Jensen Huang：末日论骗局、超级智能已至与 AI 的未来（嘉宾：President Trump）

Some people call it vision. Vision is an awfully big word to me because I I believe first of all vision matters.

We preempted the weekly show. And there's only three people we preempt the show for. President Trump, Jesus, and Jensen.

The number one podcast in the world.

That's Jensen Wong.

He's the founder, president, CEO of Nvidia.

Whether you know it or not, his decisions are shaping your future.

Nvidia is the most important stock in this market. Jensen is arguably the best executive in history.

Revenue exploded 97% year-over-year.

Not only is demand already strong, is actually accelerating.

Nvidia is the only computing platform that is a full stack AI factory. A GPU is like a time machine because it lets you see the future sooner. And if we could see the future and we can predict the future, then we have a better chance of making that future the best version of it.

Please welcome Jensen Hang. Oh, we got a standing O on the way in.

Oh, come on.

Standing O.

Standing O on the way in.

There's our guy.

Ladies and gentlemen, GPU Jesus.

They love you.

Thank you. I love you back. Number one podcast in the world.

In the world.

Absolutely.

Wow, we like the new jacket.

Well, you auctioned the opening.

I just felt you guys needed some energy. I know we're talking about serious stuff here, but we need to talk about it with energy.

1. Thoughts on Dario's blog, Frontier Labs calling to slow down AI, and Doomer psychology

Let's start with Dario's essay—

Which one?

Let's start with Dario's essay because—

Was Hemingway involved?

Actually, did anybody run it through Pangram? I don't even know how much of it was AI-assisted, but that was a pretty incredible thing. A lot of people were surprised by the coalescing of the frontier labs around the essay itself.

Jensen, unpack what happened, how you read it, and how you interpreted it. Then we'll get into some of the details that were inside of it. But maybe just the high-level thoughts to kick it off.

First of all, there was a lot of stuff in there. There's a part about safety, which we have to take very seriously. Safety is paramount. Safety and leadership are not false choices. You're able to innovate quickly, you're able to execute quickly, and America is able to lead and do it safely. I think those are false choices, but safety is obviously important.

There's a matter of internal control that I think he was speaking to. Obviously, the Kokotajlo whistleblower is a very serious matter. Whenever you have a whistleblower, you have to take it very seriously. I thought Kokotajlo had great courage to put out what his concerns were. Even then, there were some issues that were conflated within that.

I think the whistleblowing is fine. I think the scientific prediction about the future is less aligned because it's not grounded in science, obviously. It was expressed by a scientist, but it was obviously not grounded in science, and so I take issue with that. But obviously, the whistleblower part of it, and all of the pausing and pacing, are voluntary things that they could do if they feel that their company is out of control.

If Kokotajlo saw something, obviously we don't know what Kokotajlo saw—

But if he saw that the company was out of control—

Maybe it's a transition from research to engineering. As you know, these labs are transitioning from research to engineering. Extraordinary talent, extraordinary engineering—but obviously engineering is different from research. Maybe that transition is clumsy. We don't know what he saw, and ultimately only he knows.

If there was a matter of a lack of control, that's a different topic. How should the government deal with it? Now all of a sudden, regulation—I mean, it just covers everything in one blog.

Can you help us unpack this? We tried to play this game on the pod this week, and it was difficult. How do you describe it? My mom calls me and says, “Chamath, what is this whole civilizational-death thing?” I don't know how to explain it to her.

When you have very smart people quantify it, I think that's probably what's perturbing to some people. They're like, “What does that mean, a 10% chance of extinction?” Nobody knows how to explain that to the average person or how that's even possible.

First of all, we shouldn't, because it's made up. These are well-educated people—they're called researchers—obviously working in a lab. The confluence of these words, and then the prediction, is alarming and troubling. It shouldn't be done. It's irresponsible.

Now, the fact of the matter is, let's go back and look at the real facts. There was a prediction that, in 5 years' time, radiology would be completely taken over by artificial intelligence and there would be no radiologists in the world. That has proven to be exactly the opposite. We need more radiologists than ever in the world.

However, AI has taken over radiology completely, which is great. It has automated scan reading, which is great. There was a prediction that, within 6 to 12 months—wasn't it just last year?—90% of code would already be generated by AI. That has turned out to be wrong.

Within 6 to 9 months, it was predicted last year that 50% of entry-level jobs would be wiped out. That has proven to be wrong. Let's see what else has proven to be wrong. All of these predictions have been wrong.

That GPT-2 would be too unsafe to release. That Llama 3 would be too unsafe to release.

Yeah, we've heard the prediction that half of white-collar jobs would be gone next year—the jobs apocalypse.

We have to take accountability for all of the stupid predictions that were made.

Somebody has to take accountability.

Somebody has to. We ought to just keep track of all that. Of course, people do, and they remind us that those predictions are inconsistent with, ultimately, America winning the AI race.

The short form for that is that some people say, “Trust the experts.” They used COVID as the analog, which again started with researchers—educated people who had an asymmetric awareness of the thing that the rest of us did not—saying things that ultimately turned out, as we found out from the facts, not to be true.

There's this war happening right now between the “trust the experts” movement and the “let's just look at the actual history of these predictions and think more methodically” movement. Where is this coming from? It's coming from inside the places that are actually making it. What do you think is the psychological makeup, or what is the real incentive? Maybe it's a business incentive, maybe it's a political incentive. Can you guess, or how do you think about why they're doing this?

First of all, I have to tell you, these are some of the most consequential companies in history. They have extraordinary engineers, extraordinary researchers, and they do really fantastic work.

On the one hand, I work very closely with them as companies. On the other hand, we have to have conversations like this in public, and it's really unfortunate. I think these companies really ought to be built the way that we used to build companies, which is in silence.

Right. You don't allow anybody in your organization to speak for the entire organization, especially when they're having a bad weekend or they rage-quit. They're not allowed to tweet on your behalf or on the organization's behalf.

No, because that's what they decided when they came to work for us. We told them, “This is the way you behave when you work in our company.” If you like the culture of our company—and, as you know, the NVIDIA culture and the NVIDIA employee base are incredibly happy—they like the fact that the company is consistent and stable, that our core values are consistent with taking care of families and creating the conditions by which they can do their life's work.

We do meaningful work, we do it as quietly as we can, and we contribute to everybody else's success, which we're very proud of. Those core values attract people. But when you come and work in our company, there are also some things that we don't appreciate you doing.

For example, we don't welcome political discourse inside our company. Take it home. Talk about politics outside the company.

Yeah.

The company is apolitical. We're bipartisan. We want America to succeed, and whatever government is in place, we'll do everything in our power to help America succeed.

2. Sensible AI regulation and RSI

We tell people to have discourse about race, religion, politics, and all of that stuff outside the company. It's not for us.

In terms of AI regulation, more narrowly, Satya was here this morning. What he said was, before we talk about regulation that could really stymie things, why don't we just get some basics right? Why don't we get measurement right? Why don't we get standardization right?

Where do you land on—

Get engineering right?

Get the engineering right. Right. Translate the research in a more predictable way so that we're not fear-mongering. Keep it inside until we're ready to expose it. What do you think the right response is? You know, Demis had a proposal, which was sort of this more FINRA-like organization. It's not clear what Dario wants, this transnational, mutated thing that has some sort of control. Where do you land on this? What do we need right now?

Regulation should solve actual problems. And so the question is: What actual problems have we encountered? If you look at the actual problems, all of them so far have come from the labs. The reason for that, and in their defense, is because they have the most compute.

The reason for that is because they're trying to solve the frontier problems. And so, in their defense, it's sensible that the frontier labs will be where the most danger comes from. It is unlikely that a high school student did something because they simply won't have enough compute. It's unlikely that a startup will be the reason, because they won't have enough compute.

In fact, you could look across the planet and everybody won't have enough compute, with the exception of the frontier labs. And so now the question is, if you look at what actually happened, they're doing pioneering work. It's really very hard. They're transitioning from research to engineering.

I could imagine it, and they're obviously building some of the most consequential technology and companies in the world. They're building their company, their culture, the technology, engineering, and products all at the same time. And so I can understand it's a little bit hair-on-fire. Nonetheless, the 4 incidents from 1 lab and the 1 giant incident from the other lab—the first thing that you have to do is root-cause the problem from an engineering perspective.

What happened? What could we have done differently, and what are we going to implement and institutionalize, whether it's technology, methods, or processes, to make sure that we don't let it happen again? I would bet you money that in every single one of those cases, it's within their control in the future to prevent it. I'm sure those 4 incidents won't happen again. I'm sure they root-caused it and fixed it.

I'm sure they now have much better technology for sandboxes, runtimes, monitors, and continuous monitoring. I'm certain they have much better technology now. The alternative is also unlikely, which is for them to say, “Look, we had these incidents. After we're done analyzing it, we came to the conclusion that we don't know what happened, we have no idea how to control it, and we're asking society for help.”

Yeah.

Now, if that's the case, then we ought to have a bunch of companies with engineers send engineers in. I mean, we should advise them if we can, but I doubt it. I think they have extraordinary people. They've got this handled.

But we're not operating in a vacuum. David, last night you informed me that there is a Chinese lab, the makers of GLM, that are going to put 3 billion toward a recursive self-improvement run. So maybe you could tee that up for J.

Well, that's what was announced. Yeah. Zhipu AI—the founder just raised 5 billion—and said that one of their priorities is going to be trying to get to recursive self-improvement: AI that trains the next AI, and trying to automate as much of that as possible. Yeah, I think that—

Well, this is the new sexy phrase. As you guys know, RSI is a combination of a system of ideas. It starts with in-context stuff. It starts with skills. It starts with reflection. It starts with reinforcement learning and synthetic data generation. These are all very sensible ideas that cause AI to get better at solving a problem over time.

You could also have low-rank adaptation. All of that stuff doesn't include the weights. You could actually improve the weights, and it's called LoRA. LoRA could be improved with synthetic-data generation and reinforcement learning, enhancing it without training the base model itself. Then, over time, you could train the base model again with all of that experience.

I think it's a sensible thing that you're going to use the technology to enhance productivity for all kinds of tasks, including building AI. I think that's a very logical idea, and I'm certain that everybody is using it to some degree. It's just that this phrase is now being used to weaponize the technology in some way and maybe to turn the—

As if it's going to spiral out of control is the impression they're trying to give. But you don't believe that's real?

No. No, of course not. The reason for that is because you could RSI all day long inside your company, but when you release a product, you've got to evaluate it, don't you? You have to test it again, don't you? You have to make sure that there's no regression, right?

3. Hugging Face acquisition, future of Open Source, and the race with China

The basic process of control—these labs are going to, as they move from labs to engineering, have much better control, right? And when they have much better control, that comes from methods, knowledge, practice, tools, and technology. All of those things lead to better control, verification, and evals. It's going to enable RSI to be done inside the company and good products to be released outside.

Let's talk about open source for a second. This Hugging Face—we were communicating about this, and I said it's going to be one of the most consequential acquisitions. I don't even want to call it a transaction, because I think it's more important than that. Give us your first-principles explanation of open source versus closed source versus open weights, and how the ecosystem should fit together over time.

The world needs both closed models and open models. I use as many closed models as I can. This weekend, I used 4 of them, and they work terrifically. They're frontier. They're a great experience. They work incredibly well. They're getting better all the time.

The way I think about closed models is kind of like bottled water. Water is free, you guys. I don't know if I've told you guys, but water is free. I don't want to burst everybody's bubble, but water's free. This morning, I used a lot of free water taking a shower.

And so you use the right water in the right places. This is no different from electricity. This is no different from all kinds of commodities that we use in the world. You need both.

Now, in the case of open models, the reason why you need them is because it could be for sovereignty reasons, privacy reasons, or proprietary-technology reasons. Look at the facts. In the last 6 months, $400 billion of venture funding went into AI-native companies. Eighty percent of them use open models. If not for open models, how could they build their dream, right?

Because their dream could be different. Obviously, it'll be different from the frontier labs' dreams. America has so many different ways to innovate. That's one of our core strengths: great ideas just coming out of the fountain. And so open models enable that.

Open models enable every single—if we want to win the AI race, it's not about a few technology companies winning the AI race. It's about every company in America. Every company, every industry, every researcher, every teacher, every student, every startup—everybody wins.

Some of them will use closed models. A lot of them will use open models.

There's 10 million

Does it matter?

Well, let me just ask: Does it matter if the open models come from China or the U.S.?

We're doing everything we can to make a contribution to open models. However, the moment you download one, it's yours. Probably the vast majority of the world's contribution to open source today is coming from China. They just have a lot more engineers. They produce everything on a large scale because it's a larger country. And so they produce science and math students in volume, right?

That's one of our disadvantages, right?

They're producing them through amazing universities like Tsinghua University in high volume. They contribute to open source today. We download Linux. We download Kubernetes. We download all the software. A lot of it has been touched by Chinese engineers. And once you download it, it's yours. We fork it, improve it, and make it ours.

So when you download one of these Chinese models, it just happens to be made by some really great researchers in China, but it's now yours—whatever you want to do with it.

So what exactly is the race?

Yeah.

I think that's a really good point. My point is, the race is really about who exploits the technology best. The last Industrial Revolution—all of the inventors were Maxwell, Volta, and Ampère. None of them were American. They were European. The last Industrial Revolution came from Europe, but we exploited it. We took advantage of it socially better than anybody else in the world.

Look how it turned out for us.

I want to make sure that this next generation happens just like this.

Yeah. So why are the communists getting their message out so successfully here right now?

I think, first of all, the narrative is much more practical. Nobody in China is saying that there's an end to this and an end to that, and cataclysmic this and doom or that. They're much more pragmatic about it. They see AI as a technology that's going to advance their economy and advance their society, and they don't have these groups who are basically saying it's going to end civilization and we're making it up.

The part that's frustrating is, if it was true, then we ought to talk about it and go do something about it, right? Even if it's true, we ought to spend more time doing something about it than worrying a bunch of people who can't do anything about it. It's our job to build it, right?

Has there ever been a point in history where so many people have so vehemently said something that is so untrue?

And they're measurably—actually, demonstrably—untrue, and it actually makes sense that they're untrue. It's not based on science. It's not based on research. Everything that's based on science and research proves otherwise.

Is it a fear of the frontier? Humans have never been there. We've never seen it, therefore we're scared of it, and therefore it's easy to tell everyone to be scared of it.

It could be life experience as well, David. Let me give you an example. When I first graduated from school, I was an engineer and I didn't do that much typing. The reason for that is because I was the first generation before software became popular. We had to go build the computers to make software possible.

Could you imagine, in this generation, every single engineer who came into the world of engineering spending all their time typing? Literally, that's what you do when you get a job. They give you a laptop, they give you a chair, and you start typing. You type all day long, from the moment you wake up. Well, there was engineering before typing, right?

Right.

So can you imagine that the world has a mountain of engineering work to do where most of it is not typing anymore? Sure, we had busy engineers before typing. I think we're going to do a lot of great engineering after typing.

Yeah.

When I say typing, I mean coding. Even at NVIDIA, when software engineers talk to me, I tell them, "You're just typing." I've been saying that forever, obviously for fun. I tell them my favorite key is Backspace, and the reason for that is because the best software is the smallest software. So I want you to use Backspace.

Let's actually talk about NVIDIA. Let's do a little teardown of NVIDIA—meaning, just explain the pieces, because there's a lot of strategy at play. Let's start at the absolute bottom.

4. President Trump calls in live to discuss the Doomer Hoax

Oh, no. This is not planned, but we know who it is. Oh, no. No, Mr. President. Yes, sir. I have to tell you something. If it wasn't because of you calling, I would—I'm on stage with the Besties. I'm on stage with the Besties. I'm on stage with Sacks and the whole group. Jason's here, Chamath's here, and David is here.

I'm sitting in front of a few thousand people and we're talking—as it turned out, we were talking about you. Good job, sir. Good job. The fact that you saw through all of that—I mean, there's a lot of complexity, and the fact of the matter is, you saw through all of that. We're all just really grateful.

Tell them I said hi.

Do you want to say hi to the crowd? Jason would like to put you on speaker mode.

How do we put him on speaker?

Put him on speaker. Right into the microphone. Here, we're going to get a mic. Hang on a second.

Hold on, sir. We're getting a microphone.

Mr. President, you're now talking to the planet.

You see, the great thing about life is that Jensen can develop the most complex computer chip in the world that nobody can copy for 10 years, but he can't figure out how to put me on speakerphone. We have to remember this one.

It's almost a conspiracy, and the happiest group is China. China is very happy. I could even say a lot of states in the country are happy that weren't going to get anything, because they're being inundated by people who want to be there.

Now, all of a sudden, you see they're building in Finland. They want to build one. Google wants to build a big one in Finland, which I'm not happy about because they were unable to get permitting. I'm telling you, it's all a hoax.

The data centers are great. They make people wealthy, they make states wealthy, and it's the oil of the next 20–25 years. It's bigger than the internet, and AI is much more so. They're just playing right into the hands of a lot of people who don't want to see it happen. That could be political people. It could also be China. We're not going to let that happen. It's a hoax.

You're right. We're not going to let that happen, sir.

No, we're not going to let it happen. The robots are not going to be taking over the world. That's not going to happen.

My uncle was a professor at MIT for 41 or 42 years and was known as one of the most brilliant men. He was there for 41 years, at the top—top of the ladder, top of the top. He did many things, and Jensen knows all about it.

I have a little genetic strength, if you believe in the resource theory, but I do.

That explains why you know so much about AI.

Well, I know about AI. I also have common sense about AI. The robots will not be taking over. AI will not be taking over the rest of the world. The whole thing is a hoax.

With that, we have to be a little bit careful. We have to do things prudently. But that doesn't mean we're going to stop industry as we work on the next 10 years about how to destroy it. I'm with you all the way. I didn't even know how you felt about it, and I assumed you felt the same way as me.

Yes, sir.

If we're going to lead—and I have an expression—it's, "Whoever wins AI wins." That's how big it is. It's bigger than the internet. Whoever wins AI wins, and we can't let this kind of stuff happen.

That very much includes data centers. There are communities that were dying that have data centers right now, and now they're wealthy communities—really wealthy communities. We're going to make sure that everybody wins in the AI race in America: every industry, every company, every state, every person.

Good. I feel strongly about it, and I have the position that can do something about it. We're not going to let that stuff happen.

I have no idea who's at the meeting. I have no idea who the hell I'm talking to, but I'll see.

Did you hear that? Thousands of people are clapping for you, sir.

All I know is, if you're there to listen to Jensen, he's done an amazing job, and David has done an amazing job. Good luck to everybody. We're going to stay with the future.

The country has never done better. We have $20 trillion of investment coming into the country, as opposed to much less than $1 trillion under Sleepy Joe Biden, and that was for 4 years. This is in 1 year.

The country has never seen anything like it, and we're going to keep it going. Thank you all very much.

Thank you, Mr. President.

Mr. President, thank you.

I'll call you back later. Thank you, Mr. President. Thank you.

That was unique.

I thought it was a bit. Did you know that was happening?

I thought it was a bit. Yeah, that was—

No, it was real. I thought it was a bit at first when I said, "Put him on speakerphone." Wow. And he calls you. How do you think he calls you at any hour of the night, right?

Well, we were in the Oval that time when he called you. You were sleeping.

You were asleep, and he said, "Wake him up."

I felt so bad because he said, "Who's coming to this dinner?" And we go through the list. He's like, "What about Jensen?" I said, "No, sir. He's on vacation," because he had to postpone this vacation for 5 years.

What's vacation?

But why do you think he sees through the hoax? This is quite an extraordinary thing.

It's polling minus 80.

So for anyone else sitting in the Oval Office, you're going to do what's popular. You're representing the people. This is what everyone wants. They want to shut down the data centers and AI. It seems to be the popular thing in the moment. But he says it's a hoax, and he calls it. How does he do that?

I have to tell you, I'm not sure. The reason for that is because a lot of people are falling for it. The fact of the matter is, it's complicated. At first, if you look at the story—if you look at the stories—it's all anchored on 2 things. The first thing that it was anchored on was national security.

And recently, that was all blown to bits, right?

And so, no, that story is no longer anchored on national security. Now it’s anchored on safety. If you want AI to be safe, the first thing is that we need to make sure the labs that are building it are in control and that there are good tests for them. If we would like to have third parties make sure that third-party evaluators are available, that’s no different from financial controls. You guys know we have auditors.

The auditors are quite— they don’t have to be as expert as we are in our business, but they just have to ask the right questions. I think I heard somebody say that it’s good to have independent auditors or evaluators, but you just have to have multiple. I agree with that, too. Just as there are multiple evaluators and auditors, it makes sure that one company doesn’t become captured or somehow influenced for whatever reason.

There are a lot of different ways that you could solve this. I think the number-one thing is: let’s build the technology safely. Let’s make sure that the testing of it is safe. I recognize completely that what is being built is extraordinary, but these are extraordinary companies, and we ought to hold them to extraordinary standards.

I wanted to go back to open source for a second. A year ago, we weren’t taking it very seriously. It was 2 years—18 months—behind.

5. The AI boom and Nvidia's capital allocation strategy

The one thing that, as you guys know, is one of the challenges when you’re on the call with President Trump is that it’s hard to say something. I’m going to get in trouble for that. I’m sure he’s going to call me up on that. But anyhow, what I was going to tell him and all of you is that AI is creating an enormous number of jobs.

The thing that he wanted more than anything at the beginning of the administration—and in my first phone call with him, the first time I met him—is that he wants to create jobs in America. He wants to reindustrialize the United States. He wants to make sure that the United States has the energy to support the next industrial revolution. Without energy, there’s no industrial growth.

He wants to make sure that there’s energy growth, that there’s job growth, and that they’re reindustrializing the supply chain. Look at everything that we’re doing right now. All of it is happening right now as we speak. We’re creating more jobs than ever. We’re creating software jobs.

We were just talking about it earlier. About $400 billion in venture financing went into the AI industry just recently.

Yeah, 6 months.

Well, that’s created a ton of jobs. It’s created, obviously, an enormous amount of demand for compute, which I’m happy about. It’s also creating a lot of demand for data centers, and we ought to talk about that.

I was talking to Governor Abbott of Texas, and he wants to appeal to the industry to make sure that we’re empathetic to the small communities as we’re building data centers all across America—to be better listeners. Let’s actually talk about that for a second.

What’s incredible about NVIDIA, if you break down the component parts, is you’ve effectively had to become the bank of AI to get the ecosystem going, and you’ve had to do it at all the levels. You just did this thing with Cloverleaf where you’re doing land, power, and shell. You did this great thing with BlackRock and Goldman and all these folks to essentially create the financing capability.

Walk us through your capital-allocation strategy. What has to happen to get a broader ecosystem of folks to be able to come in and underwrite this next phase?

We’re creating, as you guys know, a new industrial revolution, and every aspect of it is true. This new industry requires manufacturing, just as electricity and the internet did, and now AI. With electricity, we can power anything. With the internet, we can find anything. Now, with AI, we can ask and know anything. Isn’t that right?

That’s our future. We tap into the ether, and we can ask it anything we want, and it can explain it to us. In order for that to happen, it’s got to produce the intelligence. That’s a production process, which is the reason why this infrastructure has to get built.

Once you get the infrastructure built, the question is: What about all of the other layers across the United States? This industry isn’t just about the model. It’s not just about the chips. It’s mostly about the applications on top. It’s mostly about the infrastructure layer—the data centers and all the infrastructure, the construction, the electricity, and the power generation that are involved.

I look across the entire ecosystem and look for bottlenecks.

Constraints.

Constraints. If there are extraordinary companies being built, maybe it’s a supply chain that has to get scaled up so that when we’re ready to deploy compute, they’ll be ready for us—land, power, and shell. This is no different from looking at the supply chain upstream.

I probably think about the long-term supply chain more than most because our company is really large. In order for us to succeed, a whole bunch of companies have to support me. Corning has to support me. Wendell at Corning has to support me, Lumentum, TSMC, of course, and memory companies.

We started working with all of these companies long before the revolution, before the growth came, so that the growth could happen. Now I’m doing downstream.

The compute cycle tends to be, though, that earnings over time, over long stretches of time, tend to move up the stack toward the application layer, where you can over-earn for larger periods of time. You bought Hugging Face, so now you’re actively in the serving business. Products like OpenRouter seem to make a lot of sense. It seems pretty obvious that there are better versions of ways to build things like Bedrock. I’m sure you think about it. What’s the natural conclusion?

It seems like the folks up here have no issue trying to move down.

Mm-hmm.

You have the best balance sheet, these incredible engineers, and you have the proven experience to make it right, engineer the product, and get it out. How do you think about looking up and saying, “I could probably do that”?

The reason why NVIDIA runs every single model in the world is that, about a year and a half ago, the only thing we ran was OpenAI.

Yeah.

And now look at the amazing models that are available. Meta models are available. You’ve got Grok available. Grokbot’s incredible. We now run Gemini, and Anthropic is scaling up on our platform as well.

Since a year and a half ago, you’ve got all these frontier AI models that are now open and available. The number of models is growing. There are a whole bunch of companies that I won’t mention that are building frontier models as well, and the number of AI labs is growing.

Inflection AI, Reflection AI, Physical Intelligence—the list goes on. All of these labs are building on NVIDIA. The reason for that is because, as a company, I’d rather have us help everybody succeed instead of taking a slice out.

We would go up as far as we need to, but as low as possible.

Our strategy is to go up as far as we need to and as low as possible.

And the reason for that is because if I do that—if not for NVIDIA creating cuDNN, all of the frameworks wouldn’t exist. If not for us creating Megatron Core, then all of the large-scale training wouldn’t have happened.

Wouldn’t exist.

So we go and invent all the technology necessary, as far as we need to, and then we let 1,000 flowers bloom.

Well, look, let’s be honest. I agree with you. The pushback would be that it really would be great to have more competition at the hyperscaler layer. I think you’ve done a great job supporting the neoclouds. There are some. By the way, I think you introduced me to NBS. Superb—great, everything. They’re amazing.

But we need 50 of these guys. We need 100 of them. We need 1,000 of them. It just may take some—

Yeah. You know, I’m surprisingly uncompetitive.

Really?

That’s not my thing. For example, I’d be more than happy with 5 hyperscalers. However, I noticed that the early customers of all the neoclouds—all the companies we call NCPs—were the hyperscalers.

Exactly.

The reason for that is because the hyperscalers plan once a year, but the market dynamics are so volatile right now that they’re almost always wrong. With all these regional clouds, which are agile and can move fast, they know their state, they know their country, and they know their region. They’re securing land, power, and shell in a way that’s hard for somebody who sits in Seattle or sits in Palo Alto to be able to see the planet.

We now have basically a large-scale distributed network of companies that are securing land, power, and shell for us. Now countries realize it’s strategic.

Yeah.

So many countries are saying, “I’m going to take my power and only give it to my own companies.”

Right?

Well, NVIDIA is in that country as well, and we could help the neoclouds in that country grow. Whether it’s Firmus in Australia—we just did a whole bunch of stuff in Australia.

We brought on 2 more gigawatts. Southeast Asia, of course, IOH and others bring on a few gigawatts. And so we're building gigawatts. We're scaling up.

It's pretty clear, though. I just want to get this 1 thing in. It's pretty clear that you're going pretty high up and getting very focused on open source. Obviously, you have your Nemotrons doing exceptionally well—I use them often. Hugging Face, Poolside, and Llama—very, very solid products that you're now acquihiring, hiring, whatever it is. And then you have your open-source stack for self-driving, also very disruptive.

We are the frontier model in 5 domains.

Yeah.

Yeah.

6. Nvidia's Open Source model ambitions, thoughts on Elon's Terafab

And so you don't seem to build products to get the silver medal. You seem to go for the gold. So, are you going for the gold? And will you have the best, hands-down, open-source model? Part B to that is: can open source catch up to frontier models, and are you the person to do it?

So the logic, Jason, is that we'll build it because, 1, we can—we have the skills to do it—and because our customers need us to do it.

For example, Alpamayo is the world's first thinking self-driving car. By thinking, by reasoning, you don't need as much data. You don't have to train on a few billion hours of road data because you can reason about it. Break down the problem into, “I've seen this before. It's not exactly the same, but it's largely the same as that.” Okay?

And so, why is Alpamayo necessary? Well, there's a whole bunch of car companies. Every car in the world is going to be autonomous, but beyond that, every ag-tech vehicle, every truck, every van—and most of them aren't big enough in scale to be able to build that whole stack. So, I'll build an extraordinary stack for them. They do the last-mile adapting for their application.

Now, everything that moves in the future could be autonomous. If not for us building some of the biology models, the world wouldn't have them. The ESM-2 protein foundation language model we created, ESMFold, OpenFold, AlphaFold 2—all the stuff with SE(3)-equivariance—all of that technology wouldn't have existed if we didn't build it.

One of my favorites, Proteina-Complexa, is synthesizing next-generation proteins and their binding. It's groundbreaking stuff. We built that, and so we'll build that because Lilly needs it, Merck needs it, and others need it. They don't have the capability to do it, or they're not yet there, and so we can make a real contribution.

I do everything out of need. I'm not trying to disrupt anybody. We don't wake up in the morning and try to disrupt anybody.

We just wake up in the morning and try to help everybody. Jensen, what about competitive threats that might be emerging to your core business? Can you just comment?

Just so nice. Yes. Well, I know this is—I actually want to just get your, let's just call it, take. What's your take on Terafab, the 100-million-square-foot facility Elon announced?

If anybody could do it, he can. The 2 of us were on a flight together to a country with a person who sometimes calls you on the phone. It was a nice plane, and we spent a lot of time talking about it.

I mean, you design chips; you don't fab them. Could your chips be fabbed there, or is it—

Well, we know a lot about process technology because we're pushing the limits of everything, right? And because we scale at such large scale, we have incredible memory technology inside the company. We're the world's best systems company. We've got lots of amazing—

So your take is that you've talked a lot about it.

We could talk about it. You can't discourage Elon from doing it, which is one of his superpowers. Once he decides to go do something, it's hard to stop him.

And can you give us your take on where China is with advanced lithography systems—homegrown?

They're going to get there by 2030.

By 2030.

Yeah. And 2030 is just around the corner.

Yeah. Also, that's how long? We'll all be dead by that time. And for China, does that mean the switch is flipped and then that's all going to go into mainland fabs?

Almost immediately. You know, the way to think about China is that it's really good at high-volume production, and this is just a matter of time.

Yeah. And so, as far as they're concerned, they're already there.

They're already there.

Yeah.

Jensen, Elon, and Gwynne.

We've got to run America. We've got to run.

Yeah. Speed up.

So, we've got to speed up.

Slowing down is definitely the wrong strategy.

Well, I mean, it feels apparent, I think, to most of us in the industry, that we're kind of in the AGI moment. And its definition is obviously just as smart as any other human.

I think we're already there.

We're there, right? And so then superintelligence is the next waypoint, based on what you see, based on your customer base, based on your history here.

But, Jason, I think we're there, too.

You think we're at superintelligence?

Yeah. When you take a narrow segment—I mean, my self-driving car, I don't want you to make me an omelet; I just want you to drive the car.

That is superintelligent. It's better than a human—one-tenth the accident rate.

Exactly.

Synthesizing proteins, doing virtual screening of proteins—we're already there.

Are you having fun being on the frontier of humanity?

I like it. Yeah.

Ladies and gentlemen.

Ladies and gentlemen, I like it. I like it. And guys, it's great there. The future is great, and we want to get there. Listen, right? A lot of us don't have to work. But I've got to tell you, it's too good not to be.

It's so fun, right? And so, I want everyone to be there. I want to be there. I want all of you guys there with me. We're all going to be there. We're going to be enormously successful together as a humanity.

In the meantime, we've got to encourage them, urge them on. They're doing really, really important work, as you guys know, and I want them to succeed. I also would love for us to tone down the drama. Most importantly, we need all of America to come with us. That's how we make it.

Ladies and gentlemen, Jensen Huang.

Thanks, man. Appreciate you. Thank you.

Thank you.

That was awesome. Only your part.

That was awesome. That was great.

Thanks, guys. That was great, huh? Great time.