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

Summary

Nvidia CEO Jensen Huang joins the All-In hosts at GTC for a special episode covering the full arc of Nvidia’s transformation from a GPU company to an “AI factory” company. Huang explains the Groq acquisition and how Nvidia’s Dynamo operating system enables disaggregated inference — spreading AI workloads across GPUs, CPUs, switches, and specialized processors. He argues that inference, not training, has become the most complicated computing problem in the world, with demand set to increase not 1 million times but 1 billion times as agentic AI systems begin doing real work rather than just answering questions.

The conversation spans physical AI as a $50 trillion market opportunity, digital biology approaching its “ChatGPT moment,” and humanoid robots arriving as viable commercial products within three to five years. Huang makes the provocative claim that robots will be “the greatest unlock for prosperity for more people on earth than we’ve ever seen with any technology before,” enabling anyone to stand up a business with a robot workforce. On the AI competitive landscape, he reveals that OpenAI is number one, open source is number two, and Anthropic is “a very distant third” in terms of scale — while emphasizing that Nvidia’s token allocation program now gives every employee direct access to AI compute.

Huang also addresses AI’s PR crisis head-on, warning against doomerism and criticizing those who claim “we don’t understand AI at all” as factually wrong. He urges policymakers not to let extremism drive regulation, pointing to the radiology example — where AI was predicted to eliminate the profession but instead increased demand by enabling faster, cheaper scans. His advice to young people: become expert users of AI, because knowing how to guide AI while leaving room for it to innovate requires genuine artistry.

Highlights

”Physical AI Is a $50 Trillion Industry Opportunity”

“Physical AI as a large category, it’s technology industry’s first opportunity to address a 50 trillion dollar industry that has largely been, you know, void of technology until now.” — Jensen Huang, 10:46

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”Robots Will Be the Greatest Unlock for Prosperity”

“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 could do a lot of work for them. They can stand up an Etsy store or a Shopify store. They can create anything they want with their robot.” — Jensen Huang, 54:05

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”Three to Five Years, Robots Everywhere”

“From the point of high functioning existence proof to reasonable products, technology never takes more than a couple of two-three cycles. And so a couple of 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.” — Jensen Huang, 52:18

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”OpenAI Is Number One, Open Source Number Two, Anthropic a Distant Third”

“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.” — Jensen Huang, 22:00

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”Don’t Let Doomerism Affect Policymakers”

“It is not something that we say things like we don’t understand it at all. It is not true, we don’t understand it at all. We understand a lot of things about this technology. And so I think one, we have to make sure that we continue to inform the policymakers and not allow doomerism and extremism to affect how policymakers think and understand about this technology.” — Jensen Huang, 17:27

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”Be the Expert of Using AI”

“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 concerned, who are anxious about AI, be the expert of using AI.” — Jensen Huang, 61:30

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Key Points

• Groq Acquisition and Disaggregated Inference (0:46) - Nvidia’s Dynamo operating system disaggregates inference across heterogeneous computing, leading to the Groq/Mellanox strategy of specialized chips for different workloads
• Inference Explosion: 1 Billion X (6:46) - Inference demand will increase not just 1 million times but 1 billion times as agentic AI begins doing real work
• Three Computers in the AI Stack (5:18) - Training computer, inference/agentic computer, and personal AI computer — Nvidia builds all three
• 25% of Data Centers for Groq (3:15) - Jensen recommends adding Groq to about 25% of Vera Rubin data center deployments
• Physical AI: $50 Trillion Market (10:46) - Physical AI is the tech industry’s first opportunity to address a $50 trillion industry; Nvidia’s physical AI business is approaching $10 billion annually
• Digital Biology’s ChatGPT Moment (11:10) - Within 2-5 years, AI will understand genes, proteins, and cells, creating a healthcare inflection point
• Open Source Operating System (15:51) - Nvidia’s computing platforms are open source and run everywhere, forming the blueprint of modern AI computing
• AI PR Crisis Warning (17:27) - Jensen pushes back against doomerism, arguing we understand AI technology well and must prevent extremism from driving regulation
• Anthropic Comms Critique (18:23) - When asked about Anthropic’s Department of War controversy, the discussion centers on how AI companies need better public communication
• Revenue Scale: OpenAI, Open Source, Anthropic (22:00) - OpenAI is number one by revenue, open source is second, Anthropic is “a very distant third”
• Agentic Systems Getting Work Done (22:51) - The shift from generative AI to agentic systems that actually accomplish tasks, not just generate content
• Open Source Is Essential (31:48) - Jensen believes both proprietary and open source models are essential, not an either/or choice
• Self-Driving Platform Strategy (40:36) - Everything that moves will be autonomous; Nvidia builds training, simulation, and car computers but lets partners decide how to use them
• Humanoid Robots in 3-5 Years (52:18) - From proof-of-concept to commercial products in 3-5 years; China’s rare earth and motor supply chain is critical
• Robots as Greatest Prosperity Unlock (54:05) - Personal robots will unlock economic mobility for everyone, like cars did for transportation
• Radiology Example (63:04) - AI was predicted to eliminate radiologists but instead increased demand by enabling faster, cheaper scans
• Advice to Young People (61:30) - Become expert AI users; knowing how to guide AI while leaving room for innovation requires artistry

