Jensen Huang | Does AI Revenue Justify Capex, '27 AI Bottlenecks, Singularity, Open vs Closed & more

Can AI revenues justify the spending? Jensen Huang breaks down the biggest bottlenecks heading into 2027, the path to the singularity, open vs. closed models, and why he’s 99.99% optimistic about the decade ahead. Jensen joins Brad Gerstner and Clark Tang for their third BG2 conversation, recorded days after the Xi State Dinner, the White House Accord on superintelligence, and his receipt of the National Medal of Science. They explore why building technology responsibly also means moving fast, how power constraints could shape AI’s next phase, and whether SpaceX could help put data centers in orbit. Jensen also explains why NVIDIA sees demand extending far beyond just two AI labs. Produced by STMNT Studios Music by Yung Spielberg www.bg2pod.com Follow: Brad Gerstner https://x.com/altcap Clark Tang https://x.com/_clarktang NVIDIA https://x.com/nvidia BG2 Pod https://x.com/BG2Pod The statements made and information presented in this podcast reflect each speaker's personal opinions and do not necessarily reflect the view of any other person or entity, including Altimeter Capital Management, LP ("Altimeter"). The information provided is believed to be from reliable sources, but no liability is accepted for any inaccuracies. This podcast is for informational purposes only, and statements should not be construed as providing investment advice or making an investment recommendation. Past performance does not guarantee future performance. Certain of Altimeter's funds have invested in the referenced portfolio companies, which may incentivize executives of such companies to provide favorable reviews of Altimeter. Altimeter and its respective clients trade in public securities and have made and/or may make investments in or investment decisions relating to the companies referenced in the podcast. Altimeter and its respective clients reserve the right to make investment decisions or engage in trading activity that would be (or could be construed as) consistent and/or inconsistent with the views expressed herein.

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

• 0:00Jensen Huang?

  This is an existential issue for us.

• 0:02Brad Gerstner?

  We could easily stop, but if we stop today, we lose.

• 0:05Jensen Huang?

  Functionality, capability, safety, security, they're all at exactly the same place. Priority number one.

• 0:21Brad Gerstner?

  It's our third pod back together.

• 0:22Jensen Huang?

  Yep.

• 0:23Brad Gerstner?

  In the last 10 days, you've been at the she state dinner at the White House. You were at the White House summit where we did the accord on superintelligence. Just yesterday, you were there with your parents when the president, you know, presented you with the National Medal of Science.

• 0:39Jensen Huang?

  You were the only investor at the table.

• 0:42Brad Gerstner?

  Um, there.

• 0:43Jensen Huang?

  You were the only investor at the table. Now that, now that we reflected on it.

• 0:47Brad Gerstner?

  Yeah, well, tell me, being there with your parents yesterday.

• 0:51Jensen Huang?

  Yeah.

• 0:51Brad Gerstner?

  Right. It struck me five out of the six people on stage were all born outside of the United States. How did that feel?

• 1:01Jensen Huang?

  Well, first of all, it's a great honor all by itself.

• 1:04Brad Gerstner?

  Right.

• 1:04Jensen Huang?

  And it's a reflection of the, the contribution that the company has made.

• 1:09Brad Gerstner?

  Right.

• 1:10Jensen Huang?

  And, and obviously, obviously, we created, we invented a new computing model. This is probably the most pervasive computing model in the world today. It's the only computing architecture that's in every cloud. It's every computer company is used in every field of science. It's used by gamers. It's used by creators and builders and it's used by scientists. It powers intelligence.

• 1:31Brad Gerstner?

  Right.

• 1:32Jensen Huang?

  I mean, just if you take a step, step back and you look at what the company has accomplished. You know, we started out building a graphics chip for gamers and I'm still proud of that. And of course, and, and we now shipped one and a half, 1.67 billion dollars, billion units.

• 1:48Brad Gerstner?

  Right.

• 1:48Jensen Huang?

  That's how many generations of, I mean, I raised generations of scientists and kids and designers, you know? And so, so I think the impact that, and here we are, you know, where the infrastructure that powers the intelligence of society. And so, so it's a, it's an incredibly proud moment for me to receive the award on behalf of the company. Now for my parents, you know, I wanted them to go and, and I gave them literally, you know, 24 hours of time. And they don't travel that much.

• 2:17Brad Gerstner?

  Right.

• 2:17Jensen Huang?

  They don't travel that much. And my dad didn't even have a suit.

• 2:20Brad Gerstner?

  Wow.

• 2:21Jensen Huang?

  Yeah. So he ran out and bought a suit and.

• 2:25Brad Gerstner?

  He looked great. They looked great.

• 2:27Jensen Huang?

  Yeah, it was a brand new suit and he showed up and I didn't realize he was, he had a suit. I thought we were just gonna wear some shirt. And he went out and bought a suit. My, my mom got a new dress, you know, and, and literally in 24 hours.

• 2:41Brad Gerstner?

  Yeah. Oh, I know how it works.

• 2:42Jensen Huang?

  Oh my gosh, it's incredible. And so, uh, so we got him on a, we chartered a plane for them, got him there. And, um, they were so happy to meet the president. And, and President Trump walked my mom because she has a hard time walking. And the two of them were, were walking offstage and he, he held her and walked it, walked her down the stage. And they were just incredibly, incredibly proud to meet her, you know? And, um, uh, but for them, it's their American dream. You know, my dad, I still remember, I think I was like five years old or something like that. And, and he had just come back from the United States on a business trip. He was, he was working for an air conditioning company and he.

• 3:24Brad Gerstner?

  And you were living in Taiwan.

• 3:25Jensen Huang?

  We were at the time, we had just moved to Thailand.

• 3:28Brad Gerstner?

  Thailand.

• 3:28Jensen Huang?

  And, um, oh, no, no, we were just about to move to Thailand. And, um, we were in Taiwan. He had come back and he was, he was in the United States, working for a company called Carrier. Their air conditioning company. And he was here for training. And, um, I, of all the places in the world, they landed him in New York. Okay. And so here, you could just imagine a kid in the late twenties, I guess mid, late twenties, and never been outside of Taiwan, goes to United States, New York City of all places. And he saw the future of the future. And he came back, you know, mind blown. All the things that he bought at the airport and brought it back. It was, he bought, he brought chocolate.

• 4:12Jensen Huang?

  He brought, you know, that cheese that you have in a tube back in the old days? It's kind of like.

• 4:16Brad Gerstner?

  Strange cheese.

• 4:17Jensen Huang?

  Strange cheese. It comes in a tube.

• 4:19Brad Gerstner?

  It's great.

• 4:20Jensen Huang?

  And, uh, he brought back all kinds of strange things and he's just, this is, we should go there someday. And the first opportunity they had, uh, they sent my older brother and I to, uh, United States and, and, um, you know, it was, it was his dream.

• 4:37Brad Gerstner?

  I don't think many people realize that you came to the United States ahead of your parents.

• 4:40Jensen Huang?

  Yeah.

• 4:41Brad Gerstner?

  Right? Um, and they joined a few years later, but it, it just hit me like a ton of bricks that five out of the six people on that stage yesterday were born outside the United States. You and I have talked about the need for, uh, you know, with all the other stuff going on, a real strategy and an immigration policy, policy that makes certain that we're still the place that all the great builders in the world want to come build.

• 5:06Jensen Huang?

  This, this is America's superpowers.

• 5:07Brad Gerstner?

  Right.

• 5:08Jensen Huang?

  Um, in a lot of ways, uh, the intellectual property, um, creation capability of our nation is unlike anything else in the world. And the reason for that is we attract the world's best.

• 5:18Brad Gerstner?

  True.

• 5:19Jensen Huang?

  You know, we are the place where they want to come and build their lives because of the opportunity it provides, the freedom that, that she offers, the opportunity that it provides, the spirit of, of invention and entrepreneurialism and the can do attitude. You know, in the United States, everything is, is possible.

• 5:37Brad Gerstner?

  Right.

• 5:38Jensen Huang?

  We start with, of course it's possible. Of course we could do that. Instead of, instead of, you know, I do a lot of travel and, um, of course you can't do that. Of course, you know.

• 5:47Brad Gerstner?

  In other places.

• 5:48Jensen Huang?

  In other places. And we take it for granted. But the spirit, the, the, the energy, um, that comes around with the culture that we've created here. It's just can't be understated. And, and so I think the flow, the inflow of intellectual property into this company, into this country is unlike anything else the world's ever seen.

• 6:11Brad Gerstner?

  It's true.

• 6:11Jensen Huang?

  And we want that. We want all of the world's great minds to come here, bring their great ideas, you know, make them, realize them here.

• 6:18Brad Gerstner?

  Right.

• 6:19Jensen Huang?

  Realize them here and let us, you know, let us, let us, uh, keep that flywheel going. You know, I really, it's a, it's a delicate system. It's a delicate balance I get. It's a delicate balance I get. And so we have to do it lawfully, we have to do it rightfully.

• 6:33Brad Gerstner?

  Right.

• 6:33Jensen Huang?

  Um, but we, we surely want to do it.

• 6:35Brad Gerstner?

  Yeah, for sure. So I want to start high level, just kind of where we are. Then I want to dig down a little bit into the safety and regulation and getting this right. Where we are in terms of the compute build out. And then finally, we're going to finish up, you know, drilling in a little bit to Nvidia's growth and, and, and what's going on there. But you've been really good at calling the future, right? Two years ago, we were sitting here and you said that the market was dramatically underestimating the amount of compute build out that was going to be required and was going to happen over the course of the next five years. You, you said it's not going to be a trillion, it's going to be two trillion at a minimum in terms of cumulative compute build out. And of course, now we're over that. You said before everybody was thinking about it, that inference, right?

• 7:18Brad Gerstner?

  Compute was going to one billion X because of inference time reasoning and thinking. And then last year, of course, before we saw Opus 4.6, before we saw Claude Code, you said, we're entering the age of agents, right? Where we're going to unlock a lot of these capabilities. Um, and of course, we saw Opus 4.6 and Claude Code really took off. So where are we now, right? We have Elon and Sam and.

• 7:41Jensen Huang?

  There's only three things that I said that came through. There's another 200. Why don't you just keep on going? Well, I just let that be the podcast.

• 7:49Brad Gerstner?

  So, so, so our investor day in a couple of weeks is on, you know, entering the singularity, this acceleration. We have Elon and Sam and Demis all say, hey, we're in the foothills or entering the singularity. We have, you know, model solving, Navier-Stokes, as you know. Um, so when you look into your crystal ball, where are we? And are we inflecting? Are we accelerating, you know, into a different phase of superintelligence?

• 8:22Jensen Huang?

  And so let's unpack that. First, first of all, it's fairly logical why the amount of compute that we're going to need is a billion times more than where we were just recently, let's call it last year. And the reason for that is because thinking and problem solving takes a lot more, you know, inferences than, uh, repeating something, regurgitating something that you memorized.

• 8:49Brad Gerstner?

  Right.

• 8:49Jensen Huang?

  So when you're a chat GPT, um, uh, you give it a prompt. Most of that, most of that knowledge was encoded into a large language model. Unfortunately, of course, it hallucinated. And so the next click was, um, uh, it was, it was a generative, but it was also grounded or conditioned by ground truth.

• 9:09Brad Gerstner?

  Yes.

• 9:09Jensen Huang?

  And so it would have to go out and do a search, do a quick search, comes back with the ground truth. And then from that conditioning, augment it with his own, own, what it already learned pre-training. And it would generate, uh, new, new, uh, whatever the answer is. Well, most of, most of the things that we want to really do that are, that are interesting requires you to, to reason step by step, to come up with several options, plan. And you might decide, um, let's see how these things all roll out. And then based on, based on the answers of all of those, I reflect on the best answer so far, the best path so far. I might choose one or two or three of them, roll them out.

• 9:48Brad Gerstner?

  Right.

• 9:48Jensen Huang?

  Okay. Prune the rest of it and then just keep on doing this. And you know, people, we do this, that's the reason why, why we say, um, uh, great strategists love strategic optionality. You don't want to prune things too fast on the one hand, but you, you also have to optimize your resources. Well, AIs are doing that.

• 10:06Brad Gerstner?

  Yes.

• 10:07Jensen Huang?

  Okay. So, so they're, they're keeping their strategic optionality. They're rolling things out, they're pruning, um, they're, they're reflecting, they're pruning, so on, so forth. And all of that thinking, all of that exploration just takes a lot more compute.

• 10:20Brad Gerstner?

  Right.

• 10:20Jensen Huang?

  And, and not on top of that, because it's able to now do productive things, we're going to use it for more things. And so the compounding of both, it uses more compute to do its job, but it's doing a good job. And therefore we're going to, we're going to hire it to do more jobs.

• 10:35Brad Gerstner?

  Exactly.

• 10:35Jensen Huang?

  Okay. And so that all compounds. At this point, I would, I would say that the big observation from last year is that, that on the one hand, we want the models to get better and better. And we, we still want the models to get better. And the scaling laws of models are, are going to continue. However.

