#494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution
Jensen Huang on Lex Fridman Podcast.
Passages
What is the hardest part of co-designing a system with that many complex components and design variables? Yeah, thanks for that question. So first of all, the reason why extreme co-design is necessary is because the problem no longer fits inside one computer to be accelerated by one GPU. The problem that you're trying to solve is you would like to go faster than the number of computers that you add. So you added 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... You have to break up the algorithm. You have to refactor it. You have to shard the pipeline. You have to shard the data. You have to shard the model. Now, all of a sudden, when you distribute the problem this way, not just scaling up the problem, but you're distributing the problem. then everything gets in the way. This is the Amdahl's law problem, where the amount of speed up you have for something depends on how much of the total workload it is. And so if computation represents 50% of the problem, and I sped up computation infinitely, like a million times, I only sped up the total workload by a factor of two. Now, all of a sudden, not only do you have to distribute the computation, you have to shard the pipeline somehow, you also have to solve the networking problem because you've got all of these computers are all connected together. Distributed computing at the scale that we do, 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 are a problem. It's just a massively complex computer science problem. And so we just got 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's scaling has slowed.
SPEAKER_02Check at the sourceThere's the first question, which is, what is extreme co-design? You were optimizing across the entire stack of software, from architectures to chips to systems to system software to the algorithms to the applications. That's one layer. The second thing that you and I just talked about goes beyond that. cpus and gpus and networking chips and scale up switches and scale out switches and then of course you got to include power and cooling and all of that because you know all these computers are extremely extremely power power hungry they do a lot of work and they're very energy efficient but they in aggregate still consume a lot of power and so that's one the first question is what is it The second question is, why is it? And we just spoke about the reason you want to distribute the workload so that you can exceed the benefit of just increasing the number of computers. And then the third question is, how is it? How do you do it? And that's kind of the miracle of this company. When you're designing a computer, you have to have an operating system of computers. When you're designing a company, you should first think about what is it that you want the company to produce? I see a lot of companies' organization charts, and they all look the same. Hamburger organization charts, software organization charts, and car company organization charts, they all look the same. And it doesn't make any sense to me. The goal of a company is to be the machinery, the mechanism, the system that produces the the output, and that output is the product that we'd like to create. It is also designed, the architecture of the company should reflect the environment by which it exists. It almost directly says what you should do with the organization. My direct staff is 60 people. You know, I don't have one-on-ones with them because it's impossible. You can't have 60 people on your staff if you're, you know, going to get work done.
SPEAKER_02Check at the sourceThere's experts in memory, there's experts in CPUs, there's experts in optical, GPUs and architecture, algorithms, design.
SPEAKER_02Check at the sourceAnd 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.
SPEAKER_02Check at the sourceYou 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. Something they could have contributed to, they didn't contribute to, I'm going to call them out. And so, hey, come on, let's get in here.
SPEAKER_02Check at the sourceI could reason through it just systematically. We started out as an accelerator company. But the problem with accelerators is that the application domain is too narrow. It has the benefit of being incredibly optimized for the job. 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, a very specific accelerator, we We always knew that 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. I connected two words together. there 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 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 the programmable pixel shader. So that was the first step towards programmability. You know, it was our first journey towards moving into the world of computing. The second thing that we did was we put FP32 into our shaders. That FP32 step, IEEE compatible FP32, was a huge step in the direction of 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. And which led us to create, put C on top of FP32, what we call CG. That CG path took us to eventually CUDA. 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.
SPEAKER_02Check at the sourceCan you explain that decision? That was the first... I would say that that was the first... The first strategic decision that is as close to an existential threat.
SPEAKER_02Check at the sourceYeah, it turned out to be a good decision. Here's the way it went. We invented this thing called CUDA, and it expanded the aperture of applications that we can accelerate with our accelerator. The question is, how do we attract developers to CUDA? Because a computing platform is all about developers. And developers... don't come to a computing platform just because, you know, it could perform something interesting. They come to a computing platform because the install base is large. Because a developer like anybody else wants to develop software that reaches a lot of people. So the install base is in fact the single most important part of an architecture. The architecture could attract enormous amounts of criticism. For example, No architecture has ever attracted more criticism than the x86 as a less than elegant architecture. But yet, it is the defining architecture of today. It gives you an example that in fact so many risk 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 is barely aesthetic. And so... Yet x86 survived. InstallBase is everything. InstallBase defines an architecture. Everything else is secondary. There were other architectures at the time. CUDA came out, OpenCL was here. There were several other competing architectures. But the decision that we made that was good was we said, hey, look, ultimately, it's about InstallBase, 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 would discover, and at the time, the PC was the primary computing vehicle. There was no cloud, and we could put a supercomputer in the hands of every researcher in school, every scientist, every engineer in school, every student in school, and eventually, something amazing will happen. Well, the problem was CUDA increased our cost of that gpu which is a consumer product so tremendously it it completely consumed all of the company's gross profit dollars and so at the time the company was probably you know worth i don't know at the time eight wasn't it like eight billion dollars or something six 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. You know, our market cap went down to like $1.5 billion. And so we were down there for a while, and we clawed our way back slowly. But we carried CUDA on GeForce. I always say that NVIDIA is the house that GeForce built. Because it was GeForce that took CUDA out to everybody. 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, you know, using PC components. And so that, you know, that's kind of how we got going.
SPEAKER_02Check at the sourceWell, I had to make it clear to the board what we're 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, you know, we increased our costs 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 can capture more margin. So you could reason your way into being able to afford this, but it still took a decade.
SPEAKER_02Check at the sourceWell, first of all, I'm informed by a lot of curiosity. At some point, there's a reasoning system that convinces me so clearly this outcome will happen. that this will happen. And so I believe it in my mind, and when I believe it in my mind, you know how it is, you manifest a future, and that future is so convincing, there's no way it won't happen. There's a lot of suffering in between, but you've got to believe what you believe.
