Transcript: NVIDIA CEO Jensen Huang on OpenAI, Future of Compute, and the American Dream – Bg2 Pod

Second is the OCI buildout. And I think there’s some 5, 6, 7 gigawatts that are about to be built out. And so working with OCI and OpenAI and SoftBank to build that out. Those projects are contracted, we’re working on it, lots of work to do. And then the third is Core Weave. Right. And so all of Core Weave 4. I’m talking about OpenAI still. Yes. Okay. Everything in the context of OpenAI.

And so the question is, what is this new partnership? This new partnership is about helping OpenAI working, partnering with OpenAI to build their own self-build AI infrastructure for the first time. And so this is us working directly with OpenAI at the chip level, at the software level, at the systems level, at the AI factory level to help them become a fully operated hyperscale company. I mean this is going to go on for some time.

It’s going to supplement, it’s going to supplement the amount of, they’re going through two exponentials as you know. Right. The first exponential is the number of customers is growing exponentially. And the reason for that is the AI is getting better, the use case is getting better. Just about every application is connected to OpenAI now. And so they’re going through the usage exponential.

The second exponential is the computational exponential of every use, right? Yes. Instead of just a one shot inference is now thinking before it answers. And so these two exponentials compounding their compute requirements. And so we got build out all these different projects and so this last one is an additive on top of everything that they’ve already announced, all the things that we’re already working on with them, it’s additive on top of that and it’s going to support this incredible exponential.

Building Direct Relationships with Hyperscalers

BRAD GERSTNER: One of the things you said there that’s really interesting to me is they’re going to be high probability multi-trillion dollar company in your mind, you think it’s a great investment. At the same time, they’re self-building, you’re helping them self-build their data centers. So heretofore they’ve been outsourcing to Microsoft to build the data center. Now they want to build Full Stack factories themselves.

JENSEN HUANG: They want to do. They want to. They want to basically have a relationship with us the way that Elon and X has really.

BRAD GERSTNER: Correct.

JENSEN HUANG: I mean Elon and X self-built. Exactly.

BRAD GERSTNER: But I think that’s…

JENSEN HUANG: This is a very big deal when you think about the advantage that Colossus had. They’re building Full Stack, that is a hyperscaler because if they don’t use the capacity, they could sell it to somebody else.

JENSEN HUANG: That’s right.

BRAD GERSTNER: In the same way Stargate, they’re building monstrous capacity. They think they’ll need to use most of it, but it puts them in a position to sell it to somebody else as well. It sounds very much like AWS or GCP or Azure. That’s what you’re saying.

JENSEN HUANG: Yeah, I think they’ll likely use it themselves. And just in case of X, they’ll likely use it themselves. But they would like to have the same direct relationship with us, direct working relationship and direct purchasing relationship. Meta, just as with Zuck and Meta has with us. It’s exactly a direct. Our relationship between us and Sundar and Google Direct, our partnership with Satya and Azure Direct. Isn’t that right?

And so they’ve gotten to a large enough scale that they believe it’s time for them to start building these direct relationships. I’m delighted to support that. And all of. And Satya knows it and Larry knows it and everybody’s aware of what’s going on and everybody’s very supportive of it.

Wall Street’s Skepticism vs. Reality

BRAD GERSTNER: So one of the things I find mysterious. Right. You just mentioned Oracle. $300 billion Colossus, what they’re building, we know what the sovereigns are building, we know what the hyperscalers are building. Sam’s talking in terms of trillions, but of the 25 sell-side analysts on Wall Street who cover your stock, if I look at the consensus estimate, it basically has your growth flatlining starting in 2027. 8% growth. 2027 through 2030. Okay. That is the 25 people and their only job. They get paid to forecast the growth rate for Nvidia. So clearly we’re comfortable with that, by the way. Right, right.

JENSEN HUANG: Look, we’re comfortable with that. Okay? We have no trouble beating the numbers on a regular basis.

BRAD GERSTNER: Right, No, I understand that, but there is this interesting disconnect. Right. I hear it every day on CNBC and Bloomberg and I think it goes to some of these questions around shortages leading to a glut that they don’t believe. They say, okay, we’ll give you credit for ’26, but ’27, maybe we’ll have too much and you’re not going to need that.

But it is interesting to me, and I think it’s important to point out that your consensus forecast is that this won’t happen. Right. And we also put together forecast for the company taking into account all of these numbers. And what it shows me is still, even though we’re two and a half years into the age of AI, a massive divergence of belief between what we hear Sam Altman saying, you saying, Sundar saying, Satya saying, and what Wall Street still believes. And again, you’re comfortable with that.

JENSEN HUANG: I also don’t think it’s inconsistent.

BRAD GERSTNER: Okay, so explain that a little bit.

The Three Pillars of Computing’s Future

JENSEN HUANG: So first of all, for the builders, we’re supposed to be building for opportunity, right? We’re builders. Let me give you three points to think through. And these three points, it’ll help you hopefully be more comfortable with Nvidia in this future.

So the first point, and this is the laws of physics point, this is the most important point, that general purpose computing is over and the future is accelerated computing and AI computing. That’s the first point. And so the way to think about that is there’s how many trillions of dollars of computing infrastructures in the world that has to be refreshed. Right, right.

BRAD GERSTNER: And when it gets refreshed, it’s going to be accelerated compute.

JENSEN HUANG: That’s right. And so the first thing you have to realize is that general purpose computing, and nobody disputes that, everybody goes, “Yeah, we completely agree with that. General purpose computing is over. Moore’s law is dead.” People say these things. And so what does that mean?

So general purpose computing is going to go to accelerated computing. Our partnership with Intel is recognizing that general purpose computing needs to be fused with accelerated computing to create opportunities for them. Is that right? And so one, general purpose computing is shifting to accelerated computing and AI.

Two, the first use case of AI is actually already everywhere. It’s in search, recommender engines, isn’t that right? In shopping. The basic hyperscale computing infrastructure, used to be CPUs doing recommenders, is now going to GPUs doing AI. So you just take classical computing, it’s going to accelerate computing. AI, you take hyperscale computing, is going from CPUs to accelerated computing and AI. And then now that’s the second point. Just feeding the Metas, the Googles, the ByteDances, the Amazons, and take their classical traditional way of doing hyperscaling and moving it to AI. That’s hundreds of billions of dollars.

BRAD GERSTNER: And because that may be 4 billion people on the planet today, if you take TikTok Meta into account, Google into account, who are already demanding workloads that are driven by accelerated computing.

The Future of AI and Computing Infrastructure

JENSEN HUANG: That’s exactly right. And so without even thinking about AI creating new opportunities, it’s about AI shifting how you used to do something to the new way of doing something. And then now let’s talk about the future. So far I’ve only spoken kind of largely about just mundane stuff. Just mundane stuff. The old way is now wrong. You’re no longer going to use fuel, light lanterns, you’re going to go to electricity. That’s all right. And you no longer, you know, prop planes, you’re going to go to jets. That’s all so far, you know, that’s all I’ve talked about.

And then now that the incredible thing is when you go to AI, when you go to accelerated computing, then what happens? What are the new applications that emerge as a result? And that’s all the AI stuff that we’re talking about. And that opportunity, what does that look like?

Well, the simple way of thinking about that is where motors replace labor and physical activity. We now have AI. These AI supercomputers, these AI factories that I talk about, they’re going to generate tokens to augment human intelligence, right? And human intelligence represents what, 55, 65% of the world’s GDP, let’s call it $50 trillion. And that $50 trillion is going to get augmented by something.

And so let’s use, let’s come back to a single person. Suppose I were to hire $100,000 employee, and I augmented that $100,000 employee with a $10,000 AI. And that $10,000 AI as a result made the $100,000 employee twice more productive, three times more productive. Would I do it? Heartbeat I’m doing it across every single person in our company right now. Every single have co agents. That’s right. Co workers. That’s right. Every single software engineer, every single chip designer in our company already has AIs working with them. 100% coverage.

As a result, the number of chips we’re building is better, the number is growing. The pace at which we’re doing it is right. And so we’re growing faster as a company. As a result, we’re hiring more people, our productivity is greater, our top line is greater, our profitability is greater. What’s not to love about that?

Now apply the Nvidia story to the world’s GDP. And so what’s likely to happen is that that $50 trillion is augmented by, let’s pick a number, $10 trillion. That $10 trillion needs to run on a machine. Now, the reason that AI is different than it in the past, in a way, software was written a priori and then it runs on the CPU and it runs. A person would operate it in the future. Of course, AI is generating tokens, but a machine has to generate the tokens. And it’s thinking. So that software is running all the time. Whereas in the past the software was written once. Now the software is in fact writing all the time. It’s thinking.

In order for the AI to think, it needs a factory. And so let’s say that that $10 trillion of token generated 50% gross margins and 5 trillion of it needs a factory, needs an AI infrastructure. So if you told me that on an annual basis the cap X of the world was about $5 trillion, I would say the math seems to make sense. And that’s kind of the future, right? The dis — going from general purpose computing, accelerated computing, replacing all the hyperscales with AI, and then now augmenting human intelligence for the world’s GDP.

CLARK TANG: And today that market is about, our estimate is about 400 billion annually. So the TAM, you know, is a 4 to 5x increase over where it is today.

JENSEN HUANG: Yeah, Eddie, last night, Eddie Wu Alibaba said, between now and the end of the year, now and the end of the decade, they’re going to increase their data center power by, by 10x. Right, right. You just said how much?

CLARK TANG: 4X.

JENSEN HUANG: There you go. There you go. Yeah. They’re going to increase power by 10x and we, we correlate the power. Nvidia’s revenue is almost correlated to power, isn’t that right? Yeah, that’s right. Yeah.

CLARK TANG: Because more.

