# Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution \| Lex Fridman Podcast \#494

- Channel: [Lex Fridman](https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA)
- Published: 2026-03-23
- Source video: [Watch on YouTube](https://youtube.com/watch?v=vif8NQcjVf0)
- Read on ReadTube: https://www.read.tube/p/videos/vif8NQcjVf0
- Language: English

## Summary

**Jensen Huang: NVIDIA's AI Vision**

NVIDIA, under Jensen Huang, has transformed from a GPU company to the engine of the AI revolution by pioneering "extreme co-design" at the rack and data center scale, recognizing that modern AI problems require integrated, end-to-end system optimization. This strategy involves not only hardware and software co-design but also actively shaping the entire supply chain and industry ecosystem, driven by a long-term vision of AI factories generating valuable tokens. Huang emphasizes that NVIDIA's primary moat is its extensive CUDA install base and its ability to anticipate and manifest the future of computing.

Jensen Huang discusses NVIDIA's critical role in the AI revolution, marked by its shift to extreme co-design for entire AI factories. This holistic approach, encompassing chips, networking, power, and software, is essential for tackling distributed computing challenges and overcoming the limits of traditional scaling. He outlines NVIDIA's core strategies and philosophies that have propelled its success:

- NVIDIA's primary competitive advantage is the vast install base of its CUDA computing platform, built through strategic decisions like putting CUDA on GeForce GPUs despite high costs, fostering widespread developer adoption.
- AI is fundamentally transforming computing from retrieval-based to generative, creating valuable "tokens" of intelligence that are driving an inevitable and massive increase in computational demand and global GDP.
- To address potential blockers, NVIDIA focuses on extreme energy efficiency (tokens per second per watt) in its co-design and actively engages with supply chain partners to ensure scalable production and shared future vision.
- NVIDIA anticipates future AI model architectures and agentic systems years in advance through internal research, listening to industry insights, and maintaining a flexible architecture like CUDA.
- Huang believes AGI for specific, valuable tasks is already achievable, and AI will elevate human jobs by automating tasks, making learning and adapting to AI use crucial for future professional growth.
- His leadership philosophy centers on curiosity, first-principles thinking ("speed of light"), continuously shaping belief systems across the ecosystem, and fostering trust and collaboration to manifest a shared future.

## Article

### Extreme Co-Design for AI Scaling

The reason why **extreme co-design** is necessary is that the problem no longer fits inside one computer to be accelerated by one GPU. The problem you are trying to solve is that you would like to go faster than the number of computers that you add. So you added, you know, 10,000 computers, but you would like it to go a million times faster.

Then all of a sudden, you have to take the algorithm and break it up. You have to refactor it. You have to shard the pipeline, shard the data, and shard the model. Once you distribute the problem this way, not just scaling up the problem but distributing the problem, everything gets in the way.

This is the **Amdahl’s Law** problem, where the amount of speedup you have for something depends on how much of the total workload it is. If computation represents 50% of the problem, and you speed up computation infinitely, like a million times, you only speed up the total workload by a factor of two.

Now all of a sudden, not only do you have to distribute computation, you have to shard the pipeline somehow. You also have to solve the networking problem because all of these computers are connected together. Distributed computing at the scale we do it means the CPU is a problem, the GPU is a problem, the networking is a problem, the switching is a problem, and distributing the workload across all these computers is a problem. It is just a massively complex computer science problem.

So we just have to bring every technology to bear. Otherwise, we scale up linearly, or we scale up based on the capabilities of **Moore’s Law**, which has largely slowed because **Dennard scaling** has slowed.

### How NVIDIA’s Team Solves Hard Systems Problems

There are trade-offs there, and there are complete disparate disciplines involved. There are specialists in each one of these areas: high bandwidth memory, the network and the NVLink, the NICs, the optics and the copper that you are doing, the power delivery, the cooling, all of that. There are world experts in each of those areas. The question is how you get them in a room together to figure it out.

> That is why my staff is so large.

The process starts with a question about the specialists and the generalists: how do you put together the rack when you know the set of things you have to shove into a rack together? What does that process look like when you are designing it all together?

#### Extreme co-design

The first question is: what is extreme co-design? You are optimizing across the entire stack, from software architectures to chips, to systems, to system software, to the algorithms, to the applications. That is one layer.

The second thing is broader than CPUs and GPUs and networking chips and scale up switches and scale out switches. You also have to include power and cooling, because all of these computers are extremely, extremely power hungry. They do a lot of work and they are very energy efficient, but in aggregate they still consume a lot of power.

So the first question is, what is it? The second question is, why is it? And the reason is that you want to distribute the workload so that you can exceed the benefit of just increasing the number of computers. The third question is, how is it? How do you do it? And that is the miracle of the company.

When you are designing a computer, you have to have an operating system of computers. When you are designing a company, you should first think about what it is that you want the company to produce. A lot of company organization charts all look the same: hamburger organization charts, soft organization charts, and car company organization charts. They all look the same, and that does not make any sense.

The goal of a company is to be the machinery, the mechanism, the system that produces the output. And that output is the product that you want to create. The architecture of the company should also reflect the environment by which it exists. It almost indirectly says what you should do with the organization.

The direct staff is 60 people. There are no one-on-ones with them, because it is impossible. You cannot have 60 people on your staff if you are going to get work done.

> So you still have 60 reports?

More, yes.

### CUDA, the Install Base, and Platform Lock-In

More. And most stars at least have a foot in engineering.

“Almost all of them.” There are experts in memory, experts in CPUs, experts in optical. All, all—

“That’s incredible.”

“Yeah, GPUs and—architecture, algorithms, design.”

So you constantly have an eye on the entire stack, and you are having to have intense discussions about the designer of the entire stack?

“And no conversation is ever one person. That’s why I don’t do one-on-ones. We present a problem and all of us attack it. You know, because we’re doing extreme co-design. And literally, the company is doing extreme co-design all the time.”

So even if you’re talking about a particular component, like cooling, networking, everybody’s listening in?

“Yeah, exactly.”

And they can contribute, “Well, this doesn’t work for the power distribution. This doesn’t—”

“Exactly.”

“...This doesn’t work for the memory. This doesn’t work for this.”

“Exactly. And whoever wants to tune out, tune out. You know what I’m saying? And the reason for that is because the people who are on the staff, they know when to pay attention. There’s supposed... You know, it’s something they could have contributed to, they didn’t contribute to, ‘I’m going to call them out.’ You know? And so, ‘Hey, come on, let’s get in here.’”

So, as you mentioned, NVIDIA is this company that’s adapting to the environment. So at which point can you say the environment changed and began adapting, sort of secretly, in the early days from GPU for gaming, maybe the early deep learning revolution, to we’re now going to start thinking of it as an AI factory? What does NVIDIA do? It produces AI, let’s build a factory that makes AI.

