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

Vision's an awfully big word to me because 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 yes. >> The number one podcast in the world. >> That's Jensen Wong. >> He's the founder, president, CEO of the video.

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

“>> Video's the most important stock in this work in Jensen's arguably the best executive in history.”

>> Revenue exploded 97% year over year. >> Not only is demand already strong is actually accelerating, and video's the only computing platform. That is a full stack AI factory that GPUs 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 Wong. >> We got a stand you know on the way in. >> Come on. >> Stand to go. >> Stand to go 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 the one podcast in the world. >> In the world. >> Absolutely. >> We like the new jacket.

>> Well, you know auctioned the old. >> I felt you guys needed some energy. >> Yes. >> 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 it's heavyweight 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.

And then I think a lot of people were surprised by the coalescing of the front of your 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. Maybe just a high level thoughts to kick it off. >> Well, first of all, there were a lot of stuff in there.

>> Yeah. And the 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 what 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 very serious matter. Whenever you have a whistleblower, you have to take it very seriously. I thought coxon had great courage to put out what his concerns were. And even then there were some issues that were kind of 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. And it was expressed by a scientist, but it was obviously not grounded on science. And so I take issue with that, but obviously the whistleblower part of it. You know, I think there's just a whole bunch of stuff.

I think it's surprising, pacing. Those are all the voluntary things that they could do. If they feel that their company is out of control. If coxon saw something, you know, 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. That's extraordinary talent, extraordinary engineering. But obviously engineering is different than research. Maybe that transition is clumsy.

I don't 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 block. Can you just help us sort of unpack?

We tried to play this game actually this week on the plot. How do you describe, like, you know, my mom calls me. And she's like, "Jimoth, 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.

And these are well-educated, they're called researchers.

And so 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 that a matter is, let's go back and look at the real facts. The facts are. They're not going to be the same thing. They're going to be the same thing. They're going to be the same thing.

They're going to be the same thing. They're going to be the same thing. They're going to be the same thing. They're going to be the same thing. They're going to be the same thing.

They're going to be the same thing. We'll go back and look at the real facts. The facts are. There was a prediction that in five 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. However, AI has taken over radiology completely, which is great. It has automated scan reading, which is great. There was a prediction that within six to 12 months,

90% of code would already be generated by AI. That has turned out to be wrong. Within six to nine months, that was predict that last year, 50% of entry jobs would 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. And if you're at that GPT-2 would be too unsafe to release. That Lama-3 would be too unsafe to release.

Oh, one. Yeah, we've heard the half of white collar jobs are begun 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-- No. 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, you know, they 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. So there's a difference between the trust the experts movement. And 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-- how do you think about what why they're doing this?

Well, first of all, I got to--

These are some of the most consequential companies in history. Extraordinary engineers, extraordinary researchers. Really fantastic work. On the one hand, I worked very closely with them as companies, the 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?

And so, wait, Jensen, you don't allow anybody in your organization to speak for the entire organization, especially when they're having like a bed weekend or they rage quit. They're not allowed to tweet on your behalf and the organizations behalf.

“Because that's what they decided when they came to work for us.”

And we told them, these are-- this is the way you behave when you work in our company. And if you would like to culture of our company, which, as you know, the Nvidia culture and the Nvidia employee base, incredibly happy, 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.

And so, those kind of core values people are attracted to. But when you're coming work in our company, there are also some things that we don't appreciate that you do. Like, for example, we don't welcome political discourse in our insider company. Take it home. You guys talk about politics outside the company.

Yeah. [applause] The company is an apolitical company, you know, we're bipartisan. We want America to succeed. And we want whatever government is in place.

We'll do everything in our power to help America succeed. And so 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.

And what he said is, you know, 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 that? Get engineering, right?

Get engineering, 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, Dennis had a proposal which was sort of this more fin-re-like organization.

It's not clear what Daria 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?”

You know, regulations should solve actual problems. And so the question is what actual problems have we enjoyed, right? And if you look at, look at the actual problems, all of the actual problems so far have come from the labs. And the reason for that, 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 the, in fact, you can 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, 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 the same time. I can understand it's a little bit here on fire, but nonetheless, the four 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.”

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. Now, I would bet you money that in every single one of those cases is 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 that I'm sure this for instance won't happen again.

I'm sure they root cause them and fix it. I'm sure they have now a much better technology for, you know, sandboxes and run times and monitors and continuous monitors and continuous monitors. You know, 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, you know, a bunch of, a bunch of companies with engineers, out of send engineers in.

“I mean, and we should advise them if we can, but I doubt it. I think they have extraordinary people that got this handle.”