JENSEN HUANG: Another thing. What, what else did he say? Yeah, he said token generation is doubling every few months. Yeah. What’s that saying? The perf per watt has to keep on going exponentially. That’s why Nvidia is cranking it out with perf per watt. And revenue per watt is, you know, watt is basically revenues in this future.

Historical Context and GDP Growth

BRAD GERSTNER: Embedded in this assumption. I find it very fascinating historical context.

JENSEN HUANG: Right.

BRAD GERSTNER: For 2000 years, basically GDP did not grow.

JENSEN HUANG: Okay.

BRAD GERSTNER: And then we get the industrial revolution, GDP accelerates. We get the digital revolution, GDP accelerates. And basically what you’re saying, and Scott Bessant has said it, he said, I think we’re going to have 4% GDP growth next year, basically. What you’re saying is the world’s GDP growth is going to accelerate because now we are giving the world billions of coworkers that will do work for us. And if GDP is an amount of output for a fixed amount of labor and capital, it has to accelerate.

JENSEN HUANG: It has to, it has to. Look at what’s going on with AI as a result of the technology of AI and that technology of AI, let’s just call it the large language models and all the AI agents, it’s now creating a new industry of AI agents. There’s no question about that. Okay, so that’s. OpenAI is the fastest growing revenue company in history. Right. And they’re growing exponentially. Right.

And so AI itself is a fast growing industry because of AI needs a factory behind it. Right. An infrastructure behind it. There’s this industry is growing, my industry is growing. And because my industry is growing, the industry underneath it is growing. Energy is growing. Power shell, this is the end. This is like renaissance for the energy industry, isn’t that right? Nuclear energy, gas turbines. I mean, look at all of those companies in the, in the infrastructure ecosystem underneath us. They’re doing incredibly well. Everybody’s growing.

Addressing Bubble Concerns and Market Dynamics

BRAD GERSTNER: These numbers have everybody talking about a.

JENSEN HUANG: Glut or a bubble.

BRAD GERSTNER: Right. Zuckerberg said last week on a podcast, you know, he said, “Listen, I think it’s quite possible at some point that we will have an air pocket and Meta may in fact overspend by $10 billion or whatever.” But he said it doesn’t matter. It’s so existential to the future of his business that it’s a risk that they have to take. But when you think about that, it sounds a little bit like Prisoner’s Dilemma. Right. And walk us again.

JENSEN HUANG: These are very happy prisoners.

BRAD GERSTNER: Again, through. Right. Today our estimate is that we’re going to have 100 billion of AI revenue in 2026, excluding meta and excluding, you know, the GPUs running recommender engines. Okay, so there’s. Correct. So there’s other stuff. Yeah, but let’s call it 100 billion.

JENSEN HUANG: What if that industry. Anyways, what is the industry already in hyperscale? What’s the hyperscales between trillions? Yeah, exactly. By the way, that industry is going to AI before anybody starts at zero, you got to start there.

BRAD GERSTNER: But I think the skeptics would say we need to go from 100 billion of AI revenue in 26 to at least a trillion of AI revenue in 2030. Okay, you just were talking a minute ago about 5 trillion. When you look at kind of global GDP. If you do did a bottoms up, can you see your way to a trillion dollars of AI driven revenues from 100 billion over the course of the next five years? Are we growing that fast?

JENSEN HUANG: Yes. And I would also say we’re already there.

BRAD GERSTNER: Okay, so explain that.

JENSEN HUANG: Because the hyperscalers, they went from CPUs to AI. Okay. Their entire revenue base is all now AI driven.

BRAD GERSTNER: Correct.

JENSEN HUANG: You can’t do TikTok without AI. Correct. You can’t do YouTube short without AI. You can’t, you know, you can’t do any of this stuff without AI. The, the amazing things that, that meta’s doing for, for, you know, customized content, personalized content. You can’t do that without AI. It’s all of that stuff used to be humans, you know, doing content a priori, creating four choices that are then selected by a recommender engine. Correct. And now it’s infinite number of choices generated by an AI. Right.

BRAD GERSTNER: But those things are already like we had the transition from CPUs to GPUs largely for those recommender engines, and now they’re going.

JENSEN HUANG: That’s fairly new. I would.

BRAD GERSTNER: In the last three or four years.

JENSEN HUANG: Yeah. Zuck would tell you, I was at Siggraph and Zuck would tell you, you know, they were late getting to.

BRAD GERSTNER: For sure.

JENSEN HUANG: Yeah, for sure. For sure. GPUs for, for meta is what, couple years? A year and a half. It’s pretty new. Search with GPUs. For sure. Brand spanking new. For sure. For sure. Brand spanking new. Search for GPUs on GPUs.

BRAD GERSTNER: So your argument would be the probability that we’re going to have a trillion dollars of AI revenues by 2030 is near certain because we’re almost already there.

JENSEN HUANG: Right.

BRAD GERSTNER: Let’s just talk about incremental.

JENSEN HUANG: Incremental. Now we can talk about incremental.

BRAD GERSTNER: Exactly right. As you do your bottoms up or your tops down. I just heard your tops down about percentage of global GDP.

JENSEN HUANG: Yeah.

BRAD GERSTNER: What is the percentage probability that you think will have a glut, will run into a glut in the next three or four or five years?

JENSEN HUANG: Right.

BRAD GERSTNER: It’s a distribution of we don’t know the future. It’s a distribution of until.

JENSEN HUANG: Until we fully convert all general purpose computing to accelerated computing and AI. Until we do that, I think the chances are extremely low. Okay. Okay.

BRAD GERSTNER: And that will take a few years.

JENSEN HUANG: That’ll take a few years. Yeah.

BRAD GERSTNER: Let me ask one more.

JENSEN HUANG: And then until all recommender engines are AI based, until all content generation is AI based. Because content generation Consumer oriented content generation is very largely recommender systems and so on and so forth. And all of that’s going to be AI generated until all of this stuff, what classically was hyperscale, now transitions to AI. Everything from shopping to E commerce, all that stuff, until everything goes over.

Supply Chain and Investment Dynamics

BRAD GERSTNER: But all this new build, when we’re talking about trillions, we’re investing ahead of where we are, is that at will? Are you obliged to invest the money even if you see a slowdown or a kind of a glut coming? Or is this one of these things that you’re just waving the flag to the ecosystem to say get out and build? And at some point in time, if we see some of this slow down, we can always pull back on the level of investment.

JENSEN HUANG: Actually, it’s the other way because we’re at the end of the supply chain, right? And so we respond to demand. Okay? And right now all the VCs will tell you, and you guys know the demand, there’s a shortage of compute in the world. Not because there’s a shortage of GPUs in the world, but okay, if they give me an order, I’ll build it, right?

We’ve, over the last couple of years, we’ve really plumbed the supply chain. So all of the supply chain behind me, from wafer starts to coas to HBM memories, you know, all of that technology we’ve really geared up. If we need to double, we’ll double. Yes. Okay, so the supply chain is ready now. We’re just waiting for demand signals.

And when the CSPs and the hyperscalers and our customers do their annual plan and they give us, you know, their forecast, we respond to that and we build to that. Now what’s, what’s going on, of course, is that every one of their forecasts that they provide us turns out to have been wrong. Right. Because they under forecast it. And so now we’re always in a scramble mode. And so we’ve been in the scramble mode now for, you know, a couple of years. And it’s whatever forecast we’ve been given has been always significant increase from last year, but not enough.

BRAD GERSTNER: Satya last year seemed to be pulling back a little bit. Seemed to be, some people called him the adult in the room, tamping down kind of some of these expectations. A few weeks ago he said, “Hey, I’ve also built two gigs this year and we’re going to accelerate in the future.” Do you see some of the traditional hyperscalers that may have been moving a little slower than let’s call it a core Weave or Elon X, or maybe a little slower than Stargate. Do you see them all? It sounds like to me they’re all leaning in more now.

The Second Exponential: From Memorization to Reasoning

JENSEN HUANG: And they’re all also because of the second exponential. We’ve already had one exponential we were experiencing, which was the adoption rate of AI. The engagement of AI was growing exponentially.

The second exponential that kicked in was reasoning. That was the conversation we had one year ago. You said, “Hey, listen, the moment you take AI from one shot, memorizing an answer and general memorizing and generalizing, that’s basically pre-training.” So memorizing an answer, what’s 8 times 8? Just memorize it.

And so memorizing an answer and generalizing, that was one shot AI. Now, a year ago, reasoning came about, research came about, tool use came about, and now you’re a thinking AI. One billion times, it’s going to use a lot more compute.

CLARK TANG: Certain hyperscale customers, to your point, had internal workloads that they had to migrate anyways from general purpose computing to accelerated computing. So they built through the cycle. I think maybe some hyperscalers had different workloads, so they weren’t quite sure how quickly they could digest it. But everyone has now concluded that they dramatically underbuilt.

The Future of Data Processing

JENSEN HUANG: One of the applications that my favorite is just good old fashioned data processing, structured data and unstructured data. Just good old fashioned data processing. And very soon we’re going to announce a very big initiative of accelerated data processing.

Data processing represents the vast majority of the world’s CPUs. Today it still completely runs on CPUs. If you go to Databricks, it’s mostly CPUs. You go to Snowflakes, mostly CPUs. SQL processing at Oracle, mostly CPUs. Everybody’s using CPUs to do SQL structured data.

In the future, that’s all going to move to AI data. That is one gigantic massive market that we’re going to move to. But you need everything that NVIDIA does requires acceleration layers and requires domain specific data processing recipes. We got to go build that. But that’s coming.

Addressing Circular Revenue Concerns

BRAD GERSTNER: So one of the pushbacks, I turned on CNBC yesterday, they were like, “Oh, glut bubble.” When I turned on Bloomberg, it was about round tripping and circular revenues.