“I could, I c- you could- I could reason through what just systematically. We started out as an accelerator company. But the problem with accelerators is that the application domain’s too narrow. It has the benefit of being incredibly optimized for the job. You know, any specialist has that benefit. The problem with intense specialization is that, of course, your market reach is narrower, but that’s even fine. The problem is, the market size also dictates your R&D capacity. And your R&D capacity ultimately dictates the influence and impact that you can possibly have in computing. And so, when we first started out as an accelerator, very specific accelerator, we always knew that had- that was going to be our first step. We had to find a way to become accelerated computing. But the problem is, when you become a computing company, it’s too general purpose and it takes away from your specialization. The tur- I connected two words that actually have fundamental tension. The better computing company we become, the worse we became as a specialist. The more of a specialist, the less capacity we have to do overall computing. And so, that... And I connected those two words together on purpose, that the company has to find that really narrow path, step by step by step, to expand our aperture of computing, but not give up on the most important specialization that we had.”

Okay, so the first step that we took beyond acceleration was we invented a programmable pixel shader. 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 other types of data flow processors discovered us. And they said, “Hey, all of a sudden, we might be able to use this GPU that’s incredibly computationally intensive, and it’s now compliant with IEEE. I can take my software that I was writing previously on CPUs, and I can see about using the GPU for that.”

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, step by step by step.

Well, 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 anyways 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.

#### CUDA on GeForce

“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?”

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

“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—”

“Thank you.”

“...just setting the context. It turned out to be a good decision.”

“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 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. For example, no architecture has ever attracted more criticism than the x86... you know, as a less than, 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—”

“Install base is everything.”

“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. We were already selling millions and millions of GeForce GPUs a year, and we said, ‘You know, 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...”

### The Early CUDA Bet That Nearly Broke NVIDIA

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, every engineering school, every student in school, and eventually something amazing will happen.

Well, the problem was that CUDA increased our cost of that GPU, which is a consumer product, so tremendously that it completely consumed all of the company’s gross profit dollars. At the time, the company was probably worth, I don’t know, eight billion dollars or something, six or seven billion dollars 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. Our market cap went down to like one and a half billion dollars. 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. Researchers, scientists, they discovered CUDA on GeForce because they were all, you know, many of them were gamers. Many of them built their own PCs anyways. In a university lab, many of them built clusters themselves, using PC components. And so that is kind of how we got going.

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

That was also another great, great observation. Yeah.

#### The existential decision

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

Well, I had to make it clear to the board what we were trying to do, and the management team knew our gross margins were going to 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 a certain price and it doesn’t matter what your cost is. And so we increased our cost by 50%, and that consumed... and we were a 35% gross margin company, and so 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 could capture more margin. So you could reason your way into being able to afford this, but it still took a decade.

But that is more of a conversation with the board, convincing them. Psychologically, as NVIDIA continued to make bold bets that predict the future, and in part, especially now, define the future, I am almost looking for wisdom about how you are able to make those decisions, to make leaps like that as a company.

Well, first of all, I am informed by a lot of curiosity.

### Belief, Scaling Laws, and Future Shaping

At some point, there is a reasoning system that convinces me so clearly that *this outcome will happen*. 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 is no way it will not happen. There is a lot of suffering in between, but you have got to believe what you believe.

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

Yeah. And you reason about how to get there. You reason about why it must exist. And 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 next part of it is probably a skill thing, which is that oftentimes in leadership, the leadership stays quiet or they learn about something and then they do some manifesto, and it is a brand-new year, and somehow at the end of the year, next year, we are going to have a brand-new plan. Big huge layoff this way, big huge organization change this way, new mission statement, brand-new logos, that kind of stuff. We have just never... I never do things that way.

When I learn about something and it is starting to influence how I think, I will make it very clear to everybody near me that 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 have already made up my mind, but I will take every possible opportunity — external information, new insights, new discoveries, new engineering, revelations, new milestones developed — I will take those opportunities and use them to shape everybody else’s belief system. And I am doing that literally every single day. I am doing that with my board, I am doing that with my management team, I am doing that with my employees.

I am trying to shape their belief systems such that when the day comes I say, “Hey, let’s buy Mellanox,” it is 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 had already been laying down the bricks to different organizations inside the company. Every organization and everybody — many people might have heard everything. Most of the company hears, of course, pieces of it. And on the day that I announce it, everybody is kind of bought in to many pieces of it.

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 have been shaping their belief system for some time, and therefore leadership sometimes looks like you are leading from behind, but you have been shaping their... to the point where on the day that I declared it, 100% buy-in. But that is 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 announce something about let’s go all in on this thing, and your management team, your board, your employees, your customers, they are kind of like, “Where is this coming from? This is insane.”

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

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

We do not build computers. We actually do not build clouds. As it turns out, we are a computing platform company. And so nobody can buy anything from us. That is 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 cannot 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 are going, “What took you so long?”

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

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

> So I think you have 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 are most concerned about that keep you up at night that you have to overcome in order to keep scaling?

Well, we can go back and reflect on what people thought were blockers. So in the beginning, we were the first... the pre-training scaling law. People thought, well, 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 better... with a results in a smarter AI. And so that was pre-training.

And Ilya Sutskever... Ilya said, “We are 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, of course that is obviously not true. We are going to keep on scaling the amount of data that we have to train with. A lot of that data is probably going to be synthetic, and that also confused people, you know?

And what people do not realize is they have 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, I... It is synthetic because it did not come out of nature. You created it. I am consuming it. I modify it, augment it, I regenerate it, somebody else consumes it.

And so we have now reached a level where AI is able to take ground truth, augment it, enhance it, synthetically generate an enormous amount of data. 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 model is going to continue to scale to the point where we are no longer limited... Training is no longer limited by... Data is now limited by compute. And the

### Why Inference and Test-Time Scaling Matter

The reason for that is that 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 going to be little tiny chips, and they’re not going to be like NVIDIA’s chips. Those are going to be complicated and expensive, and we could make them. In the future, inference is going to be the biggest market, and it’s going to be easy, and we’re going to commoditize it. Everybody can build their own chips.

That was always illogical to me because inference is thinking, and I think thinking is hard. Thinking is way harder than reading. Pre-training is just memorization and generalization, 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 solvable pieces that we then go off, either through first principle reasoning, or through previous examples, prior experiences. Or just exploration and search, trying different things. And that whole process of test time scaling, inference, is really about thinking. It’s about reasoning, planning, search, and so how could that possibly be compute light? And we were absolutely right about that. 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 one agentic person, and that one agentic person has a large language model that we’ve developed. But during test time, that agentic system goes off and does research and bangs on databases, and it goes out and 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. And so the next scaling law is the agentic scaling law. It’s kind of like multiplying AI. We could spin off agents as fast as you want to spin off agents. I have four scaling laws.

And as we use the agentic systems, they’re going to create a lot more data, they’re going to create a lot of experiences. Some of it we’re going to say, “Wow, this is really good. We ought to memorize this.” 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, and the agents, agentic systems, put it out to the industry. And so this loop, this cycle, is going to go on and on and on. It kind of comes down to basically intelligence is going to scale by one thing, and that’s compute.

