NVIDIA Corporation (NVDA) Earnings Call Transcript & Summary

Joe Moore from Morgan Stanley. I wonder, you gave the $5 billion number for the automotive vertical in the keynote. I assume that's still mostly data centers this year. But can you just talk about that investment and what does it tell us? It's taken longer to get to autonomy than certainly I would have thought. Can you see the investment now that there's an inflection point coming?

Yes. Thanks, Joe. There are -- yesterday, I said that we were in 3 robotic systems. Agentic AI. And Agentic AI is the perfect example of test time scaling. This is that phrase called test time, training versus testing, test time scaling. The reason why Agentic AI is test time scaling the perfect example is because it has to reason, break it down step by step, go retrieve information, read that information, understand it, put it into context, reason about it, do something, eventually generate some answers. Agentic AI could reason about things, work on things for thousands of inferences. What used to be -- what people thought inference was and it was in the beginning, computer vision inference. People used to tell me and you know I've said this before, people used to say, Jensen, inference is easier. Training is hard. That's why that's NVIDIA but inference is easier. There is nothing easy about thinking. Anybody who thinks inference is easy, they're just not thinking. And that's the absence of intelligence. And so when you reason about it, listen, you guys think about what is inference? What is inference compared to foundation learning? What is reasoning? What's thinking? What's deep thinking? You do mental simulations, you do, right? You game it out yourself. Isn't that right? You do a purposeful practice, so on and so forth. I mean those are all part of thinking. And so Agentic AI, test time scaling for sure. Fine-tuning, not much necessary because you already started with pretrained models, but test time scaling, computing intense, okay? That's one. The second is robotics and AV. That is going to be data processing and training intense. Look at the size of the AI superclusters that Elon has with NVIDIA's Hoppers and -- Amperes and Hoppers, it's gigantic. Every car company in the world will have 2 factories, a factory for building cars and the factory for updating their AIs. Makes sense? Because every single car company will have to be autonomous or you're not going to be a car company. And so we know now everything that moves will be autonomous. That's a foregone conclusion. We know now that Waymo has got -- right, they have turned a corner. I mean, that's clever, right? We think Waymo has turned a corner and it's accelerating. We know that FSD version 13 is -- I thought version 12.5 was incredible and version 13 is incredible. We know that every single EV company in China has AV capability, every single one. If it's EV, it's got AV. That's going to be the standard. And so that's going to put enormous pressure on every single car company on the planet. If you don't have 2 factories, an AI factory along with your car factory, you're not going to be a car company. And so the amount of data processing is intense. Now we're going to have robotics. You could -- there are going to be -- there can only be so many cars because there are so many humans to move around. But there's no limitations to the robots. It could very well be the largest computer industry ever. And the reason for that is we don't need more cell phones than people. But robots, you can build as many as you like. And there's a very serious population and workforce situation around the world, as you guys know. The workforce population is declining and in some manufacturing countries, fairly seriously. And they have -- it's a strategic imperative for some countries to make sure that robotics is stood up and productive in the next several years. Their population is not going to grow, not for the foreseeable future. And so now you have these 3 robot industries that -- 3 robot platforms that we serve, the NVIDIA AI enterprise, which is our Agentic AI. That's a test time scaling and then the other 2 is across the board, 3 computers. Lots of data processing, lots of training, lots of simulation. Whenever I say Omniverse simulation, think RTX Blackwell. Racks and racks and racks of RTX Blackwells. And those are used for simulation and generative AI and Cosmos and things like that. And so that's the second computer, and the third computer is the computer in the car. We're going to do about $5 billion this year in automotive. The car computer itself is very sizable. It's not insignificant, obviously, because we're doing very well in EVs and AVs and robo-taxis and all of those things are scaling now. All those things are now scaling. And so anyhow, I think -- sorry if I didn't answer your question after all of that, but we have 3 robotics. The 2 robotics businesses -- yes, the AV, the reason why that's going to continue to grow so significantly is because the amount of video data we had to train the robots with. The video data to train the cars, the video data to train the autonomous robots.

This is Ben Reitzes from Melius Research. First, I just want to say thanks for having us all here. Really grateful for how you treat all of us. Look how many people are here, obviously.

Thank you, thank you.