But we're not operating in the vacuum. David, last night, you informed me that there is a Chinese lab, the makers of GLM who are going to put three billion towards a recursive self-improvement run.

So, maybe you could tee that up for Jeff? Well, that's what was announced. Yeah. Yeah, Zepu.com, the founder just raised $5 billion and said that one of their priorities is going to be trying to get to recurs, you know, AI that trains the next AI and to try and automate as much of that as possible. Yeah, I think that, I mean.

Well, this is the new sexy phrase. Yeah. 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, you know, reinforcement learning and synthetic data generation. And these are all very sensible ideas that causes AI to get better at solving a problem, you know, over time. And you could also have low rank. You know, all of that stuff doesn't include the weights. You could actually improve the weights and it's called Laura.

Laura could be improved and synthetic data generation reinforcement learning and handset without training that 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.

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 background. As if it's going to spiral out of control is the impression they're trying to give. But you don't believe that type of thing. 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, that control comes from methods and knowledge and practice and tools and technology. All of those things that lead to the case, better control, verification and events, it's going to enable RSI to be done inside the company and for good products to be released outside.

I'll 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 open weights 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 close models as I can, this weekend I use for them. And they work terrifically, they're frontier, they're great experience, they work incredibly while they're getting better all the time.

“And the way I think about closed closed models is kind of like bottled water.”

You know, water is free, you guys. I don't know if I've told you guys, but water is free. I don't want to, you know, burst everybody's bubble. But water's free, and this morning I used a lot of free water taking a shower. And so you used the right water in the right places.

And this is no different than electricity, this is, you know, there's 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 six months, $400 billion adventure funding went into AI native companies, 80% of them use open models.

If not for open models, how could they build their dream? Because their dream could be different. Obviously, you'll be different than the labs, the frontier labs, dreams. And there's America has so many different ways to innovate that's one of our core strengths, great ideas just coming out of the fountains. 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. So let's talk about open models with a breakdown.

“Let me just ask, 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 life, 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. That's one of our disadvantages. They're manufacturing them through amazing universities like Qinghua 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 really good point. My point is, the race is really about who exploits the technology best. You know, the last industrial revolution, all of the inventors were Maxwell, Volta, Ampere, 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, or it's just like this. Yeah, yeah. So why are the communists getting their message out so successfully here right now?

The narrative is much more practical. Nobody's in China is saying that there's end of this and end of that and, you know, cataclysmic this and, you know, do more than that. You're much more pragmatic about it. They see AI's and 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's true, 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. But our job at the build it right. And that's been a point in history where so many people have so vehemently said something that is so untrue.

And they're measureably, 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.

The 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 and I think 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 that 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. So even at Nvidia, when software engineers talk to me, I tell them, you're just typing.

I've been saying that forever, but 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. Yeah. Yeah. I want you to use backspace. Let's actually talk about Nvidia.

Let's do a little tear down of Nvidia. So tear down, I mean, 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.

I, 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 sacks.

Yeah. You know, the whole group. Yeah. Jason's here. Chomats here.

David's here. Yeah. I'm sitting in front of a few thousand people. And we're talking, as it turns out, we were, 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 that a matter of you saw through all of that. And I, you know, we're all just really grateful. Tell me, I said hi.

Do you want to say hi to the crowd? Jason would like, Jason would like to put you on them. I'm on stage with the speaker mode. How do we put, how do we put on putters? On spit put on speaker?

Speaker. Yeah. Right in front of the microphone. There we go. We're getting a microphone.

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

“The great thing about life is that Jensen can develop the most complex computer”

ship in the world that nobody can copy for 10 years. But he can't figure it out and put me on speaker. [LAUGHTER] That's the thing. We have to move it this way.

So roast it. Yeah. It's almost as conspiracy. And the happy is group is China. And China is very happy.

And I can 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.

Once they build a big one in Finland, which I'm not happy about. Because they were unable to get permitting. And I'm telling you, it's all a hoax.

And they make people wealthy.

And they make states wealthy. And it's the oil of the next 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 that could also be China. And we're not going to let that happen. It's a hoax. And you're right.

We're going to let that happen, sir. Now, we're not going to let it happen. The robots are not going to be taking over the world. And that's not going to happen. You know, my uncle was a the top.

Probably maybe the best of all time, frankly. But professors at MIT for 41, 42 years. And can known as being one of the most brilliant men. And he was, 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, a little genetic strength. If you believe in the research theory, but I did.

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.

The robots will not be taking over.

“The AI will not be taking over the rest of the world.”

The whole thing is our hopes. 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 it frequently. But that doesn't mean we're going to stop it 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. And 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 includes very much includes data centers. There are communities that we're dying that have data centers right now. And now they're wealthy communities. 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. So I have no idea who's at the meeting. I have no idea who the hell I'm talking to. But you hear that? Did you hear that?

