Jensen Huang LIVE: Nvidia's Future, Physical AI, Rise of the Agent, Inference Explosion, AI PR Crisis

(0:00) Jensen Huang joins the show! (0:26) Acquiring Groq and the inference explosion (8:53) Decision making at the world's most valuable company (10:47) Physical AI's $50T market, OpenClaw's future, the new operating system for modern AI computing (16:38) AI's PR crisis, refuting doomer narratives, Anthropic's comms mistakes (20:48) Revenue capacity, token allocation for employees, Karpathy's autoresearch, agentic future (30:50) Open source, global diffusion, Iran/Taiwan supply chain impact (39:45) Self-driving platform, facing competition from active customers, responding to growth slowdown predictions (47:32) Datacenters in space, AI healthcare, Robotics (56:10) OpenAI/Anthropic revenue potential, how to build an AI moat (59:04) Advice to young people on excelling in the AI era Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason Calacanis?

  Special episode this week. We've preempted the weekly show and there's only three people we preempt the show for. President Trump, Jesus, and Jensen. And I'll let you pick which order we do that. But what an amazing run you've had and a great event.

• 0:18Jensen Huang?

  Every industry is here. Every tech company is here. Every AI company is here. Incredible.

• 0:24Jason Calacanis?

  Incredible. Extraordinary. And one of the great announcements of the past year has been Grok. When you made the purchase of Grok, did you realize how insufferable Chamath would become?

• 0:35Jensen Huang?

  I had an inkling that that

• 0:39Jason Calacanis?

  We're his friends. We have to deal with him every week.

• 0:41Jensen Huang?

  I know it.

• 0:42Jason Calacanis?

  You had to deal with him for the six week close.

• 0:44Jensen Huang?

  I know. It's like two weeks.

• 0:45Jason Calacanis?

  Two weeks.

• 0:46Jensen Huang?

  It's all coming back to me now. It's making me rather uncomfortable. The thing is, many of our strategies are presented in broad daylight at GTC years in advance of when we do it. Two and a half years ago, I introduced the operating system of the AI factory and it's called Dynamo. Dynamo, as you know, is a piece of instrument, a machine that was created by Siemens to turn essentially water into electricity. And Dynamo powered the factory of the last industrial revolution. So I thought it was the perfect name for the operating system of the next

• 1:28Jensen Huang?

  industrial revolution, the factory of that. And so inside Dynamo, the fundamental technology is disaggregated inference. Jason, I know you're super technical.

• 1:40Jason Calacanis?

  Absolutely.

• 1:41Jensen Huang?

  I know it.

• 1:41Jason Calacanis?

  I'll let you take this one. Go ahead and define it for the audience. I don't want to step on you.

• 1:44Jensen Huang?

  Yeah, thank you. I knew you wanted to jump in there for a second. But it's disaggregated inference, which means the pipeline, the processing pipeline of inference is extremely complicated. In fact, it is the most complicated computing problem today. Incredible scale, lots of mathematics of different shapes and sizes. And we came up with the idea that you would change, you would disaggregate parts of the Processing such that some of it can run on some GPUs, rest of it can run on different GPUs. And that led to us realizing that maybe even disaggregated computing could make sense. That we could have different heterogeneous nature of computing.

• 2:27Jensen Huang?

  That same sensibility led us to Mellanox. Yep. You know, today Nvidia's computing is spread across GPUs, CPUs, switches, scale up switches, scale out switches, networking processors. And now we're going to add Grok to that and we're going to put the right workload on the right chips. You know, we just really evolved from a GPU company to an AI factory company.

• 2:49Chamath Palihapitiya?

  I mean, I think that was probably the biggest takeaway that I had. You're seeing this fundamental disaggregation where we've gone from a GPU and now you have this complexion of all these different options that will eventually exist. The thing that you guys said on stage, or you said on stage was, I would like the high value inference people to take a listen to this and 25% of your data center space, you said, should be allocated to this Grok, LPU, GPU combo.

• 3:16Jensen Huang?

  We should add Grok to about 25% of the Vera Rubens in the data center.

• 3:21Chamath Palihapitiya?

  So can you tell us about how the industry looks at this idea of now basically creating this next generation form of disaggregated, pre-fill, decode, disag, and how people, do you think, will react to it?

• 3:32Jensen Huang?

  Yeah, and take a step back and at the time that we added this, we went from large language model processing to agentic processing. Now, when you're running an agent, you're accessing working memory, you're accessing long-term memory, you're using tools, you're really beating up on storage really hard. You have agents working with other agents. Some of the agents are very large models, some of them are smaller models, some of them are diffusion models, some of them are autoregressive models. And so there are all kinds of different types of models inside this data center. We created Vera Rubin to be able to run this extraordinarily diverse workload. My sense is

• 4:16Jensen Huang?

  And so we added, we used to be a one rack company, we now add a four more racks.

• 4:21David Friedberg?

  Right.

• 4:22Jensen Huang?

  So Nvidia's TAM, if you will, increased from whatever it was to probably something, call it, you know, thirty-three percent, fifty percent higher. Now, part of that thirty-three percent or fifty percent, a lot of it's going to be storage processors, it's called Bluefield, some of it will be, a lot of it, I'm hoping, will be GROC processors, and some of it will be CPUs, and they're all, and a lot of it's going to be networking processors, and so all of this is going to be running, basically, the computer of the AI revolution, called agents.

• 4:56David Friedberg?

  Right.

• 4:56Jensen Huang?

  The operating system of, of a modern, modern industry.

• 5:00David Friedberg?

  What about embedded applications? So, you know, my daughter's teddy bear at home wants to talk to her, what goes in there? Is it a custom ASIC? Or does there end up becoming much more kind of a broader set of TAM with developing tools that are maybe different for different use cases at the edge, and an embedded application?

• 5:17Jensen Huang?

  We think that there's three computers in the problem, at the, at the largest, at the largest scale, when you take a step back. There's one computer that's really about training the AI model, developing, creating the AI. Another computer for evaluating it. Depending on the type of problem you're having, like, for example, you look around, there's all kinds of robots and cars and things like that, you have to evaluate these robots inside a virtual gym that represents the physical world. So it has to be software that obeys the laws of physics. And that's a second computer, we call that Omniverse. The third computer is the computer at the edge, the robotics computer. That robotics computer, one of them could be self-driving car, another one's a robot,

• 6:01Jensen Huang?

  another one could be a teddy bear, little tiny one for a teddy bear. One of the most important ones is one that we're working on, that basically turns the telecommunications base stations into part of the AI infrastructure. So now, all of the, it's a two-trillion-dollar industry, all of that in time will be transformed into an extension of the AI infrastructure. And so radios, radios will become edge devices, factories, warehouses, you name it. And so, so there are three, these three basic computers. All of them, you know, are going to be necessary.

• 6:36Speaker 04

  Jensen, last, uh, last year, I think you were ahead of the, the rest of the world in, in, in saying inference isn't going to 1,000x.

• 6:43Jensen Huang?

  Just last year.

• 6:45Speaker 04

  Yes.

• 6:45Jensen Huang?

  Brad, you're hurting my feelings.

• 6:46Speaker 04

  Is it, is it going to 1 million x, is it going to 1 billion x?

• 6:50Jensen Huang?

  Yeah.

• 6:50Speaker 04

  Right? And I think people at the time thought it was pretty hyperbolic because the world was still focused on pre-scaling, on training. Here we are. Now inference has exploded. We're inference constrained. Um, you announced an inference factory that I think is leading edge. That's going to be 10x better in terms of throughput to the next factory. But yet if you, i- i- if I listen to what the chatter is out there, it's that your inference factory is going to cost 40 or 50 billion. And the alternatives, the custom ASICs, AMD, others are going to cost 25 to 30 billion and you're going to lose share. So why don't you talk to us? What are you seeing? How do you think about share? And does it make sense for all these folks to pay something that's a 2x premium to what others are marketing?

• 7:33Jensen Huang?

  The big takeaway, the big idea is that you should not equate the price of the factory and the price of the tokens, the cost of the tokens. It is very likely that the 50 billion dollar factory, and in fact, I can prove it, that the 50 billion dollar factory will generate for you the lowest cost tokens. And the reason for that is because we produce these tokens at extraordinary efficiency. 10x, you know, the difference between 50 billion, now it turns out 20 billion is just land power and shell, right?