It's very unique and very appreciated. You made this job fun again. So I wanted to ask you -- now that I've buttered you up, I wanted to ask you about something a lot of -- and Stacy...

Everything would have changed for me last night. [indiscernible] actually came to my hand. For a second there, they said, Jensen, you're Captain America. And I was like, if I'm Captain America, then I must be worthy. If I'm worthy, [indiscernible] must come. And then you guys didn't get that? As I was walking on stage, somebody goes, you're going to be Captain America. And then while I was on stage. If I'm Captain America, then I'm worthy. You guys know? Nobody got that. Wow, you guys work so hard.

Well, you're the most worthy person in the world [indiscernible]. My question is about the long term. And I asked you this a little bit on the call that you had for the quarter, and you said you guide 1 quarter at a time. So I don't want to end up in the same body bag as Stacy here. So talking about the...

I didn't know how to answer it.

The main question we get is what happens after '25? And you created this market. You've talked about $1 trillion in infrastructure that needs to be upgraded, a few other trillion. But Jensen, you created this. This is -- nobody knows. You have people saying, "Oh, it's going to peter out next year because of this, this, scaling loss." We're all dying to know. Like I don't think you're going to tell me, Ben, it's 37% CAGR through 2030. But how should we think about it? Are we -- what inning are we in? How do you want us to think about the long-term growth potential? Because no one knows better than you. You're most worthy.