Mentions

Companies

• Nvidia (0:00) - Host of GTC, evolved from GPU to AI factory company
• Groq (0:25) - Acquired by Nvidia, LPU inference chips to be added to 25% of data centers
• Mellanox (0:30) - Earlier Nvidia acquisition that enabled disaggregated computing
• OpenAI (22:00) - Number one AI company by revenue scale
• Anthropic (22:00) - “Very distant third” in AI revenue; criticized for communications missteps
• Tesla (40:36) - Buys Nvidia training computers for self-driving
• BYD (39:45) - New self-driving platform partner
• Mercedes (39:45) - Jensen demonstrated self-driving in a Mercedes

Products & Technologies

• Dynamo (0:46) - Nvidia’s operating system for AI factories, named after the machine that powered the last industrial revolution
• Vera Rubin (3:15) - Next-generation Nvidia GPU platform
• Grace (2:31) - Nvidia CPU being added to disaggregated computing stack
• Alpomayo (40:36) - World’s first reasoning autonomous vehicle system
• OpenClaw (10:46) - Open source robotic manipulation platform
• CUDA (57:07) - Nvidia’s almost insurmountable software moat

People

• Jensen Huang (0:00) - Nvidia CEO, interview subject
• Andrej Karpathy (27:47) - Created autoresearch tools discussed by Friedberg
• Dario Amodei (18:23) - Anthropic CEO, referenced in PR crisis discussion
• Elon Musk (40:36) - Tesla’s approach to self-driving, robot predictions of one per human

Surprising Quotes

“Three years to five years, we’re going to have robots all over the place.” — Jensen Huang, 52:18

“Time travel is also, we’re going to be able to travel at the speed of light, you know? And so, you know, clearly, we’re going to send our robots ahead of us.” — Jensen Huang, 55:26

“We literally near the ChatGPT moment of digital biology. We’re about to understand how to represent genes, proteins, cells.” — Jensen Huang, 11:10

“Special episode this week, we preempted the weekly show. And there’s only three people we preempt the show for: President Trump, Jesus, and Jensen.” — Jason Calacanis, 0:00

“I’m hoping more. Yeah, I’m hoping more.” — Jensen Huang, 53:51 (responding to whether there will be one robot per human)

Transcript

Jason Calacanis: 0:00 Special episode this week, we 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.

David Friedberg: 0:18 Every industry’s here, every tech company’s here, every AI company’s here. Incredible.

Jason Calacanis: 0:25 Extraordinary and one of the great announcements of the past year has been Groq. When you made the purchase of Mellanox, did you realize how insufferable Chamath would become?

Jensen Huang: 0:36 I had an inkling that that…

Jason Calacanis: 0:39 We’re his friends. We have to deal with him every week. You know it. You had to deal with him for the six-week close.

Jensen Huang: 0:44 Two weeks.

Jason Calacanis: 0:45 Two weeks.

Jensen Huang: 0:46 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 industrial revolution, the factory of that. And so inside Dynamo, the fundamental technology is disaggregated inference. Jason, I know you’re super technical.

Jason Calacanis: 1:22 Absolutely.

Jensen Huang: 1:23 I’ll let you take this one. Go ahead and define it for the audience, I don’t want to step on you.

Jason Calacanis: 1:24 Yeah, thank you. I know you wanted to jump in there for a second. But it’s…

Jensen Huang: 1:27 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 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. That same sensibility led us to Mellanox.

David Sacks: 2:30 Yep.

Jensen Huang: 2:31 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 Grace 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.

Chamath Palihapitiya: 2:49 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.

Jason Calacanis: 3:00 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 Groq LPU GPU combo.

Jensen Huang: 3:15 We should add Groq to about 25% of the Vera Rubins in the data center.

Jason Calacanis: 3:21 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, dis-agg, and how people do you think will react to it?

Jensen Huang: 3:32 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 auto-regressive models. And so there’s 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, and so we added what used to be a one-rack company, we now added four more racks.

Jason Calacanis: 3:56 Right.

Jensen Huang: 3:57 So Nvidia’s TAM, if you will, increased from whatever it was to probably something, call it, you know, 33%, 50% higher. Now, part of that 33% or 50%, 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 Groq processors. And some of it will be CPUs. And there’s 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, the operating system of modern industry.

Jason Calacanis: 4:57 Right. 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 in an embedded application?

Jensen Huang: 5:18 We think that there’s three computers in the problem 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, another one could be a teddy bear, little tiny one for a teddy bear. One of the most important ones is the 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 AI edge devices. Factories, warehouses, you name it. And so, so there are three of these three basic computers, all of them, you know, are going to be necessary.

Jason Calacanis: 6:37 Jensen, last, last year, I think you were ahead of the the rest of the world in in saying inference isn’t going to a thousand-

Jensen Huang: 6:43 Just last year? Brad, you’re hurting my feelings.

Jason Calacanis: 6:46 Isn’t going to a 1 million X, it’s going to 1 billion X. 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 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 do you see, 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?

Jensen Huang: 7:35 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. 10 times, you know, the difference between 50 billion, now it turns out 20 billion is just land, power, and shell, right?

Jason Calacanis: 8:18 Right.

Jensen Huang: 8:19 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 dollar data center is actually 10 times the throughput.

Jason Calacanis: 8:40 Right.

Jensen Huang: 8:41 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. Yeah.

Chamath Palihapitiya: 8:51 Can I, can I just ask a general strategy question?

Jensen Huang: 8:54 Yeah.

Chamath Palihapitiya: 8:55 I mean you’re running the most valuable company in the world. This thing is going to do-

Jason Calacanis: 9:00 350 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, how does that information get to you? How do you decide these things?

Jensen Huang: 9:27 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…

Jason Calacanis: 9:52 Lots of competitors.

Jensen Huang: 9:53 Yeah, 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 to do that meets this 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.

Jason Calacanis: 10:13 Yeah.

