You know, the services I can bring to my clients if I were a plumber, completely go berserk.

Lex Fridman (02:03:04)

And the, the people that are currently programmers and software engineers, I think they’re at the cutting edge of understanding intuitively how to communicate with the agents using natural language in order to design the best kind of software.

Lex Fridman (02:03:20)

That’s right, exactly.

Lex Fridman (02:03:20)

So over time they’ll converge, but I think there’s still value in getting, I think learning how to program, like learning what programming languages are. The old kind of programming, what are good practices for programming languages, what are design principles for programming-

Lex Fridman (02:03:39)

That’s right

Lex Fridman (02:03:40)

… Languages for large software systems?

Lex Fridman (02:03:43)

And the reason for that, Lex, and you know, as you’re saying for the audience, I think the goal of, the goal of specification, the artistry of specification, the goal and the artistry of it is going to depend on what problem you’re trying to solve. When I’m thinking, when I’m thinking about giving the company strategies and formulating corporate directions and things that we should do, I describe it at a level that is sufficiently specific that people generally understand the direction and it’s actionable. It’s specific enough that they can take action on it, but I under-specify it on purpose, so that enables 43,000 amazing people to make it even better than I imagined.

Lex Fridman (02:04:36)

And so when I’m working with engineers and when I’m working with people, I think about who, what problem am I trying to solve? Who am I working with? And the level of specification, the level of architecture definition relates to that. And so everybody’s going to have to learn how, where in the spectrum of coding they want to be. Writing a specification is coding. And so you might decide to be quite prescriptive because there’s a very specific outcome you’re looking for. You might decide that, you know, this is an area you want to be much more exploratory, and so you might under-specify and enable you to go back and forth with the AI to even push your own boundaries of creativity. And so this artistry of where you are in the spectrum, this is the future of coding.

Lex Fridman (02:05:31)

But just to linger on it outside of coding, I think a lot of people, rightfully so, are worried about their jobs, have a lot of anxiety about their jobs, especially in the white-collar sector. I don’t think any of us know what to do with tumultuous times that always come when automations and new technology arrives. And I just… First of all, I think we all need to have compassion and the responsibility to feel sort of the burden of what the actual suffering feels like for individual people and families that lose their job. I think whenever you have transformative technology like that’s coming with artificial intelligence, there’s going to be a lot of pain, and I don’t know what to do about that pain.

Lex Fridman (02:06:21)

Hopefully, it creates much more opportunities for those same people for the same kind of job as the tooling evolves and makes them more productive and makes them more fun, hopefully, as it does in the programming. I have been having so much fun programming, I have to say. Like, I’ve never had this much fun. So hopefully it makes their job, automates the boring parts and makes the creative parts the ones that the human beings are responsible for. But still there’s going to be a lot of pain and suffering.

Lex Fridman (02:06:51)

So my first recommendation before… And this is now how I deal with anxiety. In fact, we just talked about it earlier. Enormous anxiety about the future, enormous anxiety about the pressure, enormous anxiety about uncertainty, I first break it down, and then I’m gonna tell myself, “Okay, there are some things you can do something about, there’s some things you can’t do anything about. But for the stuff that you can do something about, let’s reason, reason about it and let’s go do it.”

Jensen Huang (02:07:20)

If we were to hire a new college graduate today, and I have a choice between two, one that has no clue what AI is and one that is expert in using AI, I would hire the one who’s expert in using AI. If I had an accountant, a marketing person, the one that is expert in using AI, supply chain, customer service, a salesperson, business development, a lawyer, I would hire the one who is expert in using AI. And so I would advise that every college student, every teacher should encourage their student to go use AI. Every college student should graduate and be an expert in AI. And everybody, if you’re a carpenter, if you’re an electrician, go use AI. Go see what it can do to transform your current job, elevate yourself.

Jensen Huang (02:08:21)

If I were a farmer, I would absolutely use AI. If I were a pharmacist, I would use AI. I wanna see how, what it could do to elevate my job so that I could be the innovator to revolutionize this industry myself. And so that would be the first thing that I would do. And then I would also help them… It is the case that the technology will dislocate and will eliminate many tasks. And because it will automate it, if your job is the task—then you’re very highly going to be disrupted. If your job’s purpose includes you, certain tasks- … then it’s vital that you go learn how to use AI to automate those tasks. And then there’s the world of spectrum in between.

