Conference Transcript - NVDA 2026-09-10

Jensen Huang, CEO

Good morning.

Jim Schneider, Analyst — Goldman Sachs

Good morning. Okay. Good morning, everybody. Welcome to the Goldman Sachs Communicopia and Technology Conference. My name is Jim Schneider from Goldman Sachs, and we are really thrilled to have NVIDIA and CEO Jensen Wong with us today. Welcome, Jensen. Thanks for being here.

Jensen Huang, CEO

Thank you.

Jim Schneider, Analyst — Goldman Sachs

Great to be here. Now, Jensen, last year, your prediction of $3 to $4 trillion in AI infrastructure spending by 2030 I think raised a lot of eyebrows in the investor community, but it seems like we're actually really rapidly progressing toward that figure right now um what technical advancements or market developments with respect to ai i just think we should just take a pause and acknowledge that i was right we're doing that we're doing that just take a pause take your time what people what do people miss or underappreciate and sort of where do you stand with respect to that the question is why did i know that isn't that right the question is why did i know that And it's actually fairly simple.

Jensen Huang, CEO

The last Industrial Revolution made it possible for us to power everything. And we distributed power everywhere. And then, of course, the Internet made it possible so that we can find anything. That's the big idea. You plug into the wall back in the old days, Ethernet jack or a modem and now Wi-Fi, you plug it in and you can find anything. And so it's not a small thing. It's a very big deal. And because you can find anything and because everybody wants to find everything, we had to make it part of the infrastructure. And because we can distribute power anywhere, we want to power everything, we distributed power everywhere. It became part of infrastructure. And so now we can find anything, find everything, but obviously that's not what people want. What we all want is to know everything. We want to ask anything, know everything. And that's the layer of computing that we're building now that makes it possible so that, you know, whatever you want to ask and whatever you want to know, you can. And so that's the big idea with artificial intelligence. And a computer is in the middle of doing that. The first instrument was called a dynamo. And the next one, obviously, called a computer. And now we have these AI factories, and they produce numbers, just like the internet produces numbers and at some level is that simplistic now the question is very simple how did the computer industry go from that to this and what's the implication and there are two two compounding problems two compounding challenges the first one is that that in the last generation of computers it was used by humans and because you're looking things up and you bought computers you bought the computers uh as a tool you know as as a terminal and and um so that you can find anything find everything and and so now in this new world and so so long as you prescribe to the idea that everybody wants to ask anything find and know everything if if that's a world that you you you prescribe to and and that you believe in and that it could somehow be integrated into almost everything we do then the question is how do you build that layer of computing around the world that computer as it turns out is a generative computer it's not a retrieval computer and I think I've explained this to you guys in the past the last the last 60 years everything was pre-recorded and we put it in storage somewhere and and when you touch something on the phone it goes and retrieves that piece of information and in fact it probably retrieved about somewhere between three to ten pieces of information and based on who you are the cookies that you know associated with you and the your your previous track record and your previous preferences a recommender system would recommend one of those pieces of information to you but it was all pre-recorded and so that that model of computing called is called retrieval based well if your question if you want to ask anything and know everything you want to actually know it. You don't want to find it. You want to know it. Then you have to generate the answer. You can't reasonably retrieve that. And the reason for that is because in the old days, you have this idea called a recommender system, and it's based on your preferences. Well, if you want to ask anything and know everything, then that preference has to be replaced by something else, and it's called context. So the query is called, your query is called a prompt, and the surrounding environment includes the context and also your preferences. The combination of all of that, you have to generate the answer. Now, basically what that says is that a new layer of computers that's continuously generating answers based on all the queries that are coming at it has to get built. And so that is no longer a bunch of storage, it's a bunch of computers. And what does that computer look like and what's the algorithm it runs and and so it's almost like everybody has our own recommender engine think of it that way you know instead of having one giant Rex's engine for all of meta you now have a recommender engine literally for everybody and these recommender recommendation engines are really complex because the you know the AI models are large it has to be smart it compresses a lot of the world's information and so so now the second problem goes like this it's the end of Moore's law and you guys the first time i said it you know some 15 years ago it was it was it was a lot a gasp you know i was like i said something that that hurt somebody's feelings it just says transistors don't scale anymore and it's not a big deal and so if the transistors don't scale 2x per year or get half as big every other year then the question is what's going to happen to the future as we're trying to do this other thing this you know called generative ai or artificial intelligence as you like to think about it and so in this new world where it's the end of Moore's law and we need a lot more transistors then several things have to happen the first thing that has to happen is you still want to get a hundred X and a thousand X improvement every few years and if you want to do that then you can't just live inside the chip you have to co-design which is the reason why Nvidia became a co-design company extreme co-design company you guys hear that all the time from us and the second thing that you have to do is if you want twice as many transistors, you got to build twice as many chips. And so, which is the reason why NVIDIA invented NVLink, right? And so, you see, the first thing that we did was we used COOS and that made it possible for us to fuse multiple chips together. And then we did, that wasn't even satisfying. So, we took a whole bunch of chips and we connected them together into NVLink, which is the big breakthrough today. If you don't have NVLink, if you can't have really excellent scale up and scale up technology is really hard, scale out is hard, scale up is incredibly hard. And so if you don't have that, you're dead in the water because more is your enemy, and these models are getting larger and larger and larger. And then the third thing that happens, the compound result of that is something that I predicted a while back, which is the semiconductor industry is going to be X times fat larger. And the reason for that is very simple. Demand of the semiconductor industry has been growing for some time. And yet Moore's Law was a depreciating, deflationary technology. And that was happening at the same time. If you don't have the benefit of deflationary technology, and the demand accelerates even further than that because instead of one billion people using tools and we sleep, now we have hundreds of billions of agents and have to think. So it's not like a chatbot. You don't hit it one time. You've got to think iteratively. So you compound all of this together, the semiconductor industry is going to just keep getting larger and larger, which is what we're seeing now. And so these two fundamental ideas, that we have a new layer of computing with a new application and the end of Moore's Law, meanwhile, people are expecting these AI models to be smarter and smarter because they don't like wrong answers, then the compounded result of that should result in a very large industry. And so, anyways, that's the end of my talk.

