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

20:24Jason Calacanis: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 to 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. We hear about moratoriums on data centers. So I think we have to be a lot more proactive about that. 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?

20:59Jason Calacanis: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? 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, 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, 99 % of everything that is here is all AI and it's not anthropic and open AI. 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 open source. Open ways, open source. Open AI 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.

22:12Jensen Huang:And so it's important to recognize that. 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. 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 fact 10 ,000x. Meanwhile, people pay for information, but people mostly pay for work. Talking to a chatbot and getting an answer is super great. Helping me do some research, unbelievable. But getting work done, I'll pay for. And so that's where we are. Agentic systems get work done.

23:03Jensen Huang:They're helping our software engineers get work done. And so then you take that, you got 10 ,000 X more compute. You get probably at this point, 100 X more consumption now. Yes. Yeah. And we haven't even started scaling yet. We are absolutely at a million X. Which is, I think, a great place to talk about the number of engineers you have, 20, 30 ,000 at the company. We have 43 ,000 employees. companies, I would say 38 ,000 are engineers. 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?

23:44Because 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, that you were spending -

23:56Jensen Huang:If it was well done, it would be shorter. Yeah. You didn't have time to do - Yeah. You didn't have time to write it for an hour and 45. So you guys know there is no practice, and so it's a gripping and ripping. Gripping and ripping. Yeah, yeah. Love it. So I just want to let you know I was writing the speech while I was giving the speech. Okay, so - You never know. But does that mean if we do - I apologize. Back in the envelope, man,$75 ,000 in tokens for each engineer or something like that. So are you spending in NVIDIA a billion,$2 billion on tokens for your engineering team right now? We're trying to.

24:28Jensen Huang:Let me give you the thought experiment. Let's say you have a software engineer or AI researcher and you pay them$500 ,000 a year. We do that all the time. This is happening all of the time. That$500 ,000 engineer at the end of the year, I'm going to ask them how much did you spend in tokens? and that person said$5 ,000, I will go ape something else.

24:50Jason Calacanis:Yes. Right.

24:51Jensen Huang:If that$500 ,000 engineer did not consume at least$250 ,000 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. Right, right. This is a real paradigm shift to start thinking about these all-star employees. it almost reminds me of what we learned in the NBA when LeBron James started spending a million dollars a year just on his health of his body and maintaining it. That's right. Here he is at age 41 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. Where does that go? 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? 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 the last industrial revolution. Somebody goes, boy, that building really looks heavy. Nobody says that. Wow, that mountain looks too big. Nobody says that. Everything that's too big, too heavy, takes too long.

26:12Jensen Huang:those ideas are all gone. You're reduced to creativity. That's right. What can you come up with? Exactly. Which means, now the question is, 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 help them define how to evaluate the definition of good versus bad. 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 think that every engineer is gonna have 100, 100 agents.

26:51Back to the PR problem the industry has right now. You have executives like David Freeberg 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, Freiberg, and what impact does this vision have for what you're doing? Zero-shot genomic modeling, and it works. And you have that moment, and you're like, holy s***. Honestly, 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 a whole bunch of workload.

27:3690 minutes on Claude ran this agentic system built the whole thing deployed it and we got we were on a Sunday night on a Sunday night 10 p.m. I was done at 1130. I went to bed as the CEO you replaced 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.

28:11And that would normally be a PhD thesis that would take seven years. It would be one of the most celebrated PhD thesis we've ever seen in this field. And it would be in the journal of 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. Boot it up, going to GitHub, downloaded 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.

28:40And we're experiencing it in genomics. And we're like, this is unbelievable. So I think the acceleration is widening the aperture for everyone in a way that 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 the

29:12Jensen Huang:fundamental reason why open claw is so incredible number one is it's come it's confluence its timing with the breakthroughs in large language model yeah it's 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 in the case of chip design, Synopsys and Cadence and Omniverse and Blender and Autodesk, all of these tools are going to continue to be used.

29:58Jensen Huang:Some people say that the enterprise IT software industry is going to get destroyed. 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, 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.

30:39Jensen Huang: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've ground truth. 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 LAMA model, totally distributed, with a bunch of people contributing excess compute.

31:19But they were able to do it statefully and manage a training run, which I thought was like a pretty crazy technical accomplishment. Because it's like random people and each person gets a little share.

31:29Jensen Huang:Our modern version of folding at home. Exactly. So 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 everybody? I believe we fundamentally need 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 is a technology, not a service.

32:10Jensen Huang: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 Cloud. I love to use Gemini. I love to use X. And they all have their own personalities, as you know, which just kind of depends on my mood and depends on what problem I'm trying to solve. I might do it on X or I might do it on ChatGPT. And so that segment of the industry is thriving. 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.

32:51Jensen Huang:And that 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 models as a product is going to continue to thrive. Every startup we're investing in now is open source first and then going to the proprietary models. Yeah, and the beautiful thing is because you have a great router you connect the two, 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.

