Yes, even Nvidia's head of automotive is fighting for compute

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And behind it all, thousands of experts answering your call at 2 a.m. like it's 2 p.m. One partner, powering how business gets done for companies around the globe. When you add it all up, no one does business like Comcast Business. Go to ComcastBusiness.com slash enterprise to learn more. Hello and welcome to Decoder. I'm Nilay Patel, Editor-in-Chief of The Verge, and Decoder is my show about big ideas and other problems. Today, I'm talking with Zhinzhou Wu, who is Head of Automotive at NVIDIA. NVIDIA is obviously in the news constantly right now because of the AI boom. It's one of the most valuable companies in the world because the AI industry can't get enough of the company's GPUs. But NVIDIA is also a key supplier to the auto industry. It's had chips and cars for years now. And Zhinzhou has been instrumental in building a complete autonomous driving system that automakers can just use. It's already in place in newer Mercedes EVs, as you'll hear him mentioned several times. So I really wanted to get his perspective on how the auto industry is handling the big transition to self-driving EVs. The goal that every carmaker and supplier will tell you is coming, but which seems maybe farther away in 2026 than ever. The EV adoption cycle in the United States is fully off track. Self-driving seems to forever be stuck trying to solve the final 20% of situations. And cars themselves just keep getting more expensive, even as consumers are feeling the squeeze of inflation and rising energy prices across the board. You'll hear Zhinzhou say that there's actually startling progress in reinventing the fundamental nature of the car itself, something the industry has long called the software-defined vehicle, controlled by just a handful of powerful computers instead of dozens or even hundreds of independent electronic control units, or ECUs. If you're a Decoder listener, you have heard so many carmakers talk about the need to get away from ECUs. Zhinzhou says that moment is basically here. We also talked a lot about the Chinese car industry, and how it's been able to essentially get a head start on all of this, because it began building on EV architectures and platforms instead of having to manage a transition away from gas cars and all of those ECUs. Zhinzhou used to work at a Chinese OEM, so he has quite a bit of insight here. We also talked about working at NVIDIA itself. It's a unique company with a unique leader in Jensen Huang. And Zhinzhou said his three years there so far have been a rapid learning experience. He didn't shy away from the reality of needing to compete for resources and manufacturing capacity against the company's booming AI business. And his description of what wins those arguments, especially when his customers are as slow and cost-averse as automakers, was fascinating. Of course, we also talked about AI, and how NVIDIA's approach to autonomy brings together what Zhinzhou calls the classical stack and the ability for reasoning models to actually operate the car. There's a lot here, including the idea that you'll have an AI model literally talking to itself to figure out how to drive your car, which I find both incredibly interesting and incredibly funny. Of course, you can't talk about electric cars or autonomous vehicles without talking about Tesla and Elon Musk. So I asked Zhinzhou pretty directly where Tesla is on the full self-driving curve and whether that technology can actually do what Elon claims it can do without having to put LiDAR sensors on the car. You tell me if you think his answer holds up. Okay, Zhinzhou Wu, Head of Automotive at NVIDIA. Here we go. Zhinzhou Wu, you are the Head of Automotive at NVIDIA. Welcome to Decoder. Thanks for having me. I am really excited to talk to you. It feels like the very nature of what a car is, is up for grabs. It feels like the automotive industry is in a period of massive realignment, almost as though there was a sense of where the car was going to end up as a product for several years, and that is because of EV transition difficulties, because of U.S.-China trade war difficulties. All of that seems more messy than ever before. A lot of carmakers are retrenching, and it feels like your position at NVIDIA gives you a pretty wide view of what's going on in the car industry because you supply so many of the major automakers in virtually every country. So let's just start there. What's your view of where the car industry is on this kind of long, winding road to both autonomy and electrification? That's an excellent question. Actually, I've been working in the auto industry, well, not exactly in the auto industry, but let's say working in the automotive sector for probably 15 years, starting from my career in Qualcomm. I was heading the Qualcomm automotive team for a while. Obviously, we have heard the word, basically, software-defined vehicle. And then, basically, right now, with the AI technology, it's really getting into the next phase, what do we call AI-defined vehicle, essentially. With these massive technology innovations, as you said, the auto industry is changing pretty rapidly, I would say, over the last decade. As you know, I also worked as part of a Chinese OEM for a while, for five years, heading the automotive autonomous driving team. And now, basically, I'm at NVIDIA. So what I have seen over my 15 years of career is really basically have the, I would say, the opportunity to witness this massive change. The car from a, let's say, mostly mechanical, obviously, plus electrical, basically machine, to some things that's basically we can kind of upgrade the capability through OTA, software OTA pretty rapidly. That's what we call the software-defined vehicle era. And now, basically, technology kind of advanced towards a generative AI. We are seeing, basically, we're using AI to rewrite most of the software in car. That's what we call the AI-defined vehicle, essentially. And that is also, I think, in one hand, accelerates the development pace of the vehicle capability. And on the other hand, it's also basically, as in software, it also basically changed the way how we define vehicle as well. AI is impacting the whole industry at every level. So this is really exciting to see how the world will evolve from here with just the new technology innovations. Let me pull apart some terms there. I hear them a lot from car makers who love to come on this show and tell me what's going to happen to cars. But I think some of these terms are a little bit fuzzy on the edges. You said software-defined vehicle. That's a pretty fuzzy term, right? I think the idea there is we're going to get rid of all of the ECUs in a car that currently control lots and lots of different systems. And we will centralize all of those components into maybe one or two big computers in a car. Tesla is very famous for having done this. Rivian is, they've made a huge bet on that. We'll see if that saves from Rivian, which just on the show talking about that. Other legacy car makers have tried to do this. We had GM on the show. They said, look, we don't need to do that. We're fine. We'll do it our way. Ford tried to do this in big ways. They had to set up a skunkworks and build an entirely new kind of way of making a car that they're very proud of. There'll be a truck coming out from that effort sometime soon, we're told. I don't think the industry got there. That's basically what I'm saying. Like the startup car makers got to the point where they could claim to have a software-defined vehicle, where there were one or two big computers in a car controlling every system. The legacy automakers, for the most part, have not succeeded yet. And I will just put an asterisk. Maybe Ford will succeed with this new truck, but we don't know yet. Do you think the industry broadly is going to get to software-defined vehicles or do you think the legacy automakers are going to stay where they are? A hundred percent. Again, I had the, let's say, opportunity to witness what happened in China, you know, from 2018 to 2023. And, you know, the whole industry, you know, went through this massive change just in five years. Over there, not only the new OEMs, auto OEMs, but also the legacy ones, they have to adapt. And everybody is adapting to a, basically, single central computer kind of, you know, electrical architecture because that's how you compete. In the rest of the world as well, you know, obviously, we have our partners as well through basically Drive and DriveAV basic collaboration. For example, our partner Mercedes, their current generation is basically a central computer-based architecture. It's going to be in all their vehicles. And for the other basic OEMs, we are obviously working with all of them and trying to help them basically upgrade the architecture to one or two computers because there will Chinese manufacturing ecosystem works, and they got to reset. They got to design a bunch of cars as EVs, clean sheet, basically the way the startup car makers in the United States got to do, and build globally competitive cars from a totally new foundation without having to worry about a bunch of the stuff that, I don't know, legacy American car makers would have to worry about. And then the Chinese government obviously subsidized all that at huge rates. You worked there. Was that your experience? Is that basically how it went, that they got to start fresh? I think that's just one side of it. Definitely have less legacy basically a burden to worry about. It's an advantage. But what I also see is not only, as I said, in the new AMs, but even the global players there, they have to adapt to the China pace. And basically, at least from what I learned over there, you know, everybody is going through that pace. Otherwise, again, you won't be able to compete. But again, as you said, the wave, software-defined vehicle has been there for a long time. And Tesla is the one that's really basically, you know, I think taking it to full production. I'm not sure if the first one, but basically definitely to the, I would say, largest extent. And the only way to get there is to get to, first of all, the architecture described, right? It's this kind of architect enabled kind of a software upgrade without having many, many legacy discrete ECUs. Actually, I haven't heard people arguing against that recently. Maybe you heard something different, but I think that's really the next step for everybody at this stage is really almost like table stake, you know, for the next generation architecture. Obviously, we are talking to a lot of OEMs, but this is, I think, to say the least, that's a consensus that the industry is moving towards. Yeah, I'm just curious about the pathway there, because I agree with you that many, many people have said that is the end state and that enables everything that's going to come next. It just feels like the path there has been much bumpier than the industry expected. And part of that is, I don't know, the Trump administration doesn't like EVs. So EV sales and the tax credits here went away, and maybe EV sales spiked as all that demand got pulled forward. And maybe everybody wants a gas car. And maybe all of this is harder when you don't have a giant battery that can power all of these systems in perpetuity and you actually need to start the engine to get power to all these systems instead of having a 12-volt battery. Or maybe it's the Chinese automakers are so competitive and so subsidized that the cost to do it for the legacy automakers is hard to overcome, right? Because they do have the legacy infrastructure and dealer networks in the United States to care for, and we're just going to hold off on it, right? There's something about the path to this agreed-upon future state of the car that seems harder than I thought it would be or that anyone on the show over the past five years has said it would be. And I'm curious from your perspective, like you're the supplier, you're trying to sell the vision, you're trying to put the chips in all the cars. From your perspective, what has made that path harder? The auto industry is very heavy. You know, it involves basically massive supply chain and lots of companies, lots of employees, essentially. And to make a change on the architecture, and whenever you push out a car, you have to support it for 10, 15 years. This is the Nvidia, obviously, as a supplier, we are also making a similar commitment to our customers for travel technology we supply, including chipset, including, you know, other platforms and our AV technology. We will have the commitment to support for the same generation for 10, 15 years, even for the current generation of chip. If you think about it from a, you know, Silicon Valley, from Silicon provider kind of a perspective, it's almost insane. But that's the nature of auto business. It has a, basically, it's the nature of the business. It will kind of slow things down a bit. And that's one. And the other thing is basically because of the technology is changing so fast from, let's say, the automotive as we know before and to software-defined vehicle to AI-defined vehicle. You have to come through almost like a different talent pool to be able to set up the company in proper way to adapt to this new wave of technology innovations. And that's why NVIDIA can come in and help, essentially, right? Because we believe the technology is getting to, we're talking about the autonomous vehicle, obviously, you know, mainly here. The technology is getting to a level of maturity and we are going to take in this technology to mass production. And the supplier can come in. And that's why we are not only, you know, provide AV technology, but we are providing the whole, basically, platform, you know, starting from, obviously, a chip, but also to operating system, also to open source model, and also to, you know, what we call the halos, the safety kind of operating system to help the OEM to be able to adapt to this new world faster. And as the nature of the business is, basically, not everybody can run at the same speed. So for sure. And it will take some time, obviously, for everybody to get to the finish line. But, you know, again, my job at NVIDIA is to try to help everybody to get to this everything that moves that will be autonomous, this kind of vision as soon as possible. Let me ask about your part of NVIDIA now, because I think this brings us to the decoder questions. I think everyone listening to this show is probably very familiar with the run NVIDIA has been on with AI. It's one of the most valuable companies in the world. Every GPU that NVIDIA can make is accounted for. How many people work at NVIDIA Automotive? We have actually quite a sizable team, somewhere between, basically, in the order of thousands, essentially, in the automotive team. But it's a pretty, again, because we are working on the whole platform, so there's the hardware, software, and model, and the infrastructure. So it's a pretty sizable team. And also, we have a lot of things we can leverage from the other teams as well. For example, we have, I'm pretty sure you heard about the Cosmos and EmoTron. These are our basic open source foundation models. We are leveraging heavily from work from that side as well. And how is your team organized? You mentioned you've got hardware, software. You've got models. Is that the basic structure of the team or is it organized differently? Yes, I would, well, on the engineering side, obviously, we have product, we have strategy, we have, you know, Something kind of behind the scene. Sometimes we call them unsung heroes, right? The mapping, for example, which is still very critical for L3, L4, the high-level autonomy paths. And the data infrastructure. Those are like the literal navigation maps. That's what you're talking about. Well, as HD map as well. Okay. So it's roughly that's, you know, I divide my team this way. Yes. And then is that all global? Is that mostly United States? Where's that located? Mostly in the United States, but we do have a presence in China and Europe as well. Obviously, we are building a global product, the global platform. So we need the support team everywhere. You mentioned that you rely on some of the foundation models NVIDIA has developed more broadly. How is your team structured inside of NVIDIA? Does it fit into the AI strategy? Is it set apart? Are you more siloed? How does that work? So in NVIDIA, we have, let's say, centralized hardware team, which are responsible for the hardware roadmap, you know, on our GPU, basically, and the CPU and all the chipset, basically, strategy and the production. And we have centralized software team. And the automotive is a separate, I would say, organization, which is very much more automotive, basically focused with the mission of really building the automotive platform to leverage the work from our hardware team and the software team and adapt to total automotive. And then basically, we have the model team as well. Open source model team actually part of the, you know, NVIDIA also have a culture of virtual teams. For example, our open source model for NemoTron and Cosmos, they all have a city, you know, across our research team and the software team and the hardware team. But they are virtual teams that basically work on these open source foundation models. And, you know, we can leverage, basically, those work and then basically in the automotive organization to build up Moyo, for example, as, you know, hopefully you have heard about it, to help the AV industry basically have a powerful open source model to work on. As I said, basically every GPU NVIDIA can manufacture is accounted for in some way. It's the nature of the AI industry right now. They're going to go into some Neo cloud somewhere. Do you have to fight for resources and attention against that business, which is growing at the speed and the scale that it's growing at? Yes, believe it or not, of course. So basically, for example, believe it or not, even NVIDIA, basically, we do have a limited supply of GPU for compute. So we have an internal priority, and you know, I'm working with my colleagues, basically almost on a weekly basis to decide how to set aside these, you know, different compute, sometimes for training, sometimes for test, resources for different thread of work in the company. And sometimes we need Jenssen to help, but yeah. How does that work? What does that debate look like? Is it From his strategic thinking and how he thinks about a product, how he thinks about a strategy, he's also uniquely technically deep. So it's also quite inspiring basic experience as well to just also to see how much he's keep up to date on the technical side as well. Again, it's really, I would say, a once-in-a-lifetime experience and opportunity for me to be able to learn from Jensen, yeah. When you describe the opportunity for autonomy, particularly in the future, because that seems like the big bet, right? Yes. We're going to bring to bear NVIDIA's compute excellence and the power of AI to cars and have them drive themselves. What does that revenue model look like? Does it look like you're just selling chips and software to automakers? Does it look like consumers pay a subscription and some of that flows back to you? Where does the trillion dollars come from? So basically, if you look at it, basically, right now, we firmly believe that everything that moves will be autonomous. Every mile driven by the car in the future will be autonomous. So right now, if you look at it, basically, among all the cars, we drive a certain trillion miles basically per year. And right now, the percentage of autonomous miles among all the miles all the mileage driven is probably, let's say, negligible. I think it's 0.006% or something like that. So this is really the opportunity, you know, in front of us. So NVIDIA's view is basically we'll help the ecosystem to get there, you know, as soon as possible by providing basically all the foundation technology piece again, starting from chip to operating system, and then basically to what we call Halos. Again, the Halos operating system is really important because it doesn't, you know, not only provide the SDK and the APIs for folks to develop models on hardware, but also provide basically the safety guardrail for you for developer to to put a model on it. And then basically we also define what we call the Hyperion, basically hardware platform. That's a production ready platform which includes both the computer resource, you know, the ECUs, and also the sensor suite. We think it's necessary to achieve different level of autonomy. And on top of that, we provide basically the Owl model, basically open source model, which we trained and open source not only the model architecture, but also the parameters and the data that basically you can use to fine tune the model, you know, on our platform. And on top of that, we also provide basically all the infrastructure needed, for example, simulation right now, it's really important for for developing AV. We usually call the AV problem is becoming a three computer problem, right? There's the training computer, as the simulation computer, and then then there's the inference computing in the car. All these technology piece, you know, we want to provide to the ecosystem, you know, platform, which we call NVIDIA drive, essentially so that folks can develop their technology on top of our platform. And we hope that we can get a percentage, you know, of the revenue that, you know, the ecosystem can get from every mileage that driven autonomy in the future. This is where the trillion dollar basically opportunity can come from. Let's take a quick break here. We'll be back in just a minute. Support for the show comes from Quo. Running a business means you're actually reachable until you're not. And the moment you miss that call, that text, that follow up, a competitor is the one that picks up. That's why today's episode is brought to you by Quo, spelled Q-U-O. The business phone system built so you never miss an opportunity. All your calls, texts, and voicemails live in one place. So anyone on your team can pick up a conversation, see the full history, and respond fast. Quo is the number one rated business phone system on G2, trusted by over 90,000 businesses who rely on it to stay reachable and look professional every day. Set up in minutes on any device, keep your existing number, and add teammates as you grow. No IT, no hassle. And Quo says it's easy. 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And now Vanta is helping companies like yours watch for risks that show up between audits across your vendors, your AI tools, and your whole environment. How? The Vanta agent works like a 24-7 GRC engineer in the background, finding issues, drafting fixes for you, and cutting vendor assessment time up by 50%. Whether you're a fast-growing startup or a global enterprise, Vanta is here to help you automate your security and compliance and earn and prove trust. Get started today at Vanta.com slash decoder. That's V-A-N-T-A dot com slash decoder. Welcome back. I'm talking with NVIDIA's head of automotive, Shenzhou Wu, about how NVIDIA fits into the larger auto industry. So revenue per mile, that sounds like the core metrics that you're chasing. Where does revenue per mile come from for a user? When I drive a car, do I pay a subscription? Or are you thinking it's robo-taxis everywhere and they're being monetized per ride? Where does revenue per mile come from and how does that number go up? That's right. Well, I think the world will, I think, will embrace both models. One is basically a robo-taxi. You know, as you see, there's quite a few successful ones basically doing, you know, in China and the U.S. and in the world. And we'll see more, hopefully, going down this path. And basically, you know, we'll have like a taxi-like fleet where you can enjoy taking you from, you know, place A to place B without a driver in the car. And then I think the passenger fleet will also still, you know, continue to exist for a long time because, you know, there's many people who still prefer a private, basically, let's say, space during travel. It's like, you know, many people still prefer their house as compared to rent an apartment. Obviously, there's an economy behind this as well. So we think both models will thrive. That's why we're working with both the robo-taxi companies and also the auto OEMs by providing, you know, and obviously the AV software developer, you know, companies to help them basically to by supplying different technology pieces from NVIDIA to them. One of the interesting dynamics through, I would say, at least the electrification portion of the past five years has been legacy automakers realizing that they had become insurance companies and financing companies and their suppliers were making the cars. And they had lost control of car design in like a big way. The tier one suppliers to the big automakers were in many ways in charge of big subsystems of the cars. And when they wanted to do an over-the-air update, they had to go talk to 15 different suppliers to get that done. And I've heard this complaint dozens and dozens of times on this show. And they all kind of realized, oh, we need to take back the engineering of the car. We need to be much more firmly in control of the platform of the car. It sounds like in autonomy for a variety of reasons, NVIDIA sees an opportunity to become the main supplier to a wide variety of car makers. That's obviously intention with them thinking, oh, we need to take control of the car. Right? I think Tesla might run NVIDIA chips, but they are very proud of the fact that they wrote every line of that code. And that is their platform and they've made their technology bets. Rivian, I think, WhatsApp is very proud of the fact that he is in charge of that platform company and he's going to build that platform. RJ is certainly very proud of the fact that Rivian is that kind of company. What's the dynamic there? Because it doesn't seem like every car maker can stand up the technology bet and forward invest on the hope that the revenue pay off. They will need a supplier like NVIDIA. to show up with a ready-made platform and business model. Is that tilting more in your favor now? Have we gotten out of those woods or is it still up in the air? I think the beauty of the NVIDIA business model in the automotive side is really our platform is completely open. We provide multiple layers of services and depends on basically what the OEM need or, you know, robotaxi company need, they can select what do they want to, you know, work with us, you know, basically up to which layer. For, as you mentioned, basically Tesla, you know, some OEMs, they are so capable, they will even want to build their own inference chip in the car. Even for that, we're okay. You know, we'll still continue working with them. Actually, we are working with the, you know, Tesla and the many OEMs who are building using their own inference chip by collaborating with them in the cloud, by providing them, we even try to help optimize their, you know, models, basically, you know, again, with different OEMs, we have different basic collaborations because we still have the simulation computer and the training computing in the infrastructure we're working with them. And for some of the OEMs, basically, they would like to have a more, towards a turnkey solution. We are very happy to work with them as well. In that case, we are gonna go all the way. We are working like a tier one or tier 1.5 essentially, just to go hands-by-hands. This is our drive AV kind of partners, for example, Mercedes, you know, we work very closely with them to define the products they want and then also adapt our drive AV stack to work seamlessly in that vehicle. And then actually the engineers from both sides work pretty closely to make it really, let's say, adapt well into the Mercedes, let's say, design DNA and the customer experience they would like to offer. We are not picking winners per se. We try to help OEMs based on their capability at different levels. So as I said, the openness is really important for our basically kind of engagement model with OEMs. One of the reasons I'm so curious about this is you mentioned training models, you mentioned, I think in other interviews that you're doing synthetic data to train autonomy in different ways. I'm very curious about that. It just strikes me looking at the industry, Waymo has this gigantic lead in autonomous miles driven and they're very proud of it and that's helped make their cars successful as they are in the markets they're in. Tesla obviously has a huge number as well because they're training on the actual cars that are being driven. Not every automaker can figure out how to get to a billion autonomous miles driven, right? They're going to have to rely on some third party to get them to at least the status quo, if not beyond. That feels like NVIDIA's sitting there ready to be that third party. Is that a lot of the sell to the automakers that you can just buy our technology off the shelf for and whatever open capacity that you want and we will just quickly get you to a competitive state? I would say this is one of the compelling points of the base for OEMs to engage with NVIDIA in the Hyperion ecosystem, in the drive vehicle system because one of the key things for Hyperion, basically, again, which defined the compute architecture and also the sensor architecture is the data sharing. For anybody who engage, become a drive partner, NVIDIA drive partner, we not only, we share data through our kind of existing program, which we collect basically millions of hours of data and also basically through the different car programs, basically, we are also accumulating that data to, you know, from different OEMs. And then basically, you know, we can build a model, first of all, basically, which can work with all the, you know, which are trained with all this data. And also we make sure at least the data basically collected in our different car program is shared with the OEM. That's number one. Number two is in the new era, we strongly believe compute is data as well. As you mentioned, there's a lot of synthetic data. And also there's a neural reconstruct data, which we call Unreal. This is a very important piece of technology in simulation where we have collected data from the field, but we can use neural reconstruction to sometime to fuzz the data to, you know, change the background or change the car trajectory. We can basically generate a lot of variants of the same data. And all these data, again, they need to compute, obviously, to generate this kind of millions and tens of millions of data. And we can share with them, with everybody who's engaged in our ecosystem. And in this way, collectively from all the players that engage with the drive ecosystem, we can catch up on the data gap, which is very important. So the synthetic data, I think I understand, right? You're going to collect a bunch of real-world driving examples. You'll put it into a simulator. The simulator will then blur the data. Right? I think the example that I've heard you give is there was a pedestrian that came out and we can just delay the pedestrian to make that person come out later. And the car will have to react to it as though it's real. That's right. You're going to run lots of training against lots of different variations of the same data. That's fascinating to me. I understand why all the car makers would buy into that. Why would they buy into the data sharing? Is it just a recognition that collectively they stand a better chance of catching up? Is it they just don't want to pay the money? Is it cheaper? Why would they participate in our competitors in that kind of data sharing arrangement? Both are absolutely true. And actually, the cost saving is enormous. Basically, you know, data collection, running a fleet of huge size, essentially, it's a big, I would say, capital spending, you know, for anybody who wants to do that. And also, it's kind of repetitive as well. You know, if you can find, you know, for example, what we provide in the drive platform or the drive ecosystem, it's a, it's, it can save a lot of effort and basically money from, from our customers. I'm curious about that because the idea that we're going to train stuff and then you're going to have a model in the car and we'll have an AI defined car, the sort of classical approach to self-driving was we're going to throw more and more data at the problem and eventually the car will kind of know how to do everything and it will, we'll have mapped all the roads on top of everything. Right? So you're going to, you know, I have a, I have a Cadillac EV and the way super cruise works is it works on roads that are mapped. And eventually, you know, the, the bet is they'll map more and more roads and more and more things. And the car will become more capable. It feels like NVIDIA's approach is for the car to be smart enough to do anything with or without the maps. And that requires a different approach to data collection, a different approach to commute, and then obviously a bigger bet on AI. Is that split real? Have you just made that jump? Is that the future of the platform or are you in the middle? Well, the approach we take right now for what we call the L2++, essentially it's mapless. As you said, you know, correctly. So basically the, the model would definitely need more data. Um, and to cover better, uh, more corner case and the model is obviously getting bigger, uh, as we speak as well, basically, you know, for this generation, next generation, um, we are going to use a much bigger model with, uh, with, with, with more parameters. And also a foundation models will make a play a big role here. And, uh, um, you know, being able to make this model very capable, essentially it, you know, more data is very, very critical. But on the other hand, though, the trend of using foundation model, which is already trained with internet data, um, that can help, uh, coming help as well. That's why, you know, I emphasized quite a few times on the connection with the foundation model effort inside NVIDIA. You know, with the reasoning model and the foundation model, these are the things that we can, uh, leverage from the, uh, the, let's say the frontier model perspective, um, and the leverage internet to basically um kind of scale data to be able to help the vehicle to generalize better even without the vehicle specific data. So this is one of the, you know, I would say the main, main direction we are betting on towards, let's say, higher level of autonomy, especially level four, right? This is uh one of the main um main work thread, um, you know, we are focusing on right now. And back to OEM, um, I think being able to leverage basically, you know, uh, what do we have built upon, uh, through our collaborations with the existing basically engagement, um, uh, and our massive capability of, um, uh, basically data generation using a synthetic data set and the neuro reconstruction, and also being able to leverage the foundation model, uh, capability, uh, you know, which are trained from more general data, but which will, which will help the model to reason better, to generalize better. These are the things we can offer to our customers. I feel like I have to ask about safety now. I'm sure it's more complicated than this, but you're talking about a foundation model reasoning through self-driving and all I have in my head is ChatGPT apologizing to me because it got it wrong, you know, while, while the car crashes or one of those horrible long latency loops where the model goes off in the wrong direction and realizes OS level software and the application level software, you know, to the high standard, which was very important, which is very critical to be able to deploy anything, you know, to drive the car. That's number one. And number two is basically, we take a slightly different approach than some of the, you know, players in this space. We are actually have a redundant stack, even for our L2 plus result ADAS basically function. Other than the end-to-end model, which is basically you have pixel in, you have trajectory out. We also have a classical stack. Classical stack means it's more developed based on this safety standard as we know it. With a component, basically, it's a stack with many components and each component can be verified, you know, using this known standard. That's what I refer to as a classical stack. And when you have two stacks basically kind of run in parallel, the classical stack is acting like a, sometimes we call it the big brother. But essentially, it's a safety guardrail. Try to verify all the trajectories from the end-to-end model and use it, you know, use the, let's say, known safety standard to verify it's safe at every frame. So that's a very important concept, you know, we have. And not only concept, but the implementation we have in our stack. And we will take this, obviously, this will be so critical for higher level autonomy L4. So this will, this is also the foundation of our, you know, kind of L4 stack where we have full redundancy, not only at the sensor set, but at the software architecture set. So this is the, I would say the second point I want to make to answer your safety question. And the number three, also basically, when we develop the model, basically, we are also trying to make the model reduce the hallucination as much as we can, right? So the way to do that is really basically through massive validation. You know, we are looking at, we are building basically massive simulation test data set for every model we release. Right now, we are looking at, in our program right now, we are running 5 million basically tests every day. And obviously, roughly every day, we have 10 iterations of the model, the end-to-end model of mild. So we're doing really massive validation to make sure, you know, in all these scenarios, you can think of that, you know, every test is a test scenario. You know, the model is generated by trajectory. So that's also super critical for us. So this is what we do to make sure our product is safe. Let me ask you a really dumb question I'm really curious about. You've talked a lot about the model and how it will operate the car. And yes, there's, you know, the classical stack is the safety guardrail. Is the model reasoning in language, like every other model? Is it sitting there in the background saying, I see a stop sign. What do I do? I'd better stop. I'm going to go hit the brakes the way that any sort of general model reasons in language in the background. Short answer is yes. And in our next generation model, which we are going to deploy in the next generation of, you know, vehicles, because the current generation is the Orin, which has, you know, more or less more limited compute. The next generation is Soar based. We will, you know, have the model trained with language embedded. So being able to reason through language is very important. And also you can chat with the model. You can ask the model about what he's doing. And then you can also ask a model to speed up or slow down and make a lane change, for example. As it's literally driving, it's saying to itself, I see a car over there. I need to change lanes to get ready for the exit that's coming in a couple miles. It's doing that in language to operate the car. I think it's a combination of things. Language is already embedding the model, but the visual signal is also super important, as you know. So it's, I wouldn't say it's multi-model, but language is a part of it. Obviously, as you know, the model is a black box. We don't exactly know basically what is exactly doing, but you can ask about it. And then, you know, the model will answer what it's trying to do. And you can reason with that as well. I just have this vision of a chatbot model just like freaking out as it careens on the highway at 55 miles an hour. They recently, basically, GTC, I think Jensen did it, GTC Taiwan released a video that the model is talking constantly, explaining what's trying to do. It can be quite annoying as well if you really try to hear everything the model is trying to reason about. What's the latency on that? Is that, I mean, obviously you're deploying the system, so it must be working. But is there an attempt to reduce the latency of that? I feel like language is inherently slow compared to what you need to do to drive. Like, I'm not thinking in language when I drive my car. That's why I said it's multi-model, right? But to reduce the end-to-end model, end-to-end latency is super important. Actually, that's one of the key advantages of deploying, you know, drive the car with the model. Because if you think about it, the old basically stack or the classical stack, which has multiple component, it's usually basically takes multiple hundred milliseconds. But with a model, because it's just inference time, it's separated between, you know, input, which is pixel and trajectory, you can reduce the basically, depends on the compute, obviously, capability you have. But even in the current generation, we can control it to be within a hundred millisecond, which is pretty fast. And regarding the language reasoning, obviously, if you think about it, well, that's human brain, right? But if you think about the language, basically, I would say the information rate is already abstracted. The information rate is not super high. And we are obviously using the internet data to train this kind of language-based reasoning capability. I think the latency is well under control. Let me put it that way. And again, you're not driving the car with language only. That's the key thing, as I said. Usually, the reasoning part is, I believe it's slower. Again, we don't know exactly what the model is doing, but the pixel part is, that's what drives the basic instantaneous kind of reaction of the vehicle. If you ask Anthropic, they will tell you that Claude has feelings and emotions, and it can get scared. Do you think about that? Do you think your models have emotions when they're driving the car? We will use the guardrail to make sure it doesn't get too moody. I'm just curious. I mean, like I said, we don't know how the models are working. I literally have a vision of the model being like, oh my God, I'm going so fast. But maybe the classical system will cut that down. Yeah. We have to take on a short break here. We'll be right back. Support for the show comes from Upwork. If you're looking to grow, you need people who can take your business to the next level. Upwork is a one-stop platform to find, hire, and pay expert freelancers across more than 125 categories. 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Instead, it just describes them, suggests what to do about them, and then leaves you to do it. That's not help. That's homework. ServiceNow's AI specialists are different. They're not a tool. Think of them as digital teammates who actually do the work from start to finish. Cases get resolved. Requests get processed. Loops get closed. And most importantly, no extra work for you. Because when you can truly delegate to AI, you can get back to the work only fast connectivity to be autonomous with your approach? Not necessarily, but we do require some connectivity to get navigation information and some mapping information. Most of these are navigation maps. So not only the model side and also the classical stack, which we do use some of the navigation map information to help us understand the world better, essentially. I'm only asking because I covered the launch of 5G networks in great detail, and all of the telecom companies promised me that 5G would enable autonomous cars. And it seems like your approach is the one that will lean the most heavily on low-latency networks in that way. Well, this is not wrong, but on the other hand, basically, the car has to drive autonomously in a completely blind spot as well. Real time, basically, low latency, I would say content dependency, have that dependency in the cloud, at least for the ADAS kind of application, L2 plus, which is what we call it, which is meant to work everywhere. Building that dependency is not a good idea. When you get to level 4, level 5, that's when you have the connectivity dependency. That's right. Yes. Yeah. What happens when you lose the connectivity at level 4 autonomy? When you're at level 5 and you're not steering wheel anymore and you lose connectivity, what happens? You know, you can think of connectivity as kind of a sensor. And again, the basic driving capability cannot have huge dependency on that. One of the core concepts of developing level 4 technology is you have sensor redundancy. That's not only for basically GPS, but also for camera, radar, everything you see. For every single point of failure, the car have to be able to drive safely. It's like you suddenly, you know, lost your GPS, but the car with the local perception, it need to be able to get to a safe point and pull over. That's the minimum requirement, you know, an L4 system needs to have. So this is just the L4 basic principle to be able to develop such a system. I'm very curious about where all of the sensor stacks live in the car, how much compute is in the car, how much RAM we need to put in cars at a time of increasing RAM prices. This all seems like a lot of extra cost to layer into cars, which are increasingly getting more expensive and which, you know, consumers at least in the United States, feel like they're rebelling against in lots of ways. I can look at our own website traffic and I'm like, everybody wants to buy a slate truck for $25,000 and it doesn't even have radio, right? Like that's just a battery and wheels. That's the whole car. It doesn't even have paint job, right? We're getting rid of paint jobs on the cars now to keep the cost down. You're talking about a lot of compute in the car, a lot of connectivity, maybe a bunch of RAM to load the models on. That's right. How does that play out? Does that push you more into that robo-taxi model or do you think people are just going to buy expensive self-driving cars? Definitely building autonomous car need a lot of hardware, but the other trend is the hardware cost is, I would say, is dropping pretty rapidly as well as the technology becomes more mature. For example, radar, right? Even in my career, basically, I have seen radar price probably drop by at least four or five times over 15 years because the volume just getting much bigger and the bigger and basically the cost, you know, I see have a weakness basically the drop of those sensors, actual camera sensor price dropped as well as there's more competitors and the more competition and the competition bring lower price when the volume become bigger. The scale effect is definitely there, you know, right now in ADAS and all the components become much more and more basically mature and to some level commodity. And on the computer side, as you know, the computers growing at such a rapid pace. So we talk about Moore's law, you know, in the semiconductor industry, you know, some time ago, but in the auto basically segment, in the autonomous driving segment, the compute the compute need has been growing basically at a really astonishing pace. Roughly, we are talking about 10 times every two years. It's insane. With the success of AI and obviously NVIDIA, we will be able to provide this kind of massive compute to cars at affordable price. But in the cloud or in the car? In the car, in the car. I asked you about fighting for training capacity earlier. Do you have to fight for fab capacity too? Because those costs are going up for everybody. Yes, of course. Yes. But in kind of, I'm curious, it's NVIDIA's demand that's driving up the cost for everybody. So how do you go get fab capacity when the other divisions at NVIDIA are willing to pay whatever rates anyone demands? Well, the same answer I give you, right? Again, you know, I don't know if there's anything I can say more, right? Because, you know, we are such a strategic company and, you know, our automotive business is doing well as well, but not at the pace of our data center business is doing, obviously. But basically, we are strong believer Jensen himself as well of the AV future. And we are keeping investing basically in this technology and in this future, not only from, you know, allocating external computer, but from fab capacity as well. So, but that's definitely one of the things we are looking into. Actually, most likely the even the chip price might need to go up essentially because of this, you know, intense kind of demand for every chip everybody can grab on essentially. But the positive side is basically the technology is really, you know, getting, you know, I talked about the chip side, and also I talked a little bit about sensor side. And we are looking at basically, for example, I talked about Hyperion, which is basically kind of product ready, compute plus sensor kind of platform. So we are looking, we are really trying to balance between the cost and what we can do. We are looking at what we call the, you know, sufficient necessary kind of sensor set to achieve high level of autonomy. So in Hyperion 10, for example, we really offer two versions. One is a base, which is mostly camera, 10 camera, three radar, no LiDAR. And, you know, it's a very cost effective way to build a basically kind of L2++ ADAS kind of vehicle. And on the other hand, for the high end of what we call the Hyperion high, we provide basically, you know, the sensor set required, which have like, I think, 14 camera and three LiDARs and basically seven radars essentially to be able to drive, have enough sensor redundancy to be able to drive, you know, L4. We also provide, you need the ECU redundancy as well. You need two basically kind of our next generation, well, actually to be more precise, current generation SOAR based kind of computer platform. But just imagine basically you have a car really can drive by itself. We believe with this sensor set and this computer basically architecture, we can get to that level of autonomy, which can basically justify the cost. The minimum sensor set for autonomy feels hotly debated. It's been hotly debated for a long time. I think Elon Musk saying that he thought LiDAR was a local maximum ages ago was the beginning of this debate. This debate has not quelled in any way, shape or form. Do you think level four requires LiDAR? Short answer is yes. We believe that LiDAR is the important sensor, you know, to provide the safety and the redundancy required for level four autonomy. But on the other hand, you know, it's difficult to say it's 100% necessary. We believe this is a very much feasible path to based on, as I said, Hyperion 10 high sensor configuration to get to really high level of both urban and highway level four capability. On the other hand, you know, theoretically, you know, people can prove out with massive mileage essentially to say that LiDAR may not be, you know, necessary, but it will come with the ODD limitation, essentially. Sorry, what's an ODD limitation? ODD is basically applicable basically domain, you can deploy the technology. Obviously, we have done quite a bit of analysis on this based on the our current understanding and the framework, you know, we use to do this analysis, you know, we believe that to deploy this L4 technology in all the ODDs that our customer can benefit from, it's much better to have LiDAR as compared to not having it. When you look at where Tesla is with full self-driving and their vehicles and their absolute commitment to being a vision-based system, do you think that they are currently ahead of you? Do you think they're at parity? Do you think they're behind you? So, you know, there's two levels of the answer, I guess, to this question. And obviously, basically, for the basic auto-plus-plus, basically, technology, Elon is probably ahead of everybody, essentially. You know, he has a vision a long time ago and he has stick to the vision for a long time to be able to, and develop and test the technology among massive fleet. Nobody would argue that Elon is ahead of everybody in the L2 or basically, sorry, ADAS kind of market. And everybody is playing a catch-up game, essentially. And we are very happy, actually, Elon is so successful and also, obviously, Elon is a big customer for us as well. For both SpaceX and Tesla in the GPU computer side. And we are supporting him and his team to make sure they're successful. And for level four, essentially, I think it's more open, I would say, because obviously, there's established players who have played for to the, who are already basically like Waymo, who are doing basically already um taking customers to really experience the L4 kind of experience using the methodologies they use. And Tesla is probably still trying to find the path there. And again, we don't try to pick winners, but we try to help everybody to be able to develop that technology. And our mission is really try to make the um AV ecosystem get to this vision of, you know, every my, all, everything moves that need to be, will be autonomous. This kind of vision becomes a reality. Have you had conversations with Tesla executives about using LiDAR? It seems oddly religious for no reason, especially if the costs are coming down, as you say. At some point, if the better technology solution is right there, it feels like everyone should just use it. Have you had those conversations? Well, actually, no, not myself. My team definitely has. And well, I'm looking forward to have that conversation with them. Actually, I would like to, you know, anyway, so as I said, much of this is just basically uh science and basically uh reasoning. So it's good to hear that view as well. I'm gonna wrap up by talking about something that maybe is the least in your control. Models are gonna keep getting better. NVIDIA is gonna keep making chips. Maybe customers are gonna keep demanding self-driving. That all feels like something you have a handle on. But the auto market, the cutting edge of the auto market is happening in China. I think we can just agree on this. US consumers open TikTok and see car influencers talking about BYD vehicles. And they complain in the comments that they can't get those cars. I watched a video of a Buick that is in China. It's a Buick EV that you can't get in the United States. And US customers are curious. The Buick is making better cars in China than they're making here. There's a lot of trade barriers between the United States and China. NVIDIA sits in the middle of that fight in all kinds of ways. Whether it's tariffs on imports of car components, whether it's literal blocks on what chips can be sold and where the revenue from those chips go. As you try to push the car market forward, how does the US-China trade chaos play into it? Is that something you think about? Is it something that's slowing the industry down? Is it something that you can push through? Well, basically, well, I certainly believe the, you know, policymakers, they have their reasoning and the basically rationale to make the policy, you know, as we see right now. And, you know, as NVIDIA, again, we are open ecosystem player. We still have a lot of customers in China. We try to basically help this, you know, for example, we are still supplying, you know, actually in-car inference chips because they are still basically below the, let's say, the threshold of basically, you know, what GPU is allowed to sell in the China market. And then basically, we are also working with all the Chinese OEMs. Actually, not all of them, obviously, but quite a few of them to help them on the infrastructure side by supplying them basically simulation tools. And we're working with them on open source models, Cosmos, and Amaleo. And then basically, we can, on one hand, we can help them to get their models better. On the other hand, we can also learn from the competition in the China market. Obviously, we are also working very closely with the rest of the world, basically OEMs, and try to supply, you know, all NVIDIA basically platforms and at different layers to different OEMs and help them to be successful as well. So again, we don't pick winners and we try to basically work with everybody. And the mission is super clear. And we try to, you know, make AV this vision become a reality as soon as possible. When you talk about sharing data between OEMs to train the models better and to make them more capable, are there any regulatory roadblocks or competitive roadblocks between sharing data from Chinese OEMs and American and European OEMs? Oh, yes, of course. So we have to live with the original basic, actually not only China, actually other regions have restrictions as well. For example, Europe has certain regulations regarding data. So we are conformed to all the kind of local kind of regulation to make sure we are compliant to all the basic regulations we need to be compliant to at different regions. Does that mean the regional variants of the models have different capabilities or they're better at different things? Because if the input data is different, it seems like maybe the output will be different as well. Absolutely. That's a, well, first of all, we don't, we try not to basically, for the production model, we try not to basically fork it as much as we can. But there will be basically original kind of a difference. So the model will behave differently in different regions based on the input. And some of the things are what we call the country coded. So you have to, obviously the rules are quite different in different regions, like in Europe as compared to the U.S. Some adaptation, you know, is required. And some parameters are different as well. Yeah, so it's quite an interesting journey trying to scale the technology into definitely different parts of the world. Do you think that based on the different regulatory approaches, the different data approaches, the different input data, the different configuration of the OEMs and what they're willing to invest in, the different subsidies from the governments, do you think China will get to level four as a mainstream self-driving experience first? Because if I had to look at it, I would bet that level four self-driving will happen in China way before it happens in the United States as a mainstream experience. I actually don't think that's true. As you know, basically Waymo is already getting to everybody to L4 experience, at least in the Soledos in San Francisco. And it's scaling pretty fast. And China is, you know, they're, obviously it's a much more dynamic competing kind of a market. And there's quite a few players there. But my experience, none of them has get to the maturity of Waymo, at least in San Francisco. But again, we, we're trying to help everybody in the ecosystem. So from OEM perspective, it's a different competition landscape, but even, even basically on the OEM side, I think, you know, different regions have different kind of a, well, one side is probably, you know, the China streets is also much more, much more challenging as compared to the U.S. streets. So, you know, to be able to, and the level four, I would sometime call the zero one game. Either you have it or you don't have it. Actually, as of today, I think the only one who really have proven that L4 is, can be safely deployable to every customer without driver in a kind of city kind of size region without any limitation is still in U.S., not in China. I think Waymo would, would, is going to be very flattered to hear them described as a mainstream experience. I will accept that for some subset of people in San Francisco, Waymo is a mainstream experience. I think for the vast majority of Americans, it is not yet. And that is the big turn, right? When can a Waymo work in the snow? When they're going to deploy them in Chicago, I'm, as somebody who lives in Chicago for a long time. I'm very curious how that goes in Chicago and New York city, right? The, the question I have is the mainstream experience feels like you just buy a car and just like level two ADAS is kind of a commodity in cars. Now level four will be a mainstream commodity in cars. You push the button and start driving itself. How far away do you think we are from that? Well, first of all, that's actually exactly my mission, you know, try to help the industry to get there. I would say if I need to give a time, I would say five years, less than five years. Well, that is a bold prediction. I think we're going to leave it there because we're at time. You've been really great. I'm excited to talk to you again. We'll have you back before five years to check in on that prediction. But what should we be looking for next from NVIDIA? There's quite a few things we are planning. So first of all, by I think end of this year, we are rolling out our technology on the basically ADA side in all Mercedes vehicles and some other partners as well to all over United States. And also basically, you know, starting for the next few years, this technology we're trying to roll out to the rest of the world. And meanwhile, basically, we are also working closely with Uber, for example, Uber. We announced that in GTC, try to basically roll out our L4 basically kind of service in the next few years. It's super exciting. And on top of that, obviously, we are again an ecosystem player. We are working, you know, with almost like all OEMs right now. I would say 80 percent of the mass production OEMs are in NVIDIA's Hyperion basically ecosystem for L4. So we are really building with this future with everybody. So this is a hopefully you'll see more exciting announcements from us somewhere down the road. Yeah, well, like I said, we'll have to have you back soon. Thank you so much for being on Decoder. Thanks for having me, Nilay. It's a very nice chatting with you. I'd like to thank Xinjue Wu for taking the time to speak with me and thank you for listening. I hope you enjoyed it. To let us know what you thought about this episode or really anything else at all, drop us a line. You can email us at decoder at the verge.com. We really do read all the To have the place you go to watch soccer because of the camaraderie you build with everyone else, with the bartenders, with the staff, with the other people who come to the bar. The community here at Roebling Sporting Club, it's unlike any other sports bar that I've found in New York or in Brooklyn or anywhere really. 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