YouSaid · the spoken record

Jensen Huang

lines on the record: 160
first: 2026-03-23
most recent: 2026-03-23
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“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 our backup generators for that little part of it, or we just have our computers shift a workload somewhere else, or we have the computers just run slower. You know, we could degrade our performance, reduce our power consumption, and provide for a slightly longer latency response when somebody asks for an answer. And so I think 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. Just sitting there”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“One of the areas that I would love us to talk about and just get the message out. Our power grid is designed for the worst case condition with some margin. Well, 99% of the time we're nowhere near to 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Because I told him what I needed, they understood what I need. They told me what they're going to go do. And I believe what they're going to do.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“And because they trust me and I'm very respectful of them and 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 no what to do”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“But that's impossible now because unveiling somebody too so dense. And so that's an example, and I would have to go into, you know, I fly into the supply chain, go meet my partners, and hey, guess what? So here's what I'm 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 ask them to make several billion dollars of capital investments each.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 week 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 built supercomputers in the supply chain and ship some 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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? Because, okay, let's reason about this. Because we changed the system architecture from the original DGX1 that you remembered to MVLink 72 rack scale computing. 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. We moved supercomputer, supercomputer integration at the data center into supercomputer manufacturing in the supply chain.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly the deep science, the deep engineering, the incredible manufacturing, and so much of the manufacturing is already robotics. But we have a couple of hundred suppliers that contribute the technology that goes into our 1.3 million component rack. Each rack is 1.31 and a half million components, their two hundred suppliers across the Veruben rack.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Bought three years ago. I was able to convince several of the CEOs that Even though at a 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 DEOs 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 explain 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 old companies. And so That's part of my job to. Inform and shape. Inspire, you know”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“And there were all there were several hundred CEOs. And I don't think there's ever been keynotes where several hundred CEOs show up. And part of it is I'm telling them about our business condition now. I'm telling them about the growth drivers in the very near future and what's happening. And I'm also describing where are we going to go next so that they could use all of this information and all of the dynamics that are here to inform how they want to invest. And so I inform them that way like I inform my own employees. And then of course then I make trips out to them and make sure that, hey, listen, I want you to know this quarter, this coming year, this next year, these things are going to happen. 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“All the time, and we're working on it all the time. No company in history has ever grown at a scale that we're growing while accelerating that growth. It's incredible. And it's hard for people to even understand this. In the overall world of AI computing, we're increasing share. And so supply chain, upstream and downstream are really important to us. Spend a lot of time informing all the CEOs that I work with. What are the dynamics that's going to cause the growth to continue or even accelerate? It's part of the reasons why to the entire right-hand side of me were CEOs of Practically the entire IT industry, upstream, and practically the entire industry. Infrastructure industry downstream”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 a limit so that we can keep on driving token costs down as fast as we can. Our computer price is going up, but our token generation effectiveness is going up so much faster that token cost is coming down. It's coming down an order of magnitude every year.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Power is a concern, but it's not the only concern, but that's the reason why we're pushing so hard on extreme co design, so that we can improve the tokens per second, per watt, orders of magnitude every single year. And so in the last 10 years, Moore's Law would have progressed computing about 100 times in the last 10 years. We progressed and scaled up computing by a million times in the last 10 years. And so we're going to keep on doing that through extreme co-design.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“We give you two out of three rights. Agentic systems can access sensitive information. It can execute code, and it can communicate externally. We could keep things safe if we gave you two out of those three capabilities at any time, but not all three. And out of those two out of three capabilities, we also give you access control based on whatever rights that you're given by enterprise. And then we connect it to a policy engine that all these enterprises already have. And so we're going to try to do our best to help OpenCloud become a better claw.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“We jumped on her right away and we sent a bunch of security experts his way. And we did this thing called Open Shell. It's already been integrated into OpenClaw.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Literally two years ago, at GTC, I was talking about agentic systems that exactly reflect open claw today. And of course, the confluence of many things had to happen. First of all, we needed Clauden 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 project that was sufficiently robust and sufficiently complete. And that we can all put to work. And I think OpenClaw did for agentic systems what ChatGPT did for generative systems. And I just think it's a very big deal.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 in that way, 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 openclaw? If you take the open claw schematic that I used at GTC, Will find it two years ago.