• 10:53Jensen Huang?

  The attachment of a harness, basically the exoskeleton of the large language model, you know, kind of the body of the brain that allows it to manage, manage working memory, manage knowledge. How do you go from working memory to short term, to long term, to basically formulating your knowledge graph, which is what we all do. We kind of think about, hey, you know, what did I learn? And how does it fit into everything that I know in the past? And you know how when we go to bed, we do compaction, things that don't make sense, we kind of, or don't matter, we kind of eventually forget. But the things that kind of matter, somehow it gets reconnected. And so we're doing that kind of offline.

• 11:34Jensen Huang?

  And so we got working memory, we have offline knowledge extraction and creation, knowledge graph creation. That whole part of memory is a big deal. The other thing is, of course, tool use. And we've taught these tools, these AIs, how to interact with the most important tools that the humans ever made. You know, it's no different than if you were, if you're a robot, comes into our house, it's more likely you're going to use all the tools that I have in the house than to go off and build it from scratch. And so, so spice is still going to be useful, and Verilog will be useful, and simulators will be useful, and you know, design compilers, and you know, Photoshop, and AutoCAD, and you know, Daseau,

• 12:16Jensen Huang?

  Katia, and all the Siemens tools, all of these different tools that we've been using to run our company, build our companies, design things, operate things, they're all going to be great tools for the agents to use. But the agents had to learn how to use it. And so, these two areas, knowledge, knowledge and working memory, and tool use, huge unlocks in the last year. So now, all of a sudden, you could, you could put these large language models encapsulated in their harness or exoskeleton, and you can put them into your company, connect them to the, the information, the ground truth that you have, connect them to the tools that they can use, and all of a sudden, they start doing amazing

• 13:00Jensen Huang?

  things. Now, the easiest part, frankly, was coding. That's kind of the, the first on-ramp, you know, to useful work. And in a lot of ways, coding is easy because both the customer and the work, the tools that they use are fairly accustomed to things that has tremendous use, but are not extremely easy to use. And so, in the early days of coding, it wasn't extremely easy to deploy, but software engineers found a way. And so, so now, now I think the next click for us is now, and you guys have probably been playing with Astra, OpenAI's Astra, it's really fantastic, and we're using it in all these

• 13:41Jensen Huang?

  different places. And so now, the next, the next phase of these, these agents, they're able to do, you know, design work, and a lot of creative marketing work, and we're using it to go, you take a picture and give it to Astra, it literally recreates it into CAD. It recreates it into the CAD, and so, which is incredible, because we need the CAD, so that we can, we can interact with the CAD, and make fine tuning, and adjust, and so Astra could really give us the, the first initial implementation, incredibly productive. Now, you take, you take Astra, and, and, you know, the latest release, and you apply to all of these different, different domains. I, I think that, that besides software engineering,

• 14:26Jensen Huang?

  you know, we're going to see designers, and builders, and creators, and you know, this whole area, scientists, and you know, all of, all of this whole unlocking. Now, the challenge, of course, is how do we deploy agents safely and securely into the world's enterprise? This is exactly the same question again, with safety and security, and, and we'll talk about it.

• 14:49Brad Gerstner?

  Yeah, we'll, we'll come in on that. What I'm, what I'm struck by is that all of this occurred this year, with OpenAI and Anthropic having, I don't know, two, three gigawatts of compute each, okay? So relatively small amount of compute, relative to what's coming, okay? And so, again, what I'm interested in is, the way the economy generally works, or people model the economy, or they model a company, is linear deceleration. They're not very good at modeling acceleration. But here, we have this double exponential, we have massive amount of new compute coming on board. So if OpenAI and Anthropic are, I don't know, three to five X-ing their compute

• 15:30Brad Gerstner?

  next year, does it stand to reason? I mean, we solved Navier-Stokes, we'll look back at this and say, that was a tiny amount of compute and intelligence, the worst intelligence we're ever going to have from here forward, solving a millennium problem. So when you look at what's coming online next year, you know, do you expect that we're going to have major breakthroughs in, you know, long-running agentic problems that, that we can use these swarms of agents on?

• 15:58Jensen Huang?

  Well, Navier-Stokes is really a great example. Remember, OpenAI did not solve Navier-Stokes from nothing. They solved it from the foundation of a lot of great scientists, and a lot of great mathematicians, and a lot of great previous work. Earlier, I said, we're at, we're at kind of the tipping point of a lot of these different fields, because we're kind of at that phase, that, that point, with Navier-Stokes, with a great deal of foundational work already been done. If we could just apply the exhaustive exploration of AI to the previous, previous done work, we're going to see one breakthrough after another breakthrough

• 16:40Jensen Huang?

  rather quickly. But we do need a lot of compute to do that. And, and the reason for that is because a large amount of exploration was necessary. You know, if it was, if it was simply somebody resolving some fundamental first principle theory, in order to discover the breakthrough, if it didn't require a vast amount of exploration of space, search space, not space, space, but search space, if it didn't require that, then somebody would have found it earlier. You know, people are pretty smart. And so, so I, I think that there are many problems like this, that are just on a precipice, you know, precipice, and, and really wants to just have that massive amount of intelligence exploration to, to get us there.

• 17:24Jensen Huang?

  In the, in the coming years, you know, my expectation is, is that the, the cost of intelligence would fall fairly, fairly quickly. And the reason for that is because obviously we're bringing more compute online. These models are getting smarter. When you're smarter, you could be more efficient. And so, the combination of these two things, the, the cost of intelligence will come down, which is, which is, of course, what they want. And this is no different than Moore's Law. If you go back thirty-some odd years, we, we have three decades of computing cost decreasing by a factor of two every single year. And, and we want intelligence costs to decrease by a factor of two or 20 every single year,

• 18:09Jensen Huang?

  because we, the, the consumption of it, it's going to, more than make up for it.

• 18:14Brad Gerstner?

  That's right.

• 18:15Jensen Huang?

  More than make up for it. And, and so, there's, there's a massive empty space in the world that wants to be filled, empty vacuum of, of opportunity, and, and we just got to get, get the cost down.

• 18:26Clark Tang?

  I, I guess what stands out to me about Navier-Stokes is that they had 10,000 agents working together on this. It looks like a, to me, that sounds like a, you know, hyperscale company, basically all pointing agents to work together towards a goal, right? And I think in the early days right now, it's a lot, a lot of, you know, paralyzed search exploration. But I think, you know, the, the goal is that more of these agents can work together to a common goal, and that we can solve a lot more. And to your point, you know, I think Armad says, probably cost about $4 million of compute to solve, solve, you know, this instantiation of Navier-Stokes. The costs will, the cost to do something like

• 19:07Clark Tang?

  that over the next few years is going to drop by another 5X, 10X, right? So the, the number of things that we're going to be able to do is, is rapidly expanding.

• 19:18Jensen Huang?

  Our ambition's going to go sky high. The original GPT probably cost, let's pick a number, say, you know, maybe $20 million to do. If you wanted to train GPT-1 now, it'll probably cost you, what, $1,000, $100, maybe?

• 19:33Clark Tang?

  It's like $100, yeah.

• 19:34Jensen Huang?

  Okay. And so, you, you went from, you went from $20 million, let's say $10 million, to $100 bucks. And we did that in 10 years.

• 19:42Clark Tang?

  Yeah.

• 19:43Jensen Huang?

  Okay? And so, I fully expect, meanwhile, our ambition got so large, that the models that we're training now, 3 trillion, 10 trillion, 30 trillion, we're talking about gigantic models now. And we want it to be, and so, our ambition for, for the capabilities of technology has gone up. Meanwhile, our use of it has gone up between 10 years ago to now, probably, I don't know, a million times. And so, I don't think this trend is gonna stop. I think there's a fairly insatiable demand for intelligence. And the smarter you are, the more intelligent you are,

• 20:23Jensen Huang?

  the more ideas of applying intelligence you have. You know, the more I've learned in life, the more I realize there's more to do. And that's because every time you learn something, it opens a new door. Every time you learn something, it opens a new door. And every time you open a new door, you see more opportunity, you see more challenges, you see more problems. And so that's the future. You know, people say, we're gonna have more time. I completely disagree. I think for most people, this intelligence is gonna unlock more opportunities, which is gonna, and they're gonna wanna pursue it because they're ambitious.

• 20:57Brad Gerstner?

  Yes.

• 20:58Jensen Huang?

  You know, give me an example of a family, a father, a son, you know, a husband, a wife, a mother, who doesn't want it, who don't have ambition for their families to have more. It's just, they just don't have the opportunity. And these things are gonna open up opportunities.

• 21:13Brad Gerstner?

  If you go back to 1930, Keynes talked, he made a prediction about the Industrial Revolution. And he said, you know, in a hundred years, the economy will have ADEXed, but the work week will go to 15 hours a week. He was right. The economy did ADEX, right? We had massive abundance and productivity gains from it.

• 21:30Jensen Huang?

  Yeah, except we're working 80 hours a week.

• 21:32Brad Gerstner?

  But we didn't go to a 15-hour week. And what he got wrong was the human spirit, right? He didn't realize that work is purpose. Discovery is purpose, right? It's deeply encoded in, I think, our DNA. And I'm working harder than I ever have, having more fun than I ever have, not because I have to, like my grandparents did, right? I'm doing it because I want to. And so I agree with you on that. I'm fascinated with this idea, you know, the singularity postulates that, you know, we've kind of now have all these agents that can work together on long-running tasks, they can stay on task. I love your-

• 22:11Jensen Huang?

  Keep each other on task, working as a team. Hey, you're off.

• 22:14Brad Gerstner?

  That's right. I love your idea that, you know, we build on top of all the things that have come, right? And so if I think about full self-driving as an example, we talked about it for a decade, and we really didn't get there, right? But all of the sudden, like you go on Twitter, everybody experiencing Tesla, FSD, experiencing Waymo, we're here. Somebody asked me, how was the drive here this morning? I said, I don't know, because I got in my car and I hit FSD, and I didn't pay any attention until we pulled into the parking garage, right? I was working the whole way here. But if I think about that in the fields of science, the breakthroughs that are going to come, in the fields of pharma and genetics, right? We've been laying down the tracks for 30 years. We're gonna break

• 22:54Brad Gerstner?

  through on a lot of massive cures for the world. Of course, ChatGPT is already helping parents plan their day and solve their kids' health issue and helping them get better educated. Talk to us a little bit, though, as to why that's having such a hard time breaking through, Jensen. We have all of these things that are out there improving lives and that are right around the corner. And so that's kind of curious to me as to why that's not breaking through.

• 23:24Jensen Huang?

  I guess, let me take a shot at it. And, you know, we'll ultimately not really know.

• 23:33Jensen Huang?

  I've spoken to a lot of people. They told me what they thought. I mean, one of the things, of course, is AI or superintelligence is soft matter. You can't look, you can't see it. And so you got the whole world talking about this technology, but you can't see it. You can't touch it. And even though they're already benefiting from it, when they do search, they're already benefiting from it. When they go shopping, they're benefiting from it. When they're using Netflix, they're benefiting from it. You know, you go to Walmart, you're benefiting from it. The fact that FedEx is getting stuff to us, you know?

• 24:08Brad Gerstner?

  It's all leveraging superintelligence and accelerated compute today. But we don't do a very good job telling that story. I often think-

• 24:16Jensen Huang?

  Life is, they're benefiting from it, it's just, it's invisible.

• 24:19Brad Gerstner?

  Right. So if we shut off the data centers and shut off superintelligence for a weekend, I think everybody would notice, right? I know you had a question about robotics in particular.

• 24:29Clark Tang?

  Yeah. I guess you've notably said a few times this year that the ChatGPT moment for robotics is here. And I think you've also said that it could be your next $100 billion market. So I guess, what are you seeing now in the inflection point in this year that this is around the corner?

• 24:47Jensen Huang?

  Yeah, I understated the $100 billion because we're already, the first six months of this year, we did $10 billion in physical AI. Physical AI is basically, you know, AI that's somehow interacting with the physical world. And it could be photon counting CT machines, it could be ultrasounds, it could be new imaging radars, next generation of 5G. Not only does it understand radio waves, it's gonna understand signals of all kinds. It's a sensing network. Of course, robotics, self-driving cars, manufacturing systems, visual inspections, you know, geospatial satellites, imaging the world and reconstructing

• 25:27Jensen Huang?

  it into 3D. And so there's, basically, if you look at the entire electromagnetic spectrum,

• 25:36Jensen Huang?

  there's AI stuff going on from visible to invisible spectrums. Okay? And so, and we're doing work across that entire thing. And as an AI, there's the perception part of it, there's the reasoning part of it, and then there's the planning and actuation part of it. When you finally get to planning and actuation, then that's self-driving cars and robotics and things like that, you know, in warehouse delivery vehicles, pick-and-place robots that you don't have to program, you know, as precisely, because it learns by itself, you know, all the way to people talking about consumer robots, you know, coming into your house, you know, making your bed and things. And so, or healthcare robots, hospital robots,

• 26:20Jensen Huang?

  lots of really good examples there. You know, precisely delivering exactly your prescription to the patient, having robotic care, literally 24/7, every single second, to keep patients safe. And so just lots and lots of great examples, entire surgical rooms that are gonna be completely robotic. And so, lots of great technology being deployed. And so if you look at this whole thing, we're the world's largest physical AI company. We did $10 billion in the first six months. We did $10 billion in the last year. And that was our first $10 billion year. The year before that, we did five. Okay? And so when five,

• 27:04Jensen Huang?