Jim Schneider, Analyst — Goldman Sachs

Excellent. You know, I think one thing that investors have consistently questioned is the ROI of this technology. You know, it's hard to look at a P&L today and say, oh, here's the gross margin or whatever. But coding has clearly been a killer app for the industry. I think it's fair to say we're not going back to the old way on coding for sure. As you look ahead, what other applications or tasks do you think can really move the needle on both adoption and ROI for the industry?

Jensen Huang, CEO

Everything's coding. So when you buy a cookbook, you see the recipe. That's just an American word for coding. When you ask somebody how to do something, that's coding. When you codify, you guys codify a business process, that's coding. And so everything's coding. If you want to do something, if you want to discover the best way of doing something, and then after that, repeat it over and over again without anybody deviating from that process or that methodology or that best-known process, however you guys, right, there's a lot of words about it, it's all coding. And we code in a lot of different ways. We code to make omelets. We code to repeatedly close our books and manage our supply chain. We code in order to communicate with each other in a consistent way, connecting the connecting fabric. For example, the supply chain is one giant large piece of code. Not one code, but trillions of pieces of code. And so everything that we do is, in fact, coding. And so it's a sensible thing. It's also the easiest thing for AI to learn, and the reason for that is because there's a right answer. And the best answer is hard to find, but the right answer is not hard to find. And so coding is an important part of it. A derivative of coding is, of course, bug finding. And a derivative of that, which is a very large market, is called cybersecurity. And the reason why there's so much conversation today about cybersecurity is because the industry is getting ready to launch some products. And what better way to create demand than to create a problem? You know, and so, you know, who doesn't want their market to be historical about their product and line up around the corner, you know, for it. And so there are responsible ways of doing it and there's less attractive ways of doing But, you know, there's a lot of demand creation about cybersecurity today because new products are about to be launched. And if you can code well, so you must be able to debug well. And, you know, red teaming is finding a bug, blue teaming is patching a bug. And so it's not a complicated concept, but the fact of the matter is cybersecurity will likely be the next major use case of AI, and it's going to run continuously. And so it'll be a great new business opportunity for the labs, a fabulous opportunity for CrowdStrike and we have a big partnership with them using open models to create red teaming and blue teaming and have the asymmetric advantage of swarms of NemoTron models that are running continuously. We have a partnership with Cisco, I think today, Palantir, Cisco, NVIDIA, where they're building, and Cisco is going to offer an entire AI factory platform from NVIDIA. Palantir is built on top of that. That's going to be taken out to all the countries and companies around the world to help them build either proprietary AI models based on Nemotron or cybersecurity red team, blue team models based on Nemotron. And so anyways, this is just – it's the time when – it's the next click of AI coming out to the next market. And then, of course, there will be future use cases. But at the core, code is very important. You know, I think that one of the things, Jim, that I was going to mention is if you look at – we spoke about the industry and all and technology and all, but let me – I'm here to sell some NVIDIA stock. And so I don't want this meeting, the agenda, to be unclear. We're the world's first and only growth value stock. I think we're in every way. People are trying to figure out which one we are. We're both. You can be both at the same time. And so why is it that we're both at the same time? And let me do some forensics on this for you. NVIDIA is incredibly misunderstood. As large as we are, we're insanely misunderstood. And the reason for that is this. We invented the GPU. You can't un-invent the GPU. You know, when you are NVIDIA, you can't un-invent NVIDIA. And everybody knows because they've known me for 30 years. Most of the AI researchers in the world, most of the tech CEOs, they grew up on products I built. That's how old I am. You know, when an AI researcher comes up and says, I used GeForce, you know, 2060 when I was eight years old. That's not a compliment. Okay? They're just counting the years. And so my point is, we have always been a GPU company. It's not who we are today, but unfortunately, that's where we started. Does it make sense? We're not unproud of it, but most people think NVIDIA builds a chip. I mean, you need airplanes to ship what we build. Each one of our system, each chip, if you will, one GPU is two tons. One GPU is now, it's not $399, not $399. It's, you know, $8.5 million. That's one GPU. All connected with NVLink, 2 million parts, right, 250,000 kilowatts. That's a GPU, and we ship thousands of them. I think I was just seeing the reports this morning. Grace, Blackwell, NVLink, 72 racks, month-to-month increase, month-to-month increase, 27%. And so you don't have month-to-month increase of 27%. That compounds. that's called a high growth value stock all right so number one we went from hopper which is about eight now hoppers the name of an architecture not a chip and blackwell's name of an architecture not a chip and these architectures are expanding