Thank you. I appreciate that. Yes, as you know, usually when I answer a question to you guys, I reason through it. I'm actually giving, and Colette knows when I'm -- if you're all employees, I would answer it exactly the same way. And this is how I reason through it, okay? So number one. The first reason why the growth is not -- the growth is likely sustained long term is because general-purpose computing is over, machine learning is the future. Do you guys agree with that? That first principle is vital, that general-purpose computing is over, that the path forward is accelerated computing. That first thing that I said is if there's no belief, then we're in trouble. That I believe that accelerated computing is the path forward. And the reason for that is because machine learning is the path forward. The hand coding is likely not the right answer going forward. Okay? And you're not going to hand code anyways because, guess what? You're going to have an AI assistant help you code, and that AI assistant is, guess what, machine learning-based. And so it is very likely that what I -- the first premise of what I said is very important, it's very true. And I think there's abundant evidence that, that is true. Therefore, the trailing trillion dollars' worth of general-purpose computers will be modernized over the course of, call it, a few years and pick your favorite number of years, say 4. It's going to get replaced with modern computers. Another way to say it is if you were a computer company, if you're a computing company and you're building a data center, and that data center is full of CPUs, general-purpose computers, you really ought to self-evaluate whether you understand computing at all and whether your company is moving forward. And as you know, all the cloud service providers, whatever their CapEx is, whatever their CapEx is, it's going to largely go into accelerated computing going forward. They're not going to go, why build more general-purpose computers, data centers when the gross margins of renting a CPU is basically below cost? You're renting it basically for free so that you could get their storage. Why do that? Why not invest in a modern computer where it's margin accretive, actually generates revenues, okay? So that's number one. Number two, the second thing we have to believe is that AI is a new layer above the computing stack, that it's a new layer above everything that we've done. And the reason for that is this, the last layer is called software, and software are tools, tools used by humans that -- we use them. And that layer, let's use an example. My favorite tool, Outlook, I use it. Excel, I use it. PowerPoint, I use it. And VS, I use it. Does that make sense? And so these tools, Cadence, Synopsys, we use them, they're tools. Well, what AI is, is not a tool. AI is an agent, is a robot that sits on top of tools. They use tools. That's what an AI is, right? And so what is a self-driving car? A self-driving car is a digital chauffeur. It's not an FM radio, it's not an operating system. It's a digital chauffeur. And so it's an agent that sits on top of the current stack. That layer had never existed before. Do you guys agree with that? That's why AI is a growth industry. If we're successful and there's every evidence we're successful, that AI will be a growth industry and then here comes the next part. Whereas software runs on general-purpose computing, the AI that is created, where we are fed by our cafeteria and fed off our paycheck, AI is fed off of an AI factory. This is that factory I was talking about to Joe earlier, right? We have 2 factories, a car factory and the AI factory. Every robot will be fueled by factories, and these factories are AI factories, and they're going to generate these things called tokens. And so we're going to have a whole bunch of factories. Those factories never existed before. Do you guys understand what I'm saying? They're not a replacement of data centers. They're a new thing, new thing. And so my belief is that this is -- this AI industry is a new industry. Part of that industry will be AIs that are services, and part of that industry will be the factories that power those services, which is the reason why everybody knows that AI is -- whereas software industry was CapEx-light, AI is going to be CapEx-heavy industry because AI has a factory. Makes sense? So I just reasoned about it layer by layer by layer for you, okay? And now the question is about timing. We're working as fast as we -- of course, the cloud services, AI started in cloud and it's got wonderful cloud services and things like that. But enterprises need AI as well. And the mental model for AI in enterprise is really assistants, AI agents. And those are really a digital workforce, okay? And so we're help -- that's where NeMo and NIMs and Blueprints all come in, NVIDIA's AI Enterprise. We work with ServiceNow and SAP and Cadence and Synopsys and companies who would like to create these AI agents that use their tools that they can rent to their customers, okay? These AI agents they can rent to their customers. And so that I think is -- that's our way of addressing enterprise. There's 1 billion knowledge workers. Everybody is going to have AI assistants. There are 30 million software engineers. Starting next year, if a software engineer is not assisted with an AI, if a software engineer and your company is not assisted with an AI, you are losing already fast. Every software engineer in NVIDIA has to use AI assistants next year. That's just a mandate. As soon as we -- we're using a whole bunch of different ones right now, but as Colette knows, but I would like us to gravitate towards a couple. There's a reason why there's more than one, gravitate to a couple. But every software engineer must use an AI. It's a mandatory thing because otherwise, they're not coding fast enough. Now that's 30 million of the most expensive professionals in the world. Augmenting that, accelerating that is good value. That's just one. Digital marketing. If your marketing organization is not using AI next year, it's because you're not doing marketing. You're going to be in trouble. And so one sector after another, 1 function after another function in your company, enterprise. And then we already talked about robotics, which the self-driving car, we've been working on it for 10 years. It's now kicking into gear. And then robotics, all of you see the trends. The enabling technologies, the critical technologies for humanoid robotics. Humanoid robotics, let me tell you why that's so important. It is the only robot you could deploy brownfield. We don't have to change one thing. We don't have to change a leg of the planet, and we can deploy robots. The reason why it was so easy to deploy smartphones is because we all have pockets, okay? And so if the problem with robots with wheels and tracks and things like that is you have to change -- you can only use greenfield to do that. Pick and place arms, you got to go build it into something. But humanoid robotics, just buy and deploy them. That's why you can scale them. And because we -- because you could deploy them easily, you could scale them because you could scale, the economics will work for you. The economy of scale will work for you and because you could scale it in volume, the technology flywheel will work for you. As a result of that, the innovation will accelerate. Less than 10 years from now, I'm certain of it, humanoid robotics will surprise everybody how incredibly good they are. And so that's that kind of -- now I came up through the platform stack for you and then I went through time for you, okay? That's kind of how I reason about it. And when I think through that, as I reason through that, I worked backwards. And I said, "Okay, what does NVIDIA have to do to realize that future for the world? What do I have to do to make that possible for the world to do?" And that's the reason why NVIDIA AI Enterprise, the agent toolkits are made the way they are, and that's the reason why our partners are who they are. Because we're not trying to disrupt the enterprise IT ecosystem. We're trying to enable them to be able to serve the world's enterprises with Agentic AI. We're not trying to disrupt car companies. We're trying to enable them, and so we create all the pieces. And then we can make them available to Toyota, and we can make them available to Waymo the way they want to use it. Make them available to Tesla the way Elon would like to use it. Make it available to BYD and Xiaomi and Neo, Xpeng, and Lee Auto and Mercedes and JLR. Everybody wants to use the stack differently. And so we created it in such a way that we have deep domain expertise, but they pick the parts that make sense for them because of their corporate strategy or corporate identity or whatever it is, skills or whatever it is. And so -- but that's our basic strategy. Okay.