And so for the benefit of people at home, these arrangements are when companies enter into a misleading transaction that artificially inflates revenue without any underlying economic substance. So in other words, gross propped up by financial engineering, not by customer demand. And the canonical case everybody’s referencing, of course is Cisco and Nortel from the last bubble 25 years ago.

So when you guys or Microsoft or Amazon are investing in companies that are also your big customers, in this case, you guys investing in OpenAI, while OpenAI is buying tens of billions of chips, just remind us and remind everybody else, like what are the analysts on Bloomberg and otherwise getting wrong when they’re hyperventilating about circular revenues or about round tripping?

JENSEN HUANG: Ten gigawatts is like $400 billion. And that $400 billion will have to be largely funded by their offtake. Their revenues, which is growing exponentially. It has to be funded by their capital, the money they’ve raised through equity and whatever debt they can raise. Those are the three vehicles.

And the equity that they could raise and the debt that they could raise has something to do with the confidence of the revenues that they could sustain, for sure. And so smart investors and smart lenders will consider all of these factors. Fundamentally, that’s what they’re going to do. That’s their company. It’s not my business.

And of course, we have to stay very close to them to make sure that we build in support of their continued growth. And so there’s the revenue side of it and has nothing to do with the investment side of it. The investment side of it is not tied to anything. It’s an opportunity to invest in them.

And as we were mentioning earlier, this is likely going to be the next multi-trillion dollar hyperscale company. And who doesn’t want to be an investor in that? My only regret is that they invited us to invest early on.

BRAD GERSTNER: I remember those conversations.

JENSEN HUANG: We were so poor, we were so poor we didn’t invest enough. And I should have given all my money.

BRAD GERSTNER: And the reality is if you guys don’t do your jobs and keep up with, if Vera Rubin doesn’t turn into a good chip, they can go get other chips and put them.

JENSEN HUANG: That’s right.

BRAD GERSTNER: There’s no obligation that they have to use your chips. Like you said, you’re looking at this as an opportunistic equity investment.

JENSEN HUANG: We’ve made some great investments. I got to put it out there right now. We invested in xAI, we invested in CoreWeave. Incredible. How smart was that?

The Fundamental Nature of Intelligence

BRAD GERSTNER: The other fundamental thing it seems to me, is you’re putting it out there, you’re saying this is what we’re doing and the underlying economic substance here. It’s not that you’re just somehow sending revenues back and forth between the two companies.

We got people sending money every month for ChatGPT, a billion and a half monthly users using the product. You just said every enterprise in the world is either going to do this or they will die. Every sovereign views this as existential to their national security and economic security as nuclear power.

JENSEN HUANG: What person, company or nation says intelligence is basically optional for us? I mean, it’s fundamental to them. It’s the automation of intelligence.

Annual Release Cycle and AI-Driven Development

BRAD GERSTNER: I beat the demand question to death. So let’s jump in a little bit to system design and I’m going to turn to Clark here in a second on that. But in 2024, you switched to your annual release cycle with Hopper. You then had a massive upgrade, which required significant data center overhaul with Grace Blackwell in 2025.

And in the back half of 26, we’re going to get Vera Rubin, 27, we’ll get Ultra, and 28, Feynman. How is the annual release cycle going? What were the main goals of going to an annual release cycle? And did AI inside NVIDIA allow you to execute the annual release cycle?

JENSEN HUANG: Yeah, the answer is yes. On the last question, without it, NVIDIA’s velocity, our pace, our scale would be limited. And so without AI these days, it’s just simply not possible to build what we build now.

Why do we do it? There’s something that Eddie said it at his earnings call or his conference. Satya has said it, Sam has said it. The token generation rate is going up exponentially and the customer use is going up exponentially. I think they’re at 800 million weekly active users or something like that. I mean, that’s less than two years from ChatGPT.

BRAD GERSTNER: And each of those users is generating massively more tokens because they’re using inference, time, reasoning.

The Performance Imperative

JENSEN HUANG: That’s right, exactly. And so the first thing is, because the token generation rate is going up so incredibly, two exponentials on top of each other, we have to. Unless we increase the performance at incredible rates, the cost of token generation will keep growing.

Because Moore’s Law is dead. Because transistors basically cost the same every single year now. And power is largely the same between those two fundamental laws, unless we come up with new technologies to drive the cost down. Even if there’s a slight difference in gross, you give somebody a discount of a few percent, how’s that going to make up for two exponentials?

And so we have to increase our performance annually at a pace that keeps up with that exponential. So in the case of going from Kepler all the way to Hopper was probably 100,000 times. That was the beginning of the AI journey for NVIDIA. 100,000 times in 10 years.

Between Hopper and Blackwell, we increase because of NVLink, 72, 30 times in one year. And then we’ll get another X factor again with Rubin, and then we’ll get another X factor with Feynman.

And the way we do that is because the transistors aren’t really helping us very much. Moore’s law is largely the density is growing up, but the performance is not. And so if that’s the case, one of the challenges that we have to do is we have to break the entire problem down at the system level and change every chip at the same time.

And all the software stack and all the systems all at the same time. The ultimate extreme co-design. Nobody’s ever co-designed at this level before. We change the CPU, revolutionize the CPU, a GPU, the networking chip, the NVLink. Scale up the Spectrum X, scale out.

Somebody said, I heard somebody said, “Oh yeah, it’s just Ethernet.” Okay, so Spectrum X Ethernet is not just Ethernet. And people are starting to discover, oh my God, the X factors is pretty incredible. NVIDIA’s Ethernet business, the just Ethernet business, is the fastest growing Ethernet business in the world.

And so scale out. And of course now we have to build even larger systems. So we scale across multiple AI factories connected together. And then we do this at an annual pace. And so we now have an exponential of exponentials going ourselves from technology.

And that allows our customers to drive the cost of tokens down. Keep making those tokens smarter and smarter with pre-training and post-training and thinking. And as a result, when the AI gets smarter, they get more used. When they get more used, they’re going to grow exponentially.

Understanding Extreme Co-Design

BRAD GERSTNER: For people who may not be as familiar, what is extreme co-design?

JENSEN HUANG: Extreme co-design means that you have to optimize the model algorithm, system and chip at the same time. You have to innovate outside the box. Because Moore’s law said you just have to keep making the CPU faster and faster. Everything got faster. You were innovating within the box. Just make that chip faster.

Well, if that chip doesn’t go any faster, then what are you going to do? Innovate outside the box. And so NVIDIA really changed things because we did two things. We invented CUDA, invented GPUs and we invented the idea of co-design at a very large scale. That’s why there’s all these industries we’re in. We’re creating all these libraries and co-design number one.

Full Stack Extreme is even beyond software and GPUs. It’s now at the data center level. Switches and networking and all of that software in the switches and the networking and the NICs, the scale up, the scale out, optimizing across all of that. As a result of that, Blackwell to Hopper is 30 times. No Moore’s Law could possibly achieve that.

BRAD GERSTNER: And that comes from the extreme co-design.

JENSEN HUANG: That’s because NVIDIA has. That’s why we got into networking and switching and scale up and scale out and scale across and building CPUs and building GPUs and building NICs. That’s the reason why NVIDIA is so rich in software and people.

We check in more open source software in the world than just about anybody except one other company. I think it’s AI2 or something like that. So we have such enormous richness of software and that’s just in AI. Don’t forget computer graphics and digital biology and autonomous vehicles. The amount of software we produce as a company is incredible. That allows us to do deep and extreme co-design.

Competitive Advantages and Supply Chain

BRAD GERSTNER: I heard from one of your competitors, “Yes, he’s doing this because it helps drive down the cost of token generation. But at the same time, your annual release cycle makes it almost impossible for your competitors to keep up. The supply chain gets locked up more because you’re giving three year visibility to your supply chain. So now the supply chain has confidence as to what they can build to.”

JENSEN HUANG: So think about this way before you ask the question. Think about this. In order for us to do several hundred billion dollars a year of AI infrastructure build out. Think about how much capacity we had to go start a year ago. We’re talking about building hundreds of billions of dollars of wafer starts and DRAM buys. This is now at a scale that hardly any company can keep up with.

BRAD GERSTNER: So would you say your competitive moat is greater today than it was three years ago?

The Challenge of Competition in AI Hardware

JENSEN HUANG: Yeah. You know, first of all, there’s just more competition than ever before. But it’s harder than ever before. And the reason why I say that is because wafer cost is getting higher. Which means that unless you do co-design at an extreme scale, you’re just not going to be able to deliver the X factor growth. Number one.

And so you know, unless you’re working on six, seven, eight chips a year, right? That’s amazing thing. It’s not about building an ASIC, it’s about building an AI factory system. And this system has a lot of chips in it and they’re all co-designed and together they deliver that 10x factor that we get almost regularly.

So number one, the co-design is extreme. The second thing is that the scale is extreme. When your customers deploy a gigawatt, that’s 400,000 to 500,000 GPUs. Getting 500,000 GPUs to work together is a miracle. I mean it’s just a miracle.

And so your customers are taking enormous risk on you to go buy all of this. You got to ask yourself what customer would place a $50 billion PO on an architecture? Right? On an unproven architecture. That’s right, a new one. A new architecture, yeah, you just take that, a whole new chip, you’re as excited as you are about it and everybody’s excited for you and you just show the first silicon. Who’s going to give you $50 billion PO? And why would you start $50 billion worth of wafers for a chip that just taped out?

But for NVIDIA we could do that because our architecture is so proven, so the scale of our customers so incredible now the scale of our supply chain is incredible. Who’s going to start all of that stuff, pre-build all of that stuff for a company unless they know that NVIDIA can deliver through? And they believe that we can deliver through to all of the customers around the world. They’re willing to start several hundred billion dollars at a time. The scale’s incredible.

