Jensen Huang

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likely this will be one of the largest industries in the world and um, uh, it'll take longer than a couple two, three years. It'll take less than 10. And so this will this will be our next $100 billion business.

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

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Turns out paralegals are growing like crazy. And the reason for that is because the backlog of lawsuits is really high, and now these law firms could get a lot more cases through. And in order to do so, you got to hire more people.

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The evidence would show that and it makes perfect sense that AI and automation is creating jobs everywhere. The narrative about AI destroying jobs is exactly backwards. AI eliminate tasks. AI automates tasks away. But it doesn't necessary doesn't necessarily eliminate jobs.

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

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we want we want to encourage everybody and every company to build their own AIs. And and and who knows what innovation will come from the fact that it's open source.

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We we kind of have to live in the future 5 to 10 years because it takes three or so years just to build a system, takes a couple years to ramp it up, and you're dealing and you would like them to be able to use the computer for 10 years after.

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we we kind of have coarse level uh recursive self-improvement already. And the fact that every time you use it, it improves the markdown files. Uh every time you use it, it updates its uh long-term memory.

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We don't need the the agents to be 100% accurate, 100% high quality in order for us to use it. It could, you know, literally be 80% and then we help it the rest of the way, or it could be 99% we help it the rest of the way. And so I I think controllability is probably the single biggest breakthrough that we need for agents at every single level.

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one of the most important things is systems understanding. Systems awareness, system design, system organization. Um, but systems thinking. And the reason for that is because most of the low-level things that that has to be done are going to be done agentically anyways.

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whatever it takes to fit the car to you whatever it takes to fit the organization to you, that's what you got to do. And the next CEO, whatever the personality is, they can figure it out.

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in a lot of ways I think a a CEO is in service of the company, in service of all the people that are working there. And you want to empower them with some insight. And so that's really where it's coming from. It's not so much a management technique, but a personality technique.

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the the breakthrough for us was realizing that AlexNet was not AlexNet. That AlexNet was an approach with deep deep learning that allows you to learn any function.

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You don't invest in companies, you invest in people.

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we realized early on uh that it's not about building a great chip, it's about accelerating an algorithm domain.

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one of the things I've always believed believed in is what makes great companies is a unique perspective about the world that you deeply believe in. It's not so much the technology, it's not so much uh the market even.

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the the big lesson is that for me is technology is changing all the time, and so long as you're able to confront the reality, so long as you are able to learn, the technology itself actually doesn't matter.

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Computing is not like that. There's a reason why the x86 still exists. There's a reason why ARM is so sticky. These ecosystems, these ecosystems are hard to replace. It costs an enormous amount of time and energy and most people don't want to do it.

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We have got to acknowledge that most of the advanc advances in AI came out of algorithm advances not just the raw hardware. Now if most advances came from algorithms and computer science and programming tell me that their army of AI researchers is not their fundamental advantage.

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And so you just have to first realize that chips exist in China. They manufacture 60% of the world's mainstream chips, maybe more. It's a very large industry for them. They have some of the world's greatest computer scientists.

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Pick your ASIC team where you can say I can bet the farm of I can bet my entire business that you will be here for me every single year. Your cost, your token cost will decrease by an order of magnitude every single year. I can count on it like I can count on the clock.

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Because it's it's a bad business practice. You you set your price. You set your price and then and then people decide to buy it or not. And and um uh there there I I understand that that others in the chip industry um uh change their prices when demand is higher. Uh but we just don't we just don't that's just never been a practice of ours.

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Nvidia would be the top of that list not to make it. You know, this is long before you, but Nvidia's graphics architecture was precisely wrong. It's not a little bit wrong. We created an architecture that was precisely wrong.

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the work that we do with building our our computing platform. If we don't if we don't do it, I genuinely believe it doesn't get done. If we didn't take the risk that we take, if we didn't build MVLink the way we built, if we didn't build the whole stack, if we didn't create the ecosystem the way we did it, if we didn't dedicate ourselves to 20 years of CUDA while losing money most of that time, if we didn't do it, nobody else would have done it.