Vivek Arya from Bank of America. I appreciate you hosting the event. Jensen, you mentioned physical AI and humanoid robotics several times in the keynote yesterday and today. What is NVIDIA's strategy in this and the go-to-market in this specific business, right? If this is really a multitrillion-dollar business, when will we really start to hear, right, about some near- to medium-term milestones? And is your approach essentially just at the processor level plus the software and enablement level? Or do you see NVIDIA actually making the actual robots? So just what is the strategy? And when will investors get to see some of the results?

Okay, I appreciate that. Thanks, Vivek. Our robotic strategy for automotive and robot and humanoid robots or even robotic factories are exactly the same. And the reason for that is because it is technologically exactly the same problem. And I've generalized it into an architecture that through some adaptation, I could address all 3, okay? So 3 computers: 1 computer is the training computer, train the AI. Robotics is going to be a massive data problem, lots and tons and tons of videos and human demonstrations and synthetic data generation as we showed yesterday, a huge data problem. We're going to take all that data, we're going to train AI models with them, okay? So the first business opportunity for us in robotics is training. But I have to enable everybody to be able to train at scale. And everybody lacks data. That's the reason why NVIDIA built Omniverse and Cosmos, so that between Omniverse, which is a physics grounded -- you guys know large language models when they first came out, the criticism was it hallucinated. Remember that? And notice that the criticism is starting to decline. And the reason for that is because of retrievable augmented generation. Isn't that right? Grounding it in facts, okay? Well, if we have a generative model that can generate physics, which is essentially what Cosmos is doing, physics generator, but it hallucinates, then I can ground it with physics because Omniverse is physically grounded, physics-grounded. Does it make sense? Omniverse is calculated. It's based on solvers. And so its physics is known and believed, okay? And then I connect that to a generative model. Now I can create Doctor Strange, right? I can create all these futures and all these futures could then be used to train my model. And once the model learns it and actually lives all these futures, then it becomes -- has a generalized understanding of the world, okay? And so training. I need to create a bunch of data for it, and that's why Omniverse and Cosmos is available. Now we've deployed a system for generative -- physics-grounded, generative physics, generative world models that every single robotics company, every single car company now has a torrential amount of data to train on DGX. Makes sense? That's how I'd reason through it. Second, we also want to enable the industry, period. And one of the things that we have to do, the industry needs is simulation, simulation of the alternative world. And who is a better simulation technology company than NVIDIA? This is something that we must do. If we don't do it, who is? So this is one of those mandates of our company that we have to go select work that is impactful because look at 50 trillion-dollar industries are affected. Only we can do it and we have the expertise to do it. And we have the will and mind to do it. And so the second computer is Omniverse. These 2 computers are the first to go. We see the growth -- and notice we were talking about autonomous vehicles earlier, and data centers was the bigger part. That's data center business, okay? And then when eventually they scale out, then we have the robotics processor, Orin and Thor coming in. And the architecture is exactly the same across all of them. So everything is software harmonious. You can run anything anywhere and anyhow. Does that make sense? So robotics, 3 computers, robotics, big training, big data center opportunity, big data center opportunity.

Mark Lipacis, Evercore ISI. Thanks a lot for hosting the meeting. Really appreciate it. Jensen, you guys have made some announcements on quantum computing. Can you share with us your view on how this technology develops over time, what your strategy is? And longer term, pick the time frame, 5, 10, 15 years. What is the difference between what quantum computing will be doing versus accelerating computing platforms that you have?

Sure. Quantum computing can't solve every problem. It's good at small data, big compute, big combinatorial computing problems. It's not good at large data problems. It's good at small data problems. And the reason for that is because the way you communicate with a quantum computer is microwaves. And terabytes of data is not a thing for them. And so just working backwards, there are some very, very interesting problems that you could use quantum computers for. Truly generating a random number, cryptography. So these are problems that are small data, big compute. And working backwards, we're a computing company. We're an accelerated computing company. And as you know, we work with CPUs. We obviously built Grace. We're not offended by anything around us. And we just want to build computers that can solve problems that normal computers can't. And so in the case of quantum computing, it turns out that you need a classical computer to do error correction with the quantum computer. And that classical computer better be the fastest computer that humanity can build, and that happens to be us. And so we are the perfect company to be the classical part of classical-quantum. And so we are working with -- just about every quantum computing company in the world is working with us now. And they're working with us in 2 ways. One, this quantum-classical we call it CUDA-Q. So we're extending CUDA to quantum. And they use us for simulating the algorithms, simulating the architecture, creating the architecture itself, and developing algorithms that we can use someday. And when is that someday? We're probably somewhere between -- in terms of the number of qubits, order of -- 5 orders of magnitude or 6 orders of magnitude away. And so if you kind of said 15 years for very useful quantum computers, that'd probably be on the early side. If you said 30 is probably on the late side. But if you picked 20, I think a whole bunch of us would believe it. But what we're interested in is we want to help the industry get there as fast as possible and to create the computer of the future and will be a very significant part of it.