  10, let's say we did 20, who knows? So maybe the 100 was a little understated. And I think the reasoning part of it is, is everything that matters to us in the final analysis touches the physical world. And we live in the physical world. And so I think the market opportunity for bringing software, computing, intelligence into an industry largely historically was mechanical, electrical, that's a big opportunity. I mean, look at, obviously, what Elon has done to the automotive industry, turned it into essentially a computing industry, a technology industry. And so I think every car company is gonna be a technology company.

• 27:48Jensen Huang?

  Every shipping and receiving company, logistics company is gonna be a technology company. I'm super excited for FedEx. They're obviously gonna be an AI company. They're obviously gonna be a technology company. Walmart, obviously gonna be a technology company, an AI company. So super excited for them. The entire telecommunications industry will be reinvented again into a technology industry. They were a technology industry a long time ago, then became a services industry. They're gonna be a technology industry again. And I'm delighted to work with AT&T and Verizon and, you know, in Europe, around the world, as they reinvent themselves. And so you take a look at every one of these industries that touch the physical world, and there's a great idea for reinventing them with AI.

• 28:30Brad Gerstner?

  You know, we, I was just in Washington-

• 28:33Jensen Huang?

  It's so weird, you know, people put us in chips. You know, and so we're actually in warehouse and logistics. We're in healthcare. We're in automotive. We're in telecommunications.

• 28:45Brad Gerstner?

  Every industry on the planet. Every industry on the planet.

• 28:47Jensen Huang?

  And so we're in life sciences, you know? And so all these entertainment, obviously, for a long time, Nvidia was really vastly an entertainment company, you know? And so each one of these verticals, Nvidia has a massive stack that's inside of it. And we're very deep in the ecosystem. And we have excellent partnerships there. And so, you know, one of these days, you know, people might not categorize us as a chip company anymore.

• 29:17Clark Tang?

  The playbook would be to take the Alpemayo automotive, I guess playbook, and insert it into each one of these industries.

• 29:25Jensen Huang?

  Yeah, that's the final, that's the final contribution. But in each one of these, it takes three computers to build a physical AI platform. And so you need the computer to create the brain, that's the training system. But very importantly, and this is where you're gonna see the labs also do this, they need to build a digital twin of the internet, so that they don't have to put their AI agents out into the internet to learn how to use it. And so, just like we created Omniverse as the digital twin of warehouses and buildings and cities and the physical world, we put our robot into the digital twin. The digital twin learns how to be a robot. And then we deploy it into the last computer, which is the computer that's inside, you know, for example, a

• 30:10Jensen Huang?

  robot or a car or a surgical instrument or whatever it is. And so it takes three robots, three computers, and three complete stacks. And we build complete stacks for each one of them. We're the only company in the world that does that. And then, if you're a company that wants to build it end-to-end, you could use two or three or, you know, one of our computers, we don't care. And so, and so each one of a computer has its own software stack. We do the same across all of our verticals.

• 30:36Brad Gerstner?

  But of course, these capabilities cut both ways, which is why, you know, we hear a lot of people talking about safety. You've been highly critical of the Doomer narrative.

• 30:47Jensen Huang?

  You can only get there if you build the technology responsibly.

• 30:50Brad Gerstner?

  That's right.

• 30:50Jensen Huang?

  And so the first thing is, build the technology responsibly. Notice the word build. It's an engineering word. It's about technology, it's about engineering. Engineers, scientists, researchers, we have to build this technology safely, responsibly. And the question is, can you? And the answer is absolutely yes. Because if you can't-

• 31:15Jensen Huang?

  Then you shouldn't build it at all.

• 31:16Brad Gerstner?

  Of course.

• 31:17Jensen Huang?

  And so, we either have to believe we can, or we shouldn't build it. Okay? And so, I believe we can. And so, let me just reason about it with you. So, the first part of it is, they say, some people say that, that AI is uncontrollable because it's impossible to align perfectly. Alignment means this for all the audience. You tell somebody, "I want you to do this." And so, the mission is clear. The mission is clear. And it's clear to you, because you said it in English. Okay? And so, it could be, "I want you to solve this quadratic equation." And so, the mission is to solve the quadratic equation. The alignment is how do you do it? There are many ways to do it.

• 32:01Jensen Huang?

  For example, you memorized it. Okay? And so, the answer is, you know, whatever it is. Or, you saw the answer in a textbook, so you looked it up.

• 32:12Brad Gerstner?

  Right.

• 32:12Jensen Huang?

  Okay? You refer to a reference. Or, you went to a drawer and you know that drawer had all the answers. You open a drawer, you picked out the answer. Okay? Some people call that cheating. I call that opening the drawer and retrieving the answer.

• 32:26Brad Gerstner?

  Right.

• 32:26Jensen Huang?

  Okay? And that's what computers do. There's a lot of different ways you could solve that problem. Okay? Now, of course, that one is very explicit. Here's another one. Be successful in life. The mission is easy to say. And in fact, we say it to each other all the time. You know, we say it to our children, we say it to our friends. "Let's do our best to contribute to society." Well, now, the question is how? There are so many ways. How do you reasonably align, and everybody's culture and background? Some people think that contributing to society means to be generous, to be kind. Some people might mean that contribute to society, make a lot of money and then be philanthropic. Some people might, so on and so forth. Isn't that right?

• 33:09Brad Gerstner?

  Yes.

• 33:10Jensen Huang?

  Okay? And some people might just think that, that building a company alone is, or doing something alone is, is a sufficient contribution to society. Creating a beautiful piece of art, or, all of it is true.

• 33:21Brad Gerstner?

  Correct.

• 33:22Jensen Huang?

  And so, the question is, what's the alignment? And it just depends on your background, your culture, maybe it has something to do with where you live, where you came from. There's just infinite number of ways. And so, my point is this.

• 33:35Jensen Huang?

  Alignment is going to be a forever problem.

• 33:37Brad Gerstner?

  Yes.

• 33:38Jensen Huang?

  If we depend on perfect alignment in order to ensure safety, then we're never going to get there. And so, the answer is this. The way we treat, the way we think about babies and when we're raising kids and, you know, we have pets, or whatever it is, employees, okay? You start with containment. You give them the rights that, the access to tools, the access to information, the access to whatever responsibility capabilities that they have, funds, resources. You give them access based on the job that you ask them to do. You start out with zero trust. When you hire somebody, you start out with zero trust. When you have a baby that's one-year-old, you put them in a playpen, you assume zero trust.

• 34:20Jensen Huang?

  And so, you, zero trust, you give them minimum rights, and you expand that boundary as you learn more. And so, whenever we have a new AI, we have to assume the same thing. Whenever you're testing an AI, zero trust, minimum boundaries, minimum rights, and you expand it as you test and give yourself confidence. So, containment, very tight containment is really important to any autonomous system. Second thing you do, monitoring. And the monitoring has to be out of band. And the reason for that is because if you're, you'd be tell, an intelligent person, "Hey, listen, I'm going to sit here and watch you do your work to make sure you're doing it right."

• 34:59Brad Gerstner?

  Right.

• 34:59Jensen Huang?

  Okay? And I'm going to sit here and watch you make sure that you're serving customers properly, and that you're working hard enough. Okay? That, that autonomous system is going to behave differently than when you're not watching.

• 35:12Brad Gerstner?

  It's like Brad's in the room with me or not.

• 35:14Jensen Huang?

  Exactly. Exactly. Exactly. Okay? And so, so the monitoring has to be out of band, and it has to have the ability to observe that the autonomous system is doing something that was inconsistent with the policy, or inconsistent with alignment, and you have the ability to either escalate or shut it down.

• 35:32Brad Gerstner?

  Right.

• 35:33Jensen Huang?

  Just turn it off. And so, so those kind of capabilities have to exist. That's engineering, that's technology, and it's possible. And we announced it recently, called Open Shove. We have, you know, over a hundred, 100 companies now joining us. Got lots and lots of activity around the world, because everybody wants the same thing.

• 35:49Brad Gerstner?

  Starting at the highest level, you know, when I looked around the table at the White House.

• 35:53Jensen Huang?

  Yeah.

• 35:55Brad Gerstner?

  Everybody around that table, right? Their parents, they have kids. I think there's this view in the country that there are some leaders of superintelligence that are just YOLOing, that don't really care, they're being reckless, they just want to grab all the money. And yet, when I looked around their table, that table, there wasn't a person in there that I saw that wants to be reckless, that wants to YOLO. In fact, everybody there wanted to find the right balance between fast and safe. That was the president's directive. And I, we debated a little bit what that meant, right? There are different versions of how to implement that. But, you know, what would you say to the American public who fears that we have a reckless president or reckless leaders? Did you see any evidence of that?

• 36:40Brad Gerstner?

  You have kids. Do you worry about the future that you're bringing into existence with superintelligence?

• 36:46Jensen Huang?

  Safety and security technology.

• 36:50Brad Gerstner?

  Right.

• 36:50Jensen Huang?

  Are part of AI.

• 36:52Brad Gerstner?

  Right.

• 36:53Jensen Huang?

  There's some weird formulation that AI, technology, and safety and security are orthogonal.

• 37:01Brad Gerstner?

  Yeah.

• 37:02Jensen Huang?

  It's kind of like the airbags and the, and the braking system.

• 37:05Brad Gerstner?

  Right.

• 37:06Jensen Huang?

  And the radars, the cameras, the ABS, the lane keeping. All of that technology is somehow independent of the car. Do you guys understand what I'm saying?

• 37:16Brad Gerstner?

  Right.

• 37:16Jensen Huang?

  The only thing that matters to the car is the engine.

• 37:18Brad Gerstner?

  Right.

• 37:19Jensen Huang?

  In fact, the whole car is part of the car's technology. When I say, "We need to accelerate AI's technology," what I mean by that is, we need to accelerate the security technology, we have to accelerate the safety technology. They all have to be accelerated. If you ask me right now, I think everybody would say, "Let's accelerate security. Let's accelerate safety technology." Nobody says, "No, let's pace that."

• 37:43Brad Gerstner?

  Right.

• 37:44Jensen Huang?

  We should accelerate that. It's all part of the same thing. You can't separate capability, functionality, from safety and security. They go as one. And when you deliver the service, nobody's going to say, you know, "Good, good, you know, I really like the functionality of your service, but boy, is it, you know, wrecking havoc." And so, so I, I think it's all one thing.

• 38:06Brad Gerstner?

  Right.

• 38:07Jensen Huang?

  And we treat it as all one thing, and, and the industry has to treat it as one thing. And when we say accelerate, we have to mean accelerating the entire package. And right now.

• 38:17Brad Gerstner?

  Including the safety.

• 38:18Jensen Huang?

  Including, yeah.

• 38:19Brad Gerstner?

  Right?

• 38:19Jensen Huang?

  Especially the safety.

• 38:20Brad Gerstner?

  You mentioned pacing.

• 38:21Jensen Huang?

  Especially safety.

• 38:22Brad Gerstner?

  You know, this, some people read "Pacing the Frontier" by Dario, and thought that meant slowing down. And I thought a funny moment in the room was when the president called on his new best friend, Dario. He referred to him in that way, and he said, you know, "Explain to me, you know, your version of fast and safe." And Dario started off by saying, "Mr. President, you said whoever wins AI wins." Right? And he said that we're all speeding up, that they're speeding up, that they want to go fast, that they want to, you know, win the race with China, but also acknowledge that along the way, we just, to your point you just made, we have to make sure the safety systems are also speeding up and also keeping pace. So, I thought that was an important moment, where you had, you know, because I think

• 39:07Brad Gerstner?

  people have positioned him as though he's in opposition, right, to this. But it seemed to me that you were all very much on the same page. You, you went to the cabinet room with him and, and Greg and the other Frontier Labs to sign, you know, this agreement. Was there any chatter, you know, in that room about differences of opinion? Or was everybody, you obviously got it signed. Did anybody have to be put in a headlock, or did everybody go willingly?

• 39:32Jensen Huang?

  No, everybody was very, very constructive, very productive. And I, I think that, that, without exception, everybody is dedicated to advancing the industry safely and securely. And the reason for that is because if it's not safe, it's not secure, the functionality will never get a chance to speak. And so, the capability will never get, get a chance to shine. And so, we have to, we know that the priorities are at all times, safety and security. Of course, recognizing that this industry came through a transition just recently.

• 40:07Brad Gerstner?

  Right.

• 40:08Jensen Huang?