SPEAKER_02Check at the sourceYeah. And, and you, you reason about how to get there. You reason about why it, it must exist. And, and, and, you know, I reason, we all reasoned here, the management team would reason about it. All the people that I, we spent a lot of time reasoning about it. The thing, the thing that the next part of it is probably a skill thing, which is you know, oftentimes in leadership, the leadership stays quiet or they learn about something and then they do some manifesto and it's a brand new year. And somehow at the end of the year, next year, we're going to have a brand new plan, big, huge layoff this way, big, huge organization change this way, new mission statement, brand new logos, you know, that kind of stuff. We've just never, I never do things that way. When I learned about something, And it's starting to influence how I think. I'll make it very clear to everybody near me that, you know, this is interesting. This is going to make a difference. This is going to impact that. And I reason about things step by step by step. Oftentimes I've already made up my mind, but I'll take every possible opportunity. external information, new insights, new discoveries, new engineering revelations, new milestones developed, I'll take those opportunities and I'll use it to shape everybody else's belief system. And I'm doing that literally every single day. I'm doing that with my board. I'm doing that with my management team. I'm doing that with my employees. I'm trying to shape their belief system such that when I come the day I say, hey, let's buy Mellanox. It's completely obvious to everybody that we absolutely should. On the day that I said, hey, guys, let's go all in on deep learning, and let me tell you why. I've already been laying down the bricks to different organizations inside the company. Every organization and everybody, many of the people might have heard everything. Most of the company hears, of course, pieces of it. And on the day that I announce it, everybody's kind of bought into many pieces of it. And in a lot of ways, I like to announce these things. And I imagine that the employees are kind of saying, you know, Jensen, what took you so long? And in fact, I've been shaping their belief system for some time. And therefore, leadership... Sometimes it looks like you're leading from behind, but you've been shaping there to the point where on the day that I declared it, 100% buy-in. But that's what you want. You want to bring everybody along. Otherwise, we announce something about deep learning and everybody goes, what are you talking about? You know, you announce something about let's go all in on this thing. And your management team, your board, your employees, your customers, they're kind of like, where's this coming from? You know, this is insane. And so GTC, in fact, if you go back in time, you look at the keynotes. I'm also shaping the belief system of my partners in the industry. And, and I'm using that to shape, you know, the belief system of my own employees. And, and, and so by the time that I announced something, like for example, we just now, we just announced Grok. We've been late. I've been talking about the stepping stones for two and a half years. You guys just go back and go, oh my gosh, they've been talking about it for two and a half years. And so I've been laying the foundation step by step by step. So when the time comes, you announce it, everybody's, you know, what took you so long?
SPEAKER_02Check at the sourceWe don't build computers. We actually don't build clouds. We don't, as it turns out, we're a computing platform company. And so nobody can buy anything from us. That's the weird thing. You know, we vertically... design, vertically integrate to design and optimize. But then we open up the entire platform at every single layer to be integrated into other companies' products and services and clouds and supercomputers and OEM computers. And so the amazing thing is I can't do what I do without having convinced them first. And so most of GTC is about manifesting a future that by the time that we... My product is ready. They're going... What took you so long?
SPEAKER_02Check at the sourceWell, we can go back and reflect on what people thought were blockers. So in the beginning, 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 smarter AI. And so that was pre-training. And Ilyas, Suskover, Ilyas said, we're out of data or something like that. Pre-training is over or something like that. The industry panicked. You know, that this is the end of AI. And of course, that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with. A lot of that data is probably gonna be synthetic. And that also confused people, you know? And what people don't realize is they've kind of forgotten that most of the data that we are training, that we teach each other with, inform each other with, is synthetic. You know, it's synthetic because it didn't come out of nature. You created it. I'm consuming it. I modify it, augment it. I regenerate it. Somebody else consumes it. And so we've now reached a level where AI is able to take ground truth, augment it, enhance it, synthetically generate an enormous amount of data. 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're no longer limited. Training is no longer limited by data. It's now limited by compute. And the reason for that is most of the data is synthetic. Then the next phase is test time. And I still remember people telling me that inference, oh yeah, that's easy. Pre-training, that's hard. These are giant systems that people are talking about. Inference must be easy. And so inference chips are going to be little tiny chips and they're not like NVIDIA's chips. Oh, those are going to be complicated and expensive and And in the future, inference is going to be the biggest market and it's going to be easy and we're going to commoditize and everybody can build their own chips. And that was always illogical to me because inference is thinking. And I think thinking is hard. Thinking is way harder than reading. You know, pre-training is just memorization and generalization, you know, and looking for patterns and relationships. You're reading versus thinking, reasoning, solving problems, taking un- 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 and trying different things. And That whole process of test time scaling inference is really about thinking. And it's about reasoning. It's about planning. It's about search. And so how could that possibly be compute-lite? And we were absolutely right about that. So test time scaling is intensely compute-intensive. Then the question is, okay, now we're at inference and we're at test time scaling. What's beyond that? Well, obviously, we have now created one agentic person. And that one agentic person has a large language model that we've now developed. But during test time, that agentic system goes off and does research and research. bangs on databases and it goes on and, you know, uses tools. And one of the most important things it does is spins off and spawns off a whole bunch of sub-agents, which means we're now creating large teams. It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself. And so the next scaling law is the agentic scaling law. It's kind of like multiplying the AI. Multiplying AI, we could spin off agents as fast as you want to spin off agents. And so, you know, you and 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, you know, and the agents and agentic systems, you know, put it out into the industry. And so this loop, the 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 it's computing.
SPEAKER_02Check at the sourceFor example... These AI model architectures are being invented about once every six months, right? And system architectures and hardware architectures kind of every three years. And so you need to anticipate what likely is going to happen, you know, two, three years from now. And there's a couple of 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 to the extent that we can, we try to get a sense of what are the challenges that people are experiencing.
SPEAKER_02Check at the sourceThat's right. You've 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, you know, on the one hand, an incredible accelerator. On the other hand, it's really flexible. And so that balance, incredible balance between specialization, otherwise we can't accelerate the CPU, versus generalization so that we can adapt with changing algorithms, that's really, really important. That's the reason why CUDA has been so resilient on the one hand, and yet we continue to enhance it. 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 mixture experts came out, that's the reason why we had MVLink 72 instead of MVLink 8. We could now take an entire 4 trillion, 10 trillion parameter model and put it in one computing domain as if it's running on one GPU. people probably didn't notice I said it but if you look at the architecture of the Grace Blackwell racks it was completely focused on doing one thing processing the LLM all of a sudden one year later you're looking at a Vera Rubin rack. It has storage accelerators. It has this incredible new CPU called Vera. It has Vera Rubin and MVLINK72 to run the LLMs. It also has this new additional rack called Grok. 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.