Jensen, I'm going to add my thanks for hosting this wonderful event between last night and today. I'd like to ask about the client side again. We saw this very interesting announcement with Digits, I think it's called, and the GB10. But you also made a reference to support for CUDA in WSL 2, I think it's called. And I'm wondering if you can explain that a little bit to us. Because I'm not sure that so many of us are familiar with that. Also, relate the 2 to each other, my suspicion is that these 2 are -- there's either a current relation or maybe a future one that we should anticipate coming down the road on the client side.

Okay. They're, I guess, related in the sense that they're both related to AI. But here's the way to think about that. This is a Windows subsystem for Linux, WSL. WSL is how a cloud-native developer can use Windows to develop for the cloud. And so it's basically a hypervisor of Windows. It sits in a -- it's basically 2 operating system in 1. Your Windows computer has an instance inside and that instance is Linux. That Linux instance is compatible with Docker containers and it's compatible with CUDA. As a result, WSL 2 is a great development platform for cloud-native AI. And that system is used by developers. And so from now on, we're going to take NVIDIA AI, and we're going to target that system and make sure that it's world class. And so we announced that and our -- the PC OEMs are super excited and reason for that is because although it started out as a developer's platform, as you know, AI is ready to be a end user platform or enthusiast or other application developers. And so the question is how do you make Windows an AI platform when most of the AI is done on Linux and in the cloud? Well, the answer is WSL 2. It's sitting right in front of us. And so now we're going to make that a mainstream product, and we'll support it with all the things that we do to support professional and high-quality software. And the PC OEMs will make it available to end users. Now what can you do? The really great thing is, let's say you're a software developer, Adobe or Autodesk or whoever it is, and you created an AI and it works in the cloud. You could take that AI now and put it into WSL 2 without changing anything. You'll just put it into WSL 2. And over GRPC or Rest API just the way that cloud talks to each other, you use exactly the same protocols and now this application could talk to the AI that's sitting in the PC. And so this application can now talk to an AI in the cloud or in the PC perfectly without any change. That's what we announced. And I could have done a better job explaining it, but nonetheless, it's, I think, quite exciting. We finally turned -- finally turned the PC into an AIPC because the momentum and the development and the R&D of AI in the cloud is just so significant, there's no way you're going to add another platform called Windows to it. Linux is just too powerful at this point. And so I think the answer is very clearly WSL 2, which is Linux and Windows. And so that's one. Digits works exactly like a private cloud. And so whatever runs in the cloud runs on Digits. And so the amazing thing is you put the cloud AI, you put it on Digits, you point your application on Digits, and this application used to be backed up by API in the cloud is now backed up by Digits completely invisibly. Digits is like your own personal cloud. And so for an application developer, an enthusiast or somebody you're developing, now look at this, you can run your application in all these different places, develop here, run it there, develop up there, run it down here, however you like to do it, one common cohesive platform that -- which is the way computing should be from personal to private to cloud.

I think we have a question over here, Jensen, but I think we're almost at time so I think this will have to be the last question.

Unless you guys are going somewhere?

I'm not going anywhere. Want to hang on?

We can keep going then.

Yes. Yes, it's my fault. I have this -- I over-explain because I want to make sure that you understand something. That's kind of -- don't get me started.

It's Toshiya Hari from Goldman. Thank you so much for hosting this event. Jensen, since the emergence of ChatGPT, if consensus is correct, I think you will have added around $200 billion in data center revenue between '22 and '25. You mentioned last night the ChatGPT moment for, I forget if you said physical AI or robotics is just around the corner. Is there a way to contextualize sort of the magnitude of revenue or your business from physical AI over the next 3 years vis-à-vis what we've seen over the past couple of years? And then, part B kind of unrelated. I was hoping you could give us an update on how you're thinking about sovereign AI. You've been really busy traveling the world over the past couple of years. It's really difficult to forecast that business from the outside. You know what the country leaders are thinking. How should we be thinking about that business for you over the next couple of years? If there's a methodical way of thinking about that, that would be helpful.