The GPU vs ASIC Debate

CLARK TANG: To that point, one of the biggest key debates and controversies in the world is this question of GPUs versus ASICs, Google’s TPUs, Amazon’s Trainium. And it seems like everyone from ARM to OpenAI to Anthropic are rumored to be building one. Last year you said “we’re building systems, not chips” and you’re driving performance through every single part of that stack. You also said that many of these projects may never get to production scale. But given the…

JENSEN HUANG: Most of them.

CLARK TANG: Given the seeming success of Google TPUs, you know, how are you thinking about this evolving landscape today?

JENSEN HUANG: Yeah, first of all, the advantage that Google had is foresight. Remember they started TPU1 before everything started. You know, this is no different than a startup. You’re supposed to build a startup, you’re supposed to create a startup before the market grows. You’re not supposed to come up as a startup when the market’s a trillion dollars large.

You know, this fallacy and all VCs know this, this fallacy that a large market, if you could just take a few percent market share, you could be a giant company that’s actually fundamentally wrong. You’re supposed to take 100% of a tiny company, a tiny industry. Which is what NVIDIA did, right? Which is what TPUs did. There were only the two of us.

BRAD GERSTNER: But you better hope that that industry gets really big. You’re creating an industry.

JENSEN HUANG: That’s right.

BRAD GERSTNER: And I mean the NVIDIA story, you…

The Evolution of AI Infrastructure

JENSEN HUANG: You know, so that’s the challenge for people who are building ASICs. Now it looks like a juicy market, but remember, this juicy market has evolved from a chip called a GPU to… I just described an AI factory and you guys just saw, I just announced a chip called CPX for context processing and diffusion, video generation. A very specialized workload, but an important workload inside the data center.

I just preluded to maybe AI data processing processors because guess what? You need long term memory, you need short term memory. The KV cache processing is really intense. AI memory is a big deal. You know, you kind of want your AI to have good memory and just dealing with all the KV caching around the system. Really complicated stuff. Maybe it wants to have a specialized processor. Maybe there’s other things, right?

So you see the NVIDIA our viewpoint is now not GPU, our viewpoint is looking at the entire AI infrastructure. And what does it take for these incredible companies to get all of their workload through it, which is diverse and changing. Look at the transformer. The transformer architecture is changing incredibly. If not for the fact that CUDA is easy to operate on and iterate on, how do they try all of their vast number of experiments to decide which of the transformer versions, what kind of attention algorithm to use, how do you disaggregate? CUDA helps you do all that because it’s so programmable.

And so the way to think about our business now is you look at when all of these ASIC companies or ASIC projects start three, four, five years ago, I got to tell you, that industry was super adorable and simple. There was a GPU involved, but now it’s giant and complex and in another two years it’s going to be completely massive. The scale is going to be so large. And so I think that the battle of getting into a very large market as a nascent player is just hard.

CLARK TANG: As you guys know, even for the customers who perhaps are successful with ASICs, isn’t there an optimal balance in their compute fleet? Like it’s, you know, I think investors are very much binary creatures. They just want a yes or no black and white answer. Yeah, but even if you get the ASIC to work, isn’t there an optimal balance? Because you think I’m buying the NVIDIA platform. CPX is going to come out for pre-fill for, you know, for video generation, maybe a decode, you know…

JENSEN HUANG: You know, a platform, a video transcoder. Exactly, yeah.

CLARK TANG: So there will be many different chips or parts to add to the NVIDIA ecosystem. Accelerated compute fleet. Right. As new workloads are born.

JENSEN HUANG: That’s right.

CLARK TANG: And people trying to tape out new chips today are not really anticipating what’s happening a year from now. They’re just trying to get a chip to work.

JENSEN HUANG: That’s right.

BRAD GERSTNER: Said another way, Google’s a big GPU customer.

Three Categories of Chips

JENSEN HUANG: Google is a big GPU customer. If you look at, and Google is a very special case. I mean we just have to, you know, show respect where respect is really deserved. I mean TPU is on TPU 7. Yes. Right. And so, and it’s a challenge for them as well. Right. And so the work that they do is incredibly hard.

So I think the first thing to let me do it, you know, remember there are three categories of chips. There’s the category chips that are architectural. x86 CPUs, ARM CPUs, NVIDIA GPUs architectural. And it has an ecosystem above and the architecture allows, has rich IP and rich ecosystem. Very complicated technology. It’s built by the owners like us.

There’s ASICs. I worked for the original company, LSI Logic, who invented the idea of ASICs. As you know, LSI Logic is not here anymore. And the reason for that is because ASICs is really fantastic when the market size is not very large. It’s easy to have somebody be a contractor to help you put the packaging of all that stuff together and do the manufacturing on your behalf and they charge you 50, 60 points of margin.

But when the market gets large for an ASIC, there’s a new way of doing things called COT. Customer owned tooling. And who would do something like that? Apple’s smartphone chip, the volume is so large they would never go pay somebody else 50, 60% gross margin to be an ASIC. They do customer owned tooling. And so where will TPUs go when it becomes a large business? Customer owned tooling, there’s no question about it.

But there’s a place for ASICs. Video transcoders will never be too large. SmartNICs will never be too large. And so when there’s 10, 12, 15 ASIC projects going on at an ASIC company, I’m not surprised by that, you know, because they’re probably five SmartNICs and four transcoders. And you know, are they all AI chips? Of course not.

You know, and if somebody were to build an embedded embedding processor for a specific recommender system, and that was an ASIC, of course you could do that. But would you do that as the fundamental compute engine for AI that’s changing all the time? You’ve got low latency workload, you got high throughput workload, you have token generation for chat, you have thinking workload, you have AI video generation workload. Is there a, you know, now you’re talking about a very workhorse backbone of your accelerator. That’s what NVIDIA is all about.

Building Systems vs Components

BRAD GERSTNER: Again, dumb this down, it’s like playing chess and checkers, right? The fact of the matter is the folks who are starting ASICs today, whether it’s Trainium or whether it’s some of these other print accelerators, et cetera, they’re building a chip that’s a component of a much larger machine. You’ve built a very sophisticated system platform, factory, whatever you want to call it, and now you’re opening up a little bit, right? So you mentioned CPX, GPU, right? That is, it seems to me that in some ways you’re disaggregating the workloads to the best slice of the hardware for that particular domain.

JENSEN HUANG: Look what we did. We announced this thing called Dynamo, right? Disaggregated AI workload orchestration and we open sourced it because the future AI factory is disaggregated.

BRAD GERSTNER: And you launched NVLink Fusion that even said to your competitors, including Intel, which you just invested in.

JENSEN HUANG: That’s right.

BRAD GERSTNER: The way in which you participate in this factory that we’re building, because nobody else is crazy enough to try to build the entire factory, but you can plug into that if you have a product that’s good enough, compelling enough that the end user says, “hey, we want to use this instead of an ARM GPU or we want to use this instead of your inference accelerator,” et cetera. Is that correct?

JENSEN HUANG: We’re delighted to connect you in. Yeah, tell us a little bit more NVLink Fusion. It’s such a great idea and we’re so happy to partner with Intel on that. It takes the Intel ecosystem. Most of the world’s enterprise still runs on Intel. It takes the Intel ecosystem, takes the NVIDIA AI ecosystem, accelerated computer shooting it and we fused it together. Right. And we did that with ARM. Right. And there are several others we’re going to be doing it with. And that opens up opportunities for both of us as a win for both of us. Great, great win. I’ll be a large customer of theirs and they’re going to expose us to a much, much larger market opportunity.

The Economics of Performance Per Watt

BRAD GERSTNER: Yeah, it’s deeply related to this idea is the argument you’ve made that kind of shock some people where you say our competitors building ASICs, they could literally all their chips are cheaper already, but they could literally price them at zero. Our objective is they could price them at zero and you would still buy an NVIDIA system because the total cost of operating that system, power, data center, land, et cetera, the intelligence out is still a better bet than buying a chip even if it’s given to you for free.

JENSEN HUANG: Because the land, power and shell is already $15 billion. Right.

BRAD GERSTNER: So we’ve taken a crack at kind of the math on that. But walk us through your math because I think for people who don’t spend as much time here that it just doesn’t compute. How could it possibly be that you were pricing your competitors chips at zero given the expense of your chips? And it still is a better bet.

JENSEN HUANG: There’s two ways to think about it. One way is let’s just think about it from a perspective of revenues. Yes. Okay, so everybody’s power limited and let’s say you were able to secure two more gigawatts of power. Well, that two gigawatts of power you would want to have translate to revenues. Yes.

So your performance or tokens per watt was twice as high as somebody else’s token per watt. Because I did deep and extreme co-design and my performance was much higher per unit energy, then my customer can produce twice as much revenues from their data center. And who doesn’t want twice as much revenues?

And if somebody gave them a 15% discount, you know, the difference between our gross margins was call it 75 points. And somebody else’s gross margins call it 50 to 65 points. It’s not so much as to make up for the 30 times difference between Blackwell and Hopper. Let’s pretend Hopper… Hopper’s an amazing chip, an amazing system. Let’s pretend somebody else’s ASIC is Hopper. Blackwell’s 30 times.

So you’ve got to give up 30x revenues in that 1 gigawatt. It’s too much to give up. So even if they gave it to you for free, you only have 2 gigawatts to work with. Your opportunity cost is so insanely high, you would always choose the best performance per watt.

The Performance Trajectory and Competitive Moat

BRAD GERSTNER: So I heard this from one of the CFOs at one of the hyperscalers that given the performance improvement that’s coming out of your chips again precisely to that point, tokens per gig and power being the limiting factor that they had to upgrade to the new cycle. So when you look ahead at Ruben, at Rubin Ultra, Feynman, does that trajectory continue?

JENSEN HUANG: We’re building what, six, seven chips a year now?

BRAD GERSTNER: That’s part of that system.