• 8:12Speaker 04

  Right.

• 8:13Jensen Huang?

  And then on top of that, you have storage anyways, networking anyways, you got CPUs anyways, you got servers anyways, you got cooling anyways. The difference between that GPU being 1x price or half x price. Is not between 50 billion and 30 billion. Pick your favorite number, but let's say between 50 billion and 40 billion. That is not a large percentage when the $50 billion data center is actually 10 times the throughput. Right. That's the reason why I said that even for most chips, if you can't keep up with the state of the technology and the pace that we're running, even when the chips are free, it's not cheap enough.

• 8:52Chamath Palihapitiya?

  Can I just ask a general strategy question?

• 8:55Jensen Huang?

  Yep.

• 8:55Chamath Palihapitiya?

  I mean, you're running the most valuable company in the world. This thing is going to do 350-plus billion of revenue next year, 200 billion of free cash flow. It's compounding at these crazy rates. How do you decide what to do? Like, how do you actually get the information? I mean, it's famous now, these sort of emails that are people are meant to send you, but how do you really decide to get an intuition of how to shape the market? Where to really double down? Where to maybe pull back? Where to actually go into a greenfield? How does that information get to you? How do you decide these things?

• 9:26Jensen Huang?

  In a final analysis, that's the job of the CEO. And our job is to define the strategy, define the vision, define the strategy. We're informed, of course, by amazing computer scientists, amazing technologists, great people all over the company, but we have to shape that future. Well, part of it has to do with, is this something that's insanely hard to do? If it's not hard to do, we should back away from it. And the reason for that, if it's easy to do, obviously.

• 9:52Chamath Palihapitiya?

  Lots of competitors.

• 9:53Jensen Huang?

  A lot of competitors. Is this something that has never been done before? That's insanely hard to do? And that somehow taps into the special superpowers of our company? And so I have to find this confluence of things that meets the standard. And in the end, we also know that a lot of pain and suffering is going to go into it. There are no great things that are invented because it was just easy to do and just like first try, here we are. And so if it's super hard to do, nobody's ever done it before, it's very likely that you're going to have a lot of pain and suffering. And so you better enjoy it.

• 10:26Chamath Palihapitiya?

  So can you just look at maybe three or four of the more long-tail things you announced? And just talk about the long-term viability of whether it's the data centers in space or whether it's what you're trying to do with ADAS and autos or, you know, what you're trying to do on the biology side? Just give us a sense of like how you see some of these curves inflecting upwards in some of these longer tail businesses.

• 10:46Jensen Huang?

  Excellent. Uh, physical AI, large category. We believe, and I just mentioned, we have three computing systems, all the software platforms on top of it. Physical AI as a large category.

• 10:59Jensen Huang?

  It's technology industry's first opportunity to address a fifty trillion dollar industry that has largely been, you know, void of technology until now. And so, we need to invent all of the technology necessary to do that. I felt that that was a ten-year journey. We started ten years ago, we're seeing inflecting now. It is a multi-billion dollar business for us, it's close to ten billion dollars a year now. And so, it's a big business and it's growing exponentially. And so, that's number one. I think in the case of digital biology, I think we are literally near the ChatGPT moment of digital biology. We're about to understand how to represent genes, proteins, cells, we all already know how to understand chemicals.

• 11:41Jensen Huang?

  And so, the ability for us to represent and understand the dynamics of the building blocks of biology, that's a couple, two, three, five years from now. In five years' time, I completely believe that the healthcare industry or digital biology is going to inflect. And so, these are a couple of the really great ones, and you could see they're all around us.

• 12:02Jason Calacanis?

  Agriculture.

• 12:03Jensen Huang?

  Agriculture.

• 12:04Jason Calacanis?

  Inflecting now.

• 12:05Jensen Huang?

  No question, yeah.

• 12:06Jason Calacanis?

  Jensen, I want to take you from the data center to the desktop. Uh, the company was built in large part on hobbyists, video gamers and all those graphic cards in the beginning. And you mentioned in front of, I think, ten thousand people here, just clawed, uh, open claw, clawed code and what a revolution agents have become. And specifically, the hobbyists who are really where a lot of energy, um, we see, you know, a lot of the innovation breaks, want desktops, you announced one here, uh, I believe it's the Dell sixty-eight hundred. Uh, this is a very powerful workstation to run local models, seven hundred fifty gigs of RAM. Obviously, the, the Mac Studio sold out everywhere.

• 12:48Jason Calacanis?

  In my company, we're moving to open claw everything. Freeburg just got claw pilled. You got claw pilled, I understand, and you're obsessed with these. What is this from the streets movement of creating open source agents and using open source on the desktop mean to you?

• 13:05Jensen Huang?

  So great.

• 13:06Jason Calacanis?

  Where is that going?

• 13:07Jensen Huang?

  Yeah, so great. First of all, let's take a step back. In the last two years, we saw basically three inflection points. The first one was generative. ChatGPT brought AI to the common. Everybody to our awareness. But the fact of the matter is the technology set in plain sight months before GPT. It wasn't until ChatGPT put a user interface around it, made it easy for us to use, that generative AI took off. Now generative AI, as you know, generates tokens for internal consumption as well as external consumption. Internal consumption is thinking, which led to reasoning. Oh, one and oh, three.

• 13:49Jensen Huang?

  Continue that wave of ChatGPT grounded information made AI not only answer questions, but answer questions in a more grounded way useful. We started seeing the revenues and the economic model of OpenAI start to inflect. Then the third one was only inside the industry that we saw. Claude Code, the first agentic system that was very useful, really revolutionary stuff. But Claude Code was only available for enterprises. Most people outside never saw anything about Claude Code until OpenClaw. OpenClaw basically put into the popular consciousness what an AI agent can do.

• 14:33Jensen Huang?

  That's the reason why OpenClaw is so important from a cultural perspective. Now, the second reason why so important is that OpenClaw is open, but it formulates. It structures a type of computing model that is basically reinventing computing altogether. It has a memory system, it's a short-term memory file system. It has, it has scales. Did you say skills or scales? Skills. Oh, skills. They do have scales theoretically, yeah. Yeah, so the first thing, first thing, it, you know, it has resources, it manages resources, it does scheduling. Yep.

• 15:13Jensen Huang?

  Right, and it cron jobs, it could, it could spawn off agents, it could, you know, it could decompose a task. And and cause and solve problems does scheduling. It has I O subsystems, it could, you know, input, it has output, it connect to WhatsApp. And also, it has a A P I that allows it to run multiple types of applications called skills. Yeah. These four elements fundamentally define a computer. Yeah. And therefore, what do we have? We have a personal artificial intelligence computer for the very first time. Open source. It's open source, it runs literally everywhere.

• 15:52Jensen Huang?

  And so this is now the, this is the op, this is basically the blueprint, the operating system of modern computing. Yeah. And it's going to run literally everywhere. Now, of course, one of the things that we had to help it do is whenever you have agentic software, you have to make sure that an agentic software has access to sensitive information, it execute code, it could communicate externally. We have to make sure that all of it has to be governed, all of it has to be secure, and that we have policies that that gives these agents two of the three things, but not all three things at the same time. And so the governance part of it, we contributed to Peter, Peter Steinberger was here. And and so we've got a mound of great engineers working with him to help secure and keep that thing

• 16:35Jensen Huang?

  so that it could protect our privacy, protect our security.

• 16:38David Friedberg?

  Jensen, that paradigm shift makes some of the A I legislation that has passed around the country to regulate A I, and a lot of the proposed legislation effectively moot, doesn't it? Can you just comment for a second on how quickly the paradigm shift kind of obviates a lot of the models for regulatory oversight of A I, which is becoming a very hot topic in politics right now?

• 16:59Jensen Huang?

  Well, this is, this is the part that that we just, with policymakers, we need to, we need to always get in front of them. And Brad, you do a great job doing this. We had get in front of them and inform them about the state of the technology, what it is, what it is not. It is not a biological being. It is not. Alien, it is not conscious, it is computer software.

• 17:26Chamath Palihapitiya?

  Yeah, exactly.

• 17:26Jensen Huang?