  It wasn't but six months ago. This technology barely worked.

• 40:11Brad Gerstner?

  Exactly.

• 40:12Jensen Huang?

  And so, the functionality and, without functionality and capability, what's the point in making it safe and secure? Nobody even cares about it. And so, the, obviously, the first, the first job of AI is to demonstrate it could do something useful.

• 40:23Brad Gerstner?

  Right.

• 40:23Jensen Huang?

  And it really reached that point six months ago. And so, now, it really wants to be deployed, and broadly deployed. And so, so there's no question that the industry, the entire industry now has a, has a strategic imperative, an incredible imperative, and great urgency to make sure that whatever we deploy is safe and secure. And so, I think, I think everybody is completely convinced, aligned, functionality, capability, safety, security, they're all at exactly the same place. Priority number one. And so, so, I, the question is winning. I would reframe winning.

• 41:04Jensen Huang?

  Not of the technology industry winning, not of just the AI industry winning. America winning means every industry in America winning.

• 41:13Brad Gerstner?

  Healthcare.

• 41:14Jensen Huang?

  That's right. Nothing would give me more joy. The definition of America winning is when we talk about Lilly winning,

• 41:19Brad Gerstner?

  Yes.

• 41:20Jensen Huang?

  Walmart winning, FedEx winning, every single one, Boeing winning, the US military.

• 41:26Brad Gerstner?

  Every citizen winning.

• 41:27Jensen Huang?

  Every citizen winning. And so our universities winning, our high schools winning. That's the definition of winning. The winning, there is no winning if it's just a technology one, but nobody used it.

• 41:41Brad Gerstner?

  Right, right.

• 41:42Jensen Huang?

  The greatest risk is, of course, we're gonna, you know, I'll just take it, I'll just say baseline. There will be no techno, there will be no AI technology industry if we don't build it safely and securely. Okay? This is an existential issue for us. And if we make it an existential industry for everybody else, well, that's not gonna happen because it'd be an existential issue for us first.

• 42:05Brad Gerstner?

  Right.

• 42:06Jensen Huang?

  So, so having said that, the definition of winning for us, and, and, you know, this is the, this is the part I always have to change a little bit.

• 42:14Brad Gerstner?

  Right.

• 42:14Jensen Huang?

  I want us to pivot.

• 42:16Brad Gerstner?

  I like that.

• 42:16Jensen Huang?

  I want us to pivot. And, and when I see AI winning, America AI winning, I'm looking down from, you know, these days, we spend a lot of time in the air and we're looking down, every state's winning, every county's winning, every community is winning, every company's winning.

• 42:31Brad Gerstner?

  Everybody won because we could afford to spend more on healthcare, we could afford to spend more on education, we could afford to spend more on interstate, you know, commerce, highways, et cetera, all of the infrastructure of the country. What I hear you saying is the definition of winning is that when we look back 25, 30, 40 years from now, the economy needs to be 10 times as large, everybody's lives need to be better, and existential and primary to all of that is it has to be done in a way that's aligned with human interest. Which, this is the last question I have on, on, on this, and I know we have a lot to get to. Most people don't understand what our security framework is in the country. I've said we have the most robust, super intelligent security framework in the world. We're, we're clearly the only country on the planet

• 43:15Brad Gerstner?

  that's actually sanctioned a model and pulled it off the shelf with Mythos earlier this year. But I describe it as a three leg stool. We have the frontier model review of every new frontier model by Casey and the Department of Commerce. So they do all their work. If new models coming out, they review it, they test it for whether or not they think it's gonna cause a problem in the marketplace. Now we have the White House Accord on Superintelligence that you helped to architect, internal and external audits, board oversight, and all of the liability that comes with that. And then third, we have the SI Force, now led by Jay Clayton, of course, Emil and Scott and Andrew, and that's doing critical industry coordination, cyber, et cetera. So these three legs on the stool.

• 44:00Brad Gerstner?

  There are people out there, I heard Gates recently saying, we're not doing, you know, enough, we need new laws on the books and everything else. That three legged stool seems like a lot to me in a very short period of time that's come together. As a leader of one of the most important companies, obviously, in superintelligence, do you feel comfortable, right, for that future that we're trying to usher in? That, that is a good step forward where we need to be right now?

• 44:25Jensen Huang?

  Yeah, the, the best way to get to the future is, uh, uh, take action quickly.

• 44:30Brad Gerstner?

  Yeah.

• 44:31Jensen Huang?

  And, and if we, if we argue about what is the, what's perfect versus what's first.

• 44:41Brad Gerstner?

  Right.

• 44:42Jensen Huang?

  We'll end up arguing about perfection and we'll never get there. And so I love, I love the fact that, that these three legged stool are actionable.

• 44:52Brad Gerstner?

  Right.

• 44:53Jensen Huang?

  They're tangible, they have real teeth.

• 44:56Brad Gerstner?

  Right, happening now.

• 44:57Jensen Huang?

  It's happening right, right now, you could, you know, we're doing it now, we're taking action now.

• 45:02Brad Gerstner?

  Right.

• 45:02Jensen Huang?

  And, and the reason we're taking action now is because it's good for our company, it's good for our industry, it's good for our country, it's good for society, it's good all the way around. And if we don't do these things, you know, the outcome, the outcome is, is less desirable. We don't get to pursue that optimistic future that we all dreamed, dreamt about. And so if you go back and look at another industry, I don't remember another industry that is, uh, it's currently at its current state.

• 45:28Brad Gerstner?

  Right.

• 45:29Jensen Huang?

  And have now formulated all of these cross company, cross industrial, government to industry coordination to make sure that we, we, we advance this in a responsible and optimistic.

• 45:41Brad Gerstner?

  Yeah, I just think it's so important for people to understand because when I hear senators talking about it on television, on both sides, to be perfectly honest, like, they don't even.

• 45:50Jensen Huang?

  Well, they have to learn about what we're doing here first.

• 45:52Brad Gerstner?

  Right, they don't even know what we're doing.

• 45:53Jensen Huang?

  Yeah, yeah.

• 45:54Brad Gerstner?

  Right, like, we are doing an extraordinary amount. And by the way, these aren't voluntary things. There's not gonna be a frontier model that gets released that Casey is not reviewing and the Commerce Department is not reviewing. And they've demonstrated they have the authority under export controls to pull them off the shelf.

• 46:09Jensen Huang?

  Do two things. One, come and spend some time and, and, and learn what actually has to be done. What's the problem?

• 46:17Brad Gerstner?

  Right.

• 46:18Jensen Huang?

  If you want a solution, you gotta at least understand what the problem is.

• 46:20Brad Gerstner?

  Right.

• 46:20Jensen Huang?

  Right, and so, so one, come into this, we got plenty of time, and all the time in the world for this kind of stuff. And just, you know, come talk to us, come talk to our industry and learn. The second thing is learn about the solution that we put in place and then, and try to see if we can make that, if there are ideas to make that better. Of course, we're all completely open minded.

• 46:39Brad Gerstner?

  Right.

• 46:39Jensen Huang?

  You know, and so, but, but be constructive.

• 46:41Brad Gerstner?

  Right. And I thought that was part of the, the, the fourth point on, on the accord on superintelligence, was this is not static.

• 46:48Jensen Huang?

  Yeah.

• 46:49Brad Gerstner?

  This is dynamic.

• 46:49Jensen Huang?

  Yeah.

• 46:50Brad Gerstner?

  This group is gonna get together routinely.

• 46:52Jensen Huang?

  Yeah.

• 46:52Brad Gerstner?

  We're gonna update the framework as required when the technology evolves. But I wanna move forward now to compute. Of course, long running agents that work in teams of thousands or millions to solve complicated tasks, it all consumes massively more compute. Dylan, our friend at Semi Analysis, has said we're gonna bring on board 43 gigawatts of new AI compute next year. Just to contextualize it, we'll share a chart with people online. That's more than all of the AI compute that exists in the world today. So we're gonna bring that on board next year. The year after that, another 40 gigawatts of compute. You are the one, you know where every single GPU is allocated, right?

• 47:36Brad Gerstner?

  So as you sit here, does that sound right to you? Do you think we're gonna bring on that much compute? And do you think that, you know, some of the things we're hearing out there about permitting, permitting issues, or bottlenecks in the supply chain, or, you know, pro, local protests? Do you think the number's gonna be materially different than that based upon what you're seeing?

• 48:00Jensen Huang?

  Uh, I think the demand is definitely there and more.

• 48:04Brad Gerstner?

  Yeah.

• 48:04Jensen Huang?

  The demand is definitely there and more. The challenge, of course, is that there are a lot of obstacles. When you, when you move from, uh, when you move from digital to the physical, there are a lot of obstacles.

• 48:17Brad Gerstner?

  Right.

• 48:18Jensen Huang?

  There, there're land permitting, there's power generation that you gotta bring online. Of course, you gotta, you need construction workers and outfitters and plumbers and, you know, all kinds of.

• 48:29Brad Gerstner?

  Created a million new jobs just building these things.

• 48:32Jensen Huang?

  Created a million new jobs and, and their, their skills are, their skills are not fungible. You know, they have a lot of skills and particularly the, the ones that are building these 50 billion dollar data centers, you're gonna want some people with great skills.

• 48:44Brad Gerstner?

  Right.

• 48:45Jensen Huang?

  And, and there's a critical part of every single one of the build out of these things where, where you need some very specific skills. And so, um, I, I think that, and then there's, there's, of course, whatever moratoriums that are, that are happening, whatever, whatever issues that are, that are, um, that we're seeing around, around the country. And that, and so I think, I think this is, this is, um, uh, my guess is that land power shell, um, uh, the, the build out resources will be one of the, the most important obstacles to reaching that. And, and so, so our, our company, as you know, now speaking specifically for us,

• 49:28Jensen Huang?

  there are several things that we're doing to mitigate that. And, and one, you know, because we're the only company that serves every CSP, we serve every NCP. We have, uh, great enterprise businesses through Dell and HP and Lenovo and others. And because we serve every single industry and, and we're across the entire lifecycle of AI, everything from, you know, of course, data processing to training, to post-training, to inference, to agentic processing, and all of the tool use that we accelerate. You know, our exposure to our go to market is the largest of all. Every, every contract manufacturer in the world is working on my behalf.

• 50:13Jensen Huang?

  Every go to market is, is connected to our architecture. And, and so we have one particular state, one particular community, one particular data center has a hard time.

• 50:22Brad Gerstner?

  Right.

• 50:23Jensen Huang?

  You know, our demand is so much greater than our supply at the moment. We'll just adjust our supply chain accordingly. The other thing that we have, and I, I don't love this answer as much, but as, as you know, Nvidia is also the infrastructure for the planet. And so whether it's in Europe or, um, uh, and we've, we've got early developments now in Africa, in Central Asia, in Southeast Asia, in Latin America, in, you know, in Middle East and so on and so forth. And so I, I don't, I don't love that, that, that, um, United States can't keep up.

• 50:58Brad Gerstner?

  Yeah.

• 50:59Jensen Huang?

  But for our business specifically, we have such a global reach. And, and there's build out demand all around the world. If we don't build it out here, um, you know, I think we just all have to recognize that every country in the world would love to have it built out there.

• 51:13Brad Gerstner?

  Yeah.

• 51:14Jensen Huang?

  And so, and so, um, I, I think that, that our business specifically is probably the most resilient. And the reason for that is, is all the things that I mentioned.

• 51:28Brad Gerstner?

  Right.

• 51:28Jensen Huang?

  You know, we cover the entire spectrum of, of AI. We cover the entire spectrum of computing. We're in every single cloud, every single region, you know, and so, so the resilience of our company is much greater. But I do think that land power shell is gonna be a challenging part next year.

• 51:44Clark Tang?

  And I guess what I, what I, what I hear from the street is there's this idea that, you know, maybe there's 50 gigawatts of announced chips that are underway or talked about. And maybe 35 to 40 gigawatts of computer, or land power shell that might come online. So what compute fits into that land power and shell and on what timeline is a question up in the air.

• 52:08Jensen Huang?

  Yeah.

• 52:09Clark Tang?

  But I guess like if we think of this through the entire.

• 52:12Jensen Huang?

  And just, Clark, just to interrupt you there, and, and, um, I.

• 52:19Jensen Huang?

  You know, of course, as you know, Nvidia's architecture is the most productive.

• 52:24Brad Gerstner?

  Right.

• 52:25Jensen Huang?

  It generates the most tokens per watt. And so if the world's, if the world's amount of watt power, energy reduces, it shifts their choices to the most productive version. And the reason for that is that they still want to make their revenues. And so, so all of my CEO colleagues, they all want to, they all have high expectations for revenues next year and people have, you know, they need to deliver it. Their shareholders counting on them to deliver it. All their partners need to, need them to deliver it. And so when the amount of power reduces, the quality of the compute you put into it has to, has to be the most, most extraordinary. We're the most productive computing resource you could put in the world today.

• 53:06Jensen Huang?