Lex Fridman (02:09:14)

And by the way, the beautiful thing about AI, so the chatbot versions, is you can break down… You have anxiety and you can break down the problem by talking to it. Like, I’ve recently… It’s really just incredible how much you can think through your life’s problems, and through… And I don’t mean, like, therapy problems. I mean, like, very practically, “Okay, I’m worried about my…” Literally, “I’m worried about my job. What are the skills? What are the steps I need to take?” How do I get better at AI?” Everything you just said, you could literally ask and it’s going to give you- … a point-by-point plan. I mean, it’s just a great life coach, period. This-

Lex Fridman (02:09:51)

I don’t know how to use AI, and the AI goes, “Well, let me show you.”

Lex Fridman (02:09:54)

Exactly. It’s very meta, but it’s- It’s kind of incredible. So people definitely should-

Lex Fridman (02:10:00)

You can’t walk up to Excel and say, “I don’t know how to use Excel.”

Lex Fridman (02:10:02)

Exactly.

Lex Fridman (02:10:02)

You’re done.

Lex Fridman (02:10:03)

I mean, that’s really what AI has done for me in all walks of life, is that initial friction of being a beginner of using a thing for the first time. I can literally ask about any single thing, “What are the first steps I need to take?”

Lex Fridman (02:10:16)

That’s right.

Lex Fridman (02:10:17)

And that handholding that it does, removing the friction of all the experiences that the world offers is… You know, like I mentioned to you offline, you mentioned, “I’m going to China and Taiwan.”

Lex Fridman (02:10:30)

So awesome.

Lex Fridman (02:10:31)

Just ask, “Where do I-“

Lex Fridman (02:10:31)

So excited for you.

Lex Fridman (02:10:32)

“Where do I… What do…” “You know, where do I go? How do I…” All of those questions- … immediately answered, and it’s beautiful.

Jensen Huang (02:10:37)

Well, when you go to Taiwan, just ask AI… “What are Jensen’s favorite restaurants in Taiwan?” And it’ll actually-

Lex Fridman (02:10:45)

You don’t know?

Lex Fridman (02:10:45)

Oh, yeah.

Lex Fridman (02:10:46)

Is it accurate? Okay. All right.

Lex Fridman (02:10:47)

It’s all over Taiwan.

Lex Fridman (02:10:50)

Well, you’re a rockstar over there. And like we also mentioned offline, maybe our paths will cross, which would be really wonderful in computing.

Lex Fridman (02:10:58)

COMPUTEX. NVIDIA GTC Taiwan.

Lex Fridman (02:11:01)

Do you think there’s some things about human nature, about human consciousness that is fundamentally non-computational? Maybe something a chip, no matter how powerful, can never replicate?

Jensen Huang (02:11:18)

I don’t know if the chip will ever get nervous. And that’s the, you know, of course, the conditions by which that causes anxiety or nervousness or whatever emotion. I believe that AI will be able to recognize those and understand those. I don’t think my chips will feel those. And therefore, the… How that anxiety, how that feeling, how that excitement, how that, how that, you know… All of those feelings manifest in human performance. For example, extremely amazing human performance, athletic performance, you know, average or lesser than average. That entire spectrum of human performance that comes out of exactly the same circumstances for different people, manifesting a different outcome, manifesting a different performance.

Jensen Huang (02:12:15)

I don’t think there’s anything about anything that we’re building that would suggest that two different computers being presented with all of exactly the same context would perfo- Of course, it would produce statistically different outcomes, but it’s not because it felt different.

Lex Fridman (02:12:34)

Yeah, the subjective… Boy, there’s something truly special about the subjective experience that we humans feel. Like I mentioned to you, I was pretty nervous talking to you. Like I mentioned to you, that, the hope, the fear, the anxiety, and just life itself, the richness of life. How amazing everything is. How deeply we fall in love, how deeply our hearts get broken, how afraid we are of death and how much pain we feel when our loved ones pass away. All of that, the whole thing. I know it’s very hard to- … think AI being able to… A computational device being able to do that. But there’s so many mysteries about this whole thing that we’re yet to uncover, that I am open to be surprised. I’ve been surprised a lot over the past-

Lex Fridman (02:13:23)

… few months and few years. Scaling can create some incredible miracles in the space of intelligence. It has been truly marvelous to watch, so I’m open to surprise.

Lex Fridman (02:13:34)

And it’s just really important to break down what is intelligence. You know, the word, that word we use all the time, it’s not a mysterious word. Intelligence has a meaning, you know?