SPEAKER_02Check at the sourceNo, it's easier than that. You just reason about it. First of all, there's a 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. 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, right? A lot of people would say, AI is going to 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. And you could just sit there, enjoy a glass of whiskey, and think about all these things, and it would become completely obvious. Like if I were to create... The most amazing agent that we can imagine in the next 10 years, let's say it'd be a humanoid robot. If that humanoid robot were to be created, is it more likely that the humanoid robot comes into my house and uses the tools that I have to do the work that it needs to do? Or does this hand turn into a 10-pound hammer in one instance? turns into a scalpel in another instance. In order to boil water, it beams microwaves out of its fingers, Or is it more likely just to use the microwave? And the first time it goes up to the microwave, it probably doesn't know how to use it. But that's okay. It's connected to the internet. It reads the manual of this microwave, reads it, instantly becomes an expert. And so it uses it. And so I think the, I just described, in fact, almost all of the properties of open claw you know that it's going to use tools that it's going to access files it's going to be able to do research it has io subsystem and when you're done reasoning through it reasoning about it through it in that way um then you say oh my gosh the impact to the future computing is deeply profound and the reason for that is i think we've just reinvented the computer And then now you say, okay, when did we reason about that? When did we reason about open claw? If you take the open claw schematic that I used at GTC, you will 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 Cloud and GPT and all of these models to reach a level of capability. So their innovation and their breakthroughs and their continued advances was really important. And then, of course, somebody had to create an open source, you know, project that was sufficiently robust, you know, and sufficiently complete and that we can all put to work. And I think OpenClaw did for agentic systems what ChatGPT did for generative systems. And I just think it's a very big deal.
SPEAKER_02Check at the sourceYeah, we jumped on it right away, and we sent a bunch of security experts its way. And we did this thing called Open Shell. It's already been integrated into Open CLAW.
SPEAKER_02Check at the sourceWe give you two out of three rights. Agentic systems can access sensitive information, it can execute code, and it can communicate externally. We could keep things safe if we gave you two out of those three capabilities at any time, but not all three. And out of those two out of three capabilities, we also give you access control based on whatever rights that you're given by enterprise. And then we connect it to a policy engine that all these enterprises already have. And so we're going to try to do our best to help OpenClaw become a better claw.
SPEAKER_02Check at the sourcePower 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 going to keep on doing that through extreme co-design. So energy efficiency 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, our computer price is going up, but our token generation effectiveness is going up so much faster. The token cost is coming down. It's coming down an order of magnitude every year.
SPEAKER_02Check at the sourceAll 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... 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, we're CEOs of practically the entire IT industry upstream and practically the entire industry infrastructure industry downstream. And 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. And if you look at the CEOs of the DRAM industry, the number one DRAM in the world was DDR memory for CPUs in data centers. About three years ago, I was able to convince several of the CEOs that even though at the time, HBM memory was used quite scarcely and barely by supercomputers, that this was going to be a mainstream memory for data centers in the future. 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 45-year companies. And so that's part of my job is to inform and shape inspire, you know?
SPEAKER_02Check at the sourceExactly. The deep science... The deep engineering, the incredible manufacturing, and so much of the manufacturing is already robotics. But we have a couple of hundred suppliers that contribute the technology that goes into our 1.3 million component rack. Each rack is 1.3, one and a half million components. There are 200 suppliers across the Vera Rubin rack.
SPEAKER_02Check at the sourceBut I'm doing all the things necessary to see. I can go to sleep because I checked it off. I said, okay, I can go to sleep. I go, well, let's see. Let's reason about this. What's important for us? Okay, let's reason about this. Because we changed the system architecture from the original DGX1 that you remembered, to MV-Link 72 rack-scale computing. Mm-hmm. 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're doing that, you also have to recognize you're going to move... And if your total footprint of whatever data center you're going to build, let's 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... weak in the supply chain, the supercomputers are going to need a gigawatt of power. And so we're 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, MVLink 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's impossible now because NVLink 72 is so dense. And so that's an example. And I would have to go into, you know, I'd fly into the supply chain, go meet my partners and say, hey, I said, guess what? So here's what we're going to do with, this is the way we used to build our DGXs. We're going to build them this way. This is going to be so much better because we're going to need them for inference. The market for inference is, you know, coming. The inflection point for inference is coming. It's going to be a big market. And so I first explained to them what's going on, why it's going to happen. And then I, then I ask them to make several billion dollars of capital investments each. And because they, you know, they trust me and, and I, I I'm very respectful of them. And I, I give them every opportunity to question me, and I spend time to explain things to people, and I reason about it. I draw them pictures, and I reason about it in first principles. And by the time I'm done with them, there's not what to do.
SPEAKER_02Check at the sourceOkay. Because I told them what I needed. They understood what I need. They told me what they're going to go do. And I believe in what they're going to do.
SPEAKER_02Check at the sourcethat I would love us to talk about and just get the message out. You know, our power grid is designed for the worst case condition with some margin. Well, 99% of the time, we're nowhere near the worst case condition because the worst case condition is a few days in the winter, a few days in the summer, and extreme weather. Most of the time, we're nowhere near the worst case condition and we're probably running around, call it 60% of peak. And so 99% of the time, our power grid has excess power and they're just sitting idle. But they have to be there sitting idle because just in case, when the time comes, hospitals have to be powered and infrastructure has to be powered and airports have to run and so on and so forth. And so the question that I have is whether we could go and help them understand and create contractual agreements and design computer architecture systems, data centers, such that when they need the maximum power for infrastructure in society, that the data centers would get less. But that's in a very rare instance anyways. And during that time, we either have a backup generator for that little part of it, or we just have our computers shift the workload somewhere else, or we have the computers just run slower. You know, we could degrade our performance, reduce our power consumption, and provide for, you know, slightly longer latency response, you know, when somebody asks for, you know, asks for an answer. And so I think that that way of using computers, of building data centers, instead of expecting 100% uptime, and these contracts that are really, really quite rigorous, it's putting a lot of pressure on the grid to be able to... Now they're going to have to increase from their maximum. I just want to use their excess. It's just sitting there.
