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

Some people call it vision, 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 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 in Jensen's arguably the best executive in history. Revenue exploded 97% year-over-year. Not only is demand already strong, is actually accelerated. 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 Wong. Oh, we got to stand, you know, on the way in, 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've been podcasting the world. We liked the new jacket. Well, you know, auctioned the old one. 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-- What's Hemingway involved? No. 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 was the coalescing of the Frontier Labs around the essay itself. Just Jensen unpacked 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 the high-level thoughts to kick it off. Well, first of all, there were a lot of stuff in there. And the first, there's a part about safety, which we have to take very seriously, safety's 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 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-- pausing, pacing. Those are all the voluntary things that they could do. 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, and 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 pod, and it was difficult, which is, how do you describe, like, my mom calls me, and she's like, "Gymoth, 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-- [APPLAUSE] We shouldn't, because it's made up, and these are well-educated. They're called researchers. Obviously, they're working to lab, 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 of the matter is, let's 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-- wasn't it just last year, 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 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. The GPT-2 would be two unsafe to release, the Lama-3 would be two unsafe to release. Oh, one. Yeah, we've heard these. 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-- Yeah. 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 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 fact, 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. Here 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 what's 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? [Laughter] So, Jensen, you don't allow anybody in your organization to speak for the entire organization, especially when they're having like a bad weekend or they rage quit, they're not allowed to tweet on your behalf and the organization's behalf. No, because, well, 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 the culture of our company, which, as you know, the Nvidia culture and the Nvidia employee base, incredibly happy, they liked 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, 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 come and work in our company, there are also some things that we don't appreciate that you do. Like for example, we don't welcome a political discourse in our insider company. Take it home. You guys talk about politics outside the company. We are, the company is an A political company, you know, we're bipartisan. We want America to succeed. And we want, 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. than more narrowly, Satya was here this morning 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? 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 responses, 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 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 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. 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 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. And so 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 for incidents won't happen again. I'm sure they root cause then and fixed it. I'm sure they have now much better technology for sandboxes and run times and monitors and 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, 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, they have extraordinary people. They got this handled well. 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. ZPU.com, the founder disarray is five 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 a low rank. And 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 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 I think it's a sensible thing that 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 is 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. And when they have much better control and that and control comes from methods and knowledge and practice and tools and technology, all of those things that leads to better control, verification and evils, it's going to enable RSI to be done inside the company and for good price to be released outside. That's about open source for a second. I mean, this hugging face week 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 close source versus open weights and how the ecosystem should fit together over time. The world needs both close models and open models. You want to use, I use as much close models as I can this week and I use four of them. 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 close 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. 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 of venture 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 close models. A lot of them will use open models. There's 10 million. Does it matter if 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. They're manufacturing them through amazing universities, like Qinghua University in high volume. Well, 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. And once you download it, it's yours. We fork it. We improve it. We make it ours. And so we, 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. So what exactly? 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. 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, yeah, yeah. So why are the communists getting their message out so successfully here right now? You know, I think, first of all, the narrative is much more practical. The narrative is much more practical. Nobody's in China is saying that there's end of this and end of that and cataclysmic this and do them or that, do them or that. It's 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's 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. And actually, there's never been a point in history where so many people have so vehemently said something that is so untrue. And 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. In 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 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 moon. Well, there was engineering before typing. 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. When I say typing, I mean coding. And 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. I want you to use backspace software. Let's actually talk about Nvidia. Let's do a little tear down of Nvidia. So tear down, 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'm on stage with the besties. I'm on stage with the besties. I'm on stage with SACs, and the whole group. Yeah, Jason's here, Chamat's 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 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, in a matter of you, 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, we'd like to put you on them. Even Jason. Come on, Jason. Speaker mode. How do we put on puttus? On speaker mode speaker? Speaker. Yeah. Right into the microphone. Hold on, sir. 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 chip in the world that nobody can copy for 10 years. But he can't figure it out. It put me on speaker. That's what I'm just saying. We have to remember this one. So Rusty. Yeah, yeah. It's almost as 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 interdated 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 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 that could also be China. And we're not going to let that happen. It's a hoax. You're right. We're not going to let that happen, sir. Now we're not going to let it happen. The robots are not going to be taken over the world. And that's not going to happen. You know, my uncle was probably made me the best of all time, frankly, with professors at MIT for 41, 42 years. And can 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 strength. If you believe in the resource theory, but I do. I have genetics. That explains why you know so much about AI. Well, I know about AI. I know-- I also have common sense about AI. The robots will not be taken over. The AI will not be taken over the rest of the world. The whole thing is our hoax. Now, with that, we have to be a little bit careful. We have to do things, and we have to do importantly. But that doesn't mean we're going to stop in industry, because 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 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 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. They're 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 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 I'll see you. [LAUGHTER] Did you hear that? Did you hear that? 1,000 of people are clapping for you soon. Oh, let me know if you did. [APPLAUSE] I didn't 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 before years. This isn't one year. So 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. I thought it was unique. I thought it was a bit. Did you know that was happening? Yeah, that was awesome. I thought it was a-- No, it's real. I thought it was a bit at first, and I was like, put your butt speaker phone. Wow. And he calls you. How do you-- He calls you any hour at the night, right? Well, we were in the-- we were in the oval that time when he called you. And we-- Oh, sleeping. You were sleeping. 