JENSEN HUANG: That’s right. And that system software is everywhere. And it takes the integration and the optimization across all of those six, seven chips to deliver on the 30X. Blackwell. Now imagine I’m doing this every single year. Bam, bam, bam, bam, bam, bam. And so if you build one ASIC in that soup of ASICs, in that soup of chips and we’re optimizing across that, it’s a hard problem to solve.

BRAD GERSTNER: This does bring me back to where we started about the competitive moat. We’ve been covering this and investors for a while. We’re investors throughout the ecosystem and competitors of yours, from Google to Broadcom. But when I really just first principles around this and say are you increasing or decreasing your competitive moat, you move to an annual cadence, you’re co-developing with a supply chain, the scale is massively bigger than anybody anticipated, which requires scale, both a balance sheet and of development. Right.

The moves you made both through acquisition and organically with things like NV Fusion, CPX, which we just talked about, all of those things together caused me to believe that your competitive moat is increasing vis-à-vis, at least insofar as building out the factory or the system.

JENSEN HUANG: It’s at least surprising.

BRAD GERSTNER: But I think it’s interesting that your multiple is much lower than most of those other people. And I think part of that has to do with this law of large numbers. A four and a half trillion dollar company couldn’t possibly get any bigger. But I asked you this a year and a half ago.

As you sit here today, if the market’s going to AI workloads are going to 10x or 5x, we know what CapEx is doing, et cetera. Is there any conceivable world in your mind where your top line in 5 years isn’t 2 or 3x bigger than it is in 2025? What’s the probability that it’s actually not much higher than it is today?

JENSEN HUANG: Given those advantages, I’ll answer this way. Our opportunity, as I described it, is much larger than the consensus.

BRAD GERSTNER: I’ll say it here. I think NVIDIA will likely be the first $10 trillion company and I’ve been here long enough. It wasn’t that long ago, just a decade ago, as you well remember, the people said there could never be a trillion dollar company. Now we have 10, right?

JENSEN HUANG: And today people, it’s bigger, right?

BRAD GERSTNER: And today this is back to the exponentials around GDP and the growth.

NVIDIA as AI Infrastructure Partner

JENSEN HUANG: The world is bigger and people misunderstand what they remember. We’re a chip company and we build chips. Boy do we build chips and build the most amazing chips in the world. But NVIDIA is really an AI infrastructure company. We are your AI infrastructure partner and our partnership with OpenAI is a perfect demonstration of that. That we are their AI infrastructure partner.

And we work with people in a lot of different ways. We don’t require anybody to buy everything from us. We don’t require that they buy the full rack. They could buy a chip, they could buy a component. They could buy our networking, they could buy our, we have customers buying only our CPU. Just buy our GPUs and buy somebody else’s CPUs and somebody else’s networking. We’re kind of okay selling any way you like to buy. My only request is just buy a little something from us.

The Builder Advantage: Elon Musk and Colossus

BRAD GERSTNER: You said this isn’t just about better models. We also have to build. We have to have world class builders. And you said, the most world class builder maybe that we have in the country is Elon Musk. And we talked about Colossus 1 and what he was doing there standing up a couple hundred thousand, at the time H100s, H200s in a coherent cluster. Now he’s working on Colossus 2, which may be 500,000 GPUs, millions of H100 equivalents in a coherent cluster.

JENSEN HUANG: I would not be surprised if he gets to a gigawatt before anybody else does in one.

BRAD GERSTNER: Yeah. So say a little bit about that. The advantage of being, the builder who isn’t just building the software and the models, but understands what it takes to build those clusters.

JENSEN HUANG: Well, these AI supercomputers are complicated things. The technology is complicated. Procuring it is complicated because of financing issues. Securing the land, power and shell powering it is complicated. Building it all, bringing it all up, I mean, this is unquestionably the most complex systems problem humanity has ever endeavored.

And so Elon has a great advantage that in his head, all of these systems are interoperating and the interdependencies resides in one head, including the financing.

BRAD GERSTNER: And so he’s a big GPT. He’s a big supercomputer himself.

JENSEN HUANG: He’s the ultimate GPU. Yeah. And so he has a great advantage there and he has a great sense of urgency. He has a real desire to build it. And so when will comes together with skill, unbelievable things can happen. Quite unique.

Sovereign AI and the Global Race

BRAD GERSTNER: Something you’ve been so involved in is, I want to talk about sovereign AI. I want to talk about China and the global AI race that’s going on. When I look back at you, 30 years ago, you couldn’t have imagined you were going to be hanging out in palaces with amirs and the king this week. And you’re at the White House all the time. The President has said that you and NVIDIA are critical to US national security.

So when you look at that first, just contextualize for me. It’s hard to believe that you would be in those places if sovereigns didn’t view this at least as existential, as important as maybe we did nuclear in the 1940s.

JENSEN HUANG: Right.

BRAD GERSTNER: We don’t have a Manhattan Project today, at least funded by the government, but it’s funded by NVIDIA, it’s funded by OpenAI, it’s funded by Meta, it’s funded by Google. We have companies today the size of nation states. And thank God for America, right, who are funding something that it appears to me, presidents and kings think is existential to their future economic and national security. Would you agree with that?

JENSEN HUANG: Nobody needs atomic bombs. Everybody needs AI. Well said. Here, here. And so that’s a very, very large difference. AI, as you know, is modern software. That’s where I started from general purpose computing to accelerated computing, from human written code, line of time to AI written code. That foundation can’t be forgotten. We’ve reinvented computing. There’s not a new species on Earth. We just reinvented computing.

And everybody needs computing. It needs to be democratized. Which is the reason why everybody, all of these, all of the countries realize they have to get into the AI world because everybody needs to stay in computing. There’s nobody in the world that says, “Guess what? I used to use computers yesterday. I’m pretty good with clubs and fire tomorrow.” And so everybody needs to move into computer. It’s just being modernized, that’s all.

Number one, it is the case that in order to participate in AI, you have to encode within AI your history, your culture, your values. And of course, AI is getting smarter and smarter so that even the core AI is able to learn these things fairly quickly. You don’t have to start from the ground, from ground zero.

And so I think that every country needs to have some sovereign capability. I recommend that they all use OpenAI, they all use Gemini, they all use these open models. You use Grok. And I think I recommend they all do that. I recommend they all use Anthropic. But they should also dedicate resources to learn how to build AI.

And the reason for that is because they need to learn how to build it not just for language models, but they need to build it for industrial models, manufacturing models, national security, national security models. There’s a whole bunch of intelligence. They had to go cultivate themselves. So they ought to have sovereign capability. Every country should develop it.

BRAD GERSTNER: And is that what you see? Is that what you’re hearing around the world?

JENSEN HUANG: They all realize it.

BRAD GERSTNER: They all realize.

JENSEN HUANG: They all realize it. And they all are going to be customers of OpenAI, Anthropic and Grok and Gemini. But they all really need to also build their own infrastructure. And this is the big idea that what NVIDIA does is we’re building infrastructure. Just as every country needs energy infrastructure, the communications and Internet infrastructure, now every single country needs AI infrastructure.

Working with the Trump Administration

BRAD GERSTNER: So let’s start with the rest of the world. Our good friend David Sacks, the AI czar, doing a heck of a job.

JENSEN HUANG: We are so lucky to have David and Sriram in Washington D.C. and David doing AI in the AI czar. What a smart move by President Trump to put them in the White House. Because during this pivotal time, the technology is complicated. Sriram is the only person in Washington D.C. that I think knows CUDA, which is strange anyways.

But I just love the fact that during this pivotal time when technology is complicated, policy is complicated, the impact to the future of our nation is so great that we have somebody who is clear minded, dedicating the time to understand the technology and thoughtful to help us through that.

BRAD GERSTNER: And it would seem to me, again, going back to the Manhattan Project analogy.

JENSEN HUANG: Yeah, right.

BRAD GERSTNER: That you have a president who understands how existential this is. You have governors like Greg Abbott in Texas who want to remove regulations to accelerate because they understand how important it is. You have Secretaries Wright at Energy and Doug Bergum at Interior and Lutnick at Commerce who also understand how important this is, how pro energy they are. Could you imagine, could you imagine the alternative if we had an administration right now who is not pro energy and want energy to grow in our nation so that we could have AI?

JENSEN HUANG: I find it.

BRAD GERSTNER: I just can’t even think about it.

JENSEN HUANG: I find it ironic that just a couple years ago we were saying, “China’s building 100 nuclear reactors. They’re so far ahead of us,” like, that’s the primitive to AI. But now you have people, when we go to build it, everybody says, “Oh, it’s a glut.” Right. Like, it seems to me that this is something that the government, it is in their interest.

And we have industry and government working together in a way that I haven’t seen in a long time. You’ve been around a long time. You’re very close with President Trump at this stage. Help us understand, like, what is the nature of industry, government relationships. We saw that dinner last week with all the CEOs, you spent a lot of time. Is it unique? Have you seen anything like this in your career over the last 30 years?

JENSEN HUANG: It was hard to go to D.C. in the past. As you know, getting an appointment is almost impossible. President Trump has an open door to leaders who wants to come in and help them understand the future. This is an administration that believes in growth fundamentally. President Trump wants America to grow. If we can grow economically, we will be strong militarily. If we could grow economically, we will be secure. I’ve never met somebody who is secure who’s poor.

Being rich as a nation is an essential part of national security, and he knows that. He also wants America to win the AI race. This is going to be a very long term race and he understands that this is a pivotal time. He wants the technology industry to run. He wants everybody in the world to be built on American technology. These are sensible, logical things.

The opposite is strange to me. If I take everything and I just reversed it. We want our country not to grow. And because we don’t want our country to grow, we don’t need energy because we know we need energy. To grow. And so let’s not have any energy. And in fact, we don’t want our technology industry to lead. He understands that our technology industry is our national treasure.

BRAD GERSTNER: Correct.