in their scope so hopper was about eighteen thousand dollars or so per GPU system, and Blackwell went to about 25, and Vera Rubin is about 40. And the reason for that is because we're offering more and more and more of the overall AI factory. You know, we see the AI factory in our head. We're trying to use extreme co-design to overcome the challenges of Moore's Law, and between algorithms and software and system and interconnect and new technologies that we invent along the way, we create a generation every single time that's many times faster or more productive in token generation capability than the generation before. And so the first thing that we do is we're increasing our SAM of the world's CapEx, okay? So mission number one, not only are we growing, we're also capturing more at the same time. The second thing is it is incredible, but there are more model companies today than there was a year ago, than two years ago, a lot more. There are a lot more frontier model makers today than there was a couple of years ago. And there's a whole bunch who are starting up right now with a whole bunch of great ideas. And so my point is, we're the only company in the world that actually runs every model. We didn't used to run Gemini. We run Gemini today. Of course, GrokBot is doing fantastic. Can't wait to try it. And, of course, Meta's Muse. These are all new. These are all net new. Right? And not to mention, we didn't used to run Anthropic for a lot of different reasons. We didn't have the money to invest in them as a young company. Now we have more money, and we're happy to invest in them and help them. But anyhow, our share of Anthropic is growing very quickly. And, of course, we're delighted to see Anthropic and OpenAI and all of these labs growing. And so the second growth that most people don't see is we're the only company that benefits from Frontier, closed, open models. We run everything. And so there's no company in the world who is indexed to open models, which is growing incredibly fast, except for us. But you can't find that number anywhere. Open router is a good place to go look at these things, but you could see the growth. The world needs both closed models and open models, and we address them all. And then the third thing is the AI overall market is growing incredibly fast. But what we see are just the CSPs. We just see the cloud service providers. But remember, their enterprises, some of the great, great names of enterprises building AI factories are, of course, Jane Street and Hudson River. And, you know, just about every quantitative trading company in the world is shifting into this new model of doing predictions. And, of course, Drug Discovery with Lilly and Merck and BMS and many others have now created basically their robotics lab. It's, you know, basically lab in the loop, AI in the loop for wet labs. And so you need a supercomputer to do that. And so you have enterprises, but also the regional clouds. One of the biggest challenges, and I've been talking about this, and you want to make sure you understand the strategy, upstream, the supply chain is very challenging. And the reason for that is because, obviously, we're growing super fast. So packaging is a challenge. DRAM is a challenge. LPDDR DRAM is a challenge. Connectors are challenging. Everything is challenging. Voltage regulators are challenging. Everything is challenging. Wafers are obviously challenging. All kinds of different challenges upstream. But remember, the supply chain goes all the way to the end downstream until somebody stands up a computer and turns on the service. And so we've been thinking about the supply chain upstream and downstream. And one of the advantages that we have, because our go-to-market, remember, we run everything. This is the power of general purpose versus specialization. Do you remember two years ago, three years ago, everybody just say specialization is better than general purpose because it's faster? Well, if I can make general purpose faster than specialization, then specialization is all good. And the reason for that, excuse me, generalization is all good. And the reason for that is because generalization gives you fungibility, durability, versatility, rentability, and very importantly today, because of capital constraints, everybody's balance sheets, investability. Finally, we have a computer, the NVIDIA compute is finally a computer that could be asset-backed. And so we can use it to secure loans and that's a very powerful capability. And frankly, the only computing stack in the world that allows you to be able to say that because nobody could step back in on it. So anyways, our market includes CSPs and of course the regional clouds, the neoclouds. The power of the neoclouds is this. They secure land, power, and shelf for us that the CSPs have already exhausted. Just remember this. This is a very big deal. Many countries and many counties and regions want to secure the power and land for their own companies. And so we have neoclouds around the world from N-Scale and, of course, you get CoreWeave and Nebius, and they're doing fantastically. Some new ones that are on the verge of going public or in the process of filing, whether it's N-Scale or Lambda or Firmus, you're going to see a whole new crop of really, really exciting neoclouds with hundreds of billions of dollars backlogged together. And so this is a capability that allows us to secure land power and shell downstream and be diversified in our way of going to market. And so these three ideas, the whole AI market we serve completely, all of the models we serve completely, and, of course, the world's data center, CapEx, we address a lot more of it, which is the reason why we're growing so fast.