> 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 experts with sparsity.

> Perfect.

> 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...

> So good.

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

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, 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. 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 so to the extent that we can, we try to get a sense of what are the challenges that people are experiencing.

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

That’s right. You got to listen and learn from everybody. 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, 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. 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 eight. We could now take an entire four 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. 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.

> 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 comes from what? From the whispers, from understanding what all the state-

No. It’s easier than that. You just reason about it. First of all, you just reason. 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 want to wait until this AI becomes 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 obviously, if it wants to help me, it’s got to use my tools.

### Why Agents Still Need Software and Tools

You know, a lot of people would say, "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 a thought experiment. You could just sit there, enjoy a glass of whiskey, and think about all these things, and it would become completely obvious.

If I were to create the most amazing agent that we can imagine in the next 10 years, let's say it would be a humanoid robot, then 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? 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, beam microwaves out of its fingers? Or is it more likely just to use a microwave? 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.

I just described, in fact, almost all of the properties of OpenClaw: that it's gonna use tools, that it's gonna access files, it's gonna be able to do research. It has an I/O subsystem. When you're done reasoning through 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.

Then 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. First of all, we needed Claude and GPT and all of these models to reach a level of capability. Their innovation, their breakthroughs, and their continued advances were really important.

And then, of course, somebody had to create an open source project that was sufficiently robust and sufficiently complete that we can all put to work. I think OpenClaw did for agentic systems what ChatGPT did for generative systems. I just think it's a very big deal.

> 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.

> Because consumers could reach it.

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.

> Yeah, no doubt.

Part of it is memes and the — because 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.

> 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.

> And NVIDIA put forward NemoClaw.

> Yep, exactly.

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

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. So we're going to try to do our best to help OpenClaw become a better claw.

> 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

### Power, Supply Chains, and the Next Bottlenecks

future, what do you think might be the blockers now that it's clear that agents will be everywhere? So obviously we're going to need compute. So what is going to be the blocker for that scaling?

- 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. You know, the 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.

#### Power and efficiency

- 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.
- We should also get more power.

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?

#### Supply chain pressure

- 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. 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. 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."

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, you know, and, and barely by supercomputers that this was going to be a mainstream memory for data centers in the future. And at first it sounded ridiculous, but several of the CEOs believed me and decided to invest in building HBM memories.

Another memory was rather odd to put into a data center, is 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, these are 45-year companies. And so, you know, I... That's part of my job, is to inform and shape, inspire, you know.

### From Racks to the Full Semiconductor Ecosystem

Inspiring NVIDIA, the different engineers of the company, you are *manifesting the supply chain of the future*. You are having conversations with TSMC, with ASML.

> Upstream, downstream.

> Upstream, downstream.

That is the thing. GEV, Caterpillar. Yeah, that is downstream from us. Yeah, yeah, there you go. Yeah, the whole thing.

There is 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.

Exactly: the deep science, the deep engineering, the incredible manufacturing. 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.

It is interesting that you do not list that as the thing that keeps you up at night in the list of blockers.

But I am doing all the things necessary to...

Okay.

... yeah, see? I can go to sleep because I checked it off. I said, okay, you know, I go, yeah, I can go to sleep and I go, well, let us see, what re- let us reason about this. What is important for us? Um, because okay, let us reason about this. Because we changed the system architecture from the original DGX-I that you remembered to NVLink-72 rack scale computing...

What is going to... 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. If you are doing that, you also have to recognize you are going to move one... And if your total footprint of whatever data center you are going to build, let us say you would like to have 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 going to need a gigawatt of power. And so we are going to need the supply chain to increase the amount of power it has to build, test, to build and test the supercomputers in the supply chain before I ship it.

Well, NVLink-72 literally builds supercomputers in the supply chain and ships them two, three tons at a time per rack. It used to be—they used to come in parts and we used to assemble them inside the data center. But that is impossible now because NVLink-72 is so dense. And so that is an example. And I would have to go and to, you know, I have... Fly into the supply chain, go meet my partners saying, "Hey," I said, "guess what? So here is what I am going to do with... This is the way we used to build"

### Data Centers, Inference Demand, and Grid Limits

#### Building for inference and future demand

“**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 coming. The inflection point for inference is coming. It is going to be a big market. So the process begins with explaining to partners what is happening, why it is going to happen, and then asking them to make several billion dollars of capital investments each.

Because they trust him, and because he is very respectful of them, he gives them every opportunity to question him. He spends time explaining things to people. He reasons about it, draws pictures, and reasons about it from first principles. By the time he is done with them, they know what to do.

So a lot of it is about relationships and building a shared view of the future.

#### Supply-chain bottlenecks

When asked about bottlenecks in the supply chain, including ASML’s EUV tooling and TSMC’s CoWoS packaging scale-up, the answer was simple: no.

The explanation was that he had told them what he needed, they understood what he needed, they told him what they were going to do, and he believes them.

That matters especially because the company is not only growing incredibly fast, it is accelerating its growth. The concern was whether everyone in the supply chain would have to scale up, and whether conversations were happening about how to scale faster. The answer remained: no.

#### Rethinking power and the grid

The discussion then turned to energy. One area he would love to talk about and get the message out on is the power grid.

Our power grid is designed for the worst-case condition with some margin. Yet 99% of the time we are nowhere near that worst case. The worst case is a few days in the winter, a few days in the summer, and extreme weather. Most of the time we are nowhere near the worst case condition, and we are probably running around, call it 60% of peak.

So 99% of the time, the power grid has excess power, and it is just sitting idle. It has to be there, sitting idle, because when the time comes, hospitals have to be powered, infrastructure has to be powered, airports have to run, and so on.

The question is whether we can help them understand, create contractual agreements, and design computer architecture systems and data centers so that when society needs maximum power for infrastructure, the data centers would get less. That would only be in a very rare instance anyway.

During those times, the data centers could have a backup generator for that small part of it, or the computers could shift the workload somewhere else, or the computers could just run slower. They could degrade performance, reduce power consumption, and provide a slightly longer latency response when someone asks for an answer.

That way of using computers and building data centers, instead of expecting 100% uptime, would put a lot of pressure on the grid to be able to do something it was never meant to do. The point is not to make the utilities increase from their maximum; it is to use their excess. It is just sitting there.

#### Why the grid problem persists

When asked why this is not talked about enough, and whether the blocker is regulation or bureaucracy, the answer was that it is 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. The end customer expects perfection. In order to deliver that perfection, you need a combination of backup generators and grid power suppliers to deliver on perfection. Everybody has to have six nines.

The first thing, then, is to make sure that all of the customers, the CEOs, and the customers realize what they are asking for. Right now, someone in the data center operations team is disconnected from the CEO. The CEO probably does not know this. He said he is going to talk to all the CEOs.

The CEOs are probably not paying any attention to the contracts that are being signed. Everybody wants to sign the best contract, of course. The scene of two contract negotiators sitting there negotiating these multi-year contracts was described as easy to imagine, with both sides wanting the best contract.