  And it is not something that, we say things like, we don't understand it at all. It is not true, we don't understand at all. We understand a lot of things about this technology. And so, so I think, one, we have to make sure that we continue to inform the policymakers and not affect, not allow dumerism and extremism to affect how policymakers think and understand about this technology. However, however, we still have to recognize this technology is moving really fast and don't get policy ahead of the technology too quickly. And the risk that we, we run as a nation, our greatest source of national security concern with respect to AI

• 18:06Jensen Huang?

  is that other countries adopt this technology while we are so angry at it or afraid of it or somehow paranoid of it, that our industries, our society, don't take advantage of AI. And so, I'm just mostly worried about the diffusion of AI here in the United States.

• 18:23Chamath Palihapitiya?

  Can you just double click, if you were in the seat, in the boardroom of Anthropic, over that whole scuttlebutt with the Department of War? It sort of builds on this idea of, people didn't know what to think. It's sort of added to this layer of either resentment or fear or just general mistrust that people have sometimes at the software levels of AI. What would you, do you think you would have told Dario and that team to do maybe differently? To try to change some of this outcome and some of this perception?

• 18:51Jensen Huang?

  The first thing that I would, I would say about Anthropic is, first of all, the technology is incredible.

• 18:55Chamath Palihapitiya?

  Incredible.

• 18:56Jensen Huang?

  We are a large consumer of Anthropic technology.

• 18:58Chamath Palihapitiya?

  Yeah.

• 18:58Jensen Huang?

  Really admire their focus on security, really admires their focus on safety. The, the, the, the culture by which they went about it, the, the technology excellence by which they went about it, really fantastic. I, I would say that, that, the, the desire to warn people about the capability of the technology is, is also really terrific. We just have to make sure that we understand that the world has a spectrum and that, that warning is good, scaring is less good.

• 19:31Jensen Huang?

  And because this technology is too important to us.

• 19:35Speaker 04

  Right.

• 19:35Jensen Huang?

  And and I think that it is fine to predict the future. But we need to be a little bit more circumspect. We need to have a little bit more humility that in fact, we can't completely predict the future. And the ability and to say things that that are quite extreme, quite catastrophic, that there's no evidence of it happening. Could be more damaging than people think and and of course, we are technology leaders. There were, there was a time when nobody listened to us.

• 20:08Speaker 04

  Yeah.

• 20:09Jensen Huang?

  But now, because technology is so important in the social fabric, such an important industry, so important to national security. Our words do matter, and I think we have to be much more circumspect. We have to be more moderate, we have to be more balanced, we have to be more thoughtful.

• 20:24Speaker 04

  Well, I, you know, I would nominate you. I think the industry's got to get together. Seventeen percent popularity of AI in the United States. I mean, we see what happened to nuclear, right? We basically shut down the entire nuclear industry and now we have a hundred fission reactors being built in China and zero in the United States. Um, we hear about moratoriums on data centers, so I think we have to be a lot more proactive about that. But but I want to go back to this agentic explosion that you're seeing inside your company, the efficiencies, the productivity gains inside your company. There's a lot of debate whether or not we're seeing ROI, right? And you and I entering into this year, the big question was, are the revenues going to show up? Are the revenues going to scale like intelligence?

• 21:07Speaker 04

  And then we had this kind of Oppenheimer moment, a five, six billion dollar month by Anthropic in February. Do you think as you look ahead, you announced a trillion dollar, you know, visibility into a trillion dollars of just Blackwell and Vera Rubin over the course of the next couple of years? When you see this happening at Anthropic and OpenAI, do you think we're on that curve now where we're going to see revenues scale in the way that intelligence is scaling?

• 21:32Jensen Huang?

  When you look around, when you, I'll answer this a couple of different ways. When you look around this audience, you will see that Anthropic and OpenAI is represented here. But in fact, every but ninety-nine percent of everything that is here is all AI and it's not Anthropic and OpenAI.

• 21:46Speaker 04

  Right, right.

• 21:47Jensen Huang?

  And the reason for that is because AI is very diverse. I would say that the second most popular model as a category is open models. Number one is, yeah, open, right, open source, open weights, open source. OpenAI is number one, open source is number two, very distant third is Anthropic. And that tells you something about the scale of all of the AI companies that are here. And so, so it's important to recognize, recognize that. Um, let me, let me come back and say a couple of things. One, when we went from generative to reasoning, the amount of computation we needed was about a hundred times. Right.

• 22:26Jensen Huang?

  When we went from reasoning to agentic, the computation is probably another hundred times. Now we're looking at, in just two years, computation went up by a fact, 10,000x. Meanwhile, people pay for information, but people mostly pay for work. Yes. Talking to a chatbot and getting an answer is super great. Right. Helping me do some research, unbelievable. But getting work done, I'll pay for. Indeed. And so that's where we are. Agentic systems get work done. They're helping our software engineers get work done. And, and so then you take that, you got 10,000x more compute.

• 23:11Jensen Huang?

  You get probably, at this point, 100x more consumption now. Yes.

• 23:15Jason Calacanis?

  Yeah.

• 23:15Jensen Huang?

  And we haven't even started scaling yet. We are absolutely at a millionx.

• 23:20Jason Calacanis?

  Which is, I think, a great place to talk about the number of engineers you have, 20, 30,000 at the company, something.

• 23:26Jensen Huang?

  We have 43,000 employees, you know, I would say 38,000 are engineers.

• 23:32Jason Calacanis?

  The conversation we've had on the pod a number of times is, oh my God, look at the token usage in our companies. It is growing massively. And some people are asking, hey, when I join a company, how many tokens do I get? Cause I want to be an effective employee. And you postulated, I believe during your two and a half hour keynote, pretty long keynote, well done.

• 23:54Jason Calacanis?

  That you were spending-

• 23:56Jensen Huang?

  If it was well done, it would be shorter.

• 23:57Jason Calacanis?

  Yeah, you didn't have time to do a-

• 23:59Jensen Huang?

  Yeah.

• 23:59Jason Calacanis?

  You didn't have time to write a short one.

• 24:01Jensen Huang?

  Hour forty-five. So you guys, so you guys know, so you guys know, there is no practice. And so it's a gripping and ripping. Grip and rip. Yeah, yeah.

• 24:08Jason Calacanis?

  I love it.

• 24:08Jensen Huang?

  And so, so I just want to let you know, I was writing the speech while I was giving the speech. Okay, so.

• 24:13Jason Calacanis?

  You never know, but does that mean if we do back-

• 24:17Jensen Huang?

  I apologize.

• 24:17Jason Calacanis?

  If back in the envelope math, seventy-five thousand in tokens for each engineer, something like that? So are you spending in Nvidia, a billion, two billion dollars on tokens for your engineering team right now?

• 24:28Jensen Huang?

  We're trying to. Let me give you the thought experiment. Let's say you have a software engineer or AI researcher and you pay them five hundred thousand dollars a year. We do that all the time. Okay, this is happening all of the time. That five hundred thousand dollar engineer, at the end of the year, I'm gonna ask him, how many tokens, how much did you spend in tokens? And that person said, five thousand dollars, I will go ape something else.

• 24:50Jason Calacanis?

  Yes.

• 24:50Jensen Huang?

  Right. If that, if that five hundred thousand dollar engineer did not consume at least two hundred and fifty thousand dollars worth of tokens, I am going to be deeply alarmed. Okay, and this is no different than one of our chip designers who says, guess what? I'm just gonna use paper and pencil. I don't think I'm gonna need any CAD tools. Right, right.

• 25:11Jason Calacanis?

  This is a real paradigm shift to start thinking about these all-star employees. It almost reminds me of, of what we learned in the NBA when LeBron James started spending a million dollars a year just on his health of his body, like in maintaining it.

• 25:23Jensen Huang?

  That's right.

• 25:24Jason Calacanis?

  Here he is at age forty-one, still playing. It really is, hey, if these are incredible knowledge workers, why wouldn't we give them superhuman abilities?

• 25:33Jensen Huang?

  That's exactly right.

• 25:34Jason Calacanis?

  Where does that go? If we, if we extrapolate out two or three years from now, what is the efficiency of that all-star at an Nvidia and what they're able to accomplish? What do they look like?

• 25:44Jensen Huang?

  Well, first of all, things that, that, that, um, wow, this is too hard. That thought is gone. Uh, this is gonna take a long time. That thought is gone. Uh, we're gonna need a lot of people. That thought is gone. This is no different than in this, in the last industrial rev- revolution, somebody goes, "Boy, that building really looks heavy." Nobody says that.