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 list and he's like, well, what about Jensen? I said, no, sir, he's on vacation because he had to postpone this vacation for five years. And he's like, get him on the phone. What do you think he sees through the hoax? This is the thing I was really quite an extraordinary thing. It was pulling 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. 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 it was anchored on was national security. And recently, that was all blown to bits. And so 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 evaluator, third party evaluators are available. That's no different than financial control. You guys know we have auditors 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, 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. And I recognize completely that what is being built is extraordinary. But these are extraordinary companies, and we had to hold them to extraordinary standards. And they want to be. I want to take back to open source for a second. A year ago, we weren't taking it very seriously. It was two years, 18 months behind. The one thing that, you know, one of the, as you guys know, one of the challenges when you're 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 I met him, is he wants to create jobs in America. He wants to re-industrialize 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. And so he wants to make sure that there's energy growth, that there's job growth, that there 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 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 we're happy about, which is also creating a lot of demand for data centers. And we have to talk about that. I think I was just, I was talking to Governor Abbott of Texas and he was, 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 what's incredible. But in video, if you, 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 know, 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, to essentially create the financing capability. Walk us through your capital allocation 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, and every aspect of it is true. This new industry requires manufacturing, just as, just as the, the, the, the electricity internet and now AI. We power anything. We can find anything. Now with AI, we can ask and know 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 could explain 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, the, the, 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, 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. You know, I, I probably think about the long-term supply chain more than most because our company is really large and, and in order for us to succeed, a whole bunch of companies has to support me. You know, it's got a, uh, corning has to support me, lumentum and, you know, TSMC, of course, and memory companies. And so we started working with all of these companies long before the revolution, the, the, the growth came so that the growth could happen. Now I'm going, now I'm doing a downstream. I, I have one 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. Um, 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. 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, the reason why Nvidia runs every single model in the world. We, we're 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 are available. The Metamuse is available. You got Groc is available. GrocBots 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, 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, right, the list goes on, the 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, if it, if not for Nvidia creating QDNN, all of the frameworks wouldn't exist. If not for us creating Megatron, Megatron Core, then all of the large scale training wouldn't have happened. So we, 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, let's be honest. 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 NeoClouds. There's some, and by the way, I think you introduced me to diabetes 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, Chema, 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 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, they can move fast. They know their state or they know their country. They know their region. They're securing land power in 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 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 and that country grow. And so whether it's firm us in Australia, we just did a whole bunch of stuff in Australia, brought 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 clear, 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 knee. Motrons, doing exceptionally well. I use them often, hugging face, pool side, and Laguna, very, very solid product that you're now aquahiring, hiring, whatever it is. And then you have your open source stack for self-driving, also very disruptive. So we are the frontier model in five domains. 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, 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. Right. 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. And 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 in 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-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 if we didn't build it. One of my favorites, Proteina complexa, synthesizing next generation proteins and it's binding. It's groundbreaking stuff. We built that. And so we'll build that because Lillie needs it and Mark needs it and others need it. And they don't have the capability to do it, or they're not yet there. And so we can make a real contribution. So I do everything out of need. I'm not trying to disrupt. I mean, we don't wake up in the morning, try to disrupt anybody, or just wake up in the morning, try to help everybody. Jensen, what about competitive threats that might be emerging to your core business? Can you just comment? It was 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 TerraFab? 100 million square foot facility, Elon's announced. And-- If anybody could do what he can. And the two of us were on a flight together to a country. And with a person, we sometimes call you on the phone. It was a nice plane. And we had-- you know, Elon likes to talk about these things, and so we spent a lot of time talking about it. Is that anybody could do what he could do? Because you could-- you design chips, you don't fab them. Could your chips be fab there, or is it-- Well, we know a lot about process technology, because we're pushing the limits of everything. And because we scale at such large scale, we have incredible memory technology inside the company. We're the world's best service company. We got lots and amazing. 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-- that's his superpower. And once you decide to go do something, it's hard to stop him. And so-- Can you give us your take on where China is with advanced lithography systems native growing? They're going to get there by 2030. By 2030? Yeah. And 2030 is just around the corner. Yeah, also that's-- And then-- sorry, how long will we 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 mainland fabs, almost immediately. You know, the way to think about China is really good at high volume production. And this is just 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 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 an Elon and Gwen. We've got to run. America, we've got to run. Speed run. We've got speed run. Well, slowly 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 it's a definition, obviously. It's as smart as any other human. I think we're already there. We're there, right? And so then super-intelligence is the next way point, based on what you see, based on your customer base, based on your history here. And Jason, I think we're there, too. You think we're at super-intelligence? Yeah, yeah. When you take a narrow segment, 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. Right. That is super-intelligence. Super-intelligence better than a human. Yeah, yeah. Like one-tenth of the accident rate. Exactly. Yeah. Synthesizing proteins, you know, doing virtual screening of proteins. We're already there. Yeah. Yeah, yeah. Are you having fun being on the frontier of humanity? I like it. Yeah. [LAUGHTER] Ladies and gentlemen, I like it. I like it. And guys, guys, it's great there. The future is great. And we want to get there. Listen, 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 to be there. I want all of you guys there with me. We're all going to be there. We're going to be as enormously successful together as a humanity. And 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. And most importantly, we need all of America to come with us. That's how we make it. Yeah, ladies and gentlemen, Jensen Locke. Thanks, Ben. Thank you. Thank you. Thank you. Thank you. That was awesome. Paul and you're pretty awesome, that's great. Thanks, guys. Thank you. That was great. That was great.