Jim Schneider, Analyst — Goldman Sachs

Can I follow up on a couple of those points? So one on supply constraints. You talked about a couple weeks ago on your earnings call high confidence in delivering 70% revenue growth next year, unconstrained demand growth of over 100%. You know, you talked about upstream and downstream. Are you more concerned about the upstream component shortages, or are you more concerned about the downstream, which you may have a little bit less direct control over in terms of land, power, shell, and data center availability?

Jensen Huang, CEO

Well, the downstream is where NVIDIA's advantage is incredible. And upstream, our advantage is because of our scale. We have the largest supply chain in the world, and I've been working with the supply chain and these partners now for coming up on three decades. And, you know, when I make a prediction, you know, they come true. And so they like it when there are people who are right. And so because they have to put a lot of money at play and because NVIDIA has a track record of actually being helpful and truthful and good at predicting these things, not to mention we can create our own market. Don't forget, NVIDIA's platform is the only one that you can take all the way to the end market by yourself. Remember, we go to market through the CSPs, but we go to market through the OEMs. Look at Dell. They're doing incredibly. Almost all 100% NVIDIA. I think it probably is 100% NVIDIA. Supermicro's doing well. Lenovo's doing well. HPE's doing well. And then Cisco's coming in. And we have 100% of the world's enterprise go-to-market can take NVIDIA to market because we are a full-stack AI factory platform. You come in, you can install the whole thing, add our software to it, and be basically up in a couple of weeks and get to work. And because these things are so expensive, the economics of it is so high that if you don't make it productive as fast as possible, You know, the anxiety is really quite incredible. And so if you look at our go-to-market, we have diversity of channels. We have many ways to get into market. And because of our neoclouds and because of sovereign AI capabilities and all driven by the fact that we are full stack, we have a rich ecosystem, we can reach all of these different marketplaces. And so the downstream part of it is a huge advantage for us. There is no question land power and shell is a problem. And so when I hear these people talk about these big numbers, the question, and we're tracking every single gigawatt of land power shell around the world, literally everything on the planet. I mean, just think about all my partners. How many neoclouds is reporting back to us? How many OEMs are reporting back to us? How many clouds are reporting back to us? How many AI native companies are reporting back to us? We're working with everybody. And so we kind of know where everything is. And it's kind of, it's pretty amazing. We've secured a lot of it. We've secured a lot of it. How can you otherwise be upstream? You know, obviously, TSMC would never tell you. I mean, they would never say something like that, you know, that they can somehow project how many gigawatts sold. You know, how would they know? Because it's far away from downstream. But we're all the way upstream and downstream, which is the reason why NVIDIA's, you know, our position is so good.

Jim Schneider, Analyst — Goldman Sachs

So you feel good about closing that gap?