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Use us the tools that I have to do the work that it needs to do, or does this hand turns into a ten pound hammer in one instance, turns into a scalpel in another instance, and in order to boil water it beams, you know, microwaves out of its fingers, or is it more likely just to use a 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. reads the manual of this microwave, reads it instantly becomes an expert, and so uses it. And so I think I just described, in fact, almost all of the... Properties of open claw.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 be a human robot. If that human robot were to be created, Is it more likely that the human robot comes into my house”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“All the state of the artist. No, it's easier than that. You just reason about it. First of all, just reason. No matter what happens, at some point, in order for that large language model to be a digital worker, let's just use that metaphor. Let's say that we want the LLM to be a digital worker. What does it 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. You know, a lot of people would say, you know, 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 and you wear a glass of whiskey.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 MVLink seventy two to run the LLMs. It also has its new additional rack called Grock. 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 Blackwall racks, it was completely focused on doing one thing, processing the LLM.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“That's right. You got to listen and learn from everybody and have a have a, and then the last part is to have an architecture that's flexible, that can adapt and move with the wind. And one of the benefits of CUDA is that it's, you know, on the one hand, an incredible accelerator. On the other hand, it's really flexible. And so that balance, incredible balance between specialization, otherwise we can't accelerate the CPU versus generalization so that we can adapt with changing algorithms, that's really, really important. That's the reason why CUDA has been so resilient on the one hand, and yet we continue to enhance it, where at CUDA 13.2. And so evolving the architecture so fast that we can stay with the modern algorithms. For example,”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“For example, these AI model architectures are being invented about once every six months. And system architectures and hardware architectures kind of every three years. And so you need to anticipate what likely is going to happen two, three years from now And there's a couple ways that you could do that. First of all, we could do research internally ourselves, and that's one of the reasons why we have basic research. We have applied research. We create our own models. And so we have hands-on life experience right here. This is part of the co-design that I'm talking about. We're also the only AI company in the world that works with literally every AI company in the world. And to the extent that we can, we try to get a sense of what are the challenges that people are experiencing.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“It's kind of like multiplying AI, multiplying AI, we could spin off agents as fast as you want to spin off agents. And so, you know, 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 in the agents, put it onto 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 that's compute.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 bangs on databases and it goes and uses tools. And one of the most important things it does is spins off and spawns off a whole bunch of sub-agents, which means we're now creating large teams. It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself. And so the next scaling law is the agentic scaling law”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Memorization and generalization, you know, and looking for patterns and relationships. You're reading and reading versus thinking, reasoning, solving problems, taking Unexplored experiences, new experiences, and breaking it down into decomposing it into solvable pieces that we then go off either through first principle reasoning or through previous examples, prior experiences, or just exploration and search and trying different things. That whole process of test time scaling inference is really about thinking and it's about reasoning. It's about planning. It's about search. It's about, and so how could that possibly be compute light? And we were absolutely right about that. So test time scaling is intensely compute intensive.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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, you know, they're not like NVIDIA's chips. Oh, those are going to be complicated and expensive. And, you know, we could make, and this is, and 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. Pre training”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 is now limited by compute. And the reason for that is most of the data is synthetic. Then the next phase.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Know that this is the end of AI. And of course, that's obviously not true. We're going to keep on scaling the amount of data that we have to train with. A lot of that data is probably going to be synthetic. And that also confused people, you know, and what people 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. It's synthetic because 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“We can go back and reflect on what people thought were blockers. So in the beginning, the pre-training skilling law, you know, people thought, well, rightfully so, that the amount of data that we have, high quality data that we have, will limit the intelligence that we achieve. And that scaling law was an important, very important scaling law. The larger the model, the correspondingly more data results in a better results in a smarter AI. And so that was pre-training. And Ilias, Suscover, Ilias said we're out of data or something like that pre-training is over or something like that. The industry panicked.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“We don't build computers. We actually don't build clouds. We don't, as it turns out, we're computing platform company. And so nobody can buy anything from us. That's the weird thing. 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 at GTC is about manifesting a future that by the time that we my product is ready, they're going What took you so long?”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“We've been late. I've been talking about the stepping stones for two and a half years You just go back and 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 like, you know, what took you so long?”