JENSEN HUANG: And that technology like corn and steel and things in the past are now such fundamental trade opportunities. It’s an essential part of trade. And why would you not want American technology to be coveted by everyone? So that it could be used for trade?

Export Licenses and Global AI Competition

BRAD GERSTNER: Right. So let’s talk about the Internet, Google spread around the world. We had democratic values spread around the world by way of search. And Google didn’t have to go to Washington to get permission to do it. It just happened. We diffused our technology around the world.

David Sachs has been crystal clear of the need to accelerate export licenses so that the American AI stack wins around the world. Right. We’re talking chips, we’re talking models, we’re talking data centers, et cetera. We know a year and a half ago that wasn’t happening.

JENSEN HUANG: There’s a concept that was called “small yard, tall fence” or something like that. Small yard, tall fence. And the irony of it was it was described in such a way, and it was recommended in policy in such a way. It was a small yard, tall fence around America. That was the strange part.

I think President Trump’s got it right, that we want to maximize exports, we want to maximize American influence around the world. We’re supposed to maximize those things.

BRAD GERSTNER: And do you see those licenses coming? Are you seeing the acceleration in Washington? I know it’s being said at the top, but are you seeing it flow down through government that’s accelerating us around the world?

JENSEN HUANG: Secretary Lutnick was all over it. Great.

Understanding China’s AI Market

BRAD GERSTNER: So now let’s talk about China. You know, what most people may not realize is I think you understand China as well as any leader in the United States.

JENSEN HUANG: We’ve been there for 30 years.

BRAD GERSTNER: Been there for 30 years. What most people don’t realize is up until a couple years ago, you had dominant market share within China.

JENSEN HUANG: 95% market share.

BRAD GERSTNER: 95% market share in the most important thing, arguably. And you have said that our biggest own goal that we as a country could have, under the guise of somehow trying to slow them down, is we’ve unilaterally disarmed. We forced Nvidia out of China, which has allowed Huawei to accelerate on the back of monopoly profits within China.

And I just saw this morning you’re seeing announcements out of Huawei and Baba and others that they’re going to build data centers around the world. Now, Huawei has a three year plan to pass Nvidia funded by the monopoly profits in the biggest AI market in the world.

So it’s looking like your admonition that this is a huge mistake to hand China monopoly markets is coming true. The president said after the ban on H20s now we have a situation where you can sell ships to China, but there’s a 15% export tax. But now it appears that the Chinese, perhaps offended by statements out of the United States are saying no, Nvidia is not allowed to sell here.

Now where do we stand today between Nvidia and China? And can you reiterate what you think we as a country should be doing to put ourselves in the best position to win the AI race around the world?

China’s Competitive Advantages

JENSEN HUANG: We have a competitive relationship with China. We should acknowledge that China rightfully should want their companies to do well. I don’t for a second begrudge them for that. They should do well. They should give them as much support as they like. It’s all their prerogative.

And don’t forget that China has some of the biggest, best entrepreneurs in the world because they came from some of the best STEM schools in the world. They’re the most hungry in the world. 996 as you know, this is a very…

BRAD GERSTNER: Producing the most AI engineers in the world.

JENSEN HUANG: 996 so the audience knows nine in the morning to nine at night, six days a week. That is their culture. We’re up against a formidable, innovative, hungry, fast moving, under regulated. People don’t realize this. They are very lightly regulated, less regulated.

BRAD GERSTNER: Ironically than we are in the capitalist system.

JENSEN HUANG: That’s right. People think that they’re centrally governed. But remember the genius of China was distributed economic systems. And so all of these 33 provinces and all the mayor economy has driven enormous amount of internal competition, internal economic vibrancy which of course has some of its side effects.

But this is a vibrant entrepreneurial, high tech, modern industry. And to one, some of the things I heard they could never build AI chips that just sounded insane. Two, that China can’t manufacture. China can’t manufacture. If there’s one thing they could do is manufacture. Three, they’re years behind us. Is it two years? Three years. Come on. They’re nanoseconds behind us.

BRAD GERSTNER: Nanoseconds.

JENSEN HUANG: Yeah, they’re nanoseconds behind us. And so we’ve got to go compete. We’ve got to go compete.

The Path Forward with China

And so the question then becomes what’s in the best interest? What’s in the best interest of China, of course, is that they have a vibrant industry. They also publicly say, and rightfully I believe they believe this, is that they want China to be an open market, they want to attract foreign investment. They want companies to come to China and compete in the marketplace.

And I believe that they, I hope, I believe and I hope that we return to that in our context, answering your question, what do I see in the future? I do hope, because they say it, their leaders say it, and I take it at face value and I believe it because I think it makes sense for China that what’s in the best interest of China is for foreign companies to invest in China, compete in China, and for them to also have vibrant competition themselves. And they would also like to come out of China and participate around the world. That is, I think, is a fairly sensible outcome.

And what we need to do as a country is to enable our technology industry, which today is the… I’m privileged to be working in an industry that is our national treasure. We have to acknowledge it is our national treasure. It is our best industry. It is our single best industry.

Why would we not allow this industry to go compete for its survival, for this industry to go and proliferate the technology around the world so that we could have the world be built on top of American technology so that we can maximize our economic success, maximize our geopolitical influence, maximize this technology industry during such a vibrant time, such a pivotal time to allow it to thrive.

Addressing the Skeptics

BRAD GERSTNER: The skeptic says Jensen just wants to sell more chips and if he can sell them to China, great, he’ll sell them to China. He doesn’t care about what that means for America. That’s the skeptic.

JENSEN HUANG: Can I just address the skeptics? Just because I want America, ecosystem and economy to grow, doesn’t make me wrong. Right? So first of all, everything that has been said so far that’s been made up so far about China has proven to be wrong. The facts are just wrong. The ground truth is wrong. And so just because we want America to win, just because we want this industry to grow, doesn’t make me wrong? Correct.

BRAD GERSTNER: And I think anybody who knows you, and now the president, certainly myself, you deeply care about the country, you deeply want the United States of America to win the global AI race. You just happen to believe, and I think you have as much experience or more experience than anyone that inures to our advantage the probability of us winning the global AI race actually goes up if you are competing in China because it allows us to tap into half of the world’s AI engineers, keeping them in this ecosystem.

And let’s be clear with the companies we’re talking about here, ByteDance, Alibaba, et cetera, these are companies that are largely owned by American investors.

JENSEN HUANG: Yeah, right, right.

BRAD GERSTNER: Like, these are global companies that are building recommender engines and, by the way…

JENSEN HUANG: Extraordinary technologies, incredible companies.

BRAD GERSTNER: And so I think, and I’m hopeful that the argument that you’re making vis a vis China, which is a harder argument than diffusion to the rest of the world. I understand that. And that’s why I thought when the President said, you know, I don’t know, it’s a flip of a coin, maybe Jensen’s right, maybe the other guys are right, but if Jensen’s willing to put a little bit of 15% into the US treasury as a hedge on that, then I’ll go for it.

But I was really disappointed on the heels of that. I think if the Chinese feel like they’re being taken advantage of that we’re going to send them chips that are, you know, 10 years old or something, then I get why they had that response.

JENSEN HUANG: H20 is really quite spectacular still. And of course, it’s not as good as Blackwell, and I get that. Look, you know, I’m patient and I believe that they’re wise, they’re thinking through their situation. They have larger agendas to deal with vis a vis the United States. There are a lot of discussions going on, but I’ll come back to the ground truth, fundamental truth.

I believe that is in the best interest of China, that Nvidia is able to serve that market and compete in that market. I fundamentally believe it’s the best interest of China. It is, of course, fantastic in the fantastic interest of the United States. It is fundamental. But those two truths can coexist. It is possible for both to be true, and I believe it is both true.

And so I’m rather, you know, even though I tell all of our investors that our guidance includes no China, and I appreciate all of our investors to include no China in any of our guidance, we’ve got plenty of growth opportunities outside and we’ve got… All of that is true, it doesn’t make China not important to us. It’s very important to us.

Anybody who thinks that the Chinese market is not important has their head deep in the sand. And so this is one of the most important markets in the world, smart markets, smart people doing smart things. And we want to be there. And I think it’s in the best interest of both countries that we are there.

And so I think when I take a step back, I am confident that ultimately the wisdom will prevail. Yes. I’ve always been confident that wisdom prevails. I’ve always been confident that truth prevails. And it’s taken me this far, and I believe that to be fundamentally true now. And so these things will get sorted out and we will have the opportunity to go compete in that China market.

H1B Visa Policy Discussion

CLARK TANG: I’m not very political, but very topical is the administration’s decision to charge $100,000 per H1B visa. You’ve spent a lot of time with the president. You’ve called him our secret weapon in AI. I also know you want to recruit the best and brightest to our country. So how do you think about the decision to charge $100,000 per H1B visa? Does this make it easier or harder to recruit talent? And perhaps it’s a little different for large companies or small companies. How do you think about it?

JENSEN HUANG: I’m going to start with, it’s a great start. Hold on.

BRAD GERSTNER: You said it’s a great start.

JENSEN HUANG: It’s a great start. I’m just going to start there. And the reason for that is this. That implies I hope it’s not the end, but I think it’s a great start. I just hope it’s not the end.

Here’s what I fundamentally believe. America has a singular brand reputation that no country in the world has. And no country in the world is in the position or in the horizon to be able to say, “Come to America and realize the American dream.” What country has the word “dream” behind it? Yes, it’s part of its brand. We are utterly singular.

And you’re talking to somebody who represents the American dream. My parents didn’t have any money, sent us over here. We started from nothing. You guys know I bus tables, wash dishes, clean toilets, and here I am. This is the American dream.