33:34Jensen Huang:And so you're going to have world class capabilities out to shoot every single time. Can I ask that question?

33:40Jason Calacanis:Nobody wants the U.S. 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 are we in terms of global diffusion and the rate at which we're spreading U.S. AI technology around the world? Are we an A? Are we a B? Are we a C? 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%. That's right. President Trump wants us to get back in there, and the first thing is to get licensed for the companies that we're going to be able to sell to.

34:52Jensen 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 informed the Chinese companies, and many of them have given us purchase orders. And so 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.

35:37Jensen 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. When we look forward in time and we say, what do we want? 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. 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.

36:24Jensen Huang: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. But the alternative, if it looks like solar, rare earth, magnets, motors, telecommunications, I consider that a very bad outcome for national security. 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.

37:02How much do these situations worry you? How much are you spending on them?

37:06Jensen Huang:Well, first of all, I think in the Middle East, we have 6 ,000 families there. We have a lot of Iranians at NVIDIA, and their families are still in Iran. And so we have a lot of families there. The first thing 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 are 100 % in the Middle East. I was also asked, given what's happening in the Middle East, is that an area where we believe that we can expand artificial intelligence too?

37:50Jensen Huang:I believe that there's a reason we went to war, and I believe at the end of the war, 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 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. How are we doing on that? We're doing excellent. 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.

38:43Jensen Huang: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, whether it's Japan, it's Europe, we ought to we ought to diversify the supply chain, make it more resilient. And number three, let's be let's let's demonstrate restraint. And while we're reducing, increasing our diversity and resilience, let's not press, push unnecessarily.

39:29Jensen Huang:We need to be patient. It's thoughtful. Is helium a problem? A lot of reports are helium. 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, you know. So 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 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.

40:17And 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.

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 Alpamayo 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? 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.

41:57Let 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 Tranium. 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 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. Right. Yeah. And so the confidence comes from this. One, 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.

43:12Jensen Huang:And there's just 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 business, most people don't realize this, 40 % 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.

43:44Jensen Huang: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 is gaining market share. If you look at where we are today, we're gaining share. 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 this share grows? Well, we're gaining share for several reasons. One, our velocity has gone. We help people realize it's not about building the chip. It's about building the system. And that system is really hard to build.

44:16Jensen Huang: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 all of these companies are outside the cloud, and they're growing regionally in enterprise, in industries, at the edge.

45:04Jensen Huang:And that entire segment of growth is really hard to do if it's just building an ASIC.

45:09Jason Calacanis: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%. 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?

45:44Jensen Huang:Yeah, first of all, they just don't understand the scale and the breadth of AI. Yeah, I think that's true. Most people think that AI is in the top five hyperscalers. Right, that's right. 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. It's so out of the bag. It can go to$7 trillion.

46:12Jason Calacanis:Or they just have a$10 trillion company. It's all just CYA stuff that I think ultimately... It's never happened before, so you can't say it will.

46:18Jensen Huang: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 a year. We do$25 billion a year, as you guys know, in the time that we were sitting here. And so obviously, obviously, that was a joke. No, but it's all in podcast. It's roughly true. Don't worry, everything on this show is roughly true. Don't worry about it. It's all in. Wow. That was not guidance. But anyhow, the point is how big you can be depends on what is it that you make.

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. It's AI is much, open AI is incredible. They're gonna be enormous. Anthropic is incredible. They're gonna be enormous. But AI is going to be much, much bigger than that. And we addressed that segment. Tell us about data centers in space for a second. Yep. We're already in space. 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?

47:46Jensen 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. And so you can only use radiation. And radiation requires very large surfaces. And so that's not an impossible thing to solve. And there's a 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.

48:22Jensen Huang: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 space. And so there's a lot of things that we ought to 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 years. It's okay. I got plenty of time. 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.

48:57I mean, we all look great, I think. Some better than others. I think some better than others. I don't know what your secret is, Jensen. I look pretty good. I mean, what are you taking? What's off the menu? You've got to 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 push-ups and sit-ups. Perfect. Okay. 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 seem to get half as much. It seemed like 15 to 25 % of the dollar spent were on these first GP visits. And I think we all know like chat gpt 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 health care system there's several several areas that we're involved in in um in health care one is uh ai uh physics uh and 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 Hippocrates is 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.

50:37Jensen Huang: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 CT or whatever instrument we interact with in a hospital in the future will be agentic. Open claw in a safe version will be inside every single instrument. And so in a lot of ways, that instrument's gonna be interacting with patients and nurses and doctors in a very unique way.

51:13Yeah, we're seeing so much investment in AI weapons. It'd be wonderful to see some investment in AI EMTs and paramedics and saving lives, not just taking them. Which I think is a great segue into robotics. You've got dozens of partners. We have this very weird, I don't know, I wanna 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, tied with you, Elon Musk. That was a good save, I hope.

51:50Optimus, pretty impressive. And then other companies in China. 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, this humanoid factor 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?