Did you hear that? Did you hear that? Thousands of people are clapping things up. All I know if you're there. [APPLAUSE]

And I'll listen to Jensen. But he's done an amazing job and David is 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 one trillion under Sleepy Joe Biden. And that was for four years. This isn't one year. It's really 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. I'll call you back later.

Thank you, Mr. President. Thank you. I thought it was a bit. Did you know that was happening? I thought it was a bit.

I thought it was real. I thought it was a bit at first. I was like, put a bunch of speakerphone. Wow. And he calls you.

How do you call you any hour at the night? Well, we were in the. We were in the of the bedtime when he called you. I was sleeping. I was sleeping.

You were sleeping. And he like said wake him up.

“I felt so bad because he's like, who's coming to this dinner?”

And we go through the listings like, well, what about Jensen? I said, no, sir. He's on vacation because he had to postpone this vacation. He's like, I've been here. And he's like, get him on the phone.

What's the situation? But why do you think he sees through the hoax? This is the thing. That's really quite an extraordinary thing. It was it's polling minus 80.

So 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.

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 got to tell you, I'm not sure.

And the reason for that is because a lot of people are falling for it. And so the fact of matters, it's complicated.

You know, I first, I mean, if you look at the story,

if you look at the stories, it's all anchored on two things. The first thing that was anchored on was national security. And recently that was all blown to bits. And so no, 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 they're good tests for them. If we would like to have third parties to make sure that a third party evaluators are available, that's no different than financial control.

And the auditors are quite, 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 evaluated in auditors, it makes sure that one company doesn't become, you know, piled 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. And I recognize completely that what is being built is extraordinary. But these are extraordinary companies, and we ought to hold them to extraordinary standards.

And they want to be. And they want to be.

I want to go back to open search for a second.

A year ago, we weren't taking it very seriously. It was two years, 18 months behind it. The one thing that, you know, one of the, as you guys know, one of the challenges we are on the call with President Trump is 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 enormous number of jobs. The thing that he wanted more than anything at the beginning of the administration, and that my first phone call with him, my first time in middle,

is that he wants to create jobs in America. He wants to re-industrialize 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. And so, he wants to make sure that there's energy growth that there's job growth

that we're re-industrializing 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.

And we were just talking about earlier.

400 billion dollars of venture financing went into the AI industry just recently.

Six months. Well, that's created a ton of jobs. That's created a ton of jobs. It's created, you know, 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 have to talk about that.

“I think that was just, I was talking to Governor Abbott of Texas.”

And he was, 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. That's, well, it's incredible, but in video, if you break down the component parts,

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 know, you just did this thing with cloverly for you're doing land power shell. You did this great thing with black rock and Goldman and all these folks to essentially create the financing capability.

Walk us through your capital allocations strategy. What has to happen to get a broader ecosystem 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, just as the electricity Internet and now AI, we power anything. We can find anything. Now with AI, we can ask it, no, anything.

Isn't it right? And so that's our future. We're tapping to the ether. And we can ask it of anything we want and it can 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.

That all of that is involved. And so I look across the entire ecosystem and look for bottlenecks. And if there are places where extraordinary companies are being built, constraints, extraordinary companies being built. Maybe it's supply chain that has to get scaled up so that when we're ready to deploy

compute that there will 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. You know, it's got a corning, a window, a corning has to support me.

And so we started working with all of these companies long before the revolution

“that the growth came so that the growth could happen.”

Now I'm doing a downstream. I have one cycle tends to be the earnings over time, over long stretches of time. Tens 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 open router 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? Because it seems like the folks up here have no issue trying to move down.

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 open AI.”

Yeah. And now look at amazing models are available. The Metamuse is available. You got the grock is available, grock bites incredible. We now run Gemini and Anthropicus 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 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 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 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 is because if I do that, if I solved it, if not for Nvidia creating Kudian N, all of the frameworks when it exists. If not for us creating Megatron, Megatron Core, then all of the large scale training wouldn't have happened. So we go and we invent all the technology necessary as far as we need to, and then we let a thousand flowers bloom.

“And so that posture allows us to be quite frankly the only thing that I agree with you.”

The pushback would be that it really would be great to have more competition at the HyperScare layer. And I think you've done a great job supporting the Neo Clouds. There's some, by the way, I think you introduced me to dubious superb great, everything they're amazing, but we need like 50 of these guys. We need a hundred of them, we need a thousand of them, and it just may take some... Yeah, you know, 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 five hyperscalers, however. The reason I noticed the early customers of all the Neo Clouds, 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 that they're always almost wrong.