SPEAKER_02Check at the sourceI think it's a three-way problem. It starts with the end customer. The end customer puts requirements on the data centers that they can never not be available. Okay, so that the end customer expects perfection. Now, in order to deliver that perfection, you need a combination of backup generators and your grid power supplier to deliver on perfection. And so everybody's got to have six nines. Well, I think first of all, right now, we ought to have everybody understand that when the customer asks for these things, you have somebody in your data center operations team disconnected from the CEO. I bet the CEO doesn't know this. I'm going to talk to all the CEOs. Their CEOs are probably not paying any attention to the contracts that are being signed. And so everybody wants to sign the best contract, of course. And they go down to the cloud service providers and the contract, the two contract negotiators that are, I could just see them now, you know, negotiating these multi-year contracts. Both sides want, you know, the best contract as a result. The CSPs then have to go down to the utilities and they expect the six nines. And so I think the first thing is just make sure that all of the customers, the CEOs of the customers realize what they're asking for. Now, the second thing is we have to build data centers that gracefully degrade. And so if the power, if the utility, the grid tells us, listen, we're going to have to back you down to about 80%. We're going to say that's no problem at all. We're just going to move our workload around. We're going to make sure that data is never lost, but we can reduce the computing rate and use less energy. The quality of service degrades a little bit. For the critical workloads, I shift that somewhere else right away. So I don't have that problem. And so, you know, whichever data center still has 100% uptime.
SPEAKER_02Check at the sourceBeautifully put. So long as it obeys the laws of physics on first principles, I think we're good. What was the third thing you were mentioning? So the second thing is the data centers. And the third thing is we need utilities to also recognize that this is an opportunity. And instead of saying, look, it's going to take me five years to increase my grid capability, if you're willing to take power of this level of technology, guarantee, I can make them available for you next month and at this price. And so if utilities also offered more segments of power delivery promises, then I think everybody will figure out what to do with it. But there's just way too much waste in the grid right now. We should go after it.
SPEAKER_02Check at the sourceFirst of all, Elon is deep in so many different topics, yet he's also a really good systems thinker. And so he's able to think through multiple disciplines. And, and he obviously pushes things, questions everything where they're number one, is it necessary? Number two, does it have to be done this way? And in other words, you know, does it have, does it have to take this long? And so he has the ability to question everything to the point where everything is down to its minimal amount that's necessary. You can't take anything else out. And yet the necessary capabilities of the product retains. And so 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. You know, he'll just go there. If there's a problem, he'll just go there and show me the problem. You know, when you do all of this in combination, you overcome a lot of previous, this is just the way we do it. You know, I'm waiting for them. You know, I mean, it's just, everybody has a lot of excuses. And so, and then the last thing is when you act personally with so much urgency, it causes everybody else to act with urgency. you know, and, and every supplier has a lot of customers going on. Every supplier has a lot of projects going on and he, he make it, he made it, he makes it his business that he's the top priority of everybody else's, you know, projects. And so he, he does that by demonstrating.
SPEAKER_02Check at the sourceWell, first of all, co-design is a ultimate systems engineering problem. And so we approach the work that we do from that principle. The other thing that we do, and this is a philosophy, a thought, a state of mind, I guess, a method that I started 30 years ago, and it's called the speed of light. The speed of light is not just about the speed. The speed of light is my shorthand for what's the limit of what physics can do. And so everything that we do is compared against the speed of light. Memory speed, math speed, power, cost, time, effort, number of people, manufacturing cycle time. And when you think about latency versus throughput, when you think about cost versus throughput, cost versus capacity, all of these things you test against the speed of light to achieve all of these different constraints separately. And then when you consider it together, you know you have to make compromises because a system that achieves extremely low latency versus a system that achieves very high throughput, are architected fundamentally differently. But you want to know what's the speed of light of a system that achieves high throughput? What's the speed of light of a system that achieves low latency? And then when you think about the total system, you could make trade-offs. And so I force everybody to think about what's the first principles, the limits, the physical limits of for everything before we do anything. And we test everything against that. And so that's a good frame of mind. I don't love the other methods, which is continuous improvement. The problem with continuous improvement, first of all, you should engineer something from first principles at the speed, you know, with speed of light thinking limited only by physical limits and, and physics limits. And, um, after that, of course you would improve it over time. Um, But I don't like going into a problem and somebody says, hey, it takes 74 days to do this today, right now, and we can do it for you in 72 days. I'd rather strip it all back to zero and say, first of all, explain to me why 74 days in the first place. Let's think about what's possible today. If I were to build it completely from scratch, how long would it take? Oftentimes, you'd be surprised and might come to six days. Now the rest of the six days to 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 now that you know that six days is possible, then the conversation from 74 to 6 is surprisingly much more effective.
SPEAKER_02Check at the sourceAnd Lex, we'll probably kind of crank out about 200 of these pods a week, just to put in perspective.
SPEAKER_02Check at the sourceYou know, the phrase that I use most often is we need things to be as complex as necessary, but as simple as possible. And so the question is, is all that complexity there necessary? And we ought to test for that. And we ought to challenge that. And then after that, everything else above it, you know, is gratuitous.
SPEAKER_02Check at the sourceIt is the most complex computer the world has ever made.
SPEAKER_02Check at the sourceGold medalists in every single sport all assembled right here. And have to work together and report directly to you.
SPEAKER_02Check at the sourceWhole bunch of reasons. 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's amazing researchers still in China. 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 what remains is an incredible company. They also have a social culture where it's family first, friend 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 is, you know, one schoolmate, your brother for life. And so 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 the nature of friends, and insane competition 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 I've just said is fundamental to just how the kids were grown, the fact that they have excellent education, the fact that parents want them to do well in school, the fact that their culture is that way. These are just the thing about their country. And they showed up at precisely the time when technology is going through that exponential phase.
SPEAKER_02Check at the sourceYeah, it's a builder nation. Our country's leaders, incredible, but they're mostly lawyers. Their country's leaders, because 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.
SPEAKER_02Check at the sourceFirst. 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 it's not just a pure transformer model, it's transformer and SSMs. And we were early in developing the conditional GANs, that progressive GANs, which led step-by-step to diffusion. And so the fact that we're doing basic research here 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 to not only skills, scale, and motivation to build and continue to build these AI models for as long as we shall live. And so therefore we ought to do that. We can open up, we can activate every industry, every researcher, you know, every country to be able to join the AI revolution. There's 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 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 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. So these three fundamental reasons, both in 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.
SPEAKER_02Check at the sourceYeah, I appreciate you for saying that. We open source the models, we open source the weights, we open source the data, we open source how we created it.
SPEAKER_02Check at the sourceYou know, first of all, the deepest misunderstanding about TSMC is that... Their technology is all they have. That somehow they have a really great transistor. And if somebody shows up another transistor, game over. It's the technology. And of course, you know, I don't mean just the transistor and metallization systems, the packaging, the 3D packaging, the silicon photonics that, you know, 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, the demands, the dynamic demands of hundreds of companies in the world, as they're moving up, shifting out, you know, increasing, decreasing, push, pushing out, pulling in, um, changing from customer to customer. Uh, Wafer starting, wafer stopping, emergency wafer starts, you know, all of this dynamics of the world's complexity as the world is shape shifting all the time. And somehow they're running a factory with high throughput. high yields, really great costs, excellent customer service. They take their promises seriously. Because they know that they're helping you run your company, 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.