As a result, the cloud service providers then have to go down to the utilities, and they expect six nines.

#### Data centers that gracefully degrade

The second thing is that we have to build data centers that gracefully degrade.

If the power or the utility or the grid says, “Listen, we’re going to have to back you down to about 80%,” the response should be, “That’s no problem at all.” The workload would be moved around. Data would never be lost. The computing rate could be reduced and less energy used. The quality of service would degrade a little bit.

For critical workloads, that work would be shifted somewhere else right away so that the problem would not exist. Then whichever data center still has 100% uptime could handle it.

When asked how difficult that smart, dynamic allocation of power in a data center is as an engineering problem, the answer was: as soon as you can specify it, you can engineer it. “Beautifully put.” So long as it obeys the laws of physics on first principles, it is good.

#### Utilities as part of the solution

The third thing is that the utilities also need to recognize that this is an opportunity.

Instead of saying, “Look, it’s going to take me five years to increase my grid capability,” they could say, if you are willing to take power at this level of guarantee, I can make it available for you next month and at this price.

If utilities offered more segments of power delivery promises, then everybody would figure out what to do with it. There is just way too much waste in the grid right now. It should be pursued.

#### Elon, xAI, and Colossus

The discussion then shifted to Elon and xAI’s accomplishment in Memphis: building the Colossus supercomputer in record time, in just four months. It is now at 200,000 GPUs and growing very quickly.

Asked what about Elon’s approach is instructive to data center creators more broadly, and what enabled that kind of accomplishment in engineering, construction management, and everything else, the response was that Elon is deep in so many different topics. Yet he is also a really good systems thinker.

He is able to think through multiple disciplines, and he obviously pushes things, questions everything, and asks, first of all, whether it is necessary.

### Minimalism, Urgency, and System-Level Thinking

#### Questioning everything

Number two, does it have to be done this way? And then numbers, does it have to take this long? He has the ability to question everything, to the point where everything is down to its minimal amount that is necessary. You cannot take anything else out, and yet the necessary capabilities of the product remain. He is as minimalist as you could possibly imagine, and he does it at a system scale.

I also love the fact that he is represented. He is present at the point of action. He will just go there. If there is a problem, he will just go there and then say, "Show me the problem." When you do all of this in combination, you overcome a lot of previous, "This is just the way we do it," and "I am waiting for them," and all of those excuses people make.

The last thing is that when you act personally with so much urgency, it causes everybody else to act with urgency. 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 is the top priority of everybody else’s projects. He does that by demonstrating it.

#### Asking for faster

I have been in a bunch of those meetings. It is fun to watch, because really not enough people ask the question, "Okay, so, can this be done a lot faster, and how? Why does it have to take this long?"

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 is working with an engineer on the ground that is doing that task, and he is just trying to understand what that process looks like so it can be less error-prone.

Just building up that intuition from every single task involved in putting together a data center, 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" and remove all possible blockers.

#### Co-design and systems engineering

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

Well, first of all, co-design is an ultimate systems engineering problem. And so we approach the work that we do from that first, from that

### The Physics Mindset Behind “Speed of Light”

The other thing that we do is a philosophy, a thought, a state of mind, a method that I started 30 years ago, and it is called the **speed of light**.

The speed of light is not just about speed. It is my shorthand for what the limit of physics can do. So every single thing we do is compared against the speed of light: memory speed, math speed, power, cost, time, effort, number of people, manufacturing cycle time. 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.

Then, when you consider them together, you have to make compromises, because a system that achieves extremely low latency versus a cheap system that achieves very high throughput are architected fundamentally differently. But you want to know what the speed of light of a system that achieves high throughput is, what the speed of light of a system that achieves low latency is. Then, when you think about the total system, you can make trade-offs.

So I force everybody to think about the first principles, the limits, the physical limits before we do anything. And we test everything against that. That is a good frame of mind.

I do not love the other methods, which is continuous improvement. The problem with continuous improvement is that, first of all, you should engineer something from first principles at the speed, with speed of light thinking, limited only by physical limits and physics limits. After that, of course, you would improve it over time.

But I do not 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.” I would rather strip it all back to zero and say, “First of all, explain to me why 74 days in the first place. And let’s think about what’s possible today. And if I were to build it completely from scratch, how long would it take?”

Oftentimes, you would 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 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, the conversation from 74 to six is surprisingly much more effective.

#### Simplicity versus complexity

> In such incredibly complex systems that you're working with, is simplicity sometimes a good heuristic to reach for?

I mean, the Vera Rubin pod that you announced is just incredible. We are 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 is all just one pod.

> That’s just one pod.

Yes, that is just one pod.

And even the NVL72 rack alone is 1.3 million components, 1,300 chips, 4,000 pods crammed into a single 19-inch-wide rack.

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

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

The phrase that I use most often is: we need things to be **as complex as necessary, but as simple as possible**. So the question is whether all that complexity is necessary. We ought to test for that. We have got to challenge that. And then after that, everything else above it is gratuitous.

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

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

Yes, the engineering teams, I mean, I do not know. It is not a competition, but if it were like an Olympics of engineering teams, TSMC does incredible engineering. Like I said,

### China’s Rise as a Deep-Tech Powerhouse

ASML at every scale, but NVIDIA is going to give them a run for their money. Just incredible, incredible teams.

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

And have to work together. And report directly to you. This is wonderful.

You recently traveled to China, so it is interesting to ask: China has been incredibly successful in building up its technology sector. What do you understand about how China has been 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?

A whole bunch of reasons for that. Well, first of all, 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 are 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. 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 as a result, they have insane competition internally. And, you know, what remains is an incredible company.

They also have a social culture 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. 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 so 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 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 insane competition. Among comp- 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.

### Open Source, Engineering Culture, and TSMC

and they showed up at precisely the time when technology is going through that exponential.

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

> It's a builder nation.

> It's a builder nation.

> 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.

#### Open source and the AI revolution

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.

> Love it, yeah.

And thank you for releasing open source Nemotron 3 Super, which you can also use inside Perplexity to look stuff up. Now, which is a 120 billion parameter open weight MoE model. What's your vision with open source? So you mentioned China 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?

First, if we're going to be a great AI computing company, we have to understand how AI models are evolving. One of the things that I love about Nemotron 3 is that it's not just a pure transformer model, it's transformer and SSMs. And we were early in developing 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 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. 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, the 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, every country to be able to join the AI revolution.

There is a third reason, which is 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. Maybe it's biology or chemistry or the laws of physics or 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 want to make sure every car company has access to great models. We don't discover drugs, but I want to 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 recognizing that AI is not just language, that AI is really broad, that we want to engage everybody into the world of AI, and then also co-design of AI.

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

> 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.

> It's really, it's really incredible.

#### TSMC culture and trust

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?

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, I don't mean just the transistor, the metallization systems, the packaging, the 3D packaging, the silicon photonics, all of the technology that they have. That technology is really what makes the company special. Their technology makes the company special.