Yes, okay. While I answer the first one, I'll think about the second one. As you -- one way to think about the first one is this. In order to support a few million cars on the road, you need a few billion dollars of data center to support the few million cars. And the reason for that is because those cars are going to collect more data. And when they collect more data, you need more data to process. You need more bugs to fix, more corner conditions to go deal with. And so that loop, that cycle is understandable. The more cars you have on the road, the larger data centers you're going to need. And eventually, I think you're going to have 1 billion cars on the road, and they're all going to be autonomous. Or another way of saying, there's going to be 1 trillion miles driven, they're all going to be autonomous, and that has to be backed up by a whole bunch of data centers, okay? So if it's -- let's say, if it's a couple of million cars on the road, it's, call it, maybe the simple math is just 1 million cars on the road is $1 billion worth of data center. And 1 million cars is a lot of cars and it's supporting a lot of AV service. Do you see what I'm saying? The app services, the service itself pays a lot of money. And so they're more than delighted to have a data center to support that. And now the question is how many AV cars are on the road over time? And if the math that I just kind of roughly did is -- yes. If the math that I did is wrong, let me see. Is my math right? Let's see, 10. Yes. I was off by probably a factor of 2 or 3, something like that. So it's either a couple of 2, 3. It's probably a couple, $2 billion, $3 billion of data center for every 1 million cars, say. And then we just got to scale it out, okay? So that's one easy way of thinking about it probably. And then the second answer, I think it's related to the GDP. I think it's related to the industrial GDP. Information, knowledge, information in the industrial GDP. I don't think agriculture is as much. Obviously, tourism is not going to be as much. So depending on the GDP of your country, the shape of it, manufacturing countries' information, for example, information knowledge workers, financial services industry. The financial services industry, as you guys know, the quantitative trading is moving from human engineer quants, right, to machine learning robotic trading systems, robotic trading systems. And these robotic trading systems are giant transformers and they're just cranking on new data, making recommendations that with human-in-the-loop and such. And so for the information-rich ecosystems, it's just related to the size of the GDP. I think that's probably this -- yes. Near term, it's kind of hard to gauge. Every country will put up -- you might have seen the news in Europe. They literally have -- they literally adopted our phraseology and this entire initiative across 7 supercomputers called AI factories. And so this is the European AI Factory Initiative. And so you see Europe doing that. You're going to -- Japan has already done that. They allocated a couple of billion dollars or so of government subsidies that will be also matched with -- that matches the private enterprise spend for building out their infrastructure. And so let's say, there's a few billion dollars of infrastructure being stood up in Japan shortly. And so that kind of gives you a flavor of it. But I think over time, it's probably going to relate it to the size of GDP.

Harsh Kumar, Piper Sandler. Colette, Jensen, thanks again. My thanks as well for hosting this event. Incredibly helpful. I had 2 questions. Last night, you talked about generative AI going to Agentic AI going to physical AI. Is that -- for NVIDIA, is that a function of more horsepower, more compute, more networking? Or is there a fundamental change needed from your company on the software or the hardware side? Just some thoughts would be helpful.

Each stage is harder to do and each stage is harder to deploy. So for example, we've had self-driving cars for, I want to say, 8 years, but it's taken 8 years to reach this level of maturity. And the reason for that is because of risk, safety. But recommending a movie, recommending a product to you, generating a unicorn flying under the sea surrounded by ocean turtles, that's not going to hurt anybody. And so depending on the application, each one of them has a different degree of risk. Agentic AI for enterprise applications is not likely to be high risk. Depending on the functionality, it is. In legal, you're going to have to be careful, a bit more careful. In accounting, obviously, you have to be more careful. But in marketing, you could probably take a little bit more risk. In customer service, it's probably domain-specific and you'll guardrail it very specifically. In software engineering, very low risk because humans-in-the-loop. In chip design, very low risk because humans-in-the-loop. And so there's a -- you have to gauge kind of for each one, how you would go to market. In the case of robotics, self-driving cars, that's very hard. In the case of humanoid robotics, less hard. And the reason for that is because a car has to drive all over the world. It has to drive all over the United States. It's got bumpy roads. It's got -- you got Vegas, you've got Arizona, you've got San Francisco. And so the domain variation is very high. But humanoid robot, once you bring them into your facility, the domain adaptation is rather limited, rather narrow. And so I wouldn't be surprised if humanoid robotics is much, much faster to deploy if the technology works well, okay? And I'm hoping -- I'm looking forward to technology working well. And so it's really about the rate of adoption and is a function of the complexity of the technology, how you would take it to market, the safety of the system, so on and so forth. You got to balance all that.