• 26:05Jason Calacanis?

  Right.

• 26:05Jensen Huang?

  Nobody, "Wow, that mountain looks too big." Nobody says that.

• 26:08Jason Calacanis?

  Right.

• 26:08Jensen Huang?

  Everything that's too big, too heavy, takes too long, those thought, those ideas are all gone.

• 26:14Jason Calacanis?

  You're reduced to creativity.

• 26:15Jensen Huang?

  That's right.

• 26:16Jason Calacanis?

  What can you come up with?

• 26:16Jensen Huang?

  Exactly. Which means, now, the question is, how do you, how do you work with these agents? Well, it's just a new way of doing computer programming. In the fut- in the past, we code. In the future, we're gonna, we're gonna write ideas, architectures, specifications. We're gonna organize teams. We're gonna give them, we're gonna help them define how to evaluate the definition of good versus bad. What's the, what does it look like when something is a great outcome? How to iterate with you, how to brainstorm? That's really what you're looking for. And I'm, I think that every engineer is gonna have hundred, a hundred agents.

• 26:51Jason Calacanis?

  Back to the PR problem the industry has right now. You have executives, uh, like David Friedberg with Ohalo, who's looking at literally taking, through the use of technology, your technology and AI, the number of calories produced and making high quality cal- calories. Uh, what is the factor you think you can bring the cost down, Friedberg? And what impact does this vision have for what you're doing?

• 27:16David Friedberg?

  Zero-shot genomic modeling, and it works.

• 27:20Jensen Huang?

  Yeah.

• 27:20David Friedberg?

  And you have that moment and you're like, "Holy sh-" Honestly, like, and, and that's after people are replacing entire enterprise software stacks in a night. I did something in 90 minutes I was telling the guys about. Replaced the whole software stack and like, a whole bunch of workload. 90 minutes on Claude, ran this Agentic system, built the whole thing, deployed it. And we got, we were all-

• 27:40Jason Calacanis?

  On a Sunday night.

• 27:41David Friedberg?

  On a Sunday night, 10pm. I was done at 11:30. I went to bed.

• 27:43Jason Calacanis?

  As the CEO, you replaced-

• 27:46David Friedberg?

  Yeah. And everyone on my management team had to do a similar exercise over the weekend. What we saw on Monday, I was like- It's over, but the technical stuff, the science stuff, we did something in 30 minutes using auto research and I'd love your view on auto research and what that tells us about how far we still have to go in terms of efficiency. But using auto research and a chunk of data, something was published internally that we said, oh my god. And that would normally be a PhD thesis that would take seven years. It would be one of the most celebrated PhD pieces we've ever seen in this field. And it would be in the journal Science and it was done in 30 minutes on a desktop computer running on auto research. With all the data we just ingested, we got it on Friday. We're like, hey, let's try it. Try it, boot it up, go into GitHub, download it on a research and ran it.

• 28:30David Friedberg?

  And you see everyone's face just go like. And then the potential of what this is unlocking for us is like the kind of thing that would take seven years and it happened in 30 minutes. And we're experiencing it in genomics and we're like, this is unbelievable. So I, I think like the acceleration is widening the aperture for everyone in a way that like you didn't imagine a few years ago. But just going back to the auto research point. Can you just comment on what you think about the fact that this thing got published with 600 lines of code in a weekend? And the capacity that it has to run locally and achieve what it can achieve with all of these diverse data sets? And what that tells us about the early stages we are in terms of optimization on algorithms and hardware.

• 29:11Jensen Huang?

  The fundamental reason why OpenClaw is so incredible, number one, is its confluence, its timing with the breakthroughs in large language model.

• 29:23David Friedberg?

  Yeah.

• 29:23Jensen Huang?

  Its timing was perfect. It was impeccable. Now, in a lot of ways, Peter wouldn't have come up with it probably if not for the fact that Claude and GPT and ChatGPT have reached a level that is really very good.

• 29:35David Friedberg?

  Right.

• 29:36Jensen Huang?

  It is also a new capability that allows these models to tool use. The tools that we've created over time. Web browsers and Excel spreadsheets and, you know, in the case of chip design synopses and cadence and Omniverse and Blender and Autodesk and all of these tools are going to continue to be used. There's some, some people say that, that the enterprise IT software industry is going to get destroyed. There's, it's, there's, let me give you the alternative view. The enterprise software industry is limited by butts and seats. It's about to get 100 times more agents banging on those tools. There are going to be agents banging on SQL. There are going to be agents banging on vector databases,

• 30:21Jensen Huang?

  agents banging on Blender, agents banging on Photoshop. And the reason for that is because those tools are, first of all, do a very good job. Second, those tools are the conduit between us. In the final analysis, when the work is done, it has to be represented back to me in a way that I can control. Right. And I know how to control those tools. And so I need everything to be put back into synopsis. I want everything to be put back into cadence because that's how I control it. That's how I ground truth.

• 30:50Chamath Palihapitiya?

  Let me ask you a question about open source. So we have these closed source models. They're excellent.

• 30:55Jensen Huang?

  Yeah.

• 30:55Chamath Palihapitiya?

  We have these open weight models. Many of the Chinese models are incredible. Absolutely incredible. Two days ago, you may not have seen this because you were busy on stage, but there was a training run that happened in this crypto project called BitTensor, subnet 3. They managed to train a 4 billion parameter Llama model, totally distributed with a bunch of people contributing excess compute, but they were able to do it statefully and manage a training run, which I thought was like a pretty crazy technical accomplishment.

• 31:25Jensen Huang?

  Yeah.

• 31:26Chamath Palihapitiya?

  Because it's like random people and each person gets a little share.

• 31:29Jensen Huang?

  Our, our modern version of folding at home.

• 31:31Chamath Palihapitiya?

  Exactly.

• 31:32Jensen Huang?

  Yeah.

• 31:32Chamath Palihapitiya?

  So what, what do you think about the end state of open source? Do you see this decentralization of architecture as well? And decentralization of compute to support open weights and a totally open source approach to making sure AI is broadly available to everyone?

• 31:48Jensen Huang?

  I believe we fundamentally need

• 31:51Jensen Huang?

  Models as a first-class product, proprietary product, as well as models as open source. These two things are not A or B, it's A and B. There's no question about it. And the reason for that is because models is a technology, not a product. Models are technology, not a service. For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own. I would really love to keep using ChatGPT. I love to use Claude, I love to use Gemini, I love to use X. And they all have their own personalities, as you know, which is kind of depends on my mood and depends on what problem I'm trying to solve. You know, I might, you know, do it on X or I might do it on ChatGPT.

• 32:35Jensen Huang?

  And so that, that segment of the, of the industry is thriving, it's going to be great. However, there, all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control. And that, it can only come from open models. The open model industry, we're contributing tremendously to. It is near the frontier. And quite frankly, even if it reaches the frontier, I think that products as a service, world-class products as, as a, models as a product, is going to continue to thrive.

• 33:12Jason Calacanis?

  Every startup we're investing in now is open source first and then going to the proprietary models.

• 33:19Jensen Huang?

  Yeah, and the beautiful thing is because you have a great router you're connected to by, on, on first day, every single day, you're going to have access to the world's best model. And, and then it gives you time to cost reduce and fine-tune and specialize. And so you're going to have world-class capabilities out to shoot every single time.

• 33:38Speaker 04

  Can I just ask?

• 33:39Jason Calacanis?

  Of course.

• 33:40Speaker 04

  Nobody wants the US to win the global AI race more than you, right? But a year ago, the Biden-era diffusion rule really was an anti-American diffusion of AI around the world. So here we are, a year into the new administration. Give us a grade. Where is, where are we in terms of global diffusion and the rate at which we're spreading US AI technology around the world? Are we an A, are we a B, are we a C? What, what's working, what's not working?

• 34:09Jensen Huang?

  Well, first of all, President Trump wants American industry to lead. He wants American technology industry to lead. He wants American technology industry to win. He wants us to spread American technology around the world. He wants the United States to be the wealthiest country in the world. He wants all of that. At the current moment, as we speak, Nvidia gave up a 95% market share in the second largest market in the world, and we're at 0%. President Trump, that's right. President Trump wants us to get back in there. And, and the first thing is, to get licensed, licensed for the companies that we're gonna be able to sell to.