Jensen Huang, CEO

Yeah, yeah. Yeah, I think we could grow 70% year over year, and we're confident about that. And we have a year to go work on improving that. And so, you know, we're going to come to work every day and, you know, make our living and get more supply. You know, more fried chicken.

Jim Schneider, Analyst — Goldman Sachs

More Denny's.

Jensen Huang, CEO

More, yeah, more Denny's, more dinners, whatever it takes.

Jim Schneider, Analyst — Goldman Sachs

Excellent. You know, your hyperscaler and large enterprise customers clearly can finance their spending independently.

Jensen Huang, CEO

Oh, did you guys see what we announced this morning with Australia? Let's give you an example. In Australia, there's a whole bunch of energy. And we're working with all the companies and data center companies in the region. We stood up 2 gigawatts for 2027. So just put in perspective, 2 gigawatts for 2027, it's not a lot, but it's $80 billion. That's a lot. You guys are so hard to impress these days. It's like two companies, but that's okay. Anyway, I don't even think I was. Was it a tweet or a press release? Was it a blog? But anyways, it's a very big deal, because I love working. Australia's region, they have excess energy, as you know, but they need several things. They need technology. This is where NVIDIA's AI factory, full-platform AI factory comes together. They need ecosystem off-takers. $400 billion of VC funding went into AI natives in the last six months, $400 billion. AI natives, the definition of an AI native is somebody who spends two-thirds of their raised money on compute. That's what it, right? And there is no such thing as a low-capex software company anymore. Every technology company is going to be a high capex company in the future, high op-ex or high capex, but basically high compute because there are no non-AI companies. And so we're working with just about every AI startup in the world. And so we have the whole platform. We have Optake. They bring the regional land power and shell, and we work with them to stand that up. And then lastly, capital. And sometimes we invest in them. but one of the transitions and this is the big idea that's happening right now we're working with the financial industry and many of you and really appreciate the work together we're moving NVIDIA Compute from technology to an investable asset and this transition and I think when we make this transition it will happen here fairly quickly I think this is going to be a huge needle mover for people to recognize that our computing systems have long-term value, has durable value. And at the moment, none of it's being valued. And so this is a huge untapped opportunity for us, and there's just so much evidence that the compute is productive. We sell it for, let's say, one gigawatt. Data center is probably like $60 billion. And so let's say it's over six years, $10 billion a year. Let's just do some simple math. That $10 billion a year currently is being rented out for 50, as you guys know. The revenues per gigawatt right now is about $50 billion. And so that's the economics of – that's how productive our technology is. You can still rent Voltus. Voltus is 10 years old. Obviously, Mike just talked about renting amperes. All the amperes in the world are all rented out. And so durability, fungibility. NVIDIA runs every model. Every single lab can use us. And because our capacity is so large, it's kind of like TSMC. One of the values of TSMC, of course, is technology. But one of its most important values is capacity. I can build my company on top of TSMC. You can build your startup on top of NVIDIA. No question about it. We are not going to be your problem. You don't have to engineer us into existence. We are here for you as an AI platform. So the way I see TSMC is the way that AI natives and AI companies see us. We are a foundational platform of the AI ecosystem, foundational platform of the AI industry.

Jim Schneider, Analyst — Goldman Sachs

Yeah, and to your point, you're providing, in some cases, guarantees or backstops for those AI labs or neoclouds to kind of go secure land, power, and shell for their operations. You've done that a number of different ways over time, including the $500 billion platform you talked about recently. You said on your earnings call you do not believe that circular finance. And hopefully most of that $5 billion is going to be asset-backed. That's the big idea. That's the big idea. So maybe for people who are skeptical, explain maybe why you believe it's not circular.

Jensen Huang, CEO

Well, it's not circular because we put a little bit of money in, and a lot of money comes back.

Jim Schneider, Analyst — Goldman Sachs

Yeah.

Jensen Huang, CEO

Is that finance talk? I mean, I look at the spreadsheet, you know, we put in one and a hundred comes back in.

Jim Schneider, Analyst — Goldman Sachs

Is that circular?