  We're the most rentable, we're fungible and we're durable. And so, so the, the, the forces around the world because of the shortage of resources is, is shifting interest towards us. Our market share was already very high, but surprisingly to everybody, not only are we a large company and our market share is high, our market share is actually increasing for these reasons. And so, so you're going to see us go from, and because we offer so much more per gigawatt than we used to, we used to be a GPU company, then we became the largest and fastest growing networking company. We're now going to be one of the largest CPU companies, surely the fastest growing one.

• 53:47Jensen Huang?

  And so, so because we're offering more and more and we're in this, you know, we're in computing for storage, we're computing for the knowledge infrastructure and so the amount of, and now we're in security with Open Shell and Sentry. And so, so the, the offering that we, we offer into one gigawatt went from, went from, I guess about, you know, 18 billion or so per gigawatt to 20 somewhat billion per gigawatt to now 40 plus per gigawatt. And my sense is that that's going to keep on going up. And so, so not only are we, are we growing share because, because people are shifting towards our computing platform, we're also growing share because we offer more to the world's capex.

• 54:28Clark Tang?

  And to the world's dollar, dollar share of the market. Yes.

• 54:33Jensen Huang?

  Yeah.

• 54:34Clark Tang?

  Yes.

• 54:34Jensen Huang?

  If you look at, if you look at, just go back in just six years ago.

• 54:38Clark Tang?

  Yes.

• 54:39Jensen Huang?

  Our percentage of the world's capex was in single digits.

• 54:42Clark Tang?

  Yes.

• 54:44Jensen Huang?

  Then I think, I think right now with the world CSPs, we're probably running about 40%. But we're also expanding the market beyond the CSPs. And outside of the CSPs, whether it's on-prem or Neo Clouds.

• 54:58Clark Tang?

  Yeah.

• 54:59Jensen Huang?

  Half of our business, our percentage of capex is probably what, 80%?

• 55:03Clark Tang?

  Yeah.

• 55:03Jensen Huang?

  And so, and so between 40, half of our business being 40%, half of our business being 80% and both of us growing, doubling every year. And so you could see that our percentage of the world's capex is in fact even growing.

• 55:19Clark Tang?

  Yes.

• 55:19Brad Gerstner?

  Well, one of those, one of those buyers of Nvidia is somebody we talked about two years ago, Elon, when he started building Colossus. And we were blown away a couple of years ago to see him stand up a couple hundred thousand hoppers, right in Memphis. And I remember you saying, nobody can do it like him. Now he's raising another 40 billion, gonna buy maybe million plus Vera Rubens.

• 55:45Jensen Huang?

  He's moved from the K's to the M's.

• 55:48Brad Gerstner?

  He says he wants to have 10 gigawatts by the end of 2027. That's hundreds of billions.

• 55:54Jensen Huang?

  Yeah.

• 55:54Brad Gerstner?

  Right, spent on Vera Rubin. It's an unprecedented ramp at scale. You were with him again yesterday when he was also presented with a medal. Is Elon that much better at building compute today as he was then? Is it now obvious to you, is it, is it a foregone conclusion that SpaceX will be the largest AI hyperscaler in the world?

• 56:17Jensen Huang?

  Well, we, we used to, you know, in the, in the two parts of our chart, Elon used to be under enterprise. That's the way we reported in our numbers. Now he's part of our CSPs.

• 56:29Brad Gerstner?

  Of course.

• 56:29Jensen Huang?

  Yeah, yeah.

• 56:30Brad Gerstner?

  Of course.

• 56:30Jensen Huang?

  And so.

• 56:31Brad Gerstner?

  They're massive.

• 56:31Jensen Huang?

  So I'm delighted that, that, that I, I love working with Elon. He's using our technology for his own AI in training models and such, deploying models, of course. Grokbot is super cool and, and, and the, and the demand for Grokbot, the consumption for Grokbot is really skyrocketing. And then of course he rents it.

• 56:54Brad Gerstner?

  Yeah.

• 56:54Jensen Huang?

  And so the combination of these three things, he's fairly unique in doing that. I guess, I guess Google does that.

• 57:01Brad Gerstner?

  Right.

• 57:02Jensen Huang?

  Right? That would be another, yet another example. And so I think he's, he's also has the, has the advantage that, that he loves the intersection between computing and the physical build outs.

• 57:15Brad Gerstner?

  Right.

• 57:16Jensen Huang?

  You know, he loves that, that whole area and nobody knows more about energy than he does. And, and one particular new market that we're going to go explore together is putting data centers out in space. And so our two engineering teams are working super closely together to create a new type of server, a new type of architecture that might, might be super good, you know, you know, in space and on earth.

• 57:38Brad Gerstner?

  Well, we saw that Starship launched last week, you know, had its, its, its first orbital rendezvous and was launching those V3 sats that literally captured the imagination and kind of broke Twitter for a moment. Just people mesmerized by watching these launch into space.

• 57:54Jensen Huang?

  I can't, I can't wait to be a payload. I mean, not personally.

• 57:57Brad Gerstner?

  Do you think, does that get, do you have increasing confidence that we will have a material amount of compute in space, you know, via, via Starship by, let's call it 2028, 2029? I mean, Elon's put some big numbers out there, but do you think that we can get to material inference in space in that, in that timeframe?

• 58:17Jensen Huang?

  Only Nvidia compute.

• 58:18Brad Gerstner?

  Only Nvidia.

• 58:19Jensen Huang?

  Let me tell you why. It's not, not just about the technology. Nvidia's performance per unit energy is the world's best. And so we call it performance watt. And, but the most important thing is, is this, the productivity of Nvidia's compute is so good, meaning the return on investment, invested capital is so good.

• 58:38Brad Gerstner?

  Yes.

• 58:39Jensen Huang?

  That you could put it out in space and the launch cost is a small percentage of it.

• 58:44Brad Gerstner?

  Okay.

• 58:45Jensen Huang?

  The launch cost is not inconsequential.

• 58:47Brad Gerstner?

  Right.

• 58:48Jensen Huang?

  And so you're not going to put low cost computing out there.

• 58:50Brad Gerstner?

  Correct, correct.

• 58:51Jensen Huang?

  You're going to put high value computing out there. And so we're the highest value computing system you could imagine, the highest computing system in the history of computing. And so we're the perfect fit really to launch into space right now, irrespective of the, the launch cost, it'll still be excellent ROI.

• 59:08Brad Gerstner?

  So do we get 500 megawatts in space by 29?

• 59:11Jensen Huang?

  Well, I'll let him, let him tell you, but, but I think.

• 59:14Brad Gerstner?

  Is it possible?

• 59:15Jensen Huang?

  I, I, you know, what's, Elon's what, launching 100 launches a year right now? I don't know what exactly it is.

• 59:23Brad Gerstner?

  It's gonna go to multiple a day.

• 59:24Jensen Huang?

  Yeah, right. And so you, you could, you could put Vera Rubens in there and, and it'll justify it. The demand, demand is there. His return on investment is so good on it. And so the productivity is excellent. It's fungible, meaning you put a satellite out in space, if you don't have a customer to go rent it, it's, it's idle and you certainly don't want that. And so, so Nvidia compute is going to be fully fungible and there'll be lots and lots of renters. You know, we have, we work with every single closed model company in the world. We run every single open model in the world. We have thousands of startups working with us. And so, and enterprises around in every single industry. And so, so the computing is completely fungible and is durable.

• 1:00:04Brad Gerstner?

  Yeah.

• 1:00:04Jensen Huang?

  You know, it'll last, it'll sit up there and we'll keep up. And the reason for that is because we keep CUDA upgrading the software. You know, we're the only company, hardware company in the world that you pay us eight years ago and we're still updating your software, new algorithms.

• 1:00:18Brad Gerstner?

  Right.

• 1:00:18Jensen Huang?

  You know, and so we'll, we'll update the software for as long as the satellite's up there. And so, so it's going to be really durable. And so I, I think the combination of these three traits.

• 1:00:28Brad Gerstner?

  Yes.

• 1:00:29Jensen Huang?

  Along with the technology makes it the perfect payload for, for SpaceX.

• 1:00:35Brad Gerstner?

  It costs trillions of dollars to build all this capex. You guys have leveraged your own balance sheet to help folks like the Neo Clouds build that out. In order for finance people to get it to pencil out, they need to see payback, right? You talked about, you know, some companies seeing one year payback, right? $50 billion in the first year, you know, some, some spot pricing out of, out of SpaceX perhaps. But if I look at the total incremental offtake, right? The revenue of all the labs this year. So if I add up, you know, the two frontier labs, oh yeah, get yourself a snack. And we add up, you know, some of the inference labs, it's about $200 billion, okay? We need to see that double next year to 400 billion. We need to see it double the year after that to 800 billion, right?

• 1:01:20Brad Gerstner?

  In order to get in the zip code of the payback on trillions of dollars of capex. We, we had this massive unlock this year with, with Claude Code that led to a 10x increase in Anthropic's revenues. What's your confidence that the labs can continue to two to three X their revenues every year in order that we get the payback that's going to be required to, to, to build out trillions of dollars of capex?

• 1:01:50Brad Gerstner?

  I mean, you're investing in these companies. You have you have big investments in these companies, right? Do you see that level of growth continuing in these companies?

• 1:01:58Jensen Huang?

  Oh, yeah. People misunderstand what's going on. So first of all, particularly, I mean, Nvidia represents, call it 80% of the revenue share of AI infrastructure in the world today.

• 1:02:11Jensen Huang?

  And so my description of the universe of computing, Nvidia computing matters here.

• 1:02:18Jensen Huang?

  There are really basically, call it three segments, three large segments. It's probably slightly more than that, but let me just kind of categorize it in three large segments. The first segment of our computing is just baseline computing, just meaning used for data processing, molecular dynamics, physics simulation,

• 1:02:36Jensen Huang?

  fusion simulation, fission simulation, you know, you name it, right? Quantum chemistry, quantum physics, you know? And so everything, okay? The world of simulation and data processing, not to mention computer graphics. And so all of that processing done on our GPUs, if you just kind of said there was no AI at all, just classical machine learning, XG Boost at Visa, MasterCard, you know, for fraud detection, our recommender systems to figure out what news to recommend to you, just all of that stuff combined. Classical computing, not AI, not deep learning, not agentic systems, I'm going to call it a third of our business.

• 1:03:13Brad Gerstner?

  Yeah.

• 1:03:14Jensen Huang?

  And that business is growing, you know, call it 30%, 40% per year.

• 1:03:19Brad Gerstner?

  Yeah.

• 1:03:19Jensen Huang?

  Okay. In the absence of any AI labs, Nvidia is not zero, right? We're quite a large company. And so that's number one. The second part is the AI labs, people see two, but we work with every one of them. And MetaMuse is doing fantastic, right? And it's vastly Nvidia. And, you know, there were zero last year.

• 1:03:44Brad Gerstner?

  Right.

• 1:03:45Jensen Huang?

  You know, or a year before that, it was just recommender systems. And now they're, you know, killing it with MetaMuse. And of course, Grok didn't exist three years ago. We didn't run Gemini before. We run Gemini now. And starting this year, we started working with Anthropic. And so if you just kind of look at our compute, people see a couple of labs, but I see more than that. I see five, six labs. Okay. And so, so these labs are all doing great.

• 1:04:14Brad Gerstner?

  Correct.

• 1:04:15Jensen Huang?

  And if you, if you told me that that represents

• 1:04:20Jensen Huang?

  not quite one third of our business, let's say a quarter of our compute, fantastic. Okay. So let's say that's a quarter. So when people are looking at 80 Nvidia and we now run the infrastructure of the world, and I just said 25% of it, maybe 30% of it is the frontier labs.

• 1:04:39Brad Gerstner?

  Right.

• 1:04:41Jensen Huang?

  You know, call it a third is just processing in general.

• 1:04:45Brad Gerstner?

  Right.

• 1:04:46Jensen Huang?

  And another third, this is the, the part that's really great. Because we're at the beginning of a new computing platform shift, just a vast number of new startups are coming out, so many great companies. And some of them are of course doing consumer stuff, you know, one of them really doing great. Noah Instinct, I was just going to mention that. Noah is doing great. To all kinds of physical AI, biological AI, material sciences AI.

• 1:05:16Jensen Huang?

  Jeff Bezos, this new company, Prometheus, they're doing, they're doing fantastic. And so the, the number of companies across the spectrum of industries that have been started, thousands of them, and what do they do? They rent Nvidia compute. So they're renters. And so what is a third of our business is kind of cloud computing for the next generation.

• 1:05:38Brad Gerstner?

  Right.

• 1:05:39Jensen Huang?

  It's kind of like AWS for today, you know? And so it's just not AWS for the future. And that represents about a third of our business altogether, you know? My sense is that this, we're in the early innings of the build out of this new computing architecture. And it's a very, it's, it's a real big mistake to recognize just a couple of companies and think that they're funding the infrastructure. And then one observation,

• 1:06:07Jensen Huang?

  open models, you know, that, that I've been a great advocate of open models. And the reason for that is because open models opens up new opportunities. If you look at all these startup companies, 80% of them use open models.