Lex Fridman (02:13:46)

And it’s a system that… You know, it’s something that we do that includes perception and understanding and reasoning and the ability to do plan. And, you know, that loop, that loop, is the… Fundamentally what intelligence is. Intelligence is not one word that is exactly equal to humanity. And that’s, I think it’s really important to separate the two. We have two words for that. I’m not… I don’t over-fantasize about, and I don’t over-romanticize about intelligence. Intelligence is… And people have heard me say it before, I actually think intelligence is a commodity. I’m surrounded by intelligent people. And I’m surrounded by intelligent people more intelligent than I am in each one of the spaces that they’re in.

Lex Fridman (02:14:39)

And yet, I have a role in that circle. It’s actually kind of interesting. They’re more educated than I am. They went to better schools than I did. They’re deeper in any of the fields that they’re in. All of them. I have 60 of them. They’re all superhuman to me. And somehow, I’m sitting in the middle orchestrating all 60 of them. And so you gotta ask yourself… What is it about a dishwasher that allows that dishwasher to sit in the middle of superhumans? Does that make sense?

Lex Fridman (02:15:15)

And so, but that’s my point. My point is intelligence is a functional thing. Humanity is not specified functionally. It’s a much, much bigger word. And our life experience, our tolerance for pain, our determination, those are different words than intelligence. And so the thing that I wanna help the audience understand, if I could give them one thing, is intelligence is a word that we’ve elevated to a very high form over time.

Lex Fridman (02:15:50)

The word we should really elevate is humanity.

Jensen Huang (02:15:53)

Character, humanity.

Lex Fridman (02:15:55)

All those things.

Jensen Huang (02:15:55)

All of those things. Compassion, generosity, all of the things that you say just now, I believe those are superhuman powers. And that now intelligence is gonna be commoditized. Because we’ve spoken about it, the most important thing is your education. Now, even when they said the most important thing is your education, when you went to school, there’s more than just knowledge that you gained.

Lex Fridman (02:16:22)

And so, but unfortunately, our society had put everything into one single word, and life is more than one word. And I’m just telling you, my life would suggest that being lower on the intelligence curve than everybody around me doesn’t change the fact I’m the most successful. And so, and I think that kind of is—I’m trying to hopefully to inspire everybody else—that don’t let this democratization of intelligence, this commoditization of intelligence, cause you anxiety. You should be inspired by that.

Lex Fridman (02:17:00)

Yeah. I think AI will help us celebrate humans more. And certainly humanity and human first, and I think what makes this world incredible is humans forever will be so, and just AI is this incredible tool that makes us-

Lex Fridman (02:17:18)

That’s exactly right.

Lex Fridman (02:17:18)

… humans more powerful.

Lex Fridman (02:17:19)

That’s exactly right.

Lex Fridman (02:17:21)

So much of the success of NVIDIA and the lives of millions of people that I mentioned depend on you. But you’re just one human, like we mentioned, a mortal like all of us. Do you think about your mortality? Are you afraid of death?

Jensen Huang (02:17:42)

I really don’t wanna die. I have a great life. I have a great family. I have really important work. This is not a once in a lifetime experience suggests that it has been experienced by many people, just not one person. This is a once in a humanity experience, what I’m going through. NVIDIA is one of the most consequential technology companies in history. We’re doing very important work. I take it very seriously. And so some of the things that of course are practical things, like how do we think about succession planning? And I’m famous in saying that I don’t believe in succession planning.

Lex Fridman (02:18:36)

Man.

Lex Fridman (02:18:36)

And the reason for that isn’t because I’m immortal. The reason for that is because if you’re worried about succession planning, if you’re worried all that anxiety of succession planning, then what should you do about it? Then you break it all the way back down. The most important thing you should do today, if you care about the future of your company, post you, is to pass on knowledge, information, insight, skills, experience as often and continuously as you can, which is the reason why I continuously reason about everything in front of my team. Every single meeting is a reasoning meeting. Every moment I spend inside a company, outside a company is about passing on knowledge to people as fast as I can.

Jensen Huang (02:19:23)

Nothing I learn ever sits on my desk longer than, you know, a fraction of a second. I’m passing that information, that knowledge—oh my gosh, this is cool. Before I even finish learning all of it myself, I’m already pointing it to somebody else. “Get on this. This is so cool. You’re gonna wanna learn this.” And so I’m constantly passing knowledge, empowering people, elevating the capability of everybody around me, so that the outcome that I seek, that I hope for, is that I die on the job, you know? And hopefully I die on the job instantaneously, you know? And there’s no long periods of suffering, you know? It’s, uh –

Lex Fridman (02:20:06)

Well, from a fan perspective, given your extremely enormous positive impact on civilization, of course, I hope you keep going. But also it’s just fun to watch what NVIDIA is doing, you know. It’s just the rate of innovation. And I’m a huge fan of engineering. There’s so much incredible engineering continuously being done by NVIDIA. It’s just fun to watch. It’s a celebration of humanity, a celebration of great builders, a celebration of great engineering. So, it represents something special. So I hope you and NVIDIA keep going. What gives you hope about this whole thing we got going on, about humanity, about the future of humanity? When you look out, when you think about the future quite a bit, when you look out 10, 20, 50, 100 years from now, what gives you hope?