• 34:53Jensen Huang?

  We've got many companies who have requested for licenses. We've applied for licenses for them, and we've got approved licenses from Secretary Lutnik. Now, we've, we informed the Chinese companies, and many of them have given us purchase orders. And so, we're gonna, we're gonna, we're in the process of cranking up our supply chain again to go ship. I think at the highest level, Brad, I think one of the things that we should acknowledge is this. Our national security is diminished when we don't have access to miniature motors, rare earth minerals. It's diminished when we don't control our telecommunications networks. It's diminished when we can't provide for sustainable energy for our country.

• 35:38Jensen Huang?

  It is fundamentally diminished. Every single one of these industries is an example of what I don't want the AI industry to be. Right. When we look forward in time, and we say, what do we want? What is, what does it look like? When American technology industry, American AI industry leads the world, we can all acknowledge that there is no way that AI models is one universally.

• 36:05Jensen Huang?

  It is, we can all acknowledge that that is an outcome that makes no sense. However, we can all imagine that the American tech stack, from chips to computing systems, to the platforms, are used broadly by the world, where they build their own AI, they use public AI, they use private AI, whatever, and they can build their applications in their society. I would love that the American tech stack is 90% of the world. Yes. I would love that. The alternative, if it looks like solar, rare earth, magnets, motors, telecommunications, I consider that a very bad outcome for national security. Agreed.

• 36:47David Friedberg?

  Yeah. How much are you monitoring the situation with the conflicts around the world right now, and how much does it worry you, Jensen? So China and Taiwan, and then helium availability coming out of the Middle East, I understand, can be a supply chain risk to semiconductor manufacturing. How much do these situations worry you? How much are you spending on them?

• 37:06Jensen Huang?

  Well, first of all, I think in Middle East, I have, we have 6,000 families there.

• 37:10David Friedberg?

  Yeah.

• 37:11Jensen Huang?

  Uh, we have a lot of Iranians at Nvidia, and their families are still in Iran. And so, so we have, we have a lot of families there. The first thing is, is they're quite anxious, they're quite concerned, quite scared. Um, we're thinking about them all the time, uh, we're monitoring and keeping an eye on them all the time. They have 100% of our support. Uh, I've been asked several times, are we still considering, uh, being in Israel? We are 100% in Israel. We are 100% behind the families there. We are 100% in the Middle East. I was also asked, you know, given what's happening in the Middle East, uh, i- is that an area where we believe that we can expand artificial intelligence too? Um, I believe that there's a reason we went to war, and I believe at the end of the war,

• 37:55Jensen Huang?

  Middle East will be more stable than before. And so, if we were there, if we're considering it before, we should absolutely be considering it after. And so, I'm 100% in on that. With respect to, with, with, with respect to Taiwan, we have to do three things. One, we have to make sure that we re-industrialize the United States as fast as we can.

• 38:16David Friedberg?

  Yeah.

• 38:16Jensen Huang?

  And whether it's the chip manufacturing plants, the, the computer manufacturing plants, or the AI factories.

• 38:21David Friedberg?

  How are we doing on that?

• 38:22Jensen Huang?

  We're doing excellent. With, by, by gaining the strategic support, by gaining the friendship of the supply chain of Taiwan. By gaining their friendship, by gaining their support, we were able to build Arizona and Texas, California at incredible rates. They are genuinely a strategic partner. We really, they deserve our support, they deserve our friendship, they deserve our generosity, and they're doing everything they can to accelerate the manufacturing process for us. And so, so I think that's number one. Number two, we ought to diversify the manufacturing supply chain, and whether it's South Korea,

• 39:07Jensen Huang?

  whether it's Japan, it's Europe, we ought to diversify the supply chain, make it more resilient. And number three, let's be, let's demonstrate restraint. And while we're reducing, increasing our diversity and resilience, let's not press, push. Unnecessary.

• 39:29Jason Calacanis?

  We need to be patient.

• 39:30David Friedberg?

  It's helium a problem? A lot of reports.

• 39:34Jensen Huang?

  You know, I think helium could be a problem, but it's also the case that the supply chain probably has a lot of buffer in it. These kind of things tend to have a lot of buffer, but, but, you know.

• 39:45Jason Calacanis?

  You've made massive progress in self-driving. You made a big announcement, you've added many more partners, including BYD. There was just a video of you driving around in a Mercedes. And a huge announcement with Uber that you're going to have a number of cars on the road for many different manufacturers. Your bet is, I believe that there's going to be an Android-type open-source platform that you're going to play a major part in with dozens of car providers, and then maybe on the other side, there could be an iOS with Tesla or Waymo. What's your strategy thinking there and how that chessboard emerges?

• 40:26Jason Calacanis?

  Because it feels like you have a pretty deep stack, and in some ways you're competing, and in other places you're collaborative.

• 40:34Jensen Huang?

  Yeah. It's, taking a step back, we believe that everything that moves will be autonomous, completely or partly. Someday, number one. Number two, we don't want to build self-driving cars, but we want to enable every car company in the world to build self-driving cars. And so we built all three computers, the training computer, the simulation computer, the evaluation computer, as well as the car computer. We developed the world's safest driving operating system. We also created the world's first reasoning autonomous vehicle, so that it could decompose complicated scenarios into simpler scenarios that it knows how to navigate through, just like us, reasoning systems.

• 41:17Jensen Huang?

  And so that reasoning system, called Alpamio, has enabled us to achieve incredible results.

• 41:24Jensen Huang?

  We open this, we vertical optimization, we horizontally innovate, and we let everybody decide, do you want to buy one computer from us? In the case of Elon and Tesla, they buy our training computers. Do they want to buy our training computer and our simulation computers? Or do you want to let us work with us to do all three and then put the car computer in your car? So we, you know, our attitude is, we want to solve the problem. We're not the solution provider.

• 41:55Jensen Huang?

  And we're delighted, however you work with us.

• 41:57Chamath Palihapitiya?

  Let me build on this question, because I think it's like, it's so fascinating. You actually do create this platform. A thousand flowers are blooming. But it's also true that some of those flowers want to now go back down in the stack and try to compete with you a little bit. Google has TPU, Amazon has Inference and Trainium. You know, everybody's sort of spinning up their own version of, I think I can out Nvidia Nvidia, even though they also tend to be huge customers. How do you navigate that? And what do you think happens over time? And where do those things play in the complexion of this kind of vision?

• 42:32Jensen Huang?

  Yeah, really great. You know, first of all, we're the only AI company, we're an AI company. We build foundation models. We're at the frontier in many different domains. We build every single, every single layer, every single stack. We're the only AI company in the world that works with every AI company in the world. They never show me what they're building and I always show them exactly what I'm building.

• 42:53Chamath Palihapitiya?

  Right.

• 42:54Jensen Huang?

  Yeah. And so, so the confidence comes from this. One, uh, we are delighted to compete on what is the best technology. And to the extent that, to the extent that we can continue to run fast, I believe that buying from Nvidia still is one of the most economic things they could do. And I just have incredible confidence there. Number one, number two, we're the only architecture that could be in every cloud. And that gives us some fundamental advantages. We're the only architecture you could take from a cloud and put into on-prem in the car in any region.

• 43:26Chamath Palihapitiya?

  In space.

• 43:27Jensen Huang?

  That's right, in space. And so there's a whole, whole part of our market, about forty percent of our, of our business. Most people don't realize this, forty percent of our business, unless you have the CUDA stack, unless you can build an entire AI factory, you have, the customers don't know what to do with you. They're not trying to build chips. They're not trying to buy chips. They're trying to build AI infrastructure. And so they want you to come in with a full stack and we've got the whole stack. And so surprisingly Nvidia is gaining market share. If you look at where we are today, we're gaining share.

• 43:57Chamath Palihapitiya?

  Do you think what happens is these guys try and they realize, oh my God, it's too much? And then they come back, is that why their share grows?

• 44:04Jensen Huang?

  Well, we're gaining share for several reasons. One, um, our velocity has gone, we help people realize it's not about building the chip, it's about building the system. And that system's really hard to build. Uh, and, and so their, their, their business with us is increasing. In the case of AWS, I think they just announced, I think it was yesterday, that they're gonna buy a, a million chips, uh, in the next couple of years. I mean, that's a lot of chips from, from AWS and that's on top of all the chips they've already bought. And so we're delighted to do that. But number one, we're gaining share this last couple of years because we now have Anthropic coming to Nvidia.