President Trump knows that we want legal immigrants. There’s a difference between legal immigrants and illegal immigrants. But the idea that it’s a country that’s free for all doesn’t make sense. And so now the question is, how do we go from the idea that we want to protect, fundamentally the American dream to dealing with illegal immigrants at such a large scale? How do we find a logical, pragmatic solution?

So the idea that we put $100,000 price tag on H1B probably sets the bar a little too high. But that’s the first bar. It at least eliminates illegal immigration, and that’s a good start.

BRAD GERSTNER: How does it eliminate illegal immigration?

H2: Immigration Policy and the American Dream

JENSEN HUANG: Well, at least it eliminates abuse of the H1B. At least. And that’s a good start. And at least we can have a conversation. So one of the things that we know about President Trump, he’s a good listener. He actually listens. I mean, he listens to you, he listens to me, and he doesn’t have to. And he listens to a lot of people, and he’s integrating a lot of information.

And this is obviously a very complicated issue. And so I think that this is a fine start. It’s a fine start, but I’m not confused that anyone in the administration, anyone in the White House is confused that legal immigration is the foundation of the American dream and is the ultimate brand that we want to protect, and that’s the future we want to protect.

BRAD GERSTNER: And I would also say, it seems to me that certainly Sachs and other people in the administration know that we have to recruit the world’s best and brightest. We should not sacrifice the greatness of the brand. Charging $100,000, or let’s say, you know, it got lowered to 50 or whatever the case is. It does seem like it tilts the playing field in favor of big companies who can effectively sponsor all these people. And it’s more challenging for the startup ecosystem where people are already super expensive, and now I got to pay this fee on top of it.

JENSEN HUANG: It also has an unintended consequence. It might accelerate investment outside United States. And so there are unintended consequences. But like I said, start somewhere, move towards the right answer. You know, oftentimes people want to go directly from a wrong answer, wrong condition. We don’t want this condition where we’re at. And directly jump to the perfect answer is hard to find. Just start somewhere. It’s the entrepreneurial way.

BRAD GERSTNER: It’s important to me. And the president talked about before, when he was running for office, he wanted to staple a green card to the diplomas of these STEM students. So smart people coming to the United States from China, AI researchers studying at Stanford, like, we want to keep them here. We want to, and by the way, if their families can’t get here, they’re going to leave after a few years. So you might even want to make it easier for their families to come here and others. Are you confident that we have a strategic plan in this administration? You know, this is a start, but your conversations they give you confidence that we have a broader strategic plan to make sure we’re recruiting the best and the brightest.

JENSEN HUANG: I don’t know that I have an answer for that, but I understand that where we’re at is not where we want to be. And I don’t think anybody’s lost their focus on the American dream. The importance of immigration, the importance of attracting all of the world’s best talent to the United States, create the conditions for them to stay here.

There are things that are done from time to time that works against what I just described. Making foreign students uncomfortable. And being here threatens the brand. Threatens the brand. Let’s not forget that it’s okay to be competitive with China, but be careful not to be tough on Chinese. And so we need to make sure that that slippery slope isn’t crossed. You know, and so there are all of these things that goes along with finesse and nuance.

But the fact of the matter is we know where we want to be. We know we’re in a difficult situation. We don’t want to be here. And President Trump doesn’t have much time to move us in that direction. And so to the extent that we move in that direction, I believe it’s a good start.

BRAD GERSTNER: Agreed?

JENSEN HUANG: Yeah.

H2: The Decline in Chinese AI Talent Coming to America

BRAD GERSTNER: I heard from a Chinese researcher leading one of our leading labs in the US that three years ago, 90% of the top AI researchers graduating from universities in China wanted to come to the United States and did come to the United States to work in our leading labs. And he guessed that today that’s closer to 10 or 15%.

JENSEN HUANG: Right.

BRAD GERSTNER: So seen a precipitous drop.

JENSEN HUANG: That’s precisely a concern that we have.

BRAD GERSTNER: So have you seen this? Have you? You know, you’re paying attention to both markets. Do you see this? And what are the things we need to do in order to reverse that?

JENSEN HUANG: Definitely see a greater concern of Chinese students who come here and remain here, or many of them who come here for school and are thinking about going elsewhere, many of them thinking about Europe. So I think we need to be super, super concerned about this. This is a source of existential crisis. This is definitely the early indicators of a future problem.

You know, smart people’s desire to come to America, and smart people, smart students, desire to stay. Those are what I would call KPIs. Early indicators of future success.

BRAD GERSTNER: I think of it a bit like the Warriors. You know, if they have an advantage of recruiting all the best players in the NBA, then they can continue to win championships. But the second that recruiting pipeline.

JENSEN HUANG: Right.

BRAD GERSTNER: Because the brand of the warriors gets diminished or something else happens, then they’re not going to be able to recruit the best future players, and you’re not going to win championships. And when I. You talk about the American dream so eloquently that being Brand USA.

JENSEN HUANG: Right.

BRAD GERSTNER: The right to come here and to do what you’ve done and, you know, so I hope that the feedback to this administration. It’s not just the administration, it’s also just how we as a country talk about immigration.

JENSEN HUANG: That’s right.

BRAD GERSTNER: This needs to be the place that welcomes the best and the brightest, that attracts as a strategic plan for recruiting the best and the brightest and making sure that this is the place that they want to work.

H2: China Hawks and American Confidence

JENSEN HUANG: As you know, there’s a phrase, and I didn’t hear about this phrase until just a few years ago. “China Hawks.” And apparently, if you’re a China Hawk, you get to wear that label with pride. It’s almost like a badge of honor. It’s a badge of shame. There’s no question. It’s a badge of shame.

There’s no question that although they want what’s in the best interest of our country, and we all want what’s in the best interest of our country, destroying that pipeline of the American dream is not patriotic. They think they’re doing the right thing for our country, but it’s not patriotic. Not even a little bit.

And so we need to continue to be the great country we are. To have the confidence of a great country. And to have the confidence of a great country and have somebody who wants to compete with us and to have the attitude, “bring it on.” Bring it on. Because I believe in our people. I believe in our people. I believe in the people that are here. I believe in our culture. I believe in our country. I believe in our system. Bring it on.

BRAD GERSTNER: And is it your take that that’s where the president is? Like, he’s a pragmatist. He’s a believer in the growth and the ability of the United States to compete. It seems to me that’s where he is.

JENSEN HUANG: There’s no question President Trump is the “bring it on” president.

BRAD GERSTNER: Right. And he doesn’t seem to me like the reason I’m confident. And I’ve said on this pod that I think he’ll get a big deal done with China.

JENSEN HUANG: I really, really do hope so. And I think he speaks positively, with great respect and great eloquence about his relationship and the importance of China. Not one time have I ever heard him say the word “decouple,” which we heard a lot in the last administration. You can’t decouple against the single most, the two most important relationships for the next century. That doesn’t make any sense at all. Decoupling is exactly the wrong concept.

H2: Deal Making vs. Dogmatic Approaches

BRAD GERSTNER: I mean, it seems to me he and Scott Bessen are saying, listen, we need to make America great. We need to re-industrialize America. We need to balance and make sure that we have fair trade, that we protect industries, that we need to help build, that China helps us do that, recognizing that we have helped them do it over the course of the last 25 years.

But that ultimately he said, the best way to understand me is I’m a great deal maker. I make deals. Whereas I think in other camps there are people who are iconoclastic or dogmatic. You know, it’s the Mearsheimer view of China that there’s a great power struggle, one must win and one must lose.

JENSEN HUANG: Versus this idea, this idea that every country has to look exactly like ours. We want diversity.

BRAD GERSTNER: You want America to win, but that doesn’t have to come at the expense of poking an eye and telling somebody else they have to lose.

JENSEN HUANG: Because we’re that confident. Yeah, we’re that confident. Because we’re that mighty. Because we’re that incredible. I’ve got no trouble, as you know, I’ve got no trouble working with all my colleagues in the ecosystem. And notice, we just did the ultimate deal, partnering with Intel, a company that spent most of its life trying to put us out of business. And I had no trouble partnering with them.

And the reason for that is because, number one, bring it on. And number two, the future is so much greater. It doesn’t have to be all us or them. It could be us and them. But nonetheless, bring it on.

H2: The American Dream and the Right to Rise

BRAD GERSTNER: You know, you mentioned something that’s profoundly important to both of us. You and I’ve talked a lot about this, the American dream, you know, and it was, I think, Abraham Lincoln who said, fundamental to the American dream is the right to rise.

JENSEN HUANG: Yeah.

BRAD GERSTNER: The belief that your kids can do better than you did.

JENSEN HUANG: That’s right.

BRAD GERSTNER: And you, you’ve experienced the right to rise. We’ve all experienced the right to rise in America.

JENSEN HUANG: You go to Wikipedia, you can look up American dreams. My picture.

BRAD GERSTNER: And yet we live at this time where, because of the nature of these technological systems, we have companies that are going to be worth 10 trillion. We’ll probably have individuals that are worth a trillion. Those are the incentives that give people the encouragement to rise. But at the same time, when we head into this age of abundance, something that I was deeply worried about was that too many people get left behind.

JENSEN HUANG: Right.

BRAD GERSTNER: And they feel left out and left behind. So it makes sense for them to attack this system of capitalism. Something that you and I worked on together, and I’m deeply grateful for, was the idea of Invest America, that we have to start every kid at birth on the capitalist right to rise journey. Give them a thousand bucks in great companies like Social Security and OpenAI, et cetera, and they benefit. As the country wins, they win. And they own it individually. They can see it on their.

JENSEN HUANG: Every kid is a shareholder in the future of America.

H2: Invest America Initiative

BRAD GERSTNER: So on the 200, because of your support and I wanted to take the chance on this podcast and the support.

JENSEN HUANG: Well, I want to thank you for starting it, for driving it. What a great idea.

BRAD GERSTNER: And you know, so this.

JENSEN HUANG: You’re a genius. Please.