SPEAKER_02Check at the sourceThree 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.
SPEAKER_02Check at the sourceStory is true. I didn't, I didn't dismiss it, but I was, I was deeply honored. And, and of course, of course, um, uh, I knew then, as I know now, TSMC is one of the most consequential companies in history. And, and Morris is one of the, uh, the highest regarded executive and, and, um, business and personal friend that I've, that I've had in my life. And, um, For him to ask is, I was humbled and really honored. But the work that I'm doing here is really important. And I've seen, you know, in my mind anyways, in my mind's eye, what NVIDIA was going to be and what the impact that we could have. And it was really important work. And it's my responsibility, you know, my sole responsibility to make this happen. And so I declined it, you know, not because it wasn't an incredible offer. It's an unbelievable offer, but I simply couldn't take it.
SPEAKER_02Check at the sourceOur single most important property as a company is the install base of our computing platform. Our single most important thing today is the install base of CUDA. Now, the reason why 20 years ago, of course, there was no install base. But what makes, and if somebody came up with a Gouda or a Tuda, it wouldn't make any difference at all. And the reason for that is because it's never been just about the technology. The technology, of course, was incredible visionary. But it's the fact that the company was dedicated to it, stuck with it, expanded its reach. It wasn't three people that made Kuda successful. It was 43,000 people that made Kuda 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. And so the install base is the number one most important advantage. That install base, when you amplify it with the velocity of our execution, at the scale that we're talking about, no company in history had ever built systems of this complexity, period. And then to build it once a year is impossible. And that velocity combined with the installed base, in the developer's mind, you just can now take a developer's mind. From the developer's perspective, if I support CUDA, Tomorrow it will 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. Our second one is our ecosystem. The fact that we vertically integrated this incredibly complex system, but we integrated horizontally into every single company's computers. We're in the Google Cloud, we're in Amazon, we're in Azure, you know, we're ramping up AWS like crazy right now. We're in new companies like CoreWeave and Nscale. We're in supercomputers at Lilly. We're in enterprise computers. We're at the edge in radio base stations. You know, it's just crazy. One architecture is in all these different systems. We're in cars. We're in robots. We're in satellites. We're out in space. And so the fact that you have this one architecture and the ecosystem is so broad, it basically covers every single industry in the world.
SPEAKER_02Check at the sourceThen it became a computer. Then it became a cluster. Now it's an entire AI factory. When I see a computer, when I see what NVIDIA builds in the old days, I would, you know, I visualize the chip. And then, and then when I announced a new product, you know, new generation, like ladies and gentlemen, we're announcing Ampere today. I pick up the chip. That was my mental model of what I was building today. Yeah. I wouldn't picking up the chip is kind of still adorable, but it's adorable. It's not, it's not my mental model of what I'm doing. My mental model is this giant gigawatt thing that has power generation is connected to the grid. It's got cooling systems and networking of incredible monstrosity, you know, 10,000 people are in there trying to install it, hundreds of networking engineers in there, thousands of engineers behind it trying to power it up. 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.
SPEAKER_02Check at the sourceEntire 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.
SPEAKER_02Check at the sourceYeah, we're already there. NVIDIA GPUs are the first GPUs in space. And I didn't realize it was so interesting. 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 because those satellites have really high-resolution imaging systems. And they're sweeping the earth, you know, continuously now. And you want, you know, centimeter scale, you know, imaging that is done continuously for the world so that, you know, you'll basically have real time telemetry of everything. uh you don't want to beam that back down to earth it's just you know petabytes and petabytes of data you got to just do ai right there at the edge throw away everything you don't need you've seen before didn't change and then just keep the stuff that that you need and so ai ought to be done at the edge um obviously we have we have a 24 7 solar if we put it at the polars and um uh But, you know, there's no conduction, no convection. And so, you know, you're pretty much just radiation. But, you know, space is big. I guess we're just going to put big, giant radiators out there.
SPEAKER_02Check at the sourceYou know, I'm just so much more practical. I look for where my next bucket of opportunities are first. Meanwhile, I'm cultivating space. And so I send engineers to go work on the problem. We're learning a lot about it. How do we do radiation? How do we do degrading performance? How do we deal with continuous... testing and attestation of defects and how do we deal with redundancy and how do we degrade gracefully and things like that. What about software? How do you think about software and redundancy and performance out in space? Make it so that the computer never breaks. It just gets slower. So we could start doing a lot of engineering exploration up front. But in the meantime, my favorite answer is eliminate waste. We've got all that idle power. I want to evacuate it as fast as possible.