• 44:42Jensen Huang?

  Meta SL is coming to Nvidia and the growth of open models is incredible and that's all on Nvidia. And so we're growing in share because of the number of models. We're also growing in share because all of these companies are outside of the cloud and they're growing regionally in enterprise, in industries, at the edge. And that entire segment of growth is, you know, really hard to do if it's just building an ASIC.

• 45:09Speaker 04

  Brad, related to that, and not to get in the weeds on the numbers, but analysts don't seem to believe, right? So if you look at the consensus forecast, you said compute could 1 million X, right? And yet they have you growing next year at 30%, the year after that at 20%. And in 2029, which is supposed to be a monster year, at 7%, right? So if you just, if you take your TAM and you apply their growth numbers, it suggests that your share will plummet. Do you see anything in your future order book that would make that correct?

• 45:44Jensen Huang?

  Yeah, first of all, they just don't understand the scale and the breadth of AI. Yeah.

• 45:50Chamath Palihapitiya?

  Yeah, I think that's true.

• 45:51Jensen Huang?

  Most people think that AI is in the top five hyperscalers. Right.

• 45:56Speaker 04

  That's right.

• 45:56Chamath Palihapitiya?

  There's also an orthodoxy around these law of large numbers where, you know, they have to go back to their investment banking risk committee and show some model. They're not going to believe in their minds that 5 trillion goes to 15 trillion.

• 46:09Speaker 04

  It's so out of bounds.

• 46:11Chamath Palihapitiya?

  It can go to 7.

• 46:12Speaker 04

  Or that you could have a $10 trillion company.

• 46:14Chamath Palihapitiya?

  It's all just CYA stuff that I think ultimately.

• 46:16Speaker 04

  It's never happened before, so you can't say it will.

• 46:17Jensen Huang?

  And, and because, because you have to redefine what it is that you do. There was somebody who made an observation recently that Nvidia Jensen, how can you be larger than Intel in servers? And the reason for that is because the CPU market of the entire data center was about $25 billion a year.

• 46:37Chamath Palihapitiya?

  Right.

• 46:38Jensen Huang?

  We do $25 billion a year, as you guys know, in a very, in the time that we were sitting here. And so obviously, obviously.

• 46:47Jensen Huang?

  That was a joke.

• 46:48Chamath Palihapitiya?

  No, it's but it's.

• 46:49Jensen Huang?

  All in podcast.

• 46:50David Friedberg?

  Roughly true. Don't worry, everything on the show is roughly true.

• 46:53Chamath Palihapitiya?

  Don't worry about it.

• 46:53David Friedberg?

  It's all in.

• 46:54Chamath Palihapitiya?

  Wow.

• 46:55Jensen Huang?

  Okay.

• 46:55Chamath Palihapitiya?

  God, that's a lot.

• 46:56Jensen Huang?

  That was not guidance. But anyhow, anyhow, the point is how big you can be.

• 47:02Chamath Palihapitiya?

  Yeah.

• 47:02Jensen Huang?

  Depends on what is it that you make.

• 47:04Chamath Palihapitiya?

  Right.

• 47:05Jensen Huang?

  Nvidia is not making chips. Number one. Making chips does not help you solve the AI infrastructure problem anymore. It's too complicated. Number three, most people think that AI is narrowly in the things that they talk about and hear and see.

• 47:20Chamath Palihapitiya?

  Right.

• 47:20Jensen Huang?

  It's AI is much, OpenAI is incredible. They're going to be enormous. Anthropic is incredible. They're going to be enormous. But AI is going to be much, much bigger than that.

• 47:30Chamath Palihapitiya?

  Tell us about.

• 47:31Jensen Huang?

  And we address that segment.

• 47:32Chamath Palihapitiya?

  Tell us about data centers in space for a second.

• 47:34Jensen Huang?

  Yep.

• 47:36Jensen Huang?

  We're already in space.

• 47:37Chamath Palihapitiya?

  How should the layman think about what that business is versus when you hear about these big data center build outs that's happening in, on the ground?

• 47:47Jensen Huang?

  Well, we should definitely work on the ground first because we're already here. And number one, number two, we should prepare to be out in space. And obviously, there's a lot of energy in space. The challenge, of course, is that cooling, you can't take advantage of conduction and convection.

• 48:03Chamath Palihapitiya?

  Convection.

• 48:03Jensen Huang?

  And so you can only use radiation. And radiation requires very large surfaces.

• 48:07Chamath Palihapitiya?

  Right.

• 48:08Jensen Huang?

  Now, that's not an impossible thing to solve. And there's a lot of, lot of space in space. But nonetheless, the expense is still quite there, is there. We're going to go explore it. We're already there. We're already radiation hardened. We have, we have a CUDA in satellites around the world. They're doing imaging, image processing, AI imaging. And, and that kind of stuff ought to be done in space instead of sending all the data back here and do imaging down here. We ought to just do imaging out in space. And so there's a lot of things that we ought to do, do in space. And in the meantime, we're going to explore what does the architecture of data centers look like in space. And it'll take, it'll take years. It's okay. We got, I got plenty of time.

• 48:49Chamath Palihapitiya?

  I wanted to double click on healthcare. I know you've got a big effort there. We're all of a certain age where we're thinking about lifespan, health span. I mean, we all look great, I think.

• 48:59Jason Calacanis?

  Some better than others. I think some better than others. I don't know what your secret is, Jensen. Pretty good. I mean, what are you taking, what's off the menu? You gotta talk to me when we're backstage. I want to know in the green room what you've got going on.

• 49:11Jensen Huang?

  Squats and pushups and situps.

• 49:12Jason Calacanis?

  Perfect, okay. But what you know in terms of the build out in healthcare, where is that going and what kind of progress are we making? I was just using Claude to do some analysis and saying like, where are all these billing codes? We spend twice as much money in the US, we get, seem to get half as much. It seemed like 15 to 25% of the dollars spent were on these first GP visits. And I think we all know like ChatGPT and a large language model does a better job more consistently today at a first visit. So what has to happen there to kind of break through all that regulation? And have AI have a true impact on the healthcare system?

• 49:55Jensen Huang?

  There's several areas that we're involved in healthcare. One is AI physics. And that's, or AI biology, using AI to understand, represent, predict biology, behavior, biological behavior. And so that's one, that's very important in drug discovery. There's second, which is AI agents. And that's where the assistance and helping diagnosis and things like that. Open evidence is a really good example. Hippocratic is a really good example. Love working with those companies. I really think that this is an area where agentic technology is going to revolutionize how we interact with doctors and how do we interact for healthcare. The third part that we're involved in is physical AI.

• 50:40Jensen Huang?

  The first one's AI physics, using AI to predict physics. The second one is physical AI, AI that understand the properties of the laws of physics. And that's used for robotic surgery, huge amounts of activities there. Every single instrument, whether it's ultrasound or, you know, CT or whatever instrument we interact with in a hospital in the future will be agentic.

• 51:02Jason Calacanis?

  Yeah.

• 51:02Jensen Huang?

  You know, open claw in a safe version will be inside every single instrument. And so in a lot of ways. That instrument is going to be interacting with patients and nurses and doctors.

• 51:12Jason Calacanis?

  Yeah, I mean, we're seeing so much investment in AI weapons. It would be wonderful to see some investment in AI EMTs and paramedics and saving lives, not just taking them.

• 51:22Jensen Huang?

  Yeah.

• 51:23Jason Calacanis?

  Which I think is a great segue into robotics. You've got dozens of partners.

• 51:27Jensen Huang?

  Yeah.

• 51:28Jason Calacanis?

  We had this very weird, I don't know what I want to call a lost decade or 20 years of Boston Dynamics, Google bought a bunch of companies, they then wound up selling them and spinning them out, where people just thought, ah, robotics is just not ready for primetime. And now here we have the world's greatest entrepreneur at this time, tied with you, Elon Musk, doing, well, that was a good save, I hope. Optimus, pretty impressive. And then other companies in China. How close is that to actually being in our lives where we might see a chef, robotic chef, a robotic nurse, a robotic housekeeper, you know, this humanoid factor actually working in the real world, knowing what you know with those partners and the fidelity, especially

• 52:13Jason Calacanis?

  in China where they seem to be doing as good a job as we're doing here, or maybe better?