Jensen Huang: 10:14 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.

Jason Calacanis: 10:19 Can you… 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 in 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.

Jensen Huang: 10:46 Excellent. 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, it’s technology industry’s first opportunity to address a 50 trillion dollar industry that has largely been, you know, void of technology until now.

Jason Calacanis: 11:09 Mm-hmm.

Jensen Huang: 11:10 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 an inflecting now. It is a multi-billion dollar business for us, close to 10 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 already know how to understand chemicals. And so the ability for us to represent and understand the dynamics of the building blocks of biology, that’s a couple of two, three, five years from now. In five years time, I completely believe that the healthcare industry where digital biology is going to inflect. And so these are a couple of the really great… once, and you could see they’re all around us.

David Friedberg: 12:02 Agriculture.

Chamath Palihapitiya: 12:03 Agriculture and… reflecting now.

Jensen Huang: 12:05 No question. Yeah.

Jason Calacanis: 12:06 Jensen, I, uh, I want to take you from the data center to the desktop. The company was built in large part on hobbyists, video gamers, and, and all those graphics cards in the beginning. And you mentioned in front of, I think, 10,000 people here just Claude, uh, Open Claw, Claw 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 6800. Uh, this is a very powerful workstation to run local models, 750 gigs of RAM. Obviously, the, the Mac, uh, Studio sold out everywhere. In my company, we’re moving to Open Claw everything. Friedberg just got Claw-pilled, you got Claw-pilled, I understand, and you’re obsessed with these. What does this from-the-streets movement of creating open-source agents and using open-source on the desktop mean to you and where is that going?

Jensen Huang: 13:07 Yeah, so great. First of all, let’s take a step back. Um, 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 sat 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. O1 and O3 continued 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, the economic model of OpenAI start to inflect. Then the third one was only inside the industry that we saw, Claw Code, the first agentic system that was very useful, really revolutionary stuff. But, but Claw Code was only available for enterprises. Most people outside never saw anything about Claw Code until Open Claw. Open Claw basically put into the popular consciousness what an AI agent can do. That’s the reason why Open Claw is so important from a cultural perspective. Now, the second, second reason why it’s so important is that Open Claw is open but it formulates, it structures a type of computing model that is basically reinventing computing altogether. It has a memory system, scratch is a short-term memory file system, it has, it has tools, it has skills…

David Sacks: 14:59 It scales.

Jensen Huang: 15:00 Yeah.

Jason Calacanis: 15:00 Did you say skills or scales?

Jensen Huang: 15:02 Skills.

Jason Calacanis: 15:03 Oh, skills.

David Friedberg: 15:04 Because if you have scales, theoretically, yeah.

Jensen Huang: 15:06 Yeah, skills. So the first thing, first thing it, you know, it has resources and manages resources, it’s… it does scheduling.

David Friedberg: 15:12 Yep.

Jensen Huang: 15:13 Right? And cron jobs, it could, it could spawn off agents, it could, you know, it could decompose a task and cause and solve problems, so it does scheduling. It has I/O subsystems, it could, you know, input, it has output, it connect to WhatsApp. And also, it has an API that allows it to run multiple types of applications, called skills.

David Friedberg: 15:34 Yeah.

Jensen Huang: 15:35 These four elements fundamentally define a computer. And therefore, what do we have? We have a personal artificial intelligence computer for the very first time.

Jason Calacanis: 15:50 Open source.

Jensen Huang: 15:51 It’s open source, it runs literally everywhere. And so this is now the, this is basically the blueprint, the operating system of modern computing.

David Friedberg: 15:59 Yeah.

Jensen Huang: 16:00 And it’s gonna 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 can execute code, it can 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 gives these agents two of the three things, but not all three things at the same time.

David Friedberg: 16:25 Right.

Jensen Huang: 16:26 And so the governance part of it, we contributed to. Peter, Peter Steinberger was here, and so we’ve got a mound of great engineers working with him to help secure and keep that thing so that it could protect our privacy, protect our security.

David Friedberg: 16:38 That agentic paradigm shift makes some of the AI legislation that has passed around the country to regulate AI 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 AI, which is becoming a very hot topic in politics right now?

Jensen Huang: 16:59 Well, this is the part that, you know, we just with policymakers, we need to always get in front of them and Brad, you do a great job doing this, we gotta 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. Um, it is computer software.

David Friedberg: 17:26 Yeah, exactly.

Jensen Huang: 17:27 And… 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 it at all. We understand a lot of things about this technology. And so I think one, we have to make sure that we continue to inform the policymakers and not affect, not allow doomerism and extremism to affect how policymakers think and understand about this technology. However, we still have to recognize this technology’s moving really fast and don’t get policy ahead of the technology too quickly and the risk…

David Friedberg: 18:00 that we we run as a nation, our greatest source of national security concern with respect to AI is that other countries adopt this technology while we are so angry at it or afraid of it or somehow paranoid over 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.

Jason Calacanis: 18:23 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, 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?

Jensen Huang: 18:51 The first thing that I would say about Anthropic is first of all the technology is incredible.

Jason Calacanis: 19:05 Incredible.

Jensen Huang: 19:06 We are a large consumer of Anthropic technology.

Chamath Palihapitiya: 19:09 Yeah.

Jensen Huang: 19:10 Really admire their focus on security, really admires their focus on safety, um, the the culture by which they went about it, the technology excellence by which they went about it, really fantastic. Um, I would say that that the desire to warn people about the capability of the technology 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.

David Sacks: 19:21 Right.

Jensen Huang: 19:22 And because this technology is too important to us.