SPEAKER_02Check at the sourceI think that NVIDIA's growth is extremely likely, and in my mind, inevitable. And let me explain why. We're the largest computer company in history. That alone should beg the question, why? And the reason, of course, two reasons. First, two foundational technical reasons. The first reason is that computing went from being a retrieval-based file retrieval system Almost everything is a file. We pre-write something. We pre-record something. We draw something. We put it on the web. We put it in a file. And we use a recommender system, some smart filter, to figure out what to retrieve for you. And so we were a pre-recording, human pre-recording, and file retrieving system. That's what a computer is, largely. to now AI computers are contextually aware, which means that it has to process and generate tokens in real time. So we went from a retrieval-based computing system to a generative-based computing system. We're gonna need a lot more processing in this new world than in the old world. We need a lot of storage in the old world. We need a lot of computation in this new world. And so that's the first part of it. We fundamentally changed computing and the way how computing is done. The only thing that would cause it to go back is if this way of computation, this way of computing, generating information that's contextually relevant, situationally aware, that is grounded on new insight before it generates information, this computation-intensive way of doing computing would only go back if it's not effective. So for the last 10, 15 years while working on deep learning, if at any single moment I would have come to the conclusion that, you know what, this is not gonna work out. I think this is a dead end, or it's not gonna scale, it's not gonna solve this modality, it's not gonna be used in this application. Then, of course, I would feel very differently about it. But I think the last five years has given me more confidence than the last 10 years, the previous 10 years. The second idea is computers, because it was a storage system, it was largely a warehouse. We're now building factories. Warehouses don't make much money. Factories directly correlates with a company's revenues. And so the computer did two things. Not only did it change the way it did it, its purpose in the world changed. It's no longer a computer. It's a factory. A factory is used for generation of revenues. We're now seeing not only is this factory generating products, commodities that people want to consume. We're seeing that the commodities are so interesting, so valuable to so many different audiences that the tokens are starting to segment like iPhones. You have free tokens, you have premium tokens, and you have several tokens in the middle. And so intelligence, as it turns out, it's a scalable product. There's extremely high intelligence products, tokens that are used for specialized things. People will be willing to pay. The idea that somebody's willing to pay... a thousand dollars per million tokens it's just around the corner it's not if it's only when and so so now we're seeing that the commodity that this factory makes is actually valuable and is revenue generating and profit generating how now the question is how many of these factories can does the world need how much how many tokens does the world need and um How much is society willing to pay for these tokens? And what would happen to the world's economy if the productivity were to improve so substantially? What would happen? Are we going to discover new drugs, new products, new services? And so when you take these things in combination, I am absolutely certain that the world's GDP is going to accelerate in growth. I'm absolutely certain the percentage of that GDP that will be used for computation will be a hundred times more than the past because it's no longer a storage unit. It's a product generation unit. And so when you look at it in that context, and then you back into what does NVIDIA do and how much of that new economics, new industry would we have to benefit to address? I think we're going to be a lot, lot bigger. And then the rest of it to me is, is it possible for NVIDIA to be a $3 trillion revenues company in the near future? The answer is of course, yes. And the reason for that It's because it's not limited by any physical limits. There's nothing that I see that says, you know, gosh, $3 trillion is not possible. And as it turns out, NVIDIA supply chain is the burden is shared by 200 companies. And the fact that we scale out on the backs of with the partnership of this ecosystem, the question is, do we have the energy to do so? And surely we will have the energy to do so. And so all of these things combined, that number is just a number. And I still remember NVIDIA was the first time we crossed a billion dollars. I was reminded of a CEO who told me, you know, Jensen, it's theoretically impossible for a fabulous semiconductor company to exceed a billion dollars. And I won't bore you with why, but of course it's illogical and there's a lot of evidence we're not. Then somebody told me, Jensen, you'll never be more than $25 billion because of some other company. Those aren't first principle reason 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 don't exist. That's the part that's hard. You know, if NVIDIA was a $10 billion company trying to take NVIDIA's share, then it's easy to see for shareholders that, oh yeah, if they could just take 10% share, they could be this much larger. But it's hard for people to imagine how large we could be because there's nobody I could take share from. And so I think that that's one of the challenges for the world is the imagination of the future. But I got plenty of time and I'll keep reasoning about it and I'll keep talking about it. And every single GTC will become more and more real. And then more and more people will talk about it in one of these days. We'll get there, but 100% we'll get there.
SPEAKER_02Check at the sourceYeah. And what's really interesting, the reason why I was so excited about it, the iPhone of tokens arrived. What do you call it? That's 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. Yup. There's no question. Open claw is the iPhone of tokens.
SPEAKER_02Check at the sourceAnd 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. Yeah. It's reporting back to you, I got that done. You know, what do you want me to do next? You know, that's the part that I think most people don't realize is the person who's going to be chatting with them, texting them most is their claws or lobster. Yeah.
SPEAKER_02Check at the sourceI'm conscious about the fact that NVIDIA's success is very important to the United States. We generate enormous amounts of tax revenues. We establish technology leadership for our nation. Technology leadership is important for national security. National security not just in one aspect of national security, all aspects of national security. When our country's more prosperous, we could do a better job with domestic policies and helping social benefits. Because we're generating so much reindustrialization in the United States, we're creating mountains of jobs. We're helping shift jobs. how we, how we, how we build things back to United States in so many different plants, chips, computers, and of course these factories. I'm completely aware that, that, and I have, I have the benefit and this is a real, real, a real gift that, 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, 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 positively or even through great burden, for example, to supply chain? And the question is, therefore, what are you going to 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 going to do about it? And I break it down, decompose the problem. And the decomposition of these turns it into manageable things that I can do. And the only thing that I, after that, I could do is, did you do it? Did you either do it or did you get somebody else to do it? And if you didn't do it, you reasoned that you need to do it and you didn't do it and you didn't get anybody else to do it, then stop crying about it. You know? And so, and so, so I, I'm, I'm fairly, I'm fairly, uh, tough on myself. 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?
SPEAKER_02Check at the sourceOh yeah, oh yeah, sure. All the time. All the time. And there you just break down the problem into pieces, see what you could do about it. And part of, you know, Lex, 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 don't want to carry everything. One of the things that I do very quickly is I 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. Don't freak them out. Decompose the problem into smaller parts and inspire them to be able to go do something about it. But part of it is just forgetting. A lot of it is you've got to be tough on yourself. Come on, stop crying about it. Let's get going. And then you get out of bed. And then the other part is you're attracted to the next shiny light, the next future, you know, the next opportunity, the next, okay, that's behind us. What's next? And 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 job publicly. And so, um, you know, I, I say a lot of things that, that seem sensible at the time or funny at the time, mostly it's just because it's funny to me at the time. And then, you know, you reflect on it, it's less funny, but, but yeah, trust me, I know.
SPEAKER_02Check at the sourceExactly. That is, by the way, what I was trying to explain is that there's a incredible superpower of being the mind of a child. Yeah. And I say to myself, oftentimes when I look at something, and almost everything, my first thought is, how hard can it be? And so you get yourself into that mode, how hard could it be? And nobody's ever done it. It looks gigantic. It's going to cost hundreds of billions of dollars. It's going to take, you know, all this. And you just go, yeah, but how hard could it be? You know, how hard could it be? And so you got to get yourself into that state of mind. You don't want to actually... oversimulate everything and all the setbacks and all the trials and tribulations and all the disappointments. You don't want to simulate all that in advance. You don't want to know that. You want to go into a new experience thinking it's going to be perfect. It's going to be great. It's going to be incredibly fun. And then while you're there, you need to have endurance. You need to have grit so that when the setbacks actually happened, and those setbacks are going to surprise you. The disappointments aren't going to surprise you. The embarrassments are going to surprise you. The humiliations are going to surprise you. Now you just got to turn on the other bit, which is just forget about it. Move on. Keep moving. And to the extent that To the extent that my assumptions about the future and why the future is going to manifest, so long as those assumptions and that input doesn't change or didn't change materially, then I should expect that the output won't change. And so my simulated output of the future is still gonna happen. And if it's still gonna happen, I'm still gonna go after it. I believe it's gonna, you know? And so there's a combination of two or three human characteristics the ability to go into a, into an experience, fresh minded, the ability to forget the setbacks, the ability to believe in yourself, you know, to believe what you believe and stay, stay true to that belief. Um, but you're constantly reevaluating this combination of three, four, five things I think is, is really important for resilience. And, and, um, And, you know, I'm fortunate that whatever life experience has led to this, I've got kind of those four or five things. You know, I'm always curious, always learning. I'm always learning from everybody. You know, I'm always asking, and because I'm humble about everything, I'm always thinking, gosh, they did that so nicely. They did that so wonderfully. You know, I wonder what they're thinking through. How do they, you know, so I'm simulating everybody in a lot of ways. emulating almost everybody I watch, right? You're empathetic towards everything that they do that you're observing and respect. And so you're constantly learning.