BRAD GERSTNER: This passed in the big beautiful bill. Most people don’t even realize that yet. Starting in 2026, every child born forevermore in the history of this country will start off with an investment account at birth, seeding a thousand bucks in the best American companies. And your company has agreed to add to the accounts of not only the kids who work for your employees, but maybe even kids of others. I’m going to adopt schools and lots of philanthropists and companies. We think every company across America.

JENSEN HUANG: Wonderful way for companies to give back.

BRAD GERSTNER: As part of the 401k, this seems to me to be part of the change in the social contract that needs to occur. Because if we’re seeing this exponential progress, we know that the evolution of government and the social contract needs to keep up with it. Obviously, President Trump and bipartisan group in the House and Senate pass this into law.

So maybe just talk to us a little bit. When you think about the pace and magnitude of changes that are coming. I know you believe it will be a net good, but there are also going to be a bunch of people displaced along the way. We probably need things like this and other things.

Re-industrializing America and the Future of Work

BRAD GERSTNER: In order to bring everybody along for the journey.

JENSEN HUANG: There are several things that President Trump has done, and let me just start there, that are incredibly good for bringing everybody along. The first thing is re-industrializing America. President Trump, Secretary Lutnick, they’re all behind that, all the work that they’re doing encouraging companies to come build here in the United States, investing in factories and reskilling and upskilling that skilled labor workforce. This is incredibly valuable to our country.

The idea that we no longer make it only that you get a PhD or you go to one of the great schools, and only in that way can you build a great life and deserve to have a great living – we’ve got to change all that. That doesn’t make any sense. We love craft. I love people who make things with their hands. And we’re now going to go back and build things – magnificent, incredible things. I love that. That’s going to transform America. There’s no question about that.

There’s a whole band of an economy, a whole band of society that has been largely left behind because we outsourced everything. Right now, I’m not suggesting we insource everything. All the people arguing about manufacturing tennis shoes and toothpicks – that’s denigrating a perfectly good discussion into some insane level. We’ve got to recognize that re-industrializing America is just fundamentally going to be transformative.

BRAD GERSTNER: And aspirational.

JENSEN HUANG: Oh, it’s fantastic.

BRAD GERSTNER: Elon taking us to Mars, watching spaceships caught with chopsticks out in the sky. This is not only great for the industrializing base of America, it’s aspirational.

AI as the Great Equalizer

JENSEN HUANG: Fantastic. That’s right. And then, of course, AI – it is the greatest equalizer. Just think, everybody can have an AI now. The ultimate equalizer. We’ve closed the technology divide. Remember the last time that somebody had to learn, wanted to use a computer for their economic or career benefit? They had to learn C or C++ or at least Python. Now they just have to learn human.

And if you don’t know how to program an AI, you tell the AI, “Hi, I don’t know how to program an AI. How do I program an AI?” And the AI explains it to you or does it for you. It does it for you. And so it’s incredible, isn’t that right? And we’ve now closed the technology divide with technology. This is something that everybody’s got to engage. OpenAI has 800 million active users. It really needs to be 6 billion. It really needs to be 8 billion soon.

I think the AI will change tasks. The thing that people confuse is there are many tasks that will be eliminated. There are many tasks that will actually be created. But it is very likely that for many people, their jobs are gainfully protected.

For example, I’m using AI all the time. You’re using AI all the time. My analysts are using AI all the time. My engineers, every one of them, use AIs continuously. And we’re hiring more engineers, we’re hiring more people, we’re hiring across the board. The reason for that is because we have more ideas, we can now go pursue more ideas. The reason for that is because our company became more productive, and because we became more productive, we became more rich. Because we became more rich, we can hire more people to go after those ideas.

The concept that AI comes along and therefore there’s going to be a mass destruction of jobs starts with the premise that we have no more ideas. It starts with the premise we have nothing left to do. Everything we’re doing in our lives today, this is the end. And if somebody else were to do that one task for me, I have one task less now. I have to sit there and wait for something, wait for retirement, sit on my rocking chair. That idea doesn’t make sense to me.

I think that intelligence is not a zero-sum game. The more intelligent people I’m surrounded by, the more geniuses I’m surrounded by, surprisingly, the more ideas I have, the more problems I imagine that we can go solve. The more work we create, the more jobs we create. And so I think for – I don’t know what the world looks like in a million years, that’s going to be left for my children. But for the next several decades, my sense is that economy is going to grow, lots of new jobs are going to be created, every job will be changed, some jobs will be lost, and we’re not going to be riding horses on streets. And those things will be fine.

Understanding Exponential Progress

BRAD GERSTNER: Humans are famously skeptical and terrible at understanding compounding systems. And they’re even worse at understanding exponential systems that accelerate with size. We’ve talked about exponentials a lot today. The great futurist Ray Kurzweil said, “In the 21st century, we’re not going to have 100 years of progress. We’re likely to have 20,000 years of progress.”

JENSEN HUANG: Right, right.

BRAD GERSTNER: You said earlier we’re so fortunate to be living at this moment and contributing to this moment. I’m not going to ask you to look out 10 or 20 or 30 years because I think it’s so challenging. But when we think about 20, 30…

JENSEN HUANG: Things like robots – 30 years is easier than 2030.

BRAD GERSTNER: Oh, really?

JENSEN HUANG: Yeah.

BRAD GERSTNER: Okay. So I’ll grant you license to go out 30, as you think, out over the course of… I like these shorter time frames because they have to marry bits and atoms, bits and atoms. The hard part of building this stuff, right, because everybody’s saying it’s going to happen.

JENSEN HUANG: Interesting, but not helpful.

BRAD GERSTNER: Exactly. But if we have 20,000 years of progress, reflect on that statement by Ray. Reflect on exponentials and how all of our listeners, whether you work in government, whether you’re in a startup, whether you’re running a big company, need to be thinking about the accelerating rate of change, the accelerating rate of growth and how you will be co-intelligent in this new world.

The Future of AI and Robotics

JENSEN HUANG: Well, there are a lot of things that many people have already said, and they’re all very sensible. I think in the next five years one of the things that is really cool that’s going to get solved is the fusion of artificial intelligence and mechatronics, robotics.

And so we’re going to have AIs that are going to be wandering around us and we all know that we’re going to all grow up with our own R2-D2 and our duty to remember everything about us and coach us along the way and be our companion. We already know that. And the idea that every human will have their own GPUs associated with them in the cloud and that there are 8 billion people, 8 billion GPUs – that’s a viable outcome.

BRAD GERSTNER: And each having their own model that’s…

JENSEN HUANG: Fine-tuned for them, fine-tuned for them. And that AI’s in the cloud is also embodied and a whole bunch of it’s embodied in your car, it’s embodied in your own robot, it’s everywhere with you. And so that future is a very sensible thing.

The idea that we’re going to understand the infinite complexity of biology and understanding the system of biology and how to predict it and have digital twins of everybody, our own digital twin for healthcare, like we have a digital twin for shopping at Amazon. Why wouldn’t we have our digital twin for healthcare? Of course we would.

A system that predicts how we’re going to age, what disease we’re likely going to have and anything that’s about to happen, maybe even next week or tomorrow afternoon and predict it early. Of course we don’t have all that. And so I think all of that is a given.

Getting on the Exponential Train

I think the part that I’m asked a lot by CEOs that I work with about now, given all of that, what happens? What do you do? And this is a common sense of things that move fast. If you have a train that’s about to get faster and faster and go exponential, the only thing that you really need to do is get on it. And once you get on it, you’ll figure everything else out along the way.

To predict where that train’s going to be and try to shoot a bullet at it or predict where that train’s going to be and it’s going exponentially faster every second and go figure out what intersection to wait for it – that’s impossible. Just get on it while it’s going kind of slowly and go exponential along the way.

BRAD GERSTNER: A lot of people think this just happened overnight. You’ve been at this for 35 years. I remember hearing Larry Page say probably around 2005 or 2006, that the end state of Google will be when the machine can predict the question before you even answer it, before you even ask it, and give you the answer without having to look.

JENSEN HUANG: Right.

BRAD GERSTNER: I heard Bill Gates say in 2016…

JENSEN HUANG: Because contextually, you must be asking about… Well, you must be wondering about that, right.

BRAD GERSTNER: I heard Bill Gates say in 2016 when somebody said, “Haven’t all the things been done? We’ve had the Internet, we’ve had cloud, we’ve had mobile social, et cetera.” He said, “We haven’t even started.”

JENSEN HUANG: So what do you think?

BRAD GERSTNER: Why would you say that? He said, “We won’t even begin until machines go from being dumb calculators to beginning to think for themselves, to think with us.”

JENSEN HUANG: Right.

Leadership in the AI Era

BRAD GERSTNER: That is the moment that we’re in, I think, to have leaders like you, leaders like Sam and Elon, Satya, et cetera, it’s such an extraordinary advantage for this country. And to have the cooperation that we see between a system of risk capital that I’m part of, which can provide the risk capital for people to do… We’re not relying on government having a Manhattan Project. We can actually do this ourselves and together for the benefit of the country.

JENSEN HUANG: It’s an extraordinary time, and at a scale that’s unimaginable.

BRAD GERSTNER: Right, right. It’s an extraordinary time. But I also think one of the things that I’m just grateful is that we have leaders who also understand their responsibility to the fact that we are creating change at an accelerating rate. And we know while it will most likely be great for the vast majority, there’ll be challenges along the way, and we’ll deal with those as they come and raise the floor for everybody and make sure that this is a win, not just for some elite plutocrats at the top hanging out in Silicon Valley.

JENSEN HUANG: And don’t scare them, bring them along.

BRAD GERSTNER: It’s a win.

JENSEN HUANG: Don’t scare them, bring them along.

BRAD GERSTNER: And we will. So thank you for that.

JENSEN HUANG: Exactly.

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