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Nor nor did I I would say my mistake is I didn't deeply internalize that they they really had no other options that that that a VC would never put in 510 billion of investment into an AI lab with the with the hopes of it turning out to be anthropic. And so that was my miss.

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And so so I think the the ASIC margins are are incredibly good from what I can tell and and they believe it. They believe it so too. And so they're they're quite proud of their their incredible ASIC margins.

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Just because you're going to build an ASIC, you still have to build something better. than Nvidia. And it's not that easy building something better than Nvidia.

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anthropic is is an is a unique instance um and not a trend. Uh without an anthropic, why would there be any TPU growth at all? It's 100% anthropic. Without anthropic, why would there be any tranium growth at all? It's 100% anthropic.

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Nvidia's computing stack is the best performance per TCO in the world, bar none. Nobody can demonstrate to me that any single platform in the world today has better performance TCO ratio. Not one company.

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And so our expertise um helps our our our um uh our AI labs partners get another 2x out of their stack easily. Often times it's not unusual that we you know by the time that we're done optimizing their stack or optimizing a particular kernel their model sped up by 3x 2x 50%.

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TPUs like anything else is impacted by Moore's law. And we know that Moore's law is increasing about 25% per year. And so the only way to really get 10x leaps, 100x leaps, is to fundamentally change the algorithm and how it's computed every single year.

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because our computers are designed to be operated by other people, anyone who's an operator could buy our systems. Most of these homebuilt systems you have to be your own operator because it was never designed to be flexible enough for other people to operate.

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Our market reach is far greater than any any TPU can any ASA can possibly have. And so if you look at our position, uh we're the only company that that accelerates applications of all kinds.

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And so none of that my point is that none of the bottlenecks last longer than a couple 2 three years. None of them.

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You know, radiology is is going to be the first career to go. Nobody's the world's not going to need any more radiologists. Guess what? But we're short of radiologists.

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Now why are they willing to make the investments for me and not someone else and the reason for that is because they know that I have the capacity to buy it buy their supply and sell it through my downstream.

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I actually see the opposite of what people see I think the number of agents are going to grow exponentially. The number of tool users are going to grow exponentially and it's very likely that the number of instances of all these tools are going to skyrocket.

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my mental model of our company the input is electron the output is tokens that is in the middle Nvidia and our job is to to do as much as necessary as little as possible to enable that transformation to be done at incredible capabilities

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You know, the idea that somebody's willing to pay $1000 per million tokens is just around the corner. It's not if, it's only when.

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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.

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So you get this rapid, incredible great talent, rapid innovation because of open source and just, you know, the nature of friends, and, and insane competition. Among the company, what emerges is incredible stuff. And so this is the fastest innovating country in the world today

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I don't love the other methods, which is continuous improvement. The problem with continuous improvement, it… First of all, you should engineer something from first principles at the speed, you know, with speed of light thinking. Limit it only by physical limits, and physics limits. And after that, of course you would improve it over time.

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Now, the second thing is we have to build data centers that gracefully degrade.

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

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And I think OpenClaw did for agentic systems what ChatGPT did for generative systems. And I just think it's a very big deal.

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You know, a lot of people would say, "You know AI is gonna completely destroy software. We don't need software anymore. We don't even need tools anymore." That's ridiculous.

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And so this loop, this cycle, is gonna go on and on and on. It kinda comes down to basically intelligence is gonna scale by one thing, and that's compute.

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And so the next scaling law is the agentic scaling law. It's kind of like multiplying AI. Multiplying AI, we could spin off agents as fast as you want to spin off agents. And so, you know, I… You know, I have four scaling laws.

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that was always illogical to me because inference is thinking, and I think thinking is hard. Thinking is way harder than reading.

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The amount of data that we use to train models is going to continue to scale to the point where we're no longer limited… Training is no longer limited by… Data is now limited by compute. And the reason for that is most of the data is synthetic.

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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.