Jason Calacanis: 19:28 Right.

Jensen Huang: 19:29 And I think that it is fine to, uh, 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 of course we are technology leaders, uh, there was a time when nobody listened to us,

Jason Calacanis: 20:11 Yeah.

Jensen Huang: 20:12 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.

Jason Calacanis: 20:24 Well, I, you know, I would nominate you. I think the industry’s got to get together. 17% popularity of AI in the United States. I mean we see what happened in nuclear, right? We basically shut down the entire nuclear industry and now we have 100 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 I wanted 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 and Heading into this year, the big question was, are the revenues going to show up? Are the revenues going to scale like intelligence? 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?

Jensen Huang: 21:32 When you look around, when you—I’ll answer that in a couple of different ways. When you look around this audience, you will see that Anthropic and OpenAI is represented here, but in fact, 99% of everything that is here is all AI and it’s not Anthropic and OpenAI.

Chamath Palihapitiya: 21:46 Right, right.

Jensen Huang: 21:48 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.

Jason Calacanis: 21:58 Yeah. Open weights, open source.

Jensen Huang: 22:00 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.

Jason Calacanis: 22:13 Right.

Jensen Huang: 22:14 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 100 times.

Jason Calacanis: 22:27 Right.

Jensen Huang: 22:28 When we went from reasoning to agentic, the computation is probably another 100 times. Now we’re looking at, in just two years, computation went up by a factor of 10,000X. Meanwhile, people pay for information, but people mostly pay for work.

Jason Calacanis: 22:40 Yes.

Jensen Huang: 22:40 Talking to a chatbot and getting an answer is super great.

Jason Calacanis: 22:44 Right.

Jensen Huang: 22:45 Helping me do some research, unbelievable. But getting work done, I’ll pay for.

Jason Calacanis: 22:50 Indeed.

Jensen Huang: 22:51 And so that’s where we are. Agentic systems get work done. They’re helping our software engineers get work done.

Jason Calacanis: 22:57 Yes.

Jensen Huang: 22:58 And so then you take that, you’ve got 10,000X more compute, you get probably at this point a 100X more consumption now.

Jason Calacanis: 23:08 Yes.

Jensen Huang: 23:09 And we haven’t even started scaling yet. We are absolutely at a million X.

Jason Calacanis: 23:13 Which is, I think, a great place to talk about the number of engineers you have. 20, 30,000 at the company? Something?

Jensen Huang: 23:22 We have 43,000 employees. You know, I would say 38,000 are engineers.

Jason Calacanis: 23:28 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? Because 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.

Jensen Huang: 23:54 If it was well done, it would be shorter. I just want you to know.

Jason Calacanis: 23:57 You didn’t have time to do a—

Jensen Huang: 24:00 So you guys know, so you guys know, there is no practice. And so it’s a grip it and rip it.

Jason Calacanis: 24:06 Grip and rip. Love it.

Jensen Huang: 24:08 Yeah, yeah. And so, so I just want to let you know, I was writing the speech while I was giving the speech. Okay, so never know.

Jason Calacanis: 24:14 But does that mean if we do back of the envelope math, 75,000 in tokens for each engineer, something like that? So are you spending in NVIDIA a billion, 2 billion dollars on tokens for your engineering team right now?

Jensen Huang: 24:27 We’re trying to. Let me give you a thought experiment. Let’s say you have a software engineer or AI researcher and you pay them 500,000 dollars a year. We do that all the time. Okay, this is happening all of the time. That 500,000 dollar engineer, at the end of the year, I’m going to ask them how many tokens—how much did you spend in tokens, and that person said 5,000 dollars, I will go ape something else.

Jason Calacanis: 24:48 Yes.

David Friedberg: 24:49 Right.

Jensen Huang: 24:50 If that, if that 500,000 dollar engineer did not consume at least 250,000 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 going to use paper and pencil. I don’t think I’m going to need any CAD tools.

Jason Calacanis: 25:11 Right.

David Sacks: 25:12 Right.

David Friedberg: 25:13 This is a real paradigm shift just to start thinking about these all-star employees. It almost reminds me of what we learned in the MBA when LeBron James started spending a million dollars a year just on his health of his body, like in maintaining it. Here it is at age 41, still playing. It really is hey, if these are incredible knowledge workers, why wouldn’t we give them superhuman abilities?

Jensen Huang: 25:34 That’s exactly it.

David Friedberg: 25:35 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 NVIDIA and what they’re able to accomplish? What do they look like?

Jensen Huang: 25:44 Well, first of all, things that—‘wow, this is too hard,’ that thought is gone. ‘This is going to take a long time,’ that thought is gone. ‘We’re going to need a lot of people,’ that thought is gone. This is no different than in this—in the last industrial revolution, somebody goes, ‘boy, that building really looks heavy.’ Nobody says that. Nobody… ‘wow, that mountain looks too big.’ Nobody says that. Everything that’s too big, too heavy, takes too long, those ideas are all gone.

Jason Calacanis: 26:14 You’re reduced to creativity.

Jensen Huang: 26:16 That’s right. 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 past we code. In the future we’re going to write ideas, architectures, specifications. We’re going to organize teams. We’re going to give them—we’re going to help them define how to evaluate the definition of good versus bad. What’s the—what does it look like when something is of 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 going to have a hundred, a hundred agents.

Jason Calacanis: 26:51 Back to the PR problem the industry has right now. You have executives 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 calories. What is the factor you think you can bring the cost down for your burger, and what impact does this vision have for what you’re doing?