Jensen Huang (02:20:56)

I’ve always had a great confidence in the kindness, the generosity, the compassion, the human capacity. I’ve always been extremely confident of that. Sometimes more so than I should. And I get taken advantage of, but it doesn’t ever cause me not to. I start with always that people want to do good. People want to help others. And vastly, I am proven right. Constantly proven right. And often it exceeds my expectations. And so I have complete confidence in the human capacity. I think the things that give me incredible hope is what I see now as possible, and as I extrapolate based on the things that we’re doing, what will very likely happen.

Lex Fridman (02:22:22)

And that there’s so many things that we wanna solve. There’s so many problems we wanna solve. There’s so many things that we wanna build. There’s so many good things that we wanna do that are now within our reach, and within the reach of my lifetime. You just can’t possibly not be romantic about that. You know what I’m saying? O-

Lex Fridman (02:22:46)

What an exciting time to be alive. Like, truly-

Lex Fridman (02:22:49)

How can-

Lex Fridman (02:22:49)

… truly so.

Lex Fridman (02:22:50)

How can you not be romantic about that? The fact that there is a—it’s a reasonable thing to expect the end of disease. It’s a reasonable thing to expect. It’s a reasonable thing to expect that pollution will be drastically reduced. It’s a reasonable thing to expect that traveling at the speed of light is actually in our future. And then, you know, not for long distances, but short distances. You know, and people ask me how. Well, first of all, very soon, I’m gonna put a humanoid on a spaceship, and it’s gonna be, you know, my humanoid, and we’re gonna send it out as soon as possible, and it’s gonna keep improving and enhancing along the flight.

Lex Fridman (02:23:36)

And then when it’s time, all of my consciousness has already been—you know, so much of my life has been uploaded in the internet. Take all my inbox, take everything that I’ve done, everything I’ve said. You know, it’s been collected and becoming my AI. And I’m just, when the time comes, we’ll just send that at the speed of light, catch up with my robot.

Lex Fridman (02:24:00)

Oh, that’s brilliant. I mean, but for me, that’s sorta application-focused. But also, for me, the curiosity-maxing perspective, I just, all of those mysteries. There’s so much- … fascinating scientific questions there.

Jensen Huang (02:24:14)

Understanding the biological machine is right around the corner. It’s, it’s not 10 years. It’s five years probably.

Lex Fridman (02:24:20)

And then your biological machine, the, the human mind and cracking physics, theoretical physics open. It’s so exciting.

Jensen Huang (02:24:26)

Explaining consciousness, that one would be awesome.

Lex Fridman (02:24:29)

And it’s all within our reach. Jensen, thank you so much for everything you’ve done over the years. Thank you for everything you’re doing for the world. Thank you for being who you are. I can tell you’re a great human being, and I wish you incredible success this year. I can’t wait. As a fan, I can’t wait to see what you do next, and hopefully I’ll see you in Taiwan and thank you so much for talking today.

Jensen Huang (02:24:52)

Thank you, Lex. I had a great time. And also, if I could just say one more thing. And thank you for all the interviews that you do, the depth, the respect that you go through with and the research that you do to reveal, you know, for all of us the amazing people that you’ve interviewed over the years. I’ve enjoyed them immensely. And as an innovator, to have created this long form, unbelievable, and yet, you know, it’s just captivating. So anyways, thank you for everything you do.

Lex Fridman (02:25:25)

It means the world. Thank you, Jensen.

Jensen Huang (02:25:27)

Thank you, Lex.

Lex Fridman (02:25:29)

Thank you for listening to this conversation with Jensen Huang. To support this podcast, please check out our sponsors in the description, where you can also find links to contact me, ask questions, give feedback, and so on. And now, let me leave you with some words from Alan Kay. “The best way to predict the future is to invent it.” Thank you for listening, and hope to see you next time. This entry was posted in transcripts on March 23, 2026 by

Lex Fridman

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Lex Fridman

Host of Lex Fridman Podcast. Research Scientist at MIT, working on human-AI interaction, robotics, and machine learning. View all posts by Lex Fridman →

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