David O'Connor from BNP Paribas. Colette, a question for you and a follow-up for Jensen, if I may. So just Colette on the supply chain side of things. We've gone through Hopper, that's been supply constrained. We're going into Blackwell now and that's supply constrained as well. Is there -- like how should we think about the kind of supply chain going forward if you look over the next couple of years? I mean, is this just the kind of fact of life, given the demand that's out there and the complexity of the supply chain? Or is there -- how we are thinking about that differently going forward to kind of ease those constraints? And then the follow-up, just again, going back on the ASIC side of things for you, Jensen. Your largest customers, the CSPs have kind of gone down the custom ASIC route for some workloads. Yet at the same time, you talk about those new reasoning models, and that requires a factor more thinking before on the inference side of things. So they start to look a bit more like GPUs, again, than kind of custom ASICs. So just kind of your view kind of long term, how much of -- is that an opportunity for you to help customers kind of do a bit more maybe customization but maybe not a full ASIC as you talked about, it's more a general purpose, given the market. But just maybe specific for CSPs, if you have a view for like 5, 6 years down the road, how should we kind of frame how much of the market could be dominated by that. Just kind of in 2 minds on the -- with the new reasoning models because those ASICs start to look more like GPUs again.

Let me first start in terms of our suppliers. Our suppliers have been not such an amazing group of folks that we have worked with [indiscernible] enough when we needed them over the last 7 quarters. It's been interesting to see the scale. But interestingly, yes, we would still love more. So even over those 7 quarters, it's not about just supply. It has been about building out data center scale systems, moving from just computing to a full computing platform with networking and all of the different components. When you think about the systems that are coming together now, many, many chips, many, many different types of switching, many different types of All of that having to come together. We've added more suppliers. Sure, we have the continued redundancy with multiple suppliers. Many of our suppliers, we've asked for another factory, and they have come forth absolutely willing, working hand-in-hand with us in terms of what we built. I believe what we've seen over the last 7 quarters is not a supply constraint. It has been just an amazing journey of our suppliers and our partners and our ecosystem working with us. Is there a point in time where that balances out? That depends. It really depends in terms of the sophistication and what you will likely see us building going forward and how we can scale with more and more newer suppliers and building that all together. But I look at it as been a great journey. They've been tremendously helpful rather than looking at it in a perspective that we're constrained.

We work very closely with the researchers and the AI researchers and engineers at just about every CSP. They're -- and every start-up. There are none that I think is left behind. And we get input from them. We get dialogue. We obviously have dialogue with them on how to improve our GPUs for the future. It's one of our advantages because everybody wants to see a better cluster and better infrastructure for their next-generation models. And so we work very closely with them. It doesn't ever have to go to a point where they add so much to our GPU that they want to do a custom version of it. We just -- we work together and we take the -- we decide what ideas that we could put in there on their behalf. And very few of them -- well, none of them so far has needed it to be proprietary to them. And the reason for that is because there's a lot of ideas coming from a whole bunch of other people. And inside our architecture is so complicated, we call it FP4 but there's nothing about FP4 that's simple like FP4. The transformer engine has to figure out when to -- when it can take advantage of a lower precision? When does it have to move back to a higher precision? You can't do this a priori during compile time, you have to do this during run time. And you have to do it so fast because otherwise, you're sitting around, waiting around to decide. And so that suspension system, if you will, and the numerical formats that you could drift between such that after you're done running this model, which is gigantic, the answer is actually something you're looking for, that is just incredibly hard to do. And so just numerical precisions alone is an area that we do a lot of collaboration with a bunch of people. And so we already, in a lot of ways, all of our GPUs are customized with the entire ecosystem. It's not just us coming up with ideas. Most of the ideas, I would say, largely come from us, but we're modifying and iterating and collaborating across the board. Okay. Yes, okay. We'll take 2 questions. Now listen, guys, the last question, you know the burden on the last question is very high, right? You can't leave everybody in a sad mood, okay? Whoever wants to be the last question, you better be second last is what you're saying.