David Friedberg: 27:17 Zero-shot genomic modeling. And it works. And then you have that moment and you’re like, holy shit. Honestly, like, 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 on a Sunday night. 10:00 p.m. I was done at 11:30, I went to bed.

Jason Calacanis: 27:44 As the CEO, you replaced—

David Friedberg: 27:47 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 accelerated PhD theses 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 and we’re like, hey, let’s try it. Boot it up, go into GitHub, download Auto-Research, and ran it. 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 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 these different datasets? And what that tells us about the early stages we are in terms of optimization on algorithms and hardware to unlock—

Jensen Huang: 29:11 The fundamental reason why Auto-Research is so incredible, number one, is its confluence, its timing with the breakthroughs in large language model. 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. 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, Synopsys and Cadence, Omniverse and Blender and Autodesk, all of these tools are going to continue to be used. There are some people who say— say that that the enterprise IT software industry is going to get destroyed. There’s it there’s a let me give you the alternative view. The enterprise software industry is limited by butts and seats. It’s about to get a hundred times more agents banging on those tools. There’re going to be agents banging on SQL, they’re going to be agents banging on vector databases, agents banging on Blender, agents banging on Photoshop. And the reason for that is because those tools, first of all, do a very good job. Second, those tools are the conduit between us.

David Friedberg: 30:33 Right.

Jensen Huang: 30:34 In the final analysis when the work is done, it has to be represented back to me in a way that I can control.

David Sacks: 30:39 Right.

Jensen Huang: 30:41 And I know how to control those tools. And so I need everything to be put back into Synopsys, I want everything to be put back into Cadence, because that’s how I control it, that’s how I ground truth.

David Friedberg: 30:51 Let me ask you a question about open source. So we have these closed source models, they’re excellent. 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.

Jensen Huang: 31:25 Yeah.

David Friedberg: 31:26 Because it’s like random people and each person gets a little share.

Jensen Huang: 31:29 Our modern version of folding at home.

David Friedberg: 31:31 Exactly. Yeah. 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?

Jensen Huang: 31:48 I believe we fundamentally need models as a first-class product for 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 is 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. Right. I would really love to keep using ChatGPT, I’d love to use Claude, I’d love to use Gemini, I’d love to use X, and they all have their own personalities as you know, which kind of depends on my mood and depends on what problem I’m trying to solve. You know, am I going to do it on X or am I going to do it on on ChatGPT? And so that that segment of the industry is thriving and it’s going to be great. However, 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 can only come from open models. The open model industry, we’re contributing tremendously to, it is near the frontier.

David Friedberg: 33:00 And quite frankly, even if it reaches the frontier, I think that products as a service, world-class products as a- models as a product is going to continue to thrive.

Chamath Palihapitiya: 33:13 Every startup we’re investing in now is open source first and then going to the proprietary models.

David Friedberg: 33:19 Yeah, and the beautiful thing is because you have a great router, you connected to by- on first day, every single day, you’re going to have access to the world’s best model, 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 the shoot every single time.

Jason Calacanis: 33:38 Let- Gents, can I- can I just ask a question? Nobody wants the US to win the global AI race more than you, right? But oh- 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- are we an A? Are we a B? Are we a C? What- what’s working? What’s not?

Jensen Huang: 34:14 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 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%.

Jason Calacanis: 34:43 China.

Jensen Huang: 34:44 That’s right. President Trump wants us to get back in there. And the first thing is to get license- licenses for the companies that we’re going to be able to sell to. 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 Lutnick. Now we’ve informed the Chinese companies, and many of them have given us purchase orders. And so we’re going to start- 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. It is fundamentally diminished. Every single one of these industries is an example of what I don’t want the AI industry to be. 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…

David Friedberg: 36:00 No way that AI models is one universally. 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.

David Sacks: 36:46 Agreed.

Chamath Palihapitiya: 36:47 Yeah.

Jason Calacanis: 36:49 How much are you monitoring the situation with the conflicts around the world? 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?

Chamath Palihapitiya: 37:01 Yeah.

Jensen Huang: 37:06 Well, first of all, I think in the Middle East, I have… we have 6,000 families there. 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. We’re thinking about them all the time. We’re monitoring and keeping an eye on them all the time. They have 100% of our support. I’ve been asked several times, are we still considering being in Israel? We are 100% in Israel. We are 100% behind the families there. We’re 100% in the Middle East. I was also asked, you know, given what’s happening in the Middle East, is that an area where we believe that we can expand artificial intelligence to? I believe that there’s a reason we went to war. And I believe that at the end of the war, Middle East will be more stable than before. And so, if we were there… if we were considering it before, we should absolutely be considering it after. And so, I’m 100% in on that. With respect to… 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. And whether it’s the chip manufacturing plants, the computer manufacturing plants, or the AI factories…

Jason Calacanis: 38:23 How are we doing on that?

Jensen Huang: 38:25 We’re doing excellent. With… by… 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’re… they are genuinely a strategic partner. We… we… 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, 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, you know, unnecessarily—

Jason Calacanis: 39:29 Unnecessarily.

David Sacks: 39:30 We need to be patient.

Chamath Palihapitiya: 39:31 He’s being thoughtful.

Jason Calacanis: 39:32 Is helium a problem? A lot of reports on helium.

Jensen Huang: 39:34 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, yeah.

Jason Calacanis: 39:45 You’ve made massive progress in self-driving. You’ve 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 huge announcement with Uber that you’re going to have a number of cars on the road from 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? Because it feels like you have a pretty deep stack and in some ways you’re competing and in other places you’re collaborative.