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Sometimes it looks like you're leading from behind, but you've been shaping there, you know, to the point where on the day that I declared it, 100% buy-in. But that's what you want. You want to bring everybody along. Otherwise, we announce something about deep learning and everybody goes, what are you talking about? You know, you announce something about, let's go all in on this thing. And your management team, your board, your employees, your customers, they're kind of like, where's this coming from? 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 I'm using that to shape the belief system of my own employees. And so by the time that I announce something, like for example, we just announced Grock.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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, 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 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“We've just 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 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, you know, 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 Melanox”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and you reason about how to get there, you reason about why it must exist. And we all reason, the management team will reason about it, all the people that we spend a lot of time reasoning about it. The thing that the next part of it is probably a skill thing, which is 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, first of all, I'm informed by a lot of curiosity. At some point, there's a reasoning system. That convinces me so clearly this outcome will happen. That this will happen. And so I believe it in my mind and when I believe it in my mind, you know, 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I had to make it clear to the board what we were trying to do and the management team knew our gross margins were going to get crushed. So you could imagine a world where GFOS would carry the burden of CUDA and none of the gamers would appreciate it and none of the gamers would pay for it. You know, they only pay certain price and it doesn't matter what your cost is. And so we increased our cost by 50% and that consumed and we were 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Was it like $8 billion 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 GForce. I always say that NVIDIA is the house that GForce built because it was G-Force that took CUDA out to everybody. Researchers, scientists, they discovered CUDA on G-Force because they were all 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's kind of how we got going.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“And use it as a starting point of cultivating our installed base. Meanwhile, we'll go and attract developers and we went to universities and wrote books and taught classes and put CUDA everywhere. And eventually people discover, and at the time the PC was the primary computing vehicle. There was no cloud. And we could put a supercomputer in the hands of every researcher in school, every scientist, every engineering 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 completely consumed all of the company's gross profit dollars. And so at the time, the company was probably worth, I don't know at the time, $8.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Install based defines an architecture. Not everything else is secondary, okay? And so there were other architectures at the time. CUDA came out, OpenCL was here. There were several other competing architectures. But the thing that the decision that we made that was good was we said, hey, look, ultimately it's about installed base and what is the best way we could get a new computing architecture into the world By that timeframe, G-Force had become successful. We were already selling millions and millions of GForce GPUs a year. And we said, you know, we ought to put CUDA on G force. And put it into every single PC whether customers use it or not.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Incredibly well designed by some of the brightest computer scientists in the world, largely failed, and so I've given you two examples where one is, you know, one is elegant, the other one's barely aesthetic. And so yet x86 survived.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“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 x eighty six. 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”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Turned out to have been a good decision. I think here's the way it went. So we invented this thing called CUDA, and it expanded the aperture of applications that we can accelerate with our accelerator. The question is, how do we attract developers to CUD Because a computing platform is all about developers. And developers”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Excellent. That was the first, I would say that that was the first. The first strategic decision that is as close to an existential threat.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Enormous amounts of art 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.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Too was a huge step in the direction of computing. It was the reason why all of the people who were working on stream processors and other types of data flow processors discovered us. And they said, hey, all of a sudden, we might be able to use these GPUs as incredibly computationally intensive. And it's now compliant with IEEE. I can take my software that I was writing previously on CPUs and I could see about using the GPU for that. And which led us to create, put C on top of FP32 was called we call CG, that CG path took us to eventually CUDA. CUDA step by step by step putting CUDA on GForce, that was a strategic decision that was very, very hard to do because it cost the company”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source
“Its two general purpose, and it takes away from your specialization. I connect two words. Actually, have fundamental tension. The better computing company we become, the worse we become as a specialist. The more of a specialist, the less capacity we have to do overall computing. And so, and I connected those two words together on purpose, that the company has to find that really narrow path step by step by step to expand our aperture of computing, but not give up on the most important specialization that we had. Okay, so the first step that we took beyond acceleration was we invented a programmable pixel shader. 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 created, we put FP32 into our shaders. That FP32 step, IEEE compatible FP32.”
2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source