Even Nvidia’s head of automotive fights with Nvidia for compute

Today, I’m talking with Xinzhou Wu, who is the head of automotive at Nvidia. Nvidia is obviously in the news constantly 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 in cars for years now, and Xinzhou has been instrumental in building a complete autonomous driving system that automakers can just use. It’s already in newer Mercedes EVs, for example, as you’ll hear him mention several times. So I really wanted to get his perspective on how the auto industry is handling the big transition to self-driving EVs. That’s the goal every carmaker and supplier will tell you is coming, but which maybe seems 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 percent 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 Xinzhou say there’s actually been startling progress in reinventing the fundamental nature of the car itself — something the industry calls 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; Xinzhou says that moment is basically here. We talked a lot about the Chinese car industry and how it’s been able to essentially get a head start because it began building on EV architectures and platforms, instead of having to manage a transition away from gas cars and all those ECUs. Xinzhou used to work at a Chinese original equipment manufacturer (OEM), so he has quite a bit of insight there. We also talked about working at Nvidia itself. It’s a unique company with a unique leader in Jensen Huang, and Xinzhou 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 capacity against the company’s booming AI business. His description of what wins those arguments, especially when his customers are as slow and cost-averse as automakers, was fascinating. Of course, we had to discuss AI and how Nvidia’s approach to autonomy brings together what Xinzhou calls the “classical” stack and the ability for reasoning models to 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. And, of course, you can’t talk about electric cars or vehicle autonomy in the US without talking about Elon Musk and Tesla. So I asked Xinzhou pretty directly if Tesla full self-driving can actually do what Elon claims it will be able to do without using lidar. You tell me if you think his answer holds up. Okay: Xinzhou Wu, head of automotive at Nvidia. Here we go. This interview has been lightly edited for length and clarity. Xinzhou Wu, you are the head of automotive at Nvidia. Welcome to Decoder. Thanks for having me. I’m 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 US-China trade war difficulties. All of that seems messier than ever before. A lot of car makers are retrenching, and it feels like your position in 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 long, winding road to both autonomy and electrification? That’s an excellent question. I’ve been 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. And obviously, we have heard the phrase “software-defined vehicle.” Right now with AI technology, it’s getting to the next phase, what we call an “AI-defined vehicle” essentially. With these massive technological innovations, the auto industry has changed pretty rapidly over the last decade. As you know, I also worked as part of a Chinese OEM for five years, heading their autonomous driving team. Now I’m at Nvidia. So what I have seen over my 15 years of career is the opportunity to witness this massive change. The car went from, let’s say, mostly mechanical, plus electrical machines, to some things that we can upgrade the capability through over-the-air (OTA) software pretty rapidly. That’s what we call the “software-defined vehicle” era. Now, with the technology advancing towards generative AI, we are using AI to rewrite most of the software in the car. That’s what we call the “AI-defined vehicle.” That has also, on one hand, accelerated the development pace of the vehicle capability. And on the other hand, it’s also changed the way we define “vehicle” as well. AI is impacting the whole industry at every level. It is really exciting to see how the world will evolve from here with these new technological innovations. Let me pull apart some terms there. I hear them a lot from car makers who love to come on the 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. So you said “software-defined vehicle.” That’s a pretty fuzzy term. 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 compute centers in a car. Tesla is very famous for having done this. Rivian has made a huge bet on that. Wassym Bensaid from Rivian was 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. 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 the car controlling every system. The legacy automakers for the most part have not succeeded yet. I’ll put an asterisk on that. 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? 100%. I had the opportunity to witness what happened in China from 2018 to 2023. The whole industry went through this massive change just in five years. Over there, not only the new auto OEMs, but also the legacy ones have to adapt. Everybody is adapting to a single central compute kind of electrical architecture because that’s how you compete. In the rest of the world as well — we have our partners as well through Drive and Drive autonomous vehicle (AV) collaboration, for example, with our partner Mercedes. Their current generation is essential computer-based architecture. It’s going to be in all their vehicles. For the other basic OEMs, we are working with all of them and trying to help them to convert or upgrade the architecture to a one or two computers route, because there will be infotainment, there will be basic driving or advanced driver assistance systems (ADAS), ECU. But I think the world is actually moving pretty rapidly in that direction. Some of them obviously will be slower. Some of them will be faster. That’s the nature of this business. But I have no doubt that the world is basically evolving in that direction. I’m actually curious about your history. You worked at XPeng, which is a Chinese car maker. It feels to me, sitting where I sit in the United States and being a car fan for a long time, that Chinese automakers had a fairly unique advantage in that they were not big global automakers. They were not operating at a massive scale. Electrification came. Tesla obviously built a bunch of capability in China to make cars. We all know how the 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, and build globally competitive cars from a totally new foundation without having to worry about a bunch of the stuff that 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. They definitely have less of a legacy, less of a burden to worry about and that is an advantage. But what I also see is not only the new OEMs, but even the global players there have to adapt to the Chinese pace. At least from what I learned over there, everybody is going at that pace. Again, you want to be able to compete. But as you said, the wave... Software-defined vehicles have been there for a long time and Tesla is the one that’s really taking it to full production. I’m not sure if they’re the first one, but definitely to the largest extent. I have no doubt the OEMs in the rest of the world will follow as well. I think every OEM right now will have to do this because this is how you compete, that is what you need to do to survive. Autonomy will become almost like a necessity for all the OEMs to have in their vehicles. We all believe in that future. And the only way to get there is to get to... First of all, there is the architecture I described, that enables software upgrades without having many, many discreet ECUs. Actually, I haven’t heard people arguing against that recently. Maybe you heard something different, but I think that’s a necessary step for everybody. At this stage, it’s almost like a table stake for the