Jensen Huang: 40:36 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. And so that reasoning system called Alpomayo has enabled us to achieve incredible results. 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 even put the car computer in your car? And so we, you know, our attitude is we want to solve the problem, we’re not the solution provider, and we’re delighted however you work with us.

David Friedberg: 41:58 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 Inferentia 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…

Jensen Huang: 42:32 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 layer, every single stack. Um, 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. Right. Yeah. And so, the confidence comes from this: One, um, we are delighted to compete on what is the best technology, and 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, in space. That’s right, in space. And so there’s a whole part of our market, about 40% of our market—most people don’t realize this—40% of our business, unless you have the CUDA stack, unless you can build an entire AI factory, 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 the full stack, and we’ve got the whole stack. And so surprisingly, Nvidia’s gaining market share. If you look at where we are today, we’re gaining share.

David Friedberg: 43:58 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 the share grows?

Jensen Huang: 44:04 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 so their business with us is increasing. In the case of AWS, I think they just announced, I think it was yesterday, that they’re going to buy a million chips in the next couple of years. I mean, that’s a lot of chips 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, 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 outside 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.

Jason Calacanis: 45:10 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 one 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 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?

Jensen Huang: 45:44 Yeah, first of all, they just don’t understand the scale and the breadth of AI.

Jason Calacanis: 45:49 Yeah.

David Sacks: 45:50 Yeah, I think that’s true.

Jensen Huang: 45:52 Most people think that AI is in the top five hyperscalers. Right.

David Friedberg: 45:57 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.

Chamath Palihapitiya: 46:04 They’re not going to believe in their minds that 5 trillion goes to 15 trillion. They’re like, it can go to seven.

David Friedberg: 46:08 And it can take ten years.

Jason Calacanis: 46:11 It’s all just CYA stuff that I think…

Chamath Palihapitiya: 46:14 It’s never happened before so you can’t say, well…

Jensen Huang: 46:17 And 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 dollars a year.

David Sacks: 46:32 Right.

Jensen Huang: 46:33 We do 25 billion dollars a year, as you guys know, in the time that we were sitting here.

David Friedberg: 46:37 Very… very…

Jensen Huang: 46:40 That was a joke.

Chamath Palihapitiya: 46:42 It’s roughly true.

David Friedberg: 46:44 Don’t worry, everything on this show is roughly true. Don’t worry about it.

Chamath Palihapitiya: 46:46 It’s All-In, you can say anything here.

Jensen Huang: 46:49 That was not guidance. Но anyhow, it’s… The point is how big you can be depends on what it is that you make. Nvidia’s 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. OpenAI’s 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.

Chamath Palihapitiya: 47:27 Tell us about data centers in space for a second.

Jensen Huang: 47:33 We’re already in space.

Chamath Palihapitiya: 47:35 How should the layman think about what that business is versus when you hear about these big data center build-outs that’s happening on the ground?

Jensen Huang: 47:41 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. Exactly. And so you can only use radiation and radiation requires very large surfaces. And so now that’s not an impossible thing to solve and there’s lots 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 radiation hardened. We have CUDA in satellites around the world. They’re doing imaging, image processing, AI imaging. 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 the space. And so there’s a lot of things that we ought to down do in space and in the meantime, we’re going to explore what is the architecture of data centers look like in space. And it’ll take, it’ll take years. It’s okay. I got plenty of time.

Jason Calacanis: 48:51 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, healthspan. I mean we all look great, I think.

Jensen Huang: 49:03 Some better than others.

Jason Calacanis: 49:04 I think some better than others. I don’t know what your secret is, Jensen, but it looks pretty good these days. I mean, what are you taking? What’s off the menu? You got to talk to me when we’re backstage. I want to know in the green room what you got going on.

Jensen Huang: 49:14 Squats and pushups and situps.

Jason Calacanis: 49:16 Perfect. Okay. That works. 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?

Jensen Huang: 50:08 There’s several areas of where we’re involved in in healthcare. One is AI physics. And that’s or AI biology. Using AI to understand, represent, predict biology, 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 assistants 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 we interact for healthcare. The third part that we’re involved in is physical AI. 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 inter- Interacted with in a hospital in the future will be agentic. Yeah. You know, Open Claw in a safe version will be inside every single instrument, and so in a lot of ways, that instrument’s going to be interacting with patients and nurses and doctors in a very unique way.

Jason Calacanis: 51:13 Yeah, I mean we’ve seen 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. Uh which I think is a great segue into robotics. You’ve got dozens of partners. We had this very weird I don’t know if I want to call it 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 prime time.’ And now here we have the world’s greatest entrepreneur at this time uh tied with you, Elon Musk, doing—oh, that was a good save I hope—Optimus, uh pretty impressive and then other companies in China. How how close is that to actually being in our lives where we might see a chef, a robotic chef, a robotic nurse, a robotic housekeeper, you know, these humanoid factors actually working in the real world knowing what you know with those partners and the fidelity, especially in China where they seem to be doing as good a job as we’re doing here, or maybe better?

Jensen Huang: 52:18 Um, we invented the industry largely. America invented—we, you could argue we got into it too soon. Yeah. And and we got exhausted. We got tired about five years before the enabling technology appeared. Yes, the brain. Yeah yeah. And 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 of two-three cycles. And so a couple of 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. Uh I think I think uh China is is uh 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. And so in a lot of ways, our robotics industry relies deeply on their ecosystem and their supply chain. Um and and they’re, you know, obviously moving very quickly. Uh we’re going to 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 um you’re going to see some fast fast movements here.

Jason Calacanis: 53:45 Ultimately one for one? Elon seems to think we’re going to have one robot for every human, seven billion for seven billion, eight billion for eight billion.