• 1:06:17Brad Gerstner?

  Right.

• 1:06:18Jensen Huang?

  And, and they're not using it to compete with the frontier labs. They're using it to unlock a new opportunity somewhere else. You know, it could be in physical AI, it could be manufacturing, it could be healthcare. They're doing something completely, completely different. And, and they're taking these open models and many of them taking Nematron. And the, these models are then customized into super intelligence. And so if you will, I kind of see that the, the general, the general closed labs will be artificial general intelligence. And it gets super good. And then you have all these vertical companies that are focused on a particular domain. And they have their proprietary data, they teach it to do something very specific. And they achieve super intelligence for their domain.

• 1:07:02Brad Gerstner?

  So you don't see, you know, so again, if we go to your compute math, which I think a lot of people are using to forecast the future revenues of, of, of the labs, for example. You know, you said, hey, if you stand up a gigawatt of this compute, you can get, you know, 40, 50 billion dollars out the other end. But if we took the number we talked about earlier, 40 gigawatts of compute coming online next year. And if you took 40 billion out the other end, that that would be 1.6 trillion of revenues coming out the other end of that factory. But of course, there's something, you know, it, it doesn't work that way, right? At the end of the day, there's, you got to look at the demand curve and the rate of diffusion to determine, right, the revenue that's going to come out the other end of the factory.

• 1:07:45Brad Gerstner?

  And what I hear a lot is that people are nervous, right? They've seen over the course of the year, maybe the labs revenue is slowing down a little bit. And they're nervous that, you know, at some point you're going to have the proverbial bubble popping, right, where we have more supply coming online than we have demand at a moment in time, that AI is more like these step functions rather than just this parabolic line straight up. I guess the question to you is, do, are you worried about, you know, a moment in time where everybody gets nervous about financing the build out, right? And, and then we don't get the build out.

• 1:08:21Jensen Huang?

  Well, you know, all these different things, you just got to break it down, break it out, back down to first principles. And, and

• 1:08:29Jensen Huang?

  the, the part that people are trying to extrapolate, the hundred, hundred, hundred billion dollars of ARR, 125 billion dollars where they are, they're trying to extrapolate is a small part, not a small part, it's, it's just a part of the overall infrastructure build out.

• 1:08:44Brad Gerstner?

  For sure.

• 1:08:44Jensen Huang?

  And, and so, so the question is, are people going to continue doing shopping online?

• 1:08:48Brad Gerstner?

  Right.

• 1:08:48Jensen Huang?

  You know, that's

• 1:08:49Brad Gerstner?

  So that third of the build out, let's call that a third, you said is a third of your business. Take that to the bank, right? Not like, that's not even a debate. So a third of the

• 1:08:58Jensen Huang?

  So if you say, you know, I said, I said a few, couple, was it a couple of years ago? That as we exit this decade, infrastructure build out will probably be somewhere between three to four trillion a year. You know, obviously it's closer to four than it is closer to three. And, and

• 1:09:15Brad Gerstner?

  Per year?

• 1:09:16Jensen Huang?

  Per year, yeah.

• 1:09:16Brad Gerstner?

  By the end of the decade?

• 1:09:17Jensen Huang?

  Yeah. And if you, if you said, if you said that one and a half trillion of that was just compute.

• 1:09:22Brad Gerstner?

  Right, right.

• 1:09:23Jensen Huang?

  Just compute. Now, why is it going to be so big? Well, it was half a trillion before AI started.

• 1:09:30Brad Gerstner?

  Right.

• 1:09:31Jensen Huang?

  And if I told you that over the course of some six, seven, eight years, it became one and a half trillion? Because the world's just consuming more compute?

• 1:09:39Brad Gerstner?

  Right.

• 1:09:39Jensen Huang?

  I mean, that's not illogical. On top of this,

• 1:09:44Jensen Huang?

  remember Moore's law is over.

• 1:09:46Brad Gerstner?

  Right.

• 1:09:47Jensen Huang?

  There was a time, if you want to get twice as many transistors, you just wait one year.

• 1:09:51Brad Gerstner?

  Right.

• 1:09:51Jensen Huang?

  And if you want twice as many transistors, get another, wait one year, same price. But if you want twice as many transistors now, it doesn't matter how long you wait, you have to pay twice as much. Okay. Because Moore's law is over. And so whatever, whatever computing capacity that the world wants, every single year, if you want twice as much, you're going to pay twice as much.

• 1:10:11Brad Gerstner?

  Yeah.

• 1:10:12Jensen Huang?

  And so on top of that half a trillion dollars that was kind of growing just naturally.

• 1:10:16Brad Gerstner?

  Right.

• 1:10:17Jensen Huang?

  Because the world just can use more and more digital, you're going to have to effectively, you know, given an X factor, just to account for the fact that Moore's law is over.

• 1:10:27Brad Gerstner?

  Right.

• 1:10:28Jensen Huang?

  And so without AI, the semiconductor industry was on its path to become the largest industry in the world.

• 1:10:34Brad Gerstner?

  Right.

• 1:10:35Jensen Huang?

  And I've mentioned that long ago, just people forgot, which is one of those things that I said.

• 1:10:40Brad Gerstner?

  I just said that two years ago, yeah.

• 1:10:43Jensen Huang?

  And so, so, so that's just one part.

• 1:10:44Brad Gerstner?

  Right.

• 1:10:45Jensen Huang?

  And so the only thing that we, and then, and then don't forget, because we're in a new computing platform shift, there are going to be a whole lot of startups. Innovation is part of the economy.

• 1:10:56Brad Gerstner?

  Right.

• 1:10:56Jensen Huang?

  New companies

• 1:10:57Brad Gerstner?

  And we said it's accelerating.

• 1:10:58Jensen Huang?

  Yeah.

• 1:10:59Brad Gerstner?

  You know, so if we spent, if you go back to Moore's law, general purpose compute, $5 billion per gigawatt.

• 1:11:05Jensen Huang?

  Yeah.

• 1:11:05Brad Gerstner?

  Today it's 50.

• 1:11:06Jensen Huang?

  Yeah.

• 1:11:06Brad Gerstner?

  Right. For a Vera Rubin. So the question is, is Vera Rubin deliver 10x more valuable in the world?

• 1:11:12Jensen Huang?

  So the punchline is, I don't think it's necessary to extrapolate any of the labs into multiple trillions of dollars.

• 1:11:18Brad Gerstner?

  Yeah.

• 1:11:18Jensen Huang?

  Unnecessary to do that.

• 1:11:19Brad Gerstner?

  Yeah. I think that's a, I mean, it's an important one because I think people, you know, we just saw yesterday, you know, even in the market, you know, some chatter about was OpenAI's revenue 70 billion or 50 billion? I think you and

• 1:11:32Jensen Huang?

  They're going to be insanely successful companies.

• 1:11:34Brad Gerstner?

  Correct.

• 1:11:34Jensen Huang?

  Yeah.

• 1:11:35Brad Gerstner?

  But you saw the pullback in a lot of the infrastructure companies, right? On the fear that, oh my gosh, OpenAI doesn't have the money, won't have the money in order to pay for the infrastructure. I think the point that you're hammering home is that when we look at the 40 gigawatts of infrastructure build out next year, at least a third of it is just going to, right, general purpose compute replacement, the, the, the basic stuff out of the hyperscalers, that a third of it's going to kind of NeoCloud, Sovereign Enterprises, everybody else who's using this, all the startups. And a third of it's going to not two labs, but to many labs, right? Including Grok and Thinking Machines and all the other

• 1:12:14Jensen Huang?

  We're putting way too much pressure on two labs.

• 1:12:16Brad Gerstner?

  Yeah.

• 1:12:17Jensen Huang?

  I'm delighted to partner with them and support them. They're going to be two of the most consequential technology companies ever built in history. They're going to be magnificently successful.

• 1:12:27Brad Gerstner?

  Yeah.

• 1:12:27Jensen Huang?

  But they don't have to be anywhere near a trillion dollars in revenues to be successful.

• 1:12:32Brad Gerstner?

  Right.

• 1:12:32Jensen Huang?

  Yeah. And I think we're putting way too much pressure on that. And then the rest of the industry, we still do have to build out a few trillion dollars of computing infrastructure.

• 1:12:41Brad Gerstner?

  Just to keep going, let's, let's shift a little

• 1:12:44Jensen Huang?

  How good is this, you guys? Come on.

• 1:12:46Brad Gerstner?

  By the way, what are, what are these called?

• 1:12:48Jensen Huang?

  These are dried plums.

• 1:12:49Brad Gerstner?

  Yeah. Dried plums, the old dried plum.

• 1:12:51Jensen Huang?

  Yeah, this, this one particular store in Taiwan is one of my favorites.

• 1:12:54Brad Gerstner?

  Kind of a licorice-y?

• 1:12:56Jensen Huang?

  There is licorice, yeah. Licorice, a little licorice-y, salt, sugar, cinnamon.

• 1:13:01Brad Gerstner?

  Yeah. Has a little twang to it.

• 1:13:02Jensen Huang?

  Yeah, yeah. You got it.

• 1:13:04Brad Gerstner?

  You know?

• 1:13:04Jensen Huang?

  Yeah.

• 1:13:04Brad Gerstner?

  Okay. You said as to next year, demand For Nvidia's chips, well over 100%. You guided to 70% revenue growth. You gave a year forward look because you said supply is constrained.

• 1:13:20Jensen Huang?

  That's all I had at the time.

• 1:13:21Brad Gerstner?

  That's right.

• 1:13:21Jensen Huang?

  Yeah, I got 18 months to work.

• 1:13:23Brad Gerstner?

  Right. You said you were very confident.

• 1:13:26Jensen Huang?

  I'll come to work every day.

• 1:13:27Brad Gerstner?

  You said very confident and plenty of time to work with the supply chain to do even better. You expected to do even better and then not disappoint customers. So are are things going even better? And has the order book continued to grow? Because the other part of that is, right? You took a snapshot at a moment in time. And of course, people are continuing, you know, to place orders. So it, do you think there's enough slack in the supply chain? You can squeeze more out and is the order book continuing to grow as more of these capabilities are unlocked?

• 1:14:01Jensen Huang?

  For the moment, I'll tell you why I told everybody.

• 1:14:05Jensen Huang?

  The challenge that we have is we have giant order books. Every single CSP, every single NCP, every single OEM had already placed orders on us. Startups, big companies, small companies, they already placed orders on us. And the reason for that is because we work with them on it, on it, depending on on the size of the customer, at least a year for the the largest ones, two years forecast. And so, so we already have a very large order book. And because I was in the process of allocating, and what I was allocating to all of our customers is a lot less than.

• 1:14:43Brad Gerstner?

  They want it.

• 1:14:44Jensen Huang?

  They want it.

• 1:14:44Brad Gerstner?

  Yeah.

• 1:14:45Jensen Huang?

  If I if I was, if I didn't publicly say.

• 1:14:49Brad Gerstner?

  Right.

• 1:14:50Jensen Huang?

  What we had.

• 1:14:51Brad Gerstner?

  Right.

• 1:14:52Jensen Huang?

  Everybody would be second guessing, are they shortchanged? Are we being, you know? And so, and so it was, it was my way of of essentially handling every single conversation where I'm disappointing people.

• 1:15:04Brad Gerstner?

  Right.

• 1:15:05Jensen Huang?

  And.

• 1:15:05Brad Gerstner?

  So pretty much every customer would buy more from you if you could deliver more.

• 1:15:10Jensen Huang?

  By far. And then, and then since then, of course, now we have new orders almost, almost weekly and daily. And, and so, so in addition, however, we're getting more supply. And some of the supply that that is limiting us is land power shell downstream. And so a couple of weeks ago, we announced two gigawatts in Australia. And so that's a very big deal. Two gigawatts to us is like $80 billion.

• 1:15:33Brad Gerstner?

  Huge.

• 1:15:34Jensen Huang?

  And, and then, and then I'm hoping when I go to Europe with UTC, you know, we'll be able to announce some, some new partnerships there and, and unlock more, more land power shell. And so, so this land power shell is clearly one of the obstacles. And, and this is, this is a, an area where Nvidia has a huge advantage.

• 1:15:53Brad Gerstner?

  Yeah.

• 1:15:53Jensen Huang?

  You know, because based on what we build, if, if there's a NeoCloud and this NeoCloud like CoreWeave or Nebius, and they would like to to scale out their business. Nvidia has the entire full stack, complete AI infrastructure. And so, so we could support them as they're scale out. And so we, we could build in places and we can build with partners and customers that nobody else can. And so I, I think this, this gives us a lot more resilience. But nonetheless, as we, as we continue to increase supply upstream and downstream, you know, one of the critical areas is substrate. Substrates are getting larger and there are more chips.

• 1:16:38Jensen Huang?