But their ability to orchestrate the demands, the dynamic demands of hundreds of companies in the world as they're moving up, shifting out, increasing, decreasing, pushing out, pulling in, changing from customer to customer, wafer starting, wafer stopping, emergency wafer starts, 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 work, they take their promises seriously. They, when your wafer, because they know that they're helping you run your company, when the wafers, when the wafers were promised to show up, the wafers show up, 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. There are a lot of companies who are 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 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 intangible called trust. I trust them to put my company on top of them. That's a very big deal.

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.

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.

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?

Story is true. I didn't dismiss it. But I was deeply honored and, of course, 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 executive and business and personal friend that I've had in my life. And for him to ask is, I was humbled and really honored.

### Turning CUDA Into an AI Factory Moat

But the work that I’m doing here is really important, and I’ve seen, in my mind’s eye, what NVIDIA was going to be and what the impact that we could have. It was really important work, and it’s my responsibility — my sole responsibility — to make this happen. So I declined it, not because it wasn’t an incredible offer. It’s an unbelievable offer, but I simply couldn’t take it.

> I think 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 to accomplish this kind of complexity.

Yeah. Yeah, no doubt.

> So now I can help both companies.

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

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. Twenty 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. The reason for that is because it’s never been just about the technology. The technology, of course, was incredibly visionary. But it’s the fact that the company was dedicated to it, stuck with it, and expanded its reach.

It wasn’t three people that made CUDA successful. It was 43,000 people that made CUDA successful, 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. So the install base is the number one most important advantage.

When you amplify that install base 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. That velocity combined with the install base... in the developer’s mind, you just go now and take the developer’s perspective. If I support CUDA, tomorrow it’ll be 10 times better. I just have to wait six months on average. 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 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. 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. 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. The fact that you have this one architecture and the ecosystem is so broad, it basically covers every single industry in the world.

#### The AI factory as compute

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

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 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. 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. Ten thousand 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.

> 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 collection of racks, so pods, not individual chips.

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.

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.

Cooling issues is not easy.

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

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. 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.

Those satellites have really high-resolution imaging systems, and they’re sweeping the Earth continuously now. And you want centimeter-scale imaging that is done continuously for the world, so that you’ll basically have real-time telemetry of everything. You don’t want to beam that back down to Earth. It’s just petabytes and petabytes of data. You’ve got to just do AI right there at the edge, throw away everything you don’t need, everything you’ve seen before, everything that didn’t change, and then just keep the stuff that you need. So AI had to be done at the edge.

Obviously, we have a 24/7 solar if we put it at the polars. And, you know, there’s no conduction, no convection. So you’re pretty much just radiation.

### NVIDIA’s Growth, Economics, and Space Ambitions

You know, space is big. I guess we are just going to put big, giant radiators out there.

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

Jensen Huang said he is much more practical. He looks first for where his next bucket of opportunities are. Meanwhile, he is cultivating space, sending engineers to work on the problem. They are starting to learn a lot about it: how to deal with radiation, how to deal with degrading performance, how to deal with continuous testing and attestation of defects, how to deal with redundancy, and how to degrade gracefully and things like that. He added that software matters too, including how to think about software and redundancy and performance out in space. The goal, in his words, is to make it so that the computer never breaks, it just gets slower.

He said they could start doing a lot of engineering exploration upfront. But in the meantime, his favorite answer is to eliminate waste. There is all that idle power, and he wants to evacuate it as fast as possible.

> Yeah. There is a lot of low-hanging fruit here on Earth that we can utilize for the AI scaling.

A quick pause followed for sponsors, with a reminder to check them out in the description at lexfridman.com/sponsors. The sponsors mentioned were 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 for businesses, including calls, texts, and contacts.

Back in the conversation, Lex asked whether NVIDIA may one day be worth 10 trillion dollars, and framed it another way: what does the future of the world look like where that is true?

Jensen answered that NVIDIA’s growth is extremely likely and, in his mind, inevitable. He said NVIDIA is the largest computer company in history, and that alone should prompt the question why. He gave two foundational technical reasons.

First, computing changed from a retrieval-based, file-retrieval system to a generative one. Almost everything used to be a file: we pre-write something, pre-record something, draw something, put it on the web, put it in a file. Then a recommender system or some smart filter figures out what to retrieve for you. In that sense, humans were a pre-recording, file-retrieving system. That was largely what a computer was.

Now, AI computers are contextually aware, which means they have to process and generate tokens in real time. So the world moved from retrieval-based computing to generative-based computing. That means a lot more processing is needed in the new world than in the old world. The old world needed a lot of storage; the new world needs a lot of computation. He said computing has fundamentally changed, and the way computing is done has changed with it.

The only thing that would cause this to reverse would be if this way of computing, this way of generating information that is contextually relevant, situationally aware, and grounded on new insight before it generates information, turned out not to be effective. He said that over the last 10 to 15 years working on deep learning, if at any single moment he had concluded, “You know what? This is not going to work out. I think this is a dead end,” or “It is not going to scale, it is not going to solve this modality, not going to be used in this application,” he would feel very differently. But he said the last five years have given him more confidence than the previous ten years.

The second idea is that because computers used to be storage systems, they were largely warehouses. Now we are building factories. Warehouses do not make much money. Factories directly correlate with company revenues.

So the computer changed in two ways. Not only did it change how it works, its purpose in the world changed. It is no longer a computer, he said, it is a factory. It is a factory used for generating revenues.

He said we are now seeing that this factory is not only generating products, commodities that people want to consume, but that the commodities are so interesting and so valuable to so many different audiences that tokens are starting to segment, like iPhones. You have free tokens, premium tokens, and several tokens in the middle.

Intelligence, he said, turns out to be a scalable product. There are extremely high-intelligence products, tokens that can be used for specialized things, and people will be willing to pay. The idea that somebody is willing to pay $1000 per million tokens is just around the corner. It is not if, only when.

So now, he said, we are seeing that the commodity this factory makes is actually valuable, revenue-generating, and profit-generating. The question becomes how many of these factories the world needs, how many tokens the world needs, how much society is willing to pay for these tokens, and what happens to the world economy if productivity improves so substantially. Are we going to discover new drugs, new products, new services?

Taken together, he said he is absolutely certain that the world’s GDP is going to accelerate in growth. He is absolutely certain that the percentage of that GDP used for computation will be 100 times more than in the past, because it is no longer a storage unit. It is a product-generation unit.

Looking at NVIDIA in that context, and backing into what NVIDIA does and how much of that new economics and new industry it would have to benefit from, he said they are going to be a lot, lot bigger.

Then he asked whether it is possible for NVIDIA to be a $3 trillion revenues company in the near future. The answer, he said, is of course yes. The reason is that it is not limited by any physical limits. He does not see anything that says $3 trillion is not possible.

He noted that NVIDIA’s supply chain burden is shared by 200 companies, and that their scaling happens on the backs of, and with the partnership of, this ecosystem. The question is whether they have the energy to do it. He said surely they will have the energy to do it.