Jensen Huang: 53:51 Well, I’m hoping more. Yeah, I’m hoping more. Yeah. Uh well first of all, there’s a whole bunch of robots that are going to be in factories working around the clock. There’s going to be a whole bunch of robots that… Don’t move, they move a little bit. Almost everything will be robotic.

Jason Calacanis: 54:04 What does the world look like?

Jensen Huang: 54:05 Let me just think. Like, this is one of 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 could do a lot of work for them. They can stand up an Etsy store or 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 going to end up being the greatest unlock for prosperity for more people on earth than we’ve ever seen with any technology before.

David Friedberg: 54:38 Yeah, no doubt. I mean, just the simple math at the moment is we’re millions of people short in labor today.

Jason Calacanis: 54:46 Right. Yeah.

David Friedberg: 54:48 We’re actually really desperate and in need of robotics. And so that all of these companies could grow more if they had more labor. I mean, number one. Some of the things that you mentioned are super fun. I mean, big-

Jensen Huang: 55:02 Because of robots, we’ll have virtual presence. Uh, you know, I’ll be able to go into the robot at my house and virtually operate it. I’m on a business trip. Walk around the house.

David Friedberg: 55:13 Walk the dog, rake the leaves.

Jason Calacanis: 55:15 Freak out the dog.

Jensen Huang: 55:18 Yeah, exactly. Maybe not quite that, but just, you know, just wander around and just see what’s going on in the house, you know, chat with the dogs, chat with the kids. Yeah. Time travel is also, we’re going to be able to travel at the speed of light, you know? And so, you know, clearly, we’re going to send our robots ahead of us. Not going to send myself. I’m going to send a robot, check it out. And then I’m going to upload my AI.

David Friedberg: 55:40 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 going to be the unlock for enabling that. That’s right. Distance no longer matters.

Jensen Huang: 55:59 Distance doesn’t matter. Yep.

David Sacks: 56:01 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 Dwarkesh’s podcast recently said by 2027, ‘28, we’ll have hundreds of billions of dollars of revenue out of the model companies and the agent companies, and he forecasts a trillion dollars by 2030, right? This is non-infrastructure AI revenue.

Jensen Huang: 56:27 I think he’s being very conservative. I believe Dario and Anthropic is going to do way better than that. Way better than that.

Jason Calacanis: 56:37 Wow. So 30 billion to a trillion?

Jensen Huang: 56:39 Yeah. And not, and the reason for that is the one part that he hasn’t considered is that I believe-

Chamath Palihapitiya: 56:44 Every single enterprise software company will also be a reseller, value-added reseller of Anthropic’s code, Anthropic’s tokens, value-added reseller of OpenAI. That’s right. And they’re going to, that, that part of their P&L…

Jensen Huang: 57:00 …logarithmic expansion.

Jason Calacanis: 57:01 Yes.

Jensen Huang: 57:01 Their go-to market is going to expand tremendously this year.

David Friedberg: 57:05 What do you think, 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?

Jensen Huang: 57:38 Deep specialization. Deep specialization. I believe that um these models, they’re going to have general general models that are connected into the software company’s agentic system.

Jason Calacanis: 57:51 Right.

Jensen Huang: 57:52 Many of those models are cloud models and proprietary models, but many of those models are specialized sub-agents… …that they’ve trained on their own.

David Friedberg: 58:03 Right, so the call to arms for you for entrepreneurs is look, know your vertical.

Jensen Huang: 58:10 That’s right.

David Friedberg: 58:12 Know it as deep and as better than everybody else.

Jensen Huang: 58:15 That’s right.

David Friedberg: 58:16 And then wait for these tools because they’re catching up to you and now you can imbue it with your knowledge.

Jensen Huang: 58:21 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…

David Friedberg: 58:25 It 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.

Jensen Huang: 58:35 And we trap… in fact, 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…

David Friedberg: 58:46 Exactly.

David Sacks: 58:47 And that’s arguably a five or six times bigger industry is the customization.

Jensen Huang: 58:51 It is, absolutely.

David Sacks: 58:53 Yeah, very much is.

Jensen Huang: 58:54 That’s right. So I think that these platform companies have an opportunity to become that specialist, to become that vertical domain expert.

Jason Calacanis: 58:59 Right.

Chamath Palihapitiya: 59:00 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.

Jensen Huang: 59:16 That’s right.

Jason Calacanis: 59:17 Have you changed your view? Cuz you were you were Doomer Cham for a second there.

Chamath Palihapitiya: 59:22 No, no, no, no. I’m not doomer.

Jason Calacanis: 59:24 I heard you.

Chamath Palihapitiya: 59:26 No, I do have… no and you can hold space for I think two ideas. One is there are going to be a lot of…

David Sacks: 59:29 That’s Viral JCal talking.

Chamath Palihapitiya: 59:32 No, no, no.

Jensen Huang: 59:33 But that’s just because he doesn’t hang out with me enough.

Chamath Palihapitiya: 59:38 Well I mean, we’ve hung a little bit.

Jensen Huang: 59:40 You don’t talk enough.

Jason Calacanis: 59:43 He will show up at your breakfast table.

Jensen Huang: 59:45 He’ll follow you around.

Chamath Palihapitiya: 59:47 I’m not asking for it.

Jensen Huang: 59:48 He’ll follow you around.

Chamath Palihapitiya: 59:50 I’m not asking for it. I’m just saying… You come with me and Tucker, we ski in Japan every January. Love it.

David Sacks: 59:54 Me and Tucker, we go on road trips.

Jensen Huang: 59:56 Oh wow. Okay.