• 52:20Jensen Huang?

  We invented the industry largely. America invented. We could, you could argue, we got into it too soon.

• 52:26Jason Calacanis?

  Yeah.

• 52:26Jensen Huang?

  And, and we got exhausted. We got tired. About five years before the enabling technology appeared.

• 52:34Jason Calacanis?

  Yes, the brain.

• 52:35Jensen Huang?

  Yeah, yeah. And we, we just got tired of it just a little too soon. Okay, that's number one. But it's here now. Now, the question is how much longer? From the point of high functioning, existence proof, high functioning existence, existence proof, to reasonable products, technology never takes more than a couple, two, three cycles. And so, a couple, two, three cycles would basically be somewhere around three years to five years. That's it. Three years to five years, we're going to have robots all over the place. I think, I think China is, is formidable, and the reason for that is because their microelectronics, their motors, their rare earth, their magnets, which is foundational to robotics. They are the world's best.

• 53:20Jensen Huang?

  And so, in a lot of ways, our robotics industry relies deeply on their ecosystem and their supply chain. And they're, you know, obviously, moving very quickly. We're gonna, you know, our robotics industry will have to rely a lot on it. The world's robotics industry will have to rely on a lot on it. And so, so I think you're gonna see some fast, fast movements here.

• 53:42Jason Calacanis?

  Ultimately, one for one. Elon seems to think we're gonna have one robot for every human. Seven billion for seven billion, eight billion for eight billion.

• 53:49Jensen Huang?

  Well, I'm hoping more. Yeah, I'm hoping more, yeah. Well, first of all, there's a whole bunch of robots that are gonna be in factories, working around the clock. There's gonna be a whole bunch of robots that don't move. They move a little bit. Almost everything will be robotic.

• 54:04David Friedberg?

  What does the world look like? Sorry, let me just say, I think, like, this is one of the, the robotics, for me, is one of the pieces that I think unlocks economic mobility opportunities for every individual. Everyone now, like, when everyone got a car, they could now go and do a lot of different jobs. When everyone gets a robot, their robot can do a lot of work for them. They can stand up an Etsy store, a Shopify store. They can create anything they want with their robot. They could do things that they independently cannot do. I think the robot is gonna end up being the greatest unlock for prosperity for more people on Earth than we've ever seen with any technology before.

• 54:38Jensen Huang?

  Yeah, no doubt. I mean, just the simple, the, the, the simple math at the moment is we're millions of people short in labor today.

• 54:44Jason Calacanis?

  Right.

• 54:45Jensen Huang?

  Right? We're, we're, we're actually really desperate in need of robotics. And so that all of these companies could grow more if they had more labor. I mean, we're, we're, number one. Some of the things that you've mentioned are super fun. I mean, because of robots, we'll have virtual presence. You know, I'll be able to go into the robot of my house and virtually operate it. I'm on a business trip.

• 55:11Jason Calacanis?

  Right.

• 55:11Jensen Huang?

  Walk around the house.

• 55:12Jason Calacanis?

  Walk the dog.

• 55:13Jensen Huang?

  Yeah.

• 55:14Jason Calacanis?

  Break the leaves.

• 55:15Jensen Huang?

  Yeah, exactly.

• 55:16David Friedberg?

  Break out the dog.

• 55:17Jensen Huang?

  Maybe not quite that, but just, you know, just, you know, wander around. And just see what's going on in the house, you know, chat with the dogs, chat with the kids, yeah.

• 55:24Jason Calacanis?

  Yeah.

• 55:25Jensen Huang?

  Time travel is also, we're gonna be able to travel at the speed of light, you know? And so, you know, clearly, we're gonna send our robots ahead of us.

• 55:33Jason Calacanis?

  Yeah.

• 55:33Jensen Huang?

  Not gonna send myself, I'm gonna send a robot.

• 55:35Chamath Palihapitiya?

  Right, check it out.

• 55:36Jensen Huang?

  Yeah, yeah, and then I'm gonna upload my AI.

• 55:39David Friedberg?

  Well, it's inevitable. It unlocks the moon and it unlocks Mars as targets for colonization, which gives us infinite resources. Getting back from the moon is effectively zero energy cost to move material back, because you can use solar and accelerate. So you could have factories that make everything the world needs on the moon, and the robots are gonna be the unlock for enabling that.

• 55:58Jensen Huang?

  That's right. Distance no longer matters.

• 56:00David Friedberg?

  Distance doesn't matter.

• 56:00Jensen Huang?

  Yep.

• 56:00David Friedberg?

  Yeah.

• 56:01Speaker 04

  The more, the more revenue we get out of models and agents, the more we can invest in building the infrastructure, which then unlocks more capabilities on models and agents. Dario on Dorkesh's podcast recently said, "By 27, 28, we'll have hundreds of billions of dollars of revenue out of the model companies and the agent companies," and he forecast a trillion dollars by 2030, right? This is non-infrastructure AI revenue.

• 56:27Jensen Huang?

  I think he, I think he's being very conservative. I believe Dario and Anthropic is gonna do way better than that.

• 56:33Speaker 04

  Wow.

• 56:34Jensen Huang?

  Way better than that.

• 56:34Speaker 04

  Wow.

• 56:35Chamath Palihapitiya?

  So from 30 billion to a trillion.

• 56:37Jensen Huang?

  Yep, and not, and the reason for that is the one part that he hasn't considered is that I believe every single enterprise software company will also be a reseller, value-added reseller of Anthropic code, Anthropic's tokens, value-added reseller of OpenAI.

• 56:55David Friedberg?

  Have to be.

• 56:55Jensen Huang?

  That's right.

• 56:56David Friedberg?

  Yeah.

• 56:56Jensen Huang?

  And they're gonna, that, that, that part of their-

• 56:59Chamath Palihapitiya?

  Get this logarithmic expansion.

• 57:01Jensen Huang?

  Yes.

• 57:01David Friedberg?

  Yeah.

• 57:02Jensen Huang?

  Their go-to-market is going to expand tremendously this year.

• 57:06Chamath Palihapitiya?

  What do you think in that world is the moat? What's left over? I mean, you have some moats that are frankly, I think, as this scales, almost insurmountable. The best one that nobody talks about is probably CUDA, which is just like an incredible strategic advantage. But in the future, if a model can be used to create something incredible, then the next spin of a model can be used to maybe disrupt it. Sort of in your mind, what do you think for these companies that are building at that application layer? What's their moat? Like, how do they differentiate themselves?

• 57:37Jensen Huang?

  Deep specialization. Deep specialization. I believe that these models, they're gonna have general, general models that are connected into the- Software companies, agentic system.

• 57:51Jason Calacanis?

  Right.

• 57:51Jensen Huang?

  Many of those models are cloud models and proprietary models, but many of those models are specialized subagents that they've trained on their own.

• 58:03Jason Calacanis?

  Right. So the call to arms for you, for entrepreneurs is look, know your vertical.

• 58:07Jensen Huang?

  That's right.

• 58:08Jason Calacanis?

  Know it as deep and as better than everybody else.

• 58:10Jensen Huang?

  That's right.

• 58:11Jason Calacanis?

  And then wait for these tools because they're catching up to you and now you can imbue it with your knowledge.

• 58:15Jensen Huang?

  That's right. And the sooner you connect your agent, the sooner you connect your agent with customers, that flywheel is going to cause your agent to get.

• 58:24Jason Calacanis?

  It very, very much is an inversion of what we do today because today we build a piece of software and we say, what generalizes and then let's try to sell it as broadly as possible and then sell the customization around it. And we trap.

• 58:34Jensen Huang?

  And in fact, in fact, with exactly right, we, we create a horizontal, but notice there are all these GSIs and all of these consultants who are specialists, who then take your horizontal platform and specializes it into.

• 58:49Jason Calacanis?

  Exactly. And that's arguably a five or six time bigger industry is the customization.

• 58:53Jensen Huang?

  It is absolutely.

• 58:54Jason Calacanis?

  Yeah, the whole, that very much is.

• 58:55Jensen Huang?

  That's right. So I think that these platform companies have an opportunity to become that specialist, to become that vertical.