I'm second to last. Lou Miscioscia here with Daiwa Capital Markets. So going back to Ben's question and continuing with flattery, I thought that was a great answer. I'm wondering if you could, though, relate that how things are moving forward with AI to PCs and phones. How soon do we think there will be something out there that could really start, take all the money that's being invested in capital with this training and then something that actually all people here and consumers around the world could use?

I think consumers around the world are all using ChatGPT. I use it every day. Increasingly, I use Gemini Pro as well and it's really good. And Deep Research, Google Deep Research is mind-blowing, completely, utterly mind-blowing. And so it's my tutor and I ask it a lot of questions and walk -- and many things it answers, and I just walk me through it step by step and it reasons through it step by step with me. And so I really find that almost everybody ought to be using ChatGPT. Everybody should use these AIs at least as a tutor. Every student, every kid growing up should use it as a tutor, no doubt. And then -- and of course, you know these systems now have computer vision and speech and the continuous dialogue, the interactive dialogue is insanely good, and it's going to get better. And so very soon, I'm fairly excited about the Meta smart glasses and other smart glasses coming that just connect to the cloud. And if you have a question, just ask. And what am I looking at? And just it knows. And while you're reading, explain this equation to me. You're looking at studying calculus and you've got an equation. Just explain it to me. And so there's a whole bunch of things that I can imagine the usefulness for. Obviously, autonomous vehicles, that's a sure thing now. Every car that's going to get sold in a few years, you either have autonomous capability or you're not going to get -- it's not going to sell. And just by comparison to all of the other technologies, other car options that are out there, you're going to suffer by comparison severely. And so I think AI is, from all of those applications, for consumer applications, I actually think is actually moving faster than enterprise. Enterprise is waiting for the use case. And the use case is not an extension to the tools we currently have, but it's a whole bunch of AI agents that are specialized for different domains. And all of those pieces of technology are now finally coming together. We've solved the grounding problem with retrieval. We've solved largely the guardrailing problems. We've solved the domain adaptation problems. I mean, the list of issues that people had in deploying these things are largely being solved. And so I think this year is going to be the year AI agents are going to get deployed. And I just told you, at least 1 is going to get deployed for sure, which is software engineers and digital marketing campaign managers, for sure, customer service, for sure. Those 3, you're already talking about the giant part of the information knowledge worker spend. And so I think this year, we're going to see it take off.

Jensen, I'm the final question. Ruben Roy from Stifel. So I'm going to keep this maybe a little bit high level and short. I wanted to talk a little bit about scaling and within the context of bottlenecks, and you talked about power yesterday and power going to the data center. But on the other hand, you're accelerating the cadence at which you're bringing products to market with the use of AI. So as you think about bringing more of your software engineers on to using agents, et cetera, are there other areas of your road map that you're thinking about that you could bring to market, whether it's networking or maybe we saw DSP, for instance, at the -- at a recent conference. Can you help with the bottlenecks, I guess, is the question as you get more of your own -- your -- I guess, eating your own cooking is the question.

Well, I appreciate the question. The AI agents that the technology layers, NIMs and libraries and the Blueprints, we use internally, and we use them internally and make sure that they're built like products that could be then shared with the ecosystem externally. And so we're trying to go as fast as we can ourselves in making sure that AI agents are all over NVIDIA. And we start with the most important parts of our company. And the reason for that is because why go deploy a technology of this complexity and importance in areas where it doesn't move the needle for the company? And so we started with chip design. And we have a company. We have an AI in the company called Chip NeMo. It's used every single day by all the engineers. And the second 1 is going to be software engineers. And the third will likely be our digital marketing organization. And so those are likely the first ones to go. But we're using -- we're trying to deploy it as quickly as we can ourselves. All right, you guys, thank you, everybody. Happy New Year.