David Sacks: 59:57 No comment.

Chamath Palihapitiya: 59:58 No, 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 going to see 100% of driving go away by humans. That’s just…

Jason Calacanis: 60:12 it’s that’s a beautiful thing and 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 going to happen, yes?

Jensen Huang: 60:23 I think that jobs will change. For example, um, there are many chauffeurs today, who drives 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, I mean, they’re helping you with a lot of things. And so I wouldn’t be surprised actually if the chauffeurs of the future becomes your mobility assistant and they are helping you do on a whole bunch of other stuff.

Jason Calacanis: 61:01 Yeah, check you into the hotel.

Jensen Huang: 61:03 And the car’s driving by itself.

Chamath Palihapitiya: 61:04 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.

Jason Calacanis: 61:14 And by the way, while that car is driving itself, that chauffeur is going to be doing a bunch of other work on his phone and he’s going to be making money doing other stuff.

Jensen Huang: 61:23 Arranging for example, coordinating a bunch of things for you, getting, you know, yeah.

Jason Calacanis: 61:26 It’s all, the pie just grows in a way that…

Jensen Huang: 61:30 So one of the things that that… Yes, every job will be transformed. Um, 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 concerned, who are anxious about AI, be the expert of using AI. How much look, we all want our employees to be expert at using AI, and it’s not not trivial, not trivial. And so knowing how to specify, not to overprescribe, leaving enough room for the AI to innovate and create while we guide it to the outcome we want, all of that requires artistry.

Jason Calacanis: 62:08 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? 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 like 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?

Jensen Huang: 62:34 I I still believe that deep science, deep math, language skills, you know, as you know, language is the programming language of AI now.

Chamath Palihapitiya: 62:44 The ultimate programming language.

Jensen Huang: 62:45 Yeah, the ultimate… And so as it turns out, it could be that the English major could be the most successful. And so I think, I would just advise whatever education you get, just make sure that you’re deeply, deeply expert in using AI’s. One thing 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… uh, deeply… uh, respect… uh, predicted that computer vision will completely eliminate radiologists. And that the one at the one field he advises everybody to not go into is radiology. 10 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 is skyrocketing.

Jason Calacanis: 63:39 Hmm.

Jensen Huang: 63:43 The reason for that is because everybody’s job has a purpose and it’s task. The task that you do is studying the scans.

Jason Calacanis: 63:49 Mm-hmm.

Jensen Huang: 63:52 But your purpose is to diag- helping the doctors, helping the patient diagnose disease.

Chamath Palihapitiya: 63:56 Heal the patient.

Jensen Huang: 63:58 Right. And so what’s surprising is because the scans are now being done so quickly they could do more scans improving healthcare.

Jason Calacanis: 64:03 Yes.

Jensen Huang: 64:10 But doing more scans more quickly allows patients to be onboarded a lot more quick, treated a lot more quick.

David Friedberg: 64:13 Treated a lot more quick.

Jensen Huang: 64:16 And as it turns out because hospitals enjoy making money too.

David Friedberg: 64:18 Yeah.

Jason Calacanis: 64:21 Right.

Jensen Huang: 64:23 They’re doing more scans, they’re treating more customers and more patients, the revenue’s gone up and guess what?

Chamath Palihapitiya: 64:25 Early detection becomes…

Jason Calacanis: 64:28 Perfect example.

Chamath Palihapitiya: 64:29 Perfect example.

David Sacks: 64:31 Perfect example.

David Friedberg: 64:34 And in a country that grows faster, productivity increases, a wealthier country can put more teachers in the classroom, not less teachers in the classroom.

David Sacks: 64:43 That’s right.

David Friedberg: 64:46 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…

Chamath Palihapitiya: 64:51 And the monitoring.

David Friedberg: 64:53 monitoring.

Jensen Huang: 64:57 Every single student will be assisted by AI, but every single student will need great teachers.

Jason Calacanis: 64:59 Yeah.

David Friedberg: 65:00 Yeah.

Jason Calacanis: 65:01 Amazing. Uh, Jensen, congratulations on all your success. And really this is an incredibly positive, uplifting discussion. We really appreciate you taking the time for us.

David Friedberg: 65:05 He is the steward we need.

Chamath Palihapitiya: 65:06 You are.

David Friedberg: 65:08 You are. I think you need to be more vocal.

Jensen Huang: 65:11 No, no, I’m… I’m being very, very…

David Friedberg: 65:12 Be more vocal about the positive side of it. I think there’s so much doom-erism…

Jensen Huang: 65:15 But I also think it takes the humility to have this level of success and be humble about we’re making software, guys.

Jason Calacanis: 65:22 Yeah.

Jensen Huang: 65:23 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.

Jason Calacanis: 65:33 And we get to choose, right? We have autonomy and agency. We get to pick how to deploy this.

David Friedberg: 65:35 We sure do.

Jason Calacanis: 65:36 Okay, everybody. We’ll see you next time on the All-In interview.

Jensen Huang: 65:38 Thank you.

Jason Calacanis: 65:40 Well done, brother.

David Friedberg: 65:41 Thanks, man.

Jason Calacanis: 65:42 Good job.

Chamath Palihapitiya: 65:43 Thank you, sir. That was awesome.

Jensen Huang: 65:45 Appreciate you.

Chamath Palihapitiya: 65:46 You guys are awesome.

Jason Calacanis: 65:48 Jensen, thank you. Look at this. Look at this big crowd behind you guys.

Chamath Palihapitiya: 65:52 Man, I think they’re here for you.

Jason Calacanis: 66:00 I’m going all in!