SPEAKER_02Check at the sourceSurprisingly, no. And I would actually go the other way. Because I do so much of my work publicly, when I'm wrong, Yeah. And when I'm wrong or it didn't turn out that way or, you know, most of the things that I say outside, I'm fairly certain about. And the reason for that is because it's going to impact somebody else. And I want to be quite concerned about that and quite circumspect about that. For stuff that I'm reasoning about inside a meeting, a lot of things could turn out differently. But it doesn't ever stop me from reasoning. The way that I manage and lead, I'm constantly reasoning in front of people. And even when I'm talking to you, you can kind of see me kind of reasoning through things. And I want to make sure that you understand what I'm saying, not because I told you, because I'm so humble about what I'm about to tell you. I kind of show you the steps that I got there. And then you could decide whether you believe what I said in the end. And so I'm doing that all day long in meetings with all of my employees. I'm constantly reasoning through. Let me tell you how I see it. And I reason through it. It gives everybody the opportunity to intercept and say, I disagree with that part. The nice thing about reasoning through things and letting people interact with it is that they don't have to disagree with your outcome. They can disagree with your reasoning steps. And they could pull me in different directions. And then we can reason forward. And so we're kind of, you know, collective path searching method. And it's really fantastic.
SPEAKER_02Check at the sourceYeah, well, yeah. They knew that recently my first job was cleaning toilets.
SPEAKER_02Check at the sourceBy the way, it is, 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 the beginning, it's just, you know, playing Call of Duty, you know, Fortnite. And then later, they're using CUDA. And then later, they're using NVIDIA on, you know, Blender and Dassault and Autodesk.
SPEAKER_02Check at the sourceYeah. I think their perspective makes sense and I can see where they're coming from because I don't love AI slop myself. All of the AI-generated content increasingly looks similar and they're all beautiful. So I'm empathetic towards what they're thinking. That's just not what DLSS 5 is trying to do. I showed several examples of it. But DLSS 5... is 3D conditioned, 3D guided. It's ground truth, structured data guided. And so the artist determined the geometry. We are completely truthful artists. to the geometry maintained so 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 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 tune 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 going to come out the way the games are, ship the way they do, and then we're going to 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.
SPEAKER_02Check at the sourceThat's right. It's yet another tool. And they want the generative models to generate the opposite of photoreal. Yeah, it'll do that too. And so it's just yet another tool. I think the gamers might also appreciate that in the last couple of years, we introduced skin shaders. to the game developers. And many of those games have skin shaders that include subsurface scattering that make skin look more skin-like. And so the industries, game developers are looking for more and more and more tools to express their art. And so this is just yet one more tool. They could decide what to use.
SPEAKER_02Check at the sourceI would say Doom, from the intersection of the cultural implication, as well as the industry, turning a PC into a gaming device. That was a very important moment. Now, of course, flight simulation companies were before it. 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 was.
SPEAKER_02Check at the sourceWe created this thing called RTX Mod. Yeah, it's a modding tool. Awesome. It allows the community to inject the latest technology into an old game.
SPEAKER_02Check at the sourceYou think you can 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. And so, 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. Now, we saw a whole bunch of those type of companies during the internet era, and most of those websites were not anything more sophisticated than what OpenClaw could generate today. 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?
SPEAKER_02Check at the sourceWell, by the way, it's happening right now, right? You know that when you go to China, you're going to see a whole bunch of people 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, you know, feeds your little tomogachi or something like that. And it becomes 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 success 100,000 of those agents building NVIDIA is 0%. And then the one part that I want to 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 and the tasks and the tools that you use to do your job are related, not the same. I've been doing my job for 33 years. I'm the longest running tech CEO in the world, 34 years. And the tools that I've used to do my job has changed continuously in the last 34 years, and sometimes quite dramatically over the course of a couple to three years. 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 going to go away was radiology. Because computer vision was going to achieve superhuman levels And it did. Computer vision was superhuman in 2019, 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 so much faster now, you could study more scans. You could diagnose better. You could inpatient faster. We 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 going to happen. The number of software engineers at NVIDIA is going to grow, not decline. And the reason for that is because the purpose of a software engineer and the task of a software engineer of 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. You know, none of that stuff is going to go away.
SPEAKER_02Check at the sourceWhat is the definition of coding? I believe that 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 of the year 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 just increased the value that they could deliver to the customer. Their artistry just elevated tremendously. I believe that every accountant is 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.
SPEAKER_02Check at the sourceAnd the reason for that, Lex, and you know that I was just 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 so specific enough that they can take action on it. But I underspecify 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 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 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 this is an area you want to be much more exploratory. And so you might underspecify 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.
SPEAKER_02Check at the sourceSo my first recommendation before, and this is now how I deal with anxiety. In fact, we just talked about it earlier. Enormous anxiety about the future, enormous anxiety about the pressure, enormous anxiety about uncertainty. I first break it down and then I'm going to tell myself, Okay, there are some things you can do something about. There are some things you can't do anything about. But for the stuff that you can do something about, let's reason about it and let's go do it. If we were to hire a new college graduate today, and I have a choice between two, one that is no clue what AI is and one that is expert in using AI, I would hire the one who's expert in using AI. If I had an accountant, a marketing person, the one that is expert in using AI, supply chain, customer service, a salesperson, business development, a lawyer, I would hire the one who is expert in using AI. And so I would advise that every college student, every teacher should encourage their student to go use AI. Every college student should graduate and be an expert in AI. And everybody, if you're a carpenter, if you're an electrician, go use AI. Go see what it can do to transform your current job. Elevate yourself. If I were a farmer, I would absolutely use AI. If I were a pharmacist, I would use AI. I want to see what it could do to elevate my job so that I could be the innovator to revolutionize this industry myself. That would be the first thing that I would do. I would also help them. It is the case that the technology will dislocate and will eliminate many tasks because it will automate it. If your job is the task, then you're very highly going to be disrupted. If you're
SPEAKER_02Check at the sourceI don't know how to use AI. And the AI goes, well, let me show you.