Jensen Huang, CEO

If that is, let's do more of that. And not to mention, not to mention, the companies that we're investing in, we see their pipeline because we brought their pipeline to them. People are starting to recognize that wherever I invest, it's not a bad place to invest. because I'm an informed investor. I'm not taking any risks. We're not smart like you guys. I need a sure thing. And so when I see their – we bring the AI platform to them. They're securing – they have to secure the land power and shell. We help them with financing, but a very small part of it. But the most important thing is we see their offtake. Because that financing doesn't come together without the off-take. And that off-take is $100 billion. And it's lined up contracts. It's total contract of value. It's real stuff. And we know where the demand is coming from and the quality of the demand. And so we see this bigger picture, which is the reason why it gives us confidence to do it. And also, I think it's really smart strategy. I think it's necessary for us to help the industry create this network of neoclouds, which are going to be distribution channels for NVIDIA's architecture. It is also a place where all these AI natives that are being founded could land. And remember, AI is not going to be just global. AI is going to be regional. And the reason for that is because there's so many regional intelligences. And that's, by definition, you're going to see AI becoming much, much more regionalized. And so we want to have hubs of NVIDIA partners all over the world. And so that's how the circular part of it doesn't make much sense to me because the number shows it doesn't. The returns. The returns are too great.

Jim Schneider, Analyst — Goldman Sachs

Yeah, it's too good. We've only got a couple of minutes, but I'd love to hit on physical AI before we close. That's an area where you've really seeded the market across everything from automotive to industrial robots to humanoid robots, et cetera. I mean, how quickly do you think that market is going to take off? What is going to be the killer app in physical AI, and sort of how big an opportunity do you think this could be for the company by, say, 2030?

Jensen Huang, CEO

The first killer app for physical AI is just self-driving cars. There was a time, and it made no sense to believe that, there was a time when people thought the way to build a self-driving car is just to drive billions and trillions of miles. You know, obviously there would be a lot of casualty in between, but you wouldn't want that. And what you're really looking for is a thinking car, a car that can reason. And, you know, we made some groundbreaking work in this area. It's called Alpamayo. It's the world's first reasoning and thinking car. And so it could see an environment it's never seen before, and it could reason about how to break it down, just like agentic systems are reasoning. and the reasons that breaks it down into things that it understands and can think about quite easily, just like us. And so the number of miles that you need is actually quite few post-training is necessary. And I think physical AI, for example, has arrived for self-driving cars. I think in the next couple, two, three years, you're going to see really great progress. Obviously Waymo, obviously Tesla, NVIDIA's Mercedes partnership. There's a whole bunch of others that are being teed up. Our partnership with Uber, lots and lots of partnerships being teed up. And so that's the first application. Derivative applications of that are things called AMR and warehouse delivery vehicles and inside logistics centers. And we announced a big partnership with Amazon. They have the largest fleet of inside warehouse navigation systems. And so you're going to see a whole bunch of that kind of stuff, a whole bunch of applications like that. They'll get grocery delivery vehicles and, you know, all kinds of things. And then the second part is manipulation systems. Manipulation systems are making really great progress. And today's manipulation systems are all pre-programmed, just like the old computers that we talked about. Everything was pre-recorded. The current manipulation systems are pre-recorded, which limits the market size to just the biggest car companies. And if you want manipulation systems to be useful for the mid and, you know, medium-sized manufacturing companies, which in Germany is called Mittelstadt, here in the United States they just call them supply chain partners, in Japan there's a whole, you know, their version of Mittelstadt, but basically the industrial supply chain are a couple hundred million dollar companies. If you look at ASML supply chain, it's a whole bunch of middle-starts, okay? And so a bunch of technology companies. Each one of them is probably a couple of hundred million dollars. And none of it can be robotic because none of it's big enough. And so we need smart robots, that reasoning systems. And so that capability is probably a couple of years away. And then after that, physical AI will permeate into everything from telecommunications, for example. 6G is basically physical AI. instead of using cameras that understands the physical world, is using a different spectrum of electromagnetics, radio waves, but it's basically sensing the world. And so we're going to – and we have a partnership with Nokia, and we built a platform that's based on NVIDIA's CUDA and AI, and it's called AI-RAN. And the system, I think, is going to be phenomenally successful. It's basically a distributed edge data center. All of this is, I think, probably about five years, you know, into cooking. And you'll just see more and more and more of it.

Jim Schneider, Analyst — Goldman Sachs

But in the meantime, NVIDIA is already successful in physical AI because before you deploy the model, you have to train the model.

Jensen Huang, CEO

And so, you know, people know that we have, you know, Elon and I worked together in the Tesla supercomputers that he called Dojo, and that's got a whole bunch of GPUs inside. So that's for training these self-driving cars. And there's a whole bunch of examples like that. I think I've virtually got to wrap up there. Thanks so much, Jensen, for being with us. We appreciate it.

Jim Schneider, Analyst — Goldman Sachs

All right, guys.