  And so, so it's working against them in two ways. Every single chip that's being shipped uses more of the way of the material. And so, so these, these organic materials are, are really hard to make it the precision that we need because as the packages get larger, the, the coefficient, the thermal coefficients and the way we stack things make it really hard to handle things like warpage and, you know, and various, various manufacturing defects. And, and so at the scale that we do, we have to be very precise and we need really great partners. And, and so that's, that's supply constrained. And then just, those are just some examples. Silicon photonics is, is supply constrained. And so, so everything is supply constrained. And so we're going to keep working on it.

• 1:17:24Jensen Huang?

  I have 18 months to do so. You know, every single week that goes by, we increase. But nonetheless, every, we keep increasing more supply and the order books keeps going up.

• 1:17:31Brad Gerstner?

  Right.

• 1:17:32Jensen Huang?

  And so it's going to be hard to close that gap for a few years.

• 1:17:36Brad Gerstner?

  Take a look at this chart.

• 1:17:39Brad Gerstner?

  You know.

• 1:17:40Jensen Huang?

  This is like a deposition.

• 1:17:41Brad Gerstner?

  No, that's right.

• 1:17:42Jensen Huang?

  Excuse me. No, usually it goes like this. Mr. Huang, please, please now, please now look at.

• 1:17:49Brad Gerstner?

  The deposition. Okay, so the gray line, this is, was the consensus at the start of 2025. This was the consensus for Nvidia at the start of 2025. The blue line was altimeters forecast way back then. So we were a lot higher than the consensus. The green line is now where the new consensus is based upon your guidance. And so one of the things that blows my mind about this is the 25 people who get paid to just forecast what this company is going to do. Their consensus of all their ideas, right, was off by a couple hundred percent, right? Just at the beginning of 20. Oh, no.

• 1:18:25Jensen Huang?

  This time last year.

• 1:18:26Brad Gerstner?

  At this time last year, not even 25.

• 1:18:27Jensen Huang?

  Gray line?

• 1:18:28Brad Gerstner?

  Yes. That was, that was the consensus at this time last year. Okay, one year ago.

• 1:18:33Jensen Huang?

  Last week.

• 1:18:34Brad Gerstner?

  Okay, so in a year, the consensus forecast has shifted up by 100 to 200% for this company. Extraordinary. Like in, in my 20 plus years of doing this, you don't often see consensus being off by that much. And yet the stock's only up 20%. Okay, you literally doubled what the revenue forecast was and the free cash flow forecast was for the company. You're now trading at 13 to 14 times earnings, depending upon who you're looking at. So what do you think the market is saying about Nvidia or about the confidence in the expectations of the market? Right? To cause that to be the case? Because usually if you see it shift up by that much, the stock would have shifted up by a lot more.

• 1:19:19Jensen Huang?

  First of all, the, for a company to be as large as us gaining share tells you something about the resilience.

• 1:19:25Brad Gerstner?

  Yes.

• 1:19:25Jensen Huang?

  And the, the, the size and scale of our advantage.

• 1:19:30Brad Gerstner?

  Right.

• 1:19:30Jensen Huang?

  And, and.

• 1:19:31Brad Gerstner?

  Is that advantage increasing or decreasing? Is your moat increasing or decreasing?

• 1:19:35Jensen Huang?

  Increasing.

• 1:19:35Brad Gerstner?

  Increasing.

• 1:19:36Jensen Huang?

  Increasing.

• 1:19:36Brad Gerstner?

  Your competitive moat is increasing at scale.

• 1:19:39Jensen Huang?

  Yeah, let me give you an example. Let's just do a thought experiment right now. If you had to hand somebody $100 billion check to place a purchase order right now for a next generation.

• 1:19:49Brad Gerstner?

  Yeah.

• 1:19:50Jensen Huang?

  If you, if you want to get in line for Vera Rubin Ultra, it's close to placing order time.

• 1:19:59Jensen Huang?

  Sight unseen.

• 1:20:00Brad Gerstner?

  Yeah.

• 1:20:00Jensen Huang?

  Right?

• 1:20:01Brad Gerstner?

  But they have the confidence.

• 1:20:02Jensen Huang?

  And so let's say there's a, you got to place $100 billion on somebody.

• 1:20:06Brad Gerstner?

  Yeah.

• 1:20:06Jensen Huang?

  One, how do you know it's going to be any good? But because Nvidia builds it, you know, it's going to be better than Vera Rubin.

• 1:20:12Brad Gerstner?

  Right.

• 1:20:12Jensen Huang?

  And Vera Rubin's awesome.

• 1:20:13Brad Gerstner?

  Right.

• 1:20:14Jensen Huang?

  And the reason why Vera Rubin, the reason why everybody had confidence in Vera Rubin is because Black Hole is awesome.

• 1:20:19Brad Gerstner?

  Yeah.

• 1:20:20Jensen Huang?

  And so, and, and the world of computing, the, the workflows that these computers do are so complex. You can't just have like measure one benchmark.

• 1:20:29Brad Gerstner?

  Right, right.

• 1:20:30Jensen Huang?

  You know, it's $50 billion.

• 1:20:31Brad Gerstner?

  Yes.

• 1:20:32Jensen Huang?

  And the, the number of technologies that have to come together to deliver the performance is insane, you know? And so, so, so the question is, sight unseen, who can you place a $50, $100 billion order on?

• 1:20:46Brad Gerstner?

  Yeah.

• 1:20:47Jensen Huang?

  We're literally the only one.

• 1:20:48Brad Gerstner?

  Yeah.

• 1:20:50Jensen Huang?

  Now, back when it was place a PO, a billion dollar PO, that was challenging enough. But if you had to place $100 million PO and the, the, the system is sitting right in front of you. You could just bench, you could benchmark the chips.

• 1:21:03Brad Gerstner?

  Right.

• 1:21:03Jensen Huang?

  Right?

• 1:21:04Brad Gerstner?

  Yeah.

• 1:21:04Jensen Huang?

  How do you benchmark these systems? Just building it is a miracle.

• 1:21:08Brad Gerstner?

  Right.

• 1:21:09Jensen Huang?

  You know, so if, if somebody wants to do an evaluation, I'm happy to support it. But you're going to need to give me a giant data center and we're going to need a couple of years to retrofit it and, you know? And so you can't even benchmark these things anymore.

• 1:21:22Brad Gerstner?

  Interesting.

• 1:21:23Jensen Huang?

  And so it's exactly what happened with TSMC. The reason why we give 100% of our business to TSMC is because they're the only company in the world that I can plan five years out.

• 1:21:32Brad Gerstner?

  Right.

• 1:21:33Jensen Huang?

  And sight unseen, know that that transistor is going to be really good because it's better than that one, better than that one.

• 1:21:39Brad Gerstner?

  Yeah.

• 1:21:40Jensen Huang?

  And that they will have capacity for me and that they know how to work with my business team, that they have the agility to handle all of my ups and downs. And, you know, for example, to deal with, with the fact that I went from gray to blue to green.

• 1:21:52Brad Gerstner?

  Exactly.

• 1:21:53Jensen Huang?

  Okay? They have to be big enough to go, help me go from gray to blue to green.

• 1:21:57Brad Gerstner?

  Exactly.

• 1:21:58Jensen Huang?

  There's no other company in the world that could help me do that.

• 1:22:01Brad Gerstner?

  That's right.

• 1:22:01Jensen Huang?

  And to have, and, and to have all these different process technologies from silicon photonics to logic to memory to push, pull, push, pull, packaging, I mean, the number of things that we do is so dynamic. And so how do you possibly do that with a company that is one tenth their size? It's impossible. And so I think the big idea is this, because we're building infrastructure for the planet and Nvidia has become an infrastructure company. And because these infrastructures cost so much money and they're moving and technology is moving so fast.

• 1:22:36Brad Gerstner?

  Yeah.

• 1:22:37Jensen Huang?

  We're practically, you know, the combination of the trust, the, the fact that we run everything. You know, so it started out with some, some GPT models. But look at the flash attention mechanisms that were, were, were invented. The way that we paralyzed the models and sharded across NVLink 72 and how all of that changed. MOE changed. And now people are talking about transformer diffusion models that are hybrid, SSMs that are transformers hybrids and all these different technologies are all, right? The evolution of all that, we know that it all runs on CUDA.

• 1:23:12Brad Gerstner?

  Right.

• 1:23:13Jensen Huang?

  It is the only, we run, we don't know whether you need to do more pre-training, post-training or just agentic inference. Well, it turns out we run it all anyways.

• 1:23:21Brad Gerstner?

  Right.

• 1:23:21Jensen Huang?

  And so you could build a $50 billion data center and know that the fungibility of the work. And if something were to happen, you, you know, for whatever reason unknown, you want it to, if you will, sublease half of your capacity to somebody, Nvidia is completely fungible. So we're fungible in workload, we're fungible in models, we're fungible across customers. And we're deployable everywhere in the world. You start a company in Europe, you start a company in United States, but guess what? You would like your customer, you want to serve customers in Europe, you want to serve customers in Japan. Well, it turns out we're already there. Nvidia's, Nvidia's infrastructure is already there. So OpenAI wants to go to Europe, we'll take them there in a heartbeat. If they want to go to India, in a heartbeat. If they want, isn't that right?

• 1:24:03Jensen Huang?

  If Anthropic wants to go to Japan, Korea, in a heartbeat, we're already there.

• 1:24:07Brad Gerstner?

  Right.

• 1:24:07Jensen Huang?

  All of these architectures are already there. And so I think the, the, if you take Nvidia in whole and then you come back and you say, okay, I want to do the thought experiment, I want to place $100 billion on somebody. How do you do it?

• 1:24:24Brad Gerstner?

  A year ago, we were sitting here and the consensus forecast was that you were going to be flat or shrink in 2027. Now, the consensus forecast is you're going to grow 74%. Okay, so you know, I just want to let people let that sit in because a lot of people follow these consensus forecasts, right? And it was really wrong. Okay, they were forecasting-

• 1:24:46Jensen Huang?

  If they go back and listen to your podcast from last time-

• 1:24:49Brad Gerstner?

  Right, you told them.

• 1:24:50Jensen Huang?

  Yeah, I explained everything. Ladies and gentlemen, I did explain everything.

• 1:24:55Brad Gerstner?

  And so I look, I look out at 2029.

• 1:24:58Jensen Huang?

  There was, there was, wasn't it an ROI question?

• 1:25:01Brad Gerstner?

  Yes.

• 1:25:01Jensen Huang?

  And then there was, there was this custom, yeah, custom versus general purpose. The reason why Nvidia's general purpose is so I can have all this fungibility.

• 1:25:11Brad Gerstner?

  That's right.

• 1:25:12Jensen Huang?

  The benefit of fungibility is low risk.

• 1:25:14Brad Gerstner?

  Right.

• 1:25:15Jensen Huang?

  And low risk really comes in to play when you're placing big dollars.

• 1:25:20Brad Gerstner?

  Right.

• 1:25:20Jensen Huang?

  When you're building out 50, a hundred billion dollars worth of infrastructure, you need to know that that infrastructure is going to be really good for a long time.

• 1:25:28Brad Gerstner?

  Right.

• 1:25:28Jensen Huang?

  No matter what. So specialization is the enemy of fungibility. Specialization is the enemy of durability.

• 1:25:38Brad Gerstner?

  Right.

• 1:25:39Jensen Huang?

  And so, so long as I can prove

• 1:25:43Jensen Huang?

  that our performance is extraordinary, and we prove that every single day. The moment we can prove that, then fungibility and durability translates to rentability, which translates to productivity.

• 1:25:56Brad Gerstner?

  And financeability.

• 1:25:57Jensen Huang?

  And therefore financeability, which leads to the lowest TCO.

• 1:26:01Brad Gerstner?

  Right, right, exactly.

• 1:26:02Jensen Huang?

  Yeah.

• 1:26:03Clark Tang?

  Well, now, now you're buying back the largest amount in history. And, you know, on my math, you know, you could probably buy upwards of 10% of the company back within the next two years. At Goldman, you called Nvidia, you know, the first growth value company.

• 1:26:18Jensen Huang?

  Yeah.

• 1:26:20Clark Tang?

  Sounds a lot like Apple's pitch to Warren Buffett.

• 1:26:24Jensen Huang?

  Okay, is that right?

• 1:26:25Clark Tang?

  Yeah, or more, yeah, more that they could return the capital.

• 1:26:28Jensen Huang?

  It worked.

• 1:26:28Brad Gerstner?

  For sure.

• 1:26:29Jensen Huang?

  It worked.

• 1:26:30Brad Gerstner?

  Went from 300, at the time, $30 to 300.

• 1:26:33Jensen Huang?

  Yeah.

• 1:26:34Brad Gerstner?

  300 plus.

• 1:26:34Jensen Huang?

  And in a lot of ways, we're going through a similar transition. For example, the thing I did, I forgot to mention is, and Brad, I think you were about to ask me about that.

• 1:26:46Jensen Huang?