52:20Jensen Huang:We invented the industry largely. America invented it. You could argue we got into it too soon. 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 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.

53:01Jensen Huang:That's it. Three years to five years, we're going to have robots all over the place. I think China 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. And so in a lot of ways, our robotics industry relies deeply on their ecosystem and their supply chain. And they're obviously moving very quickly. 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 I think you're going to see some fast movements here.

53:42Ultimately, 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. Well, I'm hoping more.

53:50Jensen Huang:Yeah, I'm hoping more. Yeah. 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. What does the world look like? Sorry, let me just say, I 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 can do a lot of work for them.

54:22They can stand up an Etsy store, a Shopify store. or 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 math at the moment is we're millions of people short in labor today. Yeah. 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, number one. Some of the things that you 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. Right, teleoperate. Walk around the house. Walk the dog. Yeah, walk the dog. Break the leaves. Yeah, exactly. 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. 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. Yeah, yeah. And then I'm going to upload my AI. 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.

55:52So 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.

55:58Jensen Huang:Distance no longer matters.

56:00Jason Calacanis:Distance doesn't matter. 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 27, 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.

56:26Jensen Huang:I think he's being very conservative. I believe Dario and Anthropica is going to do way better than that. Wow. Way better than that. Wow. So from 30 billion to a trillion. Yep. 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 Anthropics tokens. Value-added reseller of OpenAI. That's right. And they're going to, that part of their - Look at this logarithmic expansion. Yes. Their go-to market is going to expand tremendously this year. What do you think in that world is the moat?

57:08What'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 going to have general models that are connected into the software company's agentic system. Right. 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. Right. So the call to arms for you for entrepreneurs is, look, know your vertical. That's right. Know it as deep and as better than everybody else. That's right. And then wait for these tools because they're catching up to you, and now you can imbue it with your knowledge.

58:15That's right.

58:15Jensen Huang:And the sooner you connect your agent with customers, that flywheel is going to cause your agent to get hyper. 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. And we trap you with the system. In fact, exactly right. 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. Exactly. And that's arguably a five or six time bigger industry is the customization.

58:53It is. Absolutely. Yeah. 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 domain expert. 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. Have you changed your view? Well, I go... This is Doomer Dan.

59:29I'm not Doomer Dan. I do have... Job Doomer. No, you can hold space for, I think, two ideas. One is there are going to be a large... That's viral J-Cal.

59:37Jensen Huang:But that's just because he doesn't hang out with me enough. We fog a little bit. Be careful when you watch it. He will show you your breakfast table. He'll follow you around. I'm not asking for it. I'm just saying. He'll follow you around. I'm not asking for it. You can come with me and Tucker. We ski in Japan every January. We love it. We go and Tucker. We'll go road trips. There is going to be job displacement. And then the question becomes, do those people have the fortitude, the resolve to then go embrace these technologies. We're going to see 100 % of driving go away by humans. 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.

1:00:20And so that is going to happen, yes?

1:00:22Jensen Huang:I think that jobs will change. For example, 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 become your mobility assistant and they are helping you do a whole bunch of other stuff.

1:00:59Jensen Huang:to the hotel and the car is driving by itself. The autopilot in planes

1:01:04Jason Calacanis: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. 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 arranging, for example,

1:01:19Jensen Huang:coordinating a bunch of things for you, getting, you know. The pie just grows in a way that... 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. Yes. 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 overprescribe, leaving enough room for the AI to innovate and create while we guide it to the outcome we want.

1:02:05Jensen Huang:All of that requires artistry. 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? Yeah. 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? I still believe that deep science, deep math, language skills, as you know, language is the programming language of AI.

1:02:42Jensen Huang: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 would just advise 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, predicted that computer vision will completely eliminate radiologists. and that the one field he advises everybody to not go into is radiology.

1:03:26Jensen Huang:Ten years later, his prediction was 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, but your purpose is to help the doctors, helping 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.

1:04:12Jensen Huang: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, they're doing more scans. They're treating more customers and more patients. Their revenues go up. And guess what? Perfect example.

1:04:34Jason Calacanis:And a country that grows faster, productivity increases, a wealthier country can put more teachers in the classroom, not less teachers in the classroom. That's right. 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. Yeah, 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. He is the steward we need. You are. I think you need to be more vocal. I'm being very, very honest. about the positive side of it. I think there's so much humorism. But I also think it takes the humility to have this level of success and be humble about we're making software, guys. Yeah. And I think that that's actually really healthy for people to hear.

1:05:25We have done this before. We have invented categories and industries before. We don't need to go to this scare-mongering place. It does nothing. And we get to choose, right? We have autonomy and agency. We get to pick how to deploy this. Okay, everybody. We'll see you next time. on the All In interview. Well done, brother. Thanks, man. Good job.

1:05:47Jensen Huang:Thank you, sir. That was awesome. Good, good. Appreciate you. You guys are awesome. Look at this. Look at this big crowd behind you guys. Man, I think they're here for you.