So all of these things combined, that number is just a number. He still remembers NVIDIA crossing a billion dollars for the first time, and being reminded of a CEO who told him, “Jensen, it is theoretically impossible for a fabless semiconductor company to exceed a billion dollars.” He said he would not bore the listener with why, but the end of that story is still hanging there.

### From Tokens to an Agentic Computing Future

Of course, it is illogical, and there is a lot of evidence that we are 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 first-principled thinking.

The simple way to think about that is: what is it that we make, and how large is the opportunity that we can create? Now, NVIDIA is not in the market share business. Almost everything that I just talked about does not exist. That is the hard part. If NVIDIA were a $10 billion company trying to take market share, then it is easy to see for shareholders that, oh, yeah, if they could just take 10% share, they could be this much larger. But it is hard for people to imagine how large we could be because there is nobody I could take share from. You know?

And so I think that that is one of the challenges for the world: the imagination of the future. But I have plenty of time, and I will keep reasoning about it, and I will keep talking about it, and every single GTC will become more and more real. And then more and more people will talk about it, and one of these days, we will get there. But I am 100% sure we will get there.

#### Token factories and value

Yeah, this view of token factories essentially, this token per second per watt, and every token having value. Like, it is an actual thing that brings value, and it brings different kinds of value, different amounts of value to different people with value. That is 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 is very easy, first-principles, to imagine a future, given all the potential things that AI can solve, that you are going to need an exponential number more of token factories.

#### The iPhone of tokens

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

“What do you call it? Wait, are you saying OpenClaw’s iPhone? That is interesting.”

“Agents.”

“Yeah, agents. True.”

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

“That says something.”

“Yep, there is no question OpenClaw is the iPhone of tokens.”

#### Coding by talking

“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. Um, I mean, I am embarrassed to admit that on the way here in the airport, I have ... It is the first time I have done this in public. I was programming, quote unquote, by talking to my laptop. And I was embarrassed because I was pretending like I am talking to a human colleague. Uh, I am not sure how I feel about the future where everybody ... is walking around talking to their AI, but it is such an”

### Pressure, Responsiveness, and Mental Resilience

efficient way to get stuff done. 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 they're the person who's gonna be chatting with them, texting them most, is their claws or lobster.

> 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?

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, 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, 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. 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 it that we're doing? What is it causing? What's the impact that has on other people benefit, you know, positively or even, 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 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 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 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?

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

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

And there—

All the time.

... you just break down the problem into pieces?

Yeah. Yeah.

See what you could do about it?

And part of it is forgetting. One of the most important attributes of AI learning, as you know, is 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 want to—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. 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, so, 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, is you, you're attracted to the next shiny light, the next future, you know, the next opportunity, the next, "Okay, that's behind us. Let's—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. 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, so I do a lot of my

### Optimism, Grit, and Building Through Hardship

He says a lot of things publicly that seem sensible at the time or funny at the time, and mostly it is just because it is funny to him at the time. Then, when he reflects on it later, it is less funny. Still, the broader point remains: you let yourself be pulled by the light of the future, forget the past, and keep working toward that.

> “That’s right.”

He points to the famous line about NVIDIA: if he had known how hard it would be to build NVIDIA, and that it turned out to be a million times harder than he anticipated, he would not have done it.

> “Yeah, right.”

But he argues that this is probably true about everything worth doing. That is exactly what he was trying to explain: there is an incredible superpower in having the mind of a child. When he looks at something, almost everything, his first thought is, **“How hard can it be?”**

That mindset matters because otherwise you see only the scale of the challenge. Nobody has ever done it. It looks gigantic. It is going to cost hundreds of billions of dollars. It is going to take all this. And still the response is: **How hard could it be? How hard could it be?** You have to get yourself into that state of mind.

You do not want to over-simulate everything in advance — all the setbacks, all the trials and tribulations, all the disappointments. You do not want to know that ahead of time. You want to go into a new experience thinking it is going to be perfect, it is going to be great, it is going to be incredibly fun.

Then, while you are there, you need endurance and grit, because the setbacks are going to happen. They are going to surprise you. The disappointments are going to surprise you. The embarrassments are going to surprise you. The humiliations are going to surprise you. When that happens, you cannot stay stuck in it. You have to turn on the other part: forget about it, move on, keep moving.

He adds that to the extent his assumptions about the future — and why the future is going to manifest — do not change materially, then he should expect the output not to change. His simulated output of the future is still going to happen. And if it is still going to happen, he is still going to go after it.

He believes that this combination depends on two or three human characteristics: the ability to go into an experience with a fresh mind, and then the ability to endure when reality does not match the simulation.

### Humility, Curiosity, and Constant Reassessment

The ability to forget the setbacks, the ability to believe in yourself, to believe what you believe, and stay true to that belief, while constantly reevaluating: Jensen Huang says that combination of three, four, five things is really important for resilience. He says he is fortunate that whatever life experiences led to this have given him those four or five things.

He describes himself as always curious and always learning. He says he is always learning from everybody, always asking questions, and because he is humble about everything, he is always thinking, *“Gosh, they did that so nicely. They did that so wonderfully.”* He wonders what they are thinking through and how they do it. In that sense, he says, he is simulating everybody. In a lot of ways, he says he is emulating almost everybody he watches. He is empathetic toward everything they do that he is observing and respecting, and so he is constantly learning.

> **Lex Fridman:** You are 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.

Huang answers that surprisingly, no, and he would actually go the other way. Because he does so much of his work publicly, when he is wrong, pretty much everybody sees it.

> **Lex Fridman:** You get humbled. Fair enough.

Huang says that when he is wrong, when he is wrong or it did not turn out that way, or most of the things he says outside, he is fairly certain about them. The reason is that it is going to impact somebody else, and he wants to be quite concerned about that and quite circumspect about that. For the stuff he is reasoning about inside a meeting, a lot of things could turn out differently. But that never stops him from reasoning.

He says that the way he manages and leads, he is constantly reasoning in front of people. Even when he is talking to Lex, Lex can kind of see him reasoning through things. He wants to make sure that people understand what he is saying, not because he has told them, but because he is so humble about what he is about to tell them that he shows them the steps he got there. Then they can decide whether they believe what he said in the end.

He says he is doing that all day long in meetings. With all of his employees, he is constantly reasoning through, *“Let me tell you what, how I see it.”* Then he reasons through it. That gives everybody the opportunity to intercept and say, *“I disagree with that part.”* He says the nice thing about reasoning through things and letting people interact with

### Reasoning in Public and the Value of Tools

It is that they do not 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:** 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 makes you close, closes you down a bit.

> **Jensen:** Mm-hmm. Yeah.

> **Lex:** And I think to maintain-

> **Jensen:** Yeah. Tolerance for embarrassment, I think is...

> **Lex:** 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:** Yeah. Well, you know. They knew that recently my first job was, you know, cleaning toilets, so.

> **Lex:** I'm glad you maintained that same spirit of Denny's, the work. I mean, that, that was beautiful. Your whole journey from, starting from Denny's is a beautiful one.