  You know, most of our investment and financing

• 1:26:51Jensen Huang?

  partnership strategies is trying to, trying to set up two business models, two new business models. One business model we've been working on for some time and will be accelerated in the near future is software licensing. You know, the body of software that we have in our company is just extraordinary. And that data center is so large, imagine all the software that runs in it. And we provide the entire stack. Well, and our entire stack is completely industrial strength. For the CSPs, they want to run their own stack.

• 1:27:23Brad Gerstner?

  Right.

• 1:27:23Jensen Huang?

  And so we license or we give them our software, open source, and then they take it and they modify it and they integrate it to theirs and then they operate their services. But for a lot of the world's companies, they don't have the IT department, they don't have the software department to do it, so they need me to maintain it. And so because AI, in the last several years, was largely focused on, on training, but now they're moving into deployment.

• 1:27:47Brad Gerstner?

  Right.

• 1:27:48Jensen Huang?

  They need to operate these as factories and they need to, they need to know that I'm on the hook, I'm on the SLA to support it and maintain it, keep it secure, keep it safe for as long as we shall live. And so software license for our enterprise business will be very, very large. And so, you know, if our software licenses, you know, just roughly say for, for, for simple math, a couple of thousand dollars per GPU per year, and we have millions of GPUs.

• 1:28:13Brad Gerstner?

  Right.

• 1:28:14Jensen Huang?

  We have, we ship, you know, 20 million GPUs, say, a year, I'm growing. And so half of it goes into world's enterprise, it adds up.

• 1:28:22Brad Gerstner?

  Yeah.

• 1:28:23Jensen Huang?

  Okay, and so, so I'm fairly certain this is going to be a very big part of our business, software license. And then, of course, there, there are some, some clouds that, that rely on Nvidia for our full stack. They rely on us for our offtake, our ecosystem. They also rely on us for, maybe as an investment partner and helping them secure, secure their financing and capital. They've secured land power shell, they're a regional champion, they got all kinds of stuff working in their, in their advantage, but these three areas, they need our support on. And so in, in those kind of relationships, it's very likely that we'll move to rev share. And so we'll make an investment, we'll get the benefit of the sale upfront, but we'll also have the benefit of rev share going forward.

• 1:29:06Jensen Huang?

  And so, you know, we're, we're not a cloud service provider, but we're going to be providing offtake and providing software support for them for, for, you know, four, five, 10 years. And so we ought to get some economic return on that. And so-

• 1:29:20Clark Tang?

  It's like app store sale.

• 1:29:21Jensen Huang?

  And so notice, I just described two new growth vectors, independent of the hardware sale of our company, and it's much more related to the install base of our platform. What's the benefit of software licensing if you have no install base? And, and how could you have rev share if there's no offtake? Okay, and so the, the install base element, the richness of our software stack, the offtake, the richness of our offtake and the ecosystem that we have around the world, that's going to fuel that.

• 1:29:51Brad Gerstner?

  One of the, one of the ways, you know, so we're talking about this training 13 times. You know, when Tim and Luca went to see Warren Buffett, and one of the reasons that Buffett has said he doesn't like technology companies is they, they have this hit, they produce an enormous amount of free cash flow, and rather than ever giving any of it back to the shareholders, they go and spend it on something else. And oftentimes, the something else isn't as good as the thing that caused them to generate the cash flow in the first place. You guys are now spending over 50% of your free cash flow, buying back your stock, effectively returning it, you know, to shareholders while you have this exponential growth at the same time, as we've already discussed.

• 1:30:27Jensen Huang?

  We said 50.

• 1:30:28Brad Gerstner?

  Right.

• 1:30:28Jensen Huang?

  I think this last quarter, we did 60 some odd.

• 1:30:30Brad Gerstner?

  Correct.

• 1:30:31Jensen Huang?

  Yeah, we're, we're, we're going to increase, you know, we invest in really high quality companies, as you guys know. And, and, and, you know, the last couple of years, whoever Nvidia buys from, whoever Jensen's buys from, you had to invest in that company. And now the new, the new word on the street is whoever Jensen invests in, you had to invest in that company. And it, and it's true. And the reason for that is because, you know, like you, we're, we're in, we're in the ecosystem, we're in the, we're in the flow of information.

• 1:30:58Brad Gerstner?

  We do a lot of those investments together.

• 1:31:00Jensen Huang?

  Yeah, and we know a lot of smart people.

• 1:31:02Brad Gerstner?

  Right.

• 1:31:02Jensen Huang?

  And we want to back these people when they, when they, you know, want to do something great. And so, so we're kind of on the inside, we, we have a lot of information and so our investments are, you know, well informed.

• 1:31:13Brad Gerstner?

  Right.

• 1:31:14Jensen Huang?

  And not to mention Nvidia is also in the ecosystem, and so we can help them succeed.

• 1:31:18Brad Gerstner?

  That's right.

• 1:31:19Jensen Huang?

  And so there's a lot of different reasons why the investments turn out great. But as a percentage of, of our total

• 1:31:27Jensen Huang?

  cash generation, it's going to decline very rapidly, partly because OpenAI and Anthropic isn't going to come around again. That opportunity is not going to come around again, frankly. And, and then secondarily, our, our cash flow generation is, is growing quite fast.

• 1:31:43Brad Gerstner?

  Right. And, and to that point, it seems to me that if you're buying back that much, and the durability of the revenues, I think, as you've described here today, is longer than people expect. And I think that's the one place, people are still hanging too much on, this is only going to last as long, Ezra asked you this on the pod the other day, how long until the bubble bursts? And I think you said something about two to three years, you have good visibility that, you know, the demand is there and who knows kind of after that. And I heard some people saying, oh, it may only be two years before this burst. But when I look at this, like, this is fundamentally a re-architecture of all global compute forever, forever. So unless there's a global depression.

• 1:32:25Jensen Huang?

  Yeah.

• 1:32:26Brad Gerstner?

  Right, then every single year, people are going to have to consume an enormous amount of Nvidia compute.

• 1:32:31Jensen Huang?

  Yeah, and so, so the way to think about that is, is we were running in the, you know, the CSPs were running about half a trillion, the whole company, the whole industry, the whole world is running, call it a trillion dollars of IT.

• 1:32:44Brad Gerstner?

  Right.

• 1:32:44Jensen Huang?

  Built out each year. Okay, and every, and every year, more gets built and every five years or so, some gets, you know, depreciated or turned off and, and taken offline and so on, so forth, and some of them, some of it lasts longer. And so, so that, that old world was in the retrieval way of doing computation and the new way of doing computation, you know, I've, I've predicted that going out of the, out of the decade, so three, four, let's say four, let's say five, and I, and my sense is that we're going to continue to grow from there. If you told me that the annual, the annual capacity building, infrastructure building, refreshing, combination of new and old

• 1:33:30Jensen Huang?

  refreshes runs around $10 trillion, I'm not even a little bit surprised.

• 1:33:34Brad Gerstner?

  Right. I mean, just again, for people listening, you know, you think $4 trillion of annual spend by the end of the decade, because every time you consume tokens, every time you use intelligence, you have to run the machine, the factory has to run. And I think you, you've described that, that's about 50%, you know, if you assume that, you know, $50 trillion of global GDP is, you know, is related to knowledge work, if we get a 20% productivity gain over five years on that, like you've seen inside Nvidia, you've seen much bigger productivity gains inside Nvidia, but if you see that sort of productivity gain, then if we get $10 trillion of incremental economic activity.

• 1:34:14Jensen Huang?

  Yeah.

• 1:34:15Brad Gerstner?

  Right, by the, by, by 2029, 2030, like, that seems very reasonable. And you're just saying, we'll spend half of that on CapEx, on the intelligence, you know, to, to produce it. And so maybe, you know, that's a good place for us to take a bit of an off-ramp. You've been incredibly generous. I could go on forever. But I kind of want to wrap where we started, right? When we look back.

• 1:34:40Jensen Huang?

  If this was one of those eating, drinking podcasts.

• 1:34:45Brad Gerstner?

  We could keep going, we could keep going.

• 1:34:48Brad Gerstner?

  It wasn't so long ago that the human condition was described as nasty, brutish and short. Right?

• 1:34:55Jensen Huang?

  Was that last week or?

• 1:34:58Brad Gerstner?

  Innovation has really driven all human progress, mostly in the last 200 years.

• 1:35:05Brad Gerstner?

  More time with family, longer lives, less time doing drudgery that humans really didn't want to do, safer-

• 1:35:15Jensen Huang?

  We're the first generation where 63 years old is considered young.

• 1:35:18Brad Gerstner?

  Exactly.

• 1:35:19Jensen Huang?

  When people still go, you know?

• 1:35:21Brad Gerstner?

  Exactly.

• 1:35:21Jensen Huang?

  People call me Young Man.

• 1:35:22Brad Gerstner?

  You, safer, better lives.

• 1:35:27Brad Gerstner?

  All of this despite all of these concerns that you and I hear out there. And by the way, those same concerns were expressed at the beginning of the Industrial Revolution. Right? What's your level of confidence that in five years from now, we will have made the right decisions and the world will be that much better place, right? That we describe because of the investment and all that we're doing. Are you more confident as we sit here today than you were 12 months ago?

• 1:35:55Jensen Huang?

  I'm 99.99% just in service, in honor of a good friend, not using zero in a hundred. I'm 99.99% confident that too many of us care due deeply that the outcome turns out well. That we will dedicate our might, our intellect, our resources to ensure we get there responsibly and optimistically. And so there's just so many people doing the right things. Now oftentimes, you know, there are people who pontificate about these things and they're

• 1:36:36Jensen Huang?

  informed by several, several things and they, and they, they don't realize that there are a lot of people, industries, incredible researchers and engineers outside of their sphere of information that are doing amazing things all by themselves, independent of them. Just because they didn't say, they didn't say it doesn't mean a lot of smart people are doing smart things. And, and you know, I say this all the time inside the company, even without my leadership, without strategies, the company is doing smart things all by itself. Smart people do smart things. They do more impactful things when you organize a line.

• 1:37:16Brad Gerstner?

  Right.

• 1:37:16Jensen Huang?

  But amazing people are doing smart things all by themselves. And so when somebody tells me that, that, you know, you're, you're worried about, it's good that we worry.

• 1:37:24Brad Gerstner?

  Right.

• 1:37:25Jensen Huang?

  And we should express our worries. We should express our concerns, but we shouldn't predict the end of the world.

• 1:37:32Brad Gerstner?

  Yeah.

• 1:37:33Jensen Huang?

  Because they're underestimating the human condition. They're underestimating the human capacity and they think for some, some reason with, maybe it's just hubris, maybe it's arrogance, that unless they do it, it won't get done.

• 1:37:46Brad Gerstner?

  Right, right.

• 1:37:47Jensen Huang?

  Unless they do it, it won't get done.

• 1:37:49Brad Gerstner?

  Right.

• 1:37:51Jensen Huang?

  The fact that matters, this company is doing amazing things whether I say it or not.

• 1:37:54Brad Gerstner?

  Right.

• 1:37:55Jensen Huang?

  Okay. And I, you know, I, I get, I get all the accolades and the blame, but in both cases, in both cases unfair, especially the accolades. And, and so I, and the reason for that is just these, you know, when you surround yourself with amazing people and you create the conditions by which everybody wants the same outcome.

• 1:38:15Brad Gerstner?

  Yeah.

• 1:38:15Jensen Huang?

  The good outcome, the good version of it. And I think most people do.

• 1:38:18Brad Gerstner?

  Yes.

• 1:38:18Jensen Huang?

  That's the reason why I say we need to, we need to hold ourselves accountable and responsible, but we should express a future that's optimistic.

• 1:38:27Brad Gerstner?

  For sure.

• 1:38:28Jensen Huang?

  And I am, I am 99.99% certain.

• 1:38:31Brad Gerstner?

  Right.

• 1:38:32Jensen Huang?

  That surrounded by all the amazing people in the world, we're going to get there together.

• 1:38:35Brad Gerstner?

  Yeah, agreed. And it's a, I think you're a national treasure. We're lucky to have you.

• 1:38:41Jensen Huang?

  Thank you.

• 1:38:42Brad Gerstner?

  Helping to lead the charge. The nation's indebted to you for getting everybody together, getting us all on the same page, building the framework. There's going to be more work to do. But as a father of two, and I, I know you, you have kids, you're going to have grandkids and we don't care about anything more.

• 1:38:58Jensen Huang?

  That's right.

• 1:38:59Brad Gerstner?

  Than actually building a better world for them. Right? We could easily stop. But if we stop today, we lose. Right? We lose the opportunity at that better world. So we'll wrap it there. Thank you, Jensen.

• 1:39:10Jensen Huang?

  Thank you guys. And Brad, thank you for, I, I've never met a, a greater patriot, somebody who, who cares so much about the industry, but even more so the country and, and your, your gift, your gift and your enthusiasm and just tireless energy in, in coordinating, pulling together a lot of people in order to do something great like what you did. Incredible.

• 1:39:48Brad Gerstner?

  As a reminder to everybody, just our opinions, not investment advice.