• 59:03Jason Calacanis?

  Right.

• 59:03Jensen Huang?

  Yeah, domain expert.

• 59:04Jason Calacanis?

  You know, I just want to give you your flowers. I think it was three years ago, you said, you're not going to lose your job to AI, you're going to lose your job to somebody using AI. And here we are, the entire conversation has revolved around this concept of agents making people superhuman and the business opportunity expanding and entrepreneurship expanding. You actually saw it pretty clearly.

• 59:24Jensen Huang?

  That's right.

• 59:24Jason Calacanis?

  Have you changed your view? Well, I, I go. This is. I'm not, I'm not Dummer. I do, I do have. I'm Dummer. No, I, I, you can hold space for I think two ideas. One is there are going to be a. That's viral J. Calico. No, no.

• 59:37Jensen Huang?

  But that's just because he doesn't hang out with me enough.

• 59:40Jason Calacanis?

  But we, I mean, we fuck a little bit. Be careful what you're talking about. He will show up at your breakfast table.

• 59:45Jensen Huang?

  He'll follow you around. I'm not asking for it. I'm just saying.

• 59:48Jason Calacanis?

  Follow you around.

• 59:49Jensen Huang?

  I'm not asking for it.

• 59:50Jason Calacanis?

  You can come with me and Tucker. We ski in Japan every January. Love it. You and Tucker, we'll go road trip.

• 59:55Jensen Huang?

  Wow. Okay.

• 59:57Jason Calacanis?

  There is going to be job displacement and then the question becomes, you know, do those people have the fortitude, the resolve to then go embrace these, you know, technologies? We're, we're going to see a hundred percent of driving go away by humans. That's just.

• 1:00:12Chamath Palihapitiya?

  That's a beautiful thing in the lives saved, but we have to recognize that's 15 million people in the United States, 10 to 15 million who are employed in that way. And so that is gonna happen, yes?

• 1:00:22Jensen Huang?

  I think that jobs will change. For example, there are many chauffeurs today who drive the car. I believe that many of those chauffeurs will actually be in the car, sitting behind the steering wheel, while the car is driving by itself. And the reason for that is because, remember what a chauffeur does? In the end, these chauffeurs, they're helping you. They're your assistants. They're helping you with your luggage. They're helping you with a lot of things. And so I wouldn't be surprised, actually, if the chauffeurs of the future become your mobility assistant and they are helping you do a whole bunch of other stuff.

• 1:00:59Chamath Palihapitiya?

  To the hotel.

• 1:01:00Jensen Huang?

  And the car is driving by itself.

• 1:01:01Speaker 04

  The autopilot in planes created a lot more pilots and didn't take any of the pilots out of the cockpit. Even though the autopilot is flying the plane 90% of the time.

• 1:01:11David Friedberg?

  And by the way, while that car is driving itself, that chauffeur is gonna be doing a bunch of other work on his phone, and he's gonna be making money doing other things.

• 1:01:18Jensen Huang?

  Arranging, for example, coordinating a bunch of things for you.

• 1:01:20David Friedberg?

  Yeah, the pie just grows in a way that...

• 1:01:23Jensen Huang?

  So one of the things that, yes, every job will be transformed. Some jobs will be eliminated. However, we also know that many, many jobs will be created. The one thing that I will say to young people who are coming out of school, who are anxious about AI, be the expert of using AI.

• 1:01:43Chamath Palihapitiya?

  Yes.

• 1:01:43Jensen Huang?

  How much, look, we all want our employees to be expert at using AI. And it's not trivial. Not trivial. And so knowing how to specify, not to over-prescribe, leaving enough room for the AI to innovate and create, while we guide it to the outcome we want, all of that requires artistry.

• 1:02:06Chamath Palihapitiya?

  You had this great advice to, when you were at Stanford, I think it was, which is, "I wish to you pain and suffering." Do you remember that?

• 1:02:13Jensen Huang?

  Yeah.

• 1:02:13Chamath Palihapitiya?

  Fantastic. What's your advice to young people around what they should be studying? So if they're sort of about to leave high school, because now those are the kids that are at this really native, they haven't made a decision about college, what to study, if at all, go to college. How do you guide those kids? What would you tell them?

• 1:02:31Jensen Huang?

  I still believe that deep science, deep math, language skills. You know, as you know, language is the programming language of AI now. The ultimate programming language. And so, as it turns out, it could be that the English major could be the most successful. Yeah. And so, I think, I think, I would just advise, whatever, whatever education you get, just make sure that you're deeply, deeply expert in using AIs. One of the things that I wanted to say with respect to Jobs, and I want everybody to hear it, that in fact, at the beginning of the deep learning revolution, one of the finest computer scientists in the world, deeply, deeply, I deeply, deeply respect,

• 1:03:14Jensen Huang?

  predicted that computer vision will completely eliminate radiologists. And, and that the one, the one field he advises everybody to not going to is radiology. Ten years later, his prediction was at 100% right. Computer vision has been integrated into all of the radiology technologies and radiology platforms in the world, 100%. The surprising outcome is the number of radiologists actually went up, and the demand for radiologists has skyrocketed. The reason for that is because everybody's job has a purpose and its task. The task that you do is studying the scans.

• 1:03:59Jensen Huang?

  But your purpose is to help the doctors, help the patient diagnose disease. And so, what's surprising is, because the scans are now being done so quickly, they could do more scans, improving healthcare. Yes. But doing more scans more quickly allows patients to be onboarded a lot more quick, treated a lot more quickly. And as it turns out, because hospitals enjoy making money too. Yeah. They're doing more scans.

• 1:04:29Jason Calacanis?

  Treating more patients.

• 1:04:30Jensen Huang?

  They're treating more customers and more patients. Their revenues go up and guess what?

• 1:04:34Jason Calacanis?

  Perfect example.

• 1:04:34Speaker 04

  And a country that grows faster, productivity increases, a wealthier country can put more teachers in the classroom, not less teachers in the classroom.

• 1:04:43Jensen Huang?

  That's right.

• 1:04:44Speaker 04

  You just give every one of those teachers a personalized curriculum for every student in the room. It makes them all bionic and leads to a lot more.

• 1:04:51Jensen Huang?

  Every single student will be assisted by AI, but every single student will need great teachers.

• 1:04:57Chamath Palihapitiya?

  Yeah.

• 1:04:57Jason Calacanis?

  Amazing. Jensen, congratulations on all your success. And really, this is an incredibly positive uplifting discussion. We really appreciate you taking the time for us.

• 1:05:06Chamath Palihapitiya?

  He is the steward we need.

• 1:05:08Jason Calacanis?

  You are.

• 1:05:08Chamath Palihapitiya?

  You are the steward we need.

• 1:05:09Jason Calacanis?

  I think you need to be more vocal. I'm being very, very honest. Be more vocal about the positive side of it. I think there's too much dumerism.

• 1:05:14Chamath Palihapitiya?

  But I also think it takes the humility to have this level of success and be humble about, we're making software, guys.

• 1:05:21Jason Calacanis?

  Yeah.

• 1:05:22Chamath Palihapitiya?

  And I think that that's actually really healthy for people to hear. We have done this before. We have invented categories and industries before. We don't need to go to this scaremongering place. It does nothing.

• 1:05:33Jason Calacanis?

  And we get to choose, right? We have autonomy and agency. We get to pick how to.

• 1:05:38Jensen Huang?

  We sure do.

• 1:05:39Jason Calacanis?

  Play this. Okay, everybody, we'll see you next time.

• 1:05:41Jensen Huang?

  Thank you.

• 1:05:42Jason Calacanis?

  On the All In interview. Okay. Well done, brother.

• 1:05:45Chamath Palihapitiya?

  Thanks, man.

• 1:05:46Jensen Huang?

  Good job.

• 1:05:47Jason Calacanis?

  Thank you, sir. That was awesome.

• 1:05:48Jensen Huang?

  Good, good.

• 1:05:49Jason Calacanis?

  Appreciate you.

• 1:05:50Jensen Huang?

  You guys are awesome.

• 1:05:50Speaker 04

  Jensen, thank you.

• 1:05:51Jensen Huang?

  Look at this, look at this big crowd behind you guys.

• 1:05:53Jason Calacanis?

  Man, I think they're here for you.