#### Video games and NVIDIA

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:** 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 then in beginning is just, you know, playing Call of Duty, you know? You know, Fortnite. And then later they're using CUDA, and then later they're using NVIDIA and, you know, Blender and Dassault and Autodesk.

> **Lex:** 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."

> **Jensen:** Yeah, exactly.

> **Lex:** It's like-

> **Jensen:** Exactly.

> **Lex:** You know, there's more to it, but, yeah, yeah, people really love the ... It really brought a lot of joy to a lot of people. The, the, 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? Uh, I guess people, the gamers online were concerned that it makes games look like AI slop. Uh, what do you think of this drama?

> **Jensen:** Yeah. Uh, 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, um, looks similar and they're all beautiful, and I can... So I can... 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, maintains 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.

Now, the question is, the question about enhancing, DLSS 5 also lets, because it's, 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, you know, so you can give it even an example. And it would generate in the style of that, all consistent with the artistry, you know, 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. 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:** I think people are very sensitive to human faces. And we're now living in this moment, which I think 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 ...

> **Jensen:** Yeah, that's right.

> **Lex:** ... it's wonderful.

> **Jensen:** 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 the gamers might also appreciate that, 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 and more tools to express their art. And so this is just yet more, one more tool, and they get to decide what to use.

#### Greatest games ever made

> **Lex:** Ridiculous question. What do you think is the greatest or most influential game ever made? Maybe from NVIDIA's perspective?

> **Jensen:** Doom.

> **Lex:** Doom, unquestionably. That was the start of the 3D.

> **Jensen:** 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, 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:** And then there's games more recently, I mean, Cyberpunk 2077, really nice GPU-accelerated graphics. Like-

> **Jensen:** Fully ray traced.

> **Lex:** 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-

> **Jensen:** We love mods.

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

> **Jensen:** That's right.

> **Lex:** ... I do that all the time. One of my favorite things is just walk across Skyrim.

> **Jensen:** Uh, we created this thing called RTX Mod. Yeah, it's a modding tool.

> **Lex:** Awesome.

> **Jensen:** It allows the community to inject the latest technology into an old game.

Of course, what makes a great video game is not just graphics, it's also story and character development, but 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

### AGI Timelines and the Future of Work

The question rests on your definition of AGI, so let me ask you about possible timelines here. Let’s use this ridiculous definition, perhaps, of what AGI is: an AI system that is able to essentially do your job — run, start, grow, and run a successful technology company that’s worth more than a billion dollars.

> A good one or a one?

No. It has to be worth more than a billion, more, more than a billion dollars. You know how hard it is to do all those components. So how far are we away from that? We’re talking about OpenClaw that does all the incredibly complex stuff 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?

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

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

Possible, and the reason for that is this. You said a billion, and you didn’t say forever. For example, it is not out of the question that a Claw 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. 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 OpenClaw could generate today.

> Interesting. Achieve virality and monetize that virality.

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?

> You’re gonna get a lot of people excited with that statement. It’s like, what do you mean? I can just launch an agent and make a lot of money.

Well, by the way, it’s happening right now, right? When you go to China, you’re gonna see a whole bunch of people teaching their, getting their Claws to try to go out and look for jobs and do work, make money. And 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 feeds your little Tamagotchi or something like that, and it became, 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. And then the one part that I will, I won’t do, and I want to 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

### Coding, Changing Jobs, and Expanding Professions

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 have changed continuously in the last 34 years, and sometimes quite dramatically, over the course of a couple, two, three years.

And the one story that I really want to 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. Because computer vision was going to achieve superhuman levels, and it did. CV—computer vision—was superhuman in 2019, 20, maybe a little bit later, 2020? 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. And because we're able to study scans at so much faster now, you could study more scans, you could diagnose better, you could, you could inpatient 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. Solving problems, working as a team, diagnosing problems, evaluating the result, looking for new problems to solve, innovation, connecting dots. None of that stuff is gonna go away.

> 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?

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. 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. The services I can bring to my clients if I were a plumber, completely go berserk.

> And 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.

That's right, exactly.

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 languages for large software systems?

That's right.

And the reason for that, Lex, and as you're saying for the audience, I think 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 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 enable 43,000 amazing people to make it even better than I imagined.

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.

> But just to linger on it outside of coding, I think a lot of people, rightfully so,

### AI Anxiety, Compassion, and Human Impact

People are worried about their jobs, and there is a lot of anxiety about jobs, especially in the white-collar sector. No one really knows what to do when tumultuous times arrive with automation and new technology. But first of all, there has to be compassion, and there has to be a sense of responsibility to feel the burden of what the actual suffering feels like for individual people and families that lose their jobs.

Whenever transformative technology arrives, especially technology like artificial intelligence, there is going to be a lot of pain, and there is no clear answer for what to do about that pain. The hope is that it creates much more opportunity for those same people, for the same kind of job, as the tooling evolves and makes them more productive and, hopefully, more fun to do, as it does in programming. He says he has been having so much fun programming, and has never had this much fun. The hope is that AI automates the boring parts and leaves the creative parts to human beings, the parts human beings are responsible for. But there is still going to be a lot of pain and suffering.

#### How to deal with the anxiety

The first recommendation is also how he deals with anxiety now. They had just talked about this earlier: enormous anxiety about the future, enormous anxiety about pressure, enormous anxiety about uncertainty. The first step is to break it down, then say to yourself: there are some things you can do something about, and there are some things you cannot do anything about. For the stuff you can do something about, reason about it and go do it.

If a company were hiring a new college graduate today and had a choice between two candidates, one with no clue what AI is and one who is expert in using AI, he would hire the one who is expert in using AI. The same would be true for an accountant, a marketing person, supply chain, customer service, a salesperson, business development, or a lawyer. He would hire the one who is expert in using AI.

So his advice is that every college student should use AI, and every teacher should encourage students to use AI. Every college student should graduate as an expert in AI. And everyone else should use AI too. If you are a carpenter, an electrician, a farmer, or a pharmacist, go use AI. See what it can do to transform your current job and elevate yourself. He would want to see what it could do to elevate his job so that he could be the innovator who revolutionizes the industry himself. That would be the first thing he would do.

He also says that the technology will dislocate and eliminate many tasks. If your job is the task, then you are very likely going to be disrupted. If your job’s purpose includes you performing certain tasks, then it is vital that you learn how to use AI to automate those tasks. There is a spectrum in between.

#### AI as a life coach

The beautiful thing about AI, especially chatbot versions, is that you can break down your anxiety by talking to it. He has recently found it incredible how much you can think through your life problems through AI, and he does not mean therapy problems. He means practical questions like: “I am 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 AI will give you a point-by-point plan. It is a great life coach, period.

One response is: “I do not know how to use AI,” and the AI says, “Well, let me show you.” That is very meta, but it is kind of incredible. People definitely should use it.