SPEAKER_02Check at the sourceWell, when you go to Taiwan, just ask AI, what are Jensen's favorite restaurants in Taiwan? Yeah. And it will actually tell you. Oh, yeah, yeah. Is it accurate? Yeah, yeah. All right. It's all over Taiwan.
SPEAKER_02Check at the sourceI don't know if the chip will ever get nervous. And that's the, you know, of course, the conditions by which that causes anxiety or nervousness or whatever emotion. I believe that AI will be able to recognize those and understand those. I don't think my chips will feel those. And therefore, how that anxiety, how that feeling, how that excitement, how that, you know, all of those feelings manifest in human performance. For example, extremely amazing human performance, athletic performance, you know, average or lesser than average. That entire spectrum of human performance that comes out of Exactly the same circumstances for different people manifesting in different outcome, manifesting in different performance. I don't think there's anything about anything that we're building that would suggest that two different computers being presented with all of exactly the same context would, of course, it would produce statistically different outcomes, but it's not because it felt different.
SPEAKER_02Check at the sourceAnd it's just really important to break down what is intelligence. And the word, that word we use all the time, it's not a mysterious word. Intelligence has a meaning. And it's a system that it's something that we do that includes perception and understanding and reasoning and the ability to do plan. And that loop is fundamentally what intelligence is. Intelligence is not one word that is exactly equal to humanity. And that's, I think, really important to separate the two. We have two words for that. I'm not, I don't over fantasize about, and I don't over romanticize about intelligence. Intelligence is, and people have heard me say it before. I actually think intelligence is a commodity. I'm surrounded by intelligent people and I'm surrounded by intelligent people, more intelligent than I am in each one of the spaces that they're in. And yet I have a role in that circle. It's actually kind of interesting. They're more educated than I am. They went to better schools than I did. They're deeper than in the fields that they're in, all of them. I have 60 of them. They're all superhuman to me. And somehow I'm sitting in the middle orchestrating all 60 of them. And so you got 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's my point. My point is intelligence is a functional thing. Humanity is not specified functionally. It's a much, much bigger word. And our life experience, our tolerance for pain, our determination, those are different words than intelligence. And so the thing that I want to help the audience understand is If I could give them one thing is intelligence is a word that we've elevated to very high form over time. The word we should really elevate is humanity. Character, humanity, all of those things. Compassion, generosity. All of the things that you say just now, I believe those are superhuman powers. And that now intelligence is going to be commoditized because we've spoken about it. The most important thing is your education. Now, even when they said the most important thing is your education, when you went to school, there's more than just knowledge that you gained. But unfortunately, our society had put everything into one single word. And life is more than one word. And I'm just telling you, my life would suggest that being lower on the intelligence curve than everybody around me doesn't change the fact I'm the most successful. And I think that kind of is, I'm trying, hopefully, to inspire everybody else that don't let this democratization of intelligence, this commoditization of intelligence, you know, cause you anxiety, you should be inspired by that.
SPEAKER_02Check at the sourceI really don't 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 suggests that it has been experienced by many people, just not one person. This is a once in a humanity experience, what I'm going through. NVIDIA is one of the most consequential technology companies in history. We're doing very important work. I take it very seriously. And so some of the things that, of course, are practical things, like how do we think about succession planning? And I'm famous in saying that I don't believe in succession planning. And the reason for that isn't because I'm immortal. The reason for that is because if you're worried about succession planning. If you're worried all that anxiety of succession planning, then what should you do about it? Then you break it all the way back down. The most important thing you should do today, if you care about the future of your company post you, is to pass on knowledge, information, insight, skills, experience as often and continuously as you can, which is the reason why I continuously reason about everything in front of my team. Every single meeting is 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 a fraction of a second. I'm passing that information, that knowledge, oh my gosh, this is cool. Before I even finished learning all of it myself, I've already pointing it to somebody else. Get on this. This is so cool. You're going to want to learn this. And so I'm constantly passing knowledge, empowering people, elevating the capability of everybody around me so that But the outcome that I seek, that I hope for, is that I die on the job. And hopefully I die on the job instantaneously.
SPEAKER_02Check at the sourceI've always had great confidence in the kindness, the generosity, the compassion, the human capacity. I've always been extremely confident of that. Sometimes... More so than I should. And I get taken advantage of. But it doesn't ever cause me not to. I start with, always, that people want to do good. People want to help others. And vastly, I am proven right. constantly proven right and often exceeds my expectations. So I have complete confidence in the human capacity. I think the things that give me incredible hope is what I see as I extrapolate, as what I see now is possible, And as I extrapolate, based on the things that we're doing, what will very likely happen. And that there's so many things that we want to solve. There's so many problems we want to solve. There's so many... things that we want to build. There's so many good things that we want to do that are now within our reach and within the reach of my lifetime. You just can't possibly not be romantic about that. You know what I'm saying? Yeah. Like truly, truly so. How can you not be romantic about that? The fact that it's a reasonable thing to expect the end of disease. It's a reasonable thing to expect. It's a reasonable thing to expect that pollution will be drastically reduced. It's a reasonable thing to expect that traveling at the speed of light is actually in our future. And then, you know, not for long distances, but short distances. You know, people ask me how. Well, first of all, very soon I'm going to put a humanoid on a spaceship and it's going to be, you know, my humanoid and, and we're going to send it out as soon, you know, as soon as possible. And it's going to keep improving and enhancing along the flight. And then when it's time, all of the, 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 collect and becoming my AI. And, um, I'm just, you know, when the time comes and, you know, we'll just send that at the speed of light, catch up with my robot. Oh, that's brilliant.
SPEAKER_02Check at the sourceUnderstanding the biological machine is right around the corner. It's not 10 years, it's five years probably.
SPEAKER_02Check at the sourceThank you, Lex. I had a great time. Also, if I could just say one more thing, and thank you for all the interviews that you do, the depth, the respect that you go through with, and the research that you do to reveal 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.
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Every passage above is taken from this recording: Lex Fridman Podcast, #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution.