Transcript of Jensen Huang: The Doomer Hoax, Superintelligence Is Here, and The Future of AI (ft. President Trump) New

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

We preempted the weekly show and there's only 3 people we preempt the show for: President Trump, Jesus, and Jensen.

The number 1 podcast in the world.

That's Jensen Huang.

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's arguably the best executive in history.

97% year over year.

Not only is demand already strong, it's 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 Huang.

Oh, we got a stand. Yo, I'm On the way in.

Oh, come on. Standing O.

Standing O on the way in. That's our guy.

Ladies and gentlemen, GPU Jesus.

They love you.

They love you.

Thank you. I love you back.

We like—

number one podcast in the world.

In the world.

Absolutely. Wow.

We like the new jacket.

Well, you know, I just— I felt you guys needed some energy.

Yes, this is the right time.

I know we're talking about serious stuff here, but we need to talk about it with energy.

Yes.

Let's start with this essay from this weekend.

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 helped, but that was a pretty incredible thing. I think what a lot of people were surprised by was the coalescing of the frontier labs around the essay itself. Just, Jensen, unpack what happened, how you read it, how you interpreted it, and then we'll get into some details that were inside of it. But maybe just high-level thoughts to kick it off.

Well, first of all, there were a lot of stuff in there. Yeah. And first, there's a part about safety, which we have to take very seriously. Safety is paramount, obviously. Safety and leadership are not false. They're false choices. You're able to innovate quickly, you're able to execute quickly, and America's able to lead and to 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 Coxon whistleblower is a very serious matter. Whenever you have a whistleblower, you got to take it very seriously. I thought Coxon 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 fine because it's not grounded on science, obviously. It was expressed by a scientist, but it was obviously not grounded on science. I take issue with that. Obviously, the whistleblower part of it, I think there's just a whole bunch of stuff. Pausing, pacing, those are all voluntary things that they could do if they feel that their company is out of control.

If Coxon saw something— Obviously, we don't know what Coxon saw, But if he saw that the company was out of control, and 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 than research. Maybe that transition is clumsy. We don't know what he saw, and ultimately only he knows. But if there was a matter of 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 just help us sort of unpack? We tried to play this game actually this week on the pod, and it was difficult, which is, how do you describe, like, my mom calls me and she's like, Chamath, what is this whole civilizational death thing? I don't know how to explain it to her. So when you have very smart people like that quantize it and quantify it, I think that's probably what's perturbing to some people. They're like, what does that mean, 10% of extinction? Nobody knows how to explain that to the average person, how that's even possible.

Well, first of all, we shouldn't because it's made up. First of all, I think that we shouldn't because it's made up. These are well-educated. They're called researchers. Obviously, they work in a lab. The confluence of these words And then the prediction is alarming and troubling, and 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. The facts are there was a prediction that in 5 years' time, radiology will be completely taken over by artificial intelligence and there'll be no radiologists in the world. That has proven to be exactly the opposite. We need more radiologists than ever in the world.

Right.

However, AI has taken over radiology completely, which is great. It's automated scan reading, which is great. There was a prediction that within 6 to 12 months— wasn't it just last year? Within 6 to 12 months, 90% of code would already be generated by AI. That has turned out to be wrong. Within 6 to 9 months, that was predicted last year, 50% of entry jobs will be wiped out. That has proven to be wrong. Let's see, what else has proven to be wrong? I mean, all of these predictions have been wrong. Right.

Like that GPT-2 would be too unsafe to release, that Llama 3 would be too unsafe to release.

1.

Yeah, we've heard these.

Half of white-collar jobs would be gone next year.

The jobs apocalypse. Yeah.

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

Right.

Somebody has to take— and so we ought to just keep track of all that. And of course, people do and remind us that those predictions are inconsistent with ultimately America winning the AI race.

The short form for that is some people are saying, they say, trust the experts, and they use the analog of COVID which again started with people that were researchers, educated people that had an asymmetric awareness of the thing that the rest of us did not, saying things that ultimately turned out we find out in facts not to be true. And so there's this war that's happening right now between the trust the experts movement and the, you know, well, let's just look at the actual history of these predictions and let's just think more methodically. Where is this coming from? Because it's coming from inside the places that's actually making it. Like, 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 just maybe guess, or how do you think about why they're doing this?

Well, first of all, I got to tell you, these are some of the most consequential companies in history. Extraordinary engineers, extraordinary researchers, really fantastic work. On the one hand, I work very closely with them as companies to companies. On the other hand, we have to have conversations like this in public. And it's really unfortunate. And I think that these companies really ought to be built the way that we used to build companies, which is in silence.

Right.

You know? So wait, wait.

Jensen, 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 and the organization's behalf?

Because, well, that's what they decided when they came to work for us. And we told them, this is the way you behave when you work in our company. And if you like the culture of our company, which, as you know, the Nvidia culture and the Nvidia employee base, incredibly happy.

Yeah.

They like the fact that the company is consistent, that we're stable, that our core values are consistent with taking care of the families and creating the conditions by which they can do their life's work, that 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, people are attracted to. But when you come and work in our company, there are also some things that we don't appreciate that you do. For example, we don't welcome political discourse inside our company. Take it home. You guys talk about politics outside the company. The company is an apolitical company. We're bipartisan. We want America to succeed.

Right.

We want whatever government is in place, we'll do everything in our power to help America succeed. The discourse about race and religion and politics and all of that stuff, we tell people, do it outside the company. It's not for us.

In terms of maybe AI regulation then more narrowly, Satya was here this morning and what he said is, 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 engineering right, right. Translate the research in a more predictable way so that we're not fearmongering. Keep it inside until we're ready to expose it. What do you think the right response is? 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, the sort of perspective of what do we need right now?

Regulation should solve actual problems. And so the question is, what actual problems have we enjoyed?

Right.

And if you look at the actual problems, all of the actual problems so far have come from the labs. And the reason for that, and just in their defense, the reason for that is because they have the most compute.

Right.

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

Right.

And so it's unlikely that a startup will be the reason because they won't have enough compute. It's, you know, 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, and they're doing pioneering work, it's really very hard. They're transitioning from research to engineering. I could imagine, and they're obviously building some of the most consequential technology and companies in the world. They're building their company, they're building their culture, they're building the technology, they're building engineering, they're building products all at the same time. And so I can understand it's a little bit hair on fire. But nonetheless, the 4 incidents from one lab, the one giant incident from the other lab, the first thing that you have to do is just root cause the problem from an engineering perspective.

Right.

What happened? What could we have done differently? And what are we going to implement and institutionalize, whether it's technology or methods or processes, and 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. Because the alternative, if it's not in their control, and I'm sure that they are, I'm sure those 4 incidents won't happen again. I'm sure they root-caused it and fixed it. I'm sure they have now much better technology for sandboxes and runtimes and monitors and continuous—

Yeah.

continuous monitors. And so I'm certain they have much, 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 we don't know anything that happened and 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— a bunch of companies with engineers ought to send engineers in. I mean, We should advise them if we can, but I doubt it. I think they have extraordinary people. They got this handled.

We're not operating in a vacuum. David, last night you informed me that there is a Chinese lab, the makers of GLM, who are going to put $3 billion towards a recursive self-improvement run. Maybe you could tee that up for Jeff.

That's what was announced. Zhipu.com, the founder just raised $5 billion and said that one of their priorities is going to be trying to get to AI that trains the next AI and to try and automate as much of that as possible. Yeah, I think that—

Well, this is the new sexy phrase. But as you guys know, RSI is a combination of a system of ideas. It starts everything with in-context stuff. It starts with skills. It starts with reflection. It starts with reinforcement learning and synthetic data generation. And these are all very sensible ideas. That causes AI to get better at solving a problem over time. And you could also have low rank. All of that stuff doesn't include the weights. You could actually improve the weights, and it's called LoRA. LoRA could be improved in synthetic data generation, reinforcement learning, enhance it without training the base model itself. And then over time, you could train the base model again. with all of that experience. And so I think it's a sensible thing that you're going to use the technology to enhance productivity of all kinds of tasks, including building AI. I think that's a very logical idea. And I'm certain that everybody is using it in some degree. It's just 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. the impression they're trying to give, but you don't believe that's real.

No, no, of course not. And 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? And so the basic process of control, these labs are going to— as they move from labs to engineering, they will have much, much better control.

Right.

And when they have much better control, and control comes from methods and knowledge and practice and tools and technology, all of those things that lead to better control, verification, and evals, it's going to enable RSI to be done inside the company and for good products to be released outside.

Let's talk about open source for a second. I mean, 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 OpenWeights and how the ecosystem should fit together over time.

The world needs both closed models and open models. You want to use— I use as much closed models as I can. This weekend I used 4 of them, and they work terrifically. They're frontier. They're Great experience. They work incredibly well. They're getting better all the time. And 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 is free. And this morning I used a lot of free water taking a shower. And so you use the right water in the right places. And this is no different than electricity. This is no different than all kinds of commodities that we use in the world. You need both. Now, in the case of open, the reason why you need it is because it could be for sovereignty reasons, privacy reasons, proprietary technology reasons. Look at the facts. The facts are, in the last 6 months, $400 billion of venture funding went into AI-native companies.

Yeah.

80% 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 than the labs, the Frontier Labs dreams. And 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 enables that. Open models enables 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 wonderful—

Does it matter if the model the open models come from China or the US?

Well, we're doing everything we can to make a contribution in open models. However, the moment you download, like, for example, 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 in 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 manufacturing them through amazing universities like Tsinghua University in high volume. Well, they contribute open source today. We download Linux, we download Kubernetes, we download all the software. A lot of it has been touched by Chinese. And once you download it, it's yours. We fork it, we improve it, we make it ours. And 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?

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, Ampère. None of them were American. They were, right? The last industrial revolution came from Europe, but we exploited it. We took advantage of it socially better than anybody else in the world, and look how it turned out for us. I want to make sure that this next generation happens just like this.

Yeah, that's right. 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. The narrative is much more practical. Nobody in China is saying that there's end of this and end of that and cataclysmic this and—

Doom or that, doom or that.

They're much more pragmatic about it. They see AI as a technology that's going to advance their economy, 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 is frustrating is if it was true, if it was true, then we ought to talk about it and go do something about it. 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, they're actually demonstrably untrue. and it actually makes sense, is 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. So let me give you an example. When I first graduated from school, I was an engineer, and I didn't do that much typing. And 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, you spend all your 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. You type from the moment you wake up to the moment— well, there was engineering before typing.

Right.

And 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. I mean, so 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. And 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 software.

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

Oh, no.

This is not planned, but we know who it is.

Oh no.

No. Mr. President.

Oh.

Yes, sir. I got 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 the besties. I'm on stage with Sax and Oh, man. The whole group. Yeah, Jason's here, Chamath's here, David is here. Yeah, I'm sitting in front of a few thousand people and we're talking. As it turns 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 and we're all just really grateful. Tell him I said hi.

Do you want to say hi to the crowd?

Jason would like to put you on the mic.

Even Jason.

Put him on speaker mode.

How do we put on POTUS?

Put him on speaker.

Speaker?

Yeah.

Right into the microphone. Hang on a second, sir.

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 speaker. We have to remember this one. So roasted the AI. It's almost this conspiracy. And the happiest group is China. And China is very happy. And I could even say in the country, a lot of states are happy that weren't going to get anything because they're being inundated by people that want to be there. But 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 weren't able to get permitting. And I'm telling you, it's all a hoax. The data centers are great and they make people wealthy and they make states wealthy. And it's the oil of the next 20, 25 years. It's bigger than the internet and the AI, you know, much more so. And they're just playing right into the hands of a lot of people that don't want to see it happen. And that could be political people.

It could also be China. And we're not going to let that happen. It's a, it's a hoax.

And—

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

No, we're not going to let it happen. The, the robots are not going to be taking over the world. And that's not gonna happen. My uncle was the top, probably maybe the best of all time, frankly, but professors at MIT for 41, 42 years and known as being one of the most brilliant men. And he was there for 41 years as the top. He was like at the top, top of the ladder, top of— did many things. Jensen knows all about it, but did many things. So I have a little genetic Uh, a little genetic strength, if you believe in the resource theory, but I do. I have genetics.

That explains why you know so much about AI.

Yeah, well, I know about AI. I know. I also have common sense about AI. Uh, the robots will not be taking over. Uh, the AI will not be taking over the rest of the world. The whole thing is a hoax. Now, with that, we have to be a little bit careful. We have to very be, you know, we have to do things and we have to do them prudently. But that doesn't mean we're going to stop an industry because, you know, as we work on the next 10 years about how to destroy it. So I'm with you all the way. I didn't even know how you felt about it. I assumed you felt the same way as me.

Yes, sir.

And we— 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. And whoever wins AI wins. And we can't let this kind of stuff happen. And that That includes, very much includes data centers. There are communities that were dying that have data centers right now, and now they're wealthy communities.

Yeah.

Really wealthy communities.

We're going to make sure that— we're going to make sure that everybody— we're going to make sure that everybody wins in the AI race in America. Every industry, every company, every state, every people.

Good. Well, 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 you there.

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

All I know is you're there. I'm going to listen to Jensen, but he's done an amazing job and David has done an amazing job. And good luck to everybody. And we're going to stay with the future. The country has never done better. We have $20 trillion of investment coming into the country. And that's as opposed to much less than $1 trillion under Sleepy Joe Biden. And that was for 4 years. This is in 1 year. So, you know, it's, it's really— the country is— there's never— the country has never seen anything like it. And we're going to keep it going. And so thank you all very much.

Thank you, Mr. President.

Thank you, Mr. President.

Thank you.

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

Well, that was unique.

I thought it was a bit.

Did you know that was happening?

I thought it was a bit.

Yeah, that was awesome.

You thought it was a lie?

No, it was real.

I thought it was a bit at first, and I was like, put him on speakerphone.

Wow.

And he calls you—

How do you think—

He calls you any hour of the night, right?

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

I was sleeping.

I was in there. You were asleep, and he said, wake him up.

I felt so bad because he's like, who's coming to this dinner? And we go through the He's like, well, what about Jensen? I said, no, sir, he's on vacation because he had to postpone this vacation for 5 years.

He's like, vacation?

He's like, get him on the phone.

What vacation? Why do you think he sees through the hoax? This is the thing I was asking.

It was really quite an extraordinary thing.

It's polling -80. For anyone else that's 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, end 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 got 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. Recently, that was all blown to bits. That story is no longer anchored on national security. Now it's anchored on safety. Now, if you want AI to be safe, the first thing is we need to make sure that the labs that are building it are in control, that there are good tests for them. If we would like to have third parties to make sure that a third-party evaluator, third-party evaluators are available, that's no different than financial control.

Yeah.

You guys know we have auditors, and 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. And I think I heard somebody say that it's good to have independent auditors or evaluators, but they just have to have multiple. I agree with that too. Just as there's multiple evaluators and auditors, it makes sure that one company doesn't become, you know, pilled or somehow influenced for whatever reason. And so, you know, there's a lot of different ways that you could solve this. And so 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. They want to be.

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.

The one thing that, as you guys know, one of the challenges When you're on the call with President Trump, 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 my first phone call with him, my 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 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, that we'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 earlier, $400 billion of venture financing went into the AI industry just recently.

Yeah.

6 months. Well, that's created a ton of jobs. That's created a ton of jobs. It's created obviously enormous amount of demand for compute, which I'm happy about, which is also creating a lot of demand for data centers. And we got to talk about that. I think I was talking to Governor Abbott of Texas, and he wants to appeal to the industry to make sure that we are empathetic to the small communities as we're building data centers all across America, just 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 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?

Well, We're creating, as you guys know, this is a new industrial revolution, and every aspect of it is true. This new industry requires manufacturing, just as the electricity, internet, and now AI. We power anything, we can find anything. Now with AI, we can ask and know anything. Isn't that right? And so that's our future. We tap into the ether and we can ask it of anything we want, and it could explain it to us. Now, in order for that to happen, it's got to produce the intelligence. And so that's a production process, which is the reason why this infrastructure has to get built. But 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, the power generation, all of that is involved. I look across the entire ecosystem, I look for bottlenecks. If there are places where extraordinary companies are being built—

Constraints.

Constraints, 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, that they'll be ready for us, land, power, shell. And so this is no different than looking at the supply chain upstream. I probably think about the long-term supply chain more than most because our company is really large. And in order for us to succeed, a whole bunch of companies has to support me. Corning has to support me, Lumentum. TSMC, of course, and memory companies. And so we started working with all of these companies long before the revolution, the growth came, so that the growth could happen. Now I'm doing a downstream. I have a question.

The compute cycle tends to be, though, that the earnings over time, over long stretches of time, tends to move up the stack, right towards the application layer where you can over-earn for larger periods of time. I mean, you bought Hugging Face. Now you're sort of in the actively in the serving business, I mean, it seems pretty natural that products like OpenRouter make a lot of sense. It seems pretty obvious that, you know, there are better versions of ways to build things like Bedrock. I'm sure you think about it. What's the natural conclusion? Because it seems like the folks up here have no issue trying to move down.

Mm-hmm.

And you have the best balance sheet, these incredible engineers, and you have the proven experience to make it right and engineer the product and get it out. So 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, we were the only— it's incredible. Last year, about a year and a half ago, the only thing we ran was OpenAI.

Yeah.

And now look at amazing models that are available. The Meta Muse is available. You got Grok is 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 got all these frontier AI models that are now open, that are available. So the number of models that are growing, there's a whole bunch of companies that I won't mention that are building frontier models as well. And the number of AI labs are growing. The ineffables, the reflections, the—

Thinking machines.

Right? The list goes on. Physical intelligence. The list goes on. Okay? And so all of these labs are building on Nvidia. And the reason for that is because as a company, I'd rather for us to help everybody succeed instead of taking a slice out. And so we would go up as far as we need to, but as low as possible. Our strategy is go up as far as we need to and as low as possible. And the reason for that It's because if I do that, if I solve the— 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.

Training wouldn't exist.

So we go and we invent all the technology necessary as far as we need to, and then we let 1,000 flowers bloom. And so that posture allows us to be, quite frankly, 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. And I think you've done a great job supporting the NeoClouds. There's some, and by the way, I think you introduced me to Nebula. Superb, great, everything, they're amazing. But we need like 50 of these guys. We need 100 of them. We need 1,000 of them. And it just may take some—

Yeah. You know, Chamath, it's just, I'm surprisingly uncompetitive.

Really?

Yeah. That's not my thing. You know, my thing is kind of like, for example, I'd be more than happy with 5 hyperscalers. However, the reason I noticed the early customers of all the NeoClouds, all the what we call NCPs, all the early customers were the hyperscalers.

Exactly.

And the reason for that is because the hyperscalers plan once a year, but the market dynamics is so volatile right now.

Right.

that they're always almost wrong. And so with all these regional clouds who are agile and they can move fast, they know their state or they know their country, 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.

Right.

And so we now have basically a large-scale distributed network of companies that are building securing land power shelf for us. And now countries realize it's strategic. 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. And so whether it's Firmus in Australia, we just did a whole bunch of stuff in Australia, brought on 2 more gigabytes. Southeast Asia, of of course, IOH and others bring on a few gigabytes. And so we're building gigawatts. So we're building, you know, we're scaling up, you know, pretty—

Clear though, I just want to get this one 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 Laguna, very, very solid product that that you're now acquihiring, hiring, whatever it is. Then you have your open-source stack for self-driving, also very disruptive.

We are the frontier model in 5 domains.

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

The logic, Jason, is that we will build it because, one, we can, we have the skills to do it, and because our customers need us to do it. For example, Alpa Romeo is the world's first thinking, self-driving car. By thinking, by reasoning, you don't need as much data as— you don't have to train on a few billion hours of road data because you could reason about it, break down the problem into, oh, I've seen this before. It's not exactly the same, but it's largely the same as that. Okay? And so Alpa Mayo, why is it necessary? Well, there's a whole bunch of car companies. Every car in the world is going to be autonomous. But beyond that, every agtech, 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 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 it. The ESM2 protein language model, we created that. ESMFold, OpenFold, AlphaFold 2, all the stuff with Q-Equivariant.

All of that stuff, technology wouldn't have existed if we didn't build it. One of my favorites, Proteina Complexa, synthesizing next-generation proteins and its binding. It's groundbreaking stuff. We built that. We'll build that because Lilly needs it and Merck needs it and others need it, and they don't have the capability to do it or they're not yet there. We can make a real contribution. I do everything out of need. I'm not trying to disrupt. I mean, we don't wake up in the morning trying to disrupt anybody. We just wake up in the morning trying to help everybody.

Jensen, what about competitive threats that might be emerging to your core business? Can you just comment?

We're just so nice.

Yes. Well, I know this is— well, I actually want to just get your— let's just call it a take. What's your take on TeraFab, 100 million square foot facility Elon's announced?

If anybody could do it, he can. The two 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. Elon likes to talk about these things, and so we spent a lot of time talking about it.

Anybody could do it, he could do it. You design chips, you don't fab them. Could your chips be fabbed there?

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

Your take is you've talked a lot about it.

Yeah, we could talk about it. You can't discourage Elon from doing it, which is one of his incredible That's a superpower. And once he decides to go do something, it's hard to stop him.

Can you give us your take on where China is with advanced lithography systems, native GRON?

They're going to get there by 2030.

By 2030?

Yeah. And 2030 is just around the corner. Also, that's—

sorry, how long?

We'll all be dead at 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?

The way to think about China is they're really good at high-volume production. And this is just a matter of time. And I think in decades as well. I've been around a long time. And for Nvidia, I've got to think about what happens next decade and decade after that. So 2 or 3 years is just a click. That's nothing. As far as they're concerned, they're already there.

They're already there.

Yeah.

Jensen, Elon, and Gwen.

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

Speed run.

We got to speed run. 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 in the AGI moment, and it's a definition, obviously, just as smart as any other human.

I think we're already there.

We're there, right?

Then superintelligence is the next waypoint based on what you see, based on your customer base, based on your history here.

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 superintelligence.

Superintelligence.

It's better than a human.

Yeah.

Like 1/10 the accident rate.

Exactly.

Yeah.

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

Yeah.

Yeah.

Are you having fun being on the frontier of humanity? I like it. Yeah.

Ladies and gentlemen.

I like it. 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.

Ride the wake.

A lot of us don't have to work, but I got to tell you, it's too good not to be.

So fun.

Right? And so I want every— 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. And in the meantime, we 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. And 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 great. That was awesome. Pulling your purse.

That was awesome. That was great.

Thanks, guys.

That was great, huh?

That was great timing.

Yeah.