You cannot walk up to Excel and say, “I do not know how to use Excel.” You are done. That initial friction of being a beginner, of using a thing for the first time, is what AI has removed in all walks of life. You can literally ask about any single thing, “What are the first steps I need to take?” and that handholding removes the friction of all the experiences the world offers.

He had mentioned offline that the other person was going to China and Taiwan, and he says that is awesome. The response is to just ask: “Where do I go? How do I…” All of those questions are immediately answered, and it is beautiful.

When you go to Taiwan, just ask AI, “What are Jensen’s favorite restaurants in Taiwan?” and it will actually answer. The question is whether that is accurate. The answer is yes. It is all over Taiwan. He is a rockstar over there. They had also mentioned offline that their paths might cross, which would be really wonderful in computing.

> COMPUTEX. NVIDIA GTC Taiwan.

#### Consciousness and computation

The question comes up whether there are things about human nature or human consciousness that are fundamentally non-computational, something a chip, no matter how powerful, can never replicate. The answer is that he does not know whether a chip will ever get nervous. The conditions that cause anxiety or nervousness, or whatever emotion, are part of that question.

He believes AI will be able to recognize those things and understand them. He does not think his chips will feel them. And therefore, all of those feelings—anxiety, feeling, excitement, and so on—manifest in human performance. There can be extremely amazing human performance, athletic performance, average performance, or lesser-than-average performance. That entire spectrum of human performance comes out of exactly the same circumstances for different people, manifesting different outcomes and different performance.

He does not think anything they are building suggests that two different computers, presented with exactly the same context, would perform identically. Of course, they would produce statistically different outcomes, but not because they felt different.

The subjective experience is something truly special that humans feel. He says he was pretty nervous talking to you, and mentions the hope, the fear, the anxiety, and 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. How much pain we feel when our loved ones pass away. The whole thing.

It is very hard to think of AI, a computational device, being able to do that. But there are so many mysteries about the whole thing that remain to be uncovered that he is open to being surprised. He has been surprised a lot over the past few months and few years. Scaling can create some incredible miracles in the space of intelligence, and it has been truly marvelous to watch, so he is open to surprise.

And it is just really important to break down what is...

### Intelligence, Mortality, and Human Meaning

Intelligence. You know, the word we use all the time, it is not a mysterious word. Intelligence has a meaning. It is a system that includes perception, understanding, reasoning, and the ability to plan. That loop is fundamentally what intelligence is. Intelligence is not one word that is exactly equal to humanity, and that is really important to separate. We have two words for that.

I do not over-fantasize about intelligence, and I do not over-romanticize about intelligence. Intelligence is, and people have heard me say it before, I actually think intelligence is a commodity. I am surrounded by intelligent people. I am surrounded by intelligent people more intelligent than I am in each one of the spaces that they are in. And yet, I have a role in that circle. It is actually kind of interesting. They are more educated than I am. They went to better schools than I did. They are deeper than, in any of the fields that they are in. All of them. I have 60 of them. They are all superhuman to me. And somehow, I am sitting in the middle orchestrating all 60 of them.

So you have to ask yourself: what is it about a dishwasher that allows that dishwasher to sit in the middle of superhumans? Does that make sense? But that is my point. My point is intelligence is a functional thing. Humanity is not a functionally specified thing. It is 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 want to help the audience understand, if I could give them one thing, is that intelligence is a word that we have elevated to a very high form over time.

> The word we should really elevate is humanity.

Character. Humanity.

> All those things.

Compassion, generosity, all of the things that you said just now, I believe those are superhuman powers. And now intelligence is going to be commoditized. Because we have spoken about it, the most important thing is your education. The most important thing is your education. Even when they said the most important thing is your education, when you went to school, there is more than just knowledge that you gained. Unfortunately, our society has put everything into one single word, and life is more than one word.

I am just telling you, my life would suggest that being lower on the intelligence curve than everybody around me does not change the fact that I am the most successful. And so I think that kind of is what I am trying to hopefully inspire everybody else with: do not let this democratization of intelligence, this commoditization of intelligence, cause you anxiety. You should be inspired by that.

> 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-

> That is exactly right.

> ... humans more powerful.

> That is exactly right.

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

I really do not want to 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; it suggests that it has been experienced by many people, just not one person. This is a once in a humanity experience, what I am going through. NVIDIA is one of the most consequential technology companies in history. We are doing very important work. I take it very seriously.

And so some of the practical things are, of course, things like how do we think about succession planning? And I am famous in saying that I do not believe in succession planning.

> Man.

And the reason for that is not because I am immortal. The reason for that is because if you are worried about succession planning, if you are 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.

That is the reason why I continuously reason about everything in front of my team. Every single meeting is about 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. Nothing I learn ever sits on my desk longer than, you know, a fraction of a second. I am passing that information, that knowl- oh my gosh, this is cool. Before I even finish learning all of it myself, I am already pointing it to somebody else. "Get on this. This is so cool. You are gonna wanna, you are gonna wanna learn this."

And so I am 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 is no long periods of suffering, you know? It is,

Well, from a fan perspective, given your enormous positive impact on civilization, of course, I hope you keep going. But also it is just fun to watch what NVIDIA is doing, you know. It is just the rate of innovation. And I am a huge fan of engineering. There is so much incredible engineering continuously being done by NVIDIA. It is just fun to watch. It is 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?

I have always had a great confidence in the kindness, the generosity, the compassion, the human capacity. I have always been extremely confident of that. Sometimes more so than I should. And I get taken advantage of, but it does not 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.

So I have complete confidence in the human capacity. I think the things that give me incredible hope are what I see as I extrapolate, what I see now is possible, and as I extrapolate, based on the things that we are doing, what will very likely happen. There are so many things that we want to solve. There are so many problems we want to solve. There are so many things that we want to build. There are so many good things that we want to do that are now within our reach, and within the reach of my lifetime.

### The Romance of Future Possibilities

You just can’t possibly not be romantic about that. You know what I’m saying?

> What an exciting time to be alive. Truly.

How can you not be romantic about that? The fact that there is a reasonable thing to expect the end of disease, a reasonable thing to expect pollution will be drastically reduced, and a reasonable thing to expect that traveling at the speed of light is actually in our future. And not for long distances, but short distances.

You know, 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. 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, you know, when the time comes, we’ll just send that at the speed of light, catch up with my robot.

> Oh, that’s brilliant.

For me, that’s sort of application-focused. But also, for me, the curiosity-maxing perspective, I just... all of those mysteries. There’s so much fascinating scientific questions there.

Understanding the biological machine is right around the corner. It’s not 10 years. It’s five years probably. And then your biological machine, the human mind, and cracking physics, theoretical physics, open. It’s so exciting. Explaining consciousness, that one would be awesome. 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.

> Thank you, Lex. I had a great time. And also, if I could just say one more thing.

> Yes.

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.

> It means the world. Thank you, Jensen.

> Thank you, Lex.

Thank you for listening to this conversation with Jensen Huang. To support this

### Closing Thoughts and Alan Kay’s Quote

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.