And so with all these regional clouds who are agile, they can move fast, they know their state or they know their country, they know their region, their 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. And so we now have basically a large-scale distributed network of companies that are building securing land power shell for us, and, and 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 Neo Clouds in that country grow, and so whether it's, whether it's firm us in Australia, we just did a whole bunch of stuff in Australia, and we could run on two more gigabytes, southeast Asia, of course, IOH, and others bring on a few gigabytes. And so we're building a gigawatts. So we're building, you know, we're scaling up, you know. Pretty good there, though.

What do you 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, a very, very solid product that you're now acquiring, hiring whatever it is. And then you have your open source stack for self-driving, also very disruptive.

Yeah. And so you don't seem to build products to get the silver metal. 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?”

And then part B to that is, can open source catch up to frontier models and are you the person to do it? So the logic, 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. So for example, Alpomayo is the world's first thinking, self-driving car.

And by thinking, by reasoning, you don't need as much data as, you know, you don't have to train on a few billion hours of road data.

Because you could reason about it, break down the problem and say, "Oh, I've seen this before. It's not exactly the same, but it's largely the same as that." So Alpomayo, 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 ag tech, 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 ESM-2 protein-found language model, we created that, ESM-fold, open-fold, alpha-fold-2. All the stuff with co-equivariant, all of that stuff technology wouldn't have existed. We didn't build it. One of my favorites, Protena Complexa, synthesizing next generation proteins and it's binding. It's groundbreaking stuff.

We built that. We'll build that because the Lily needs it, and Mark needs it, and others need it. They don't have the capability to do it, or they're not yet there. So we can make a real contribution. So I do everything out of need.

I'm not trying to disrupt. We don't wake up in the morning, trying to disrupt anybody. We're just wake up in the morning, trying to help everybody.

“What about competitive threats that might be emerging to your core business?”

Can you just comment? It's just so nice. Yes. Well, I actually want to just get your, let's just call it a take.

What's your take on tariffab, 100 million square foot facility, E-Lons announced?

Anybody could do it, he can. And the two of us were on a flight together to a country. And with a person, which sometimes calls you on the phone. It was the nice plane. And, and we had like, you know, you know, 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. Because I mean, you could, you design chips. You don't fab them. Could your chips be fab there, or is it?

Well, we know, we know a lot about process technology, because we're pushing the limits of everything. Right. And, you know, because we scale at such large scale, we have incredible memory technology inside the company.

The world's best 30s company, you know, we got lots of amazing things. So your take is, you've talked a lot about it. So we could just, yeah, we could talk about it.

And you can't discourage Elon from doing it, which is one of his incredible,

that's the superpower. And once you decide to go do something, it's hard to stop them.

“And so I think, can you give us your take on where China is with advanced lithography systems?”

Native grown. They're going to get there by 2030. By 2030. Yeah. And 2030 is just around the corner.

Yeah. Also, that's, and then for, sorry, how long will it be dead at that time? So. And for China, does that mean the switch is flipped? And then that's all going to go into main landfabs, almost immediately.

You know, the way to think about China is really good at high volume production. And this is just matter of time. Yeah. And, you know, I think in decades as well, you know, I've been around a long time.

You know, for a video, I've got to think about what happens next decade and decade after that. So, two or three years, it's just a click. It's nothing. And so as far as they're concerned, they're already there.

They're already there. Yeah. Just in Elon and Gwen. We've got to run. America, we've got to run.

Yeah. Speedrun. So we got to speedrun. Slowly down. There's definitely the wrong strategy.

But I mean, it feels apparent, I think, to most of us in the industry, that were kind of in the AGI moment. And it's a definition, obviously, as smart as any other here. I think we're already there. We're there, right?

And so then superintelligence is the next way point. Based on what you see based on your customer based on your history. And Jason, I think we're there too. You think we're at superintelligence? Yeah, yeah.

When you take a narrow segment, a narrow segment. I mean, myself driving car, I don't want you to make me an omelet.

Right.

Superintelligence better.

It's better than a human. Yeah, yeah. I want to attempt the accident rate. Exactly. Yeah.

Synthesizing proteins. You know, doing virtual screening of proteins. Yeah, already there. Yeah.

“Are you having fun being on the front here of humanity?”

I like it. Yeah. Ladies and gentlemen. Ladies and gentlemen. I like it.

I like it. 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 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.

“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 make it. And most importantly, we need all of America to come with us. That's how we make it. Ladies and gentlemen. Gentlemen.

Gentlemen. Thank you. Thank you. Thank you. Thank you.

Thank you. Thank you. Thank you. That was awesome. Yeah.

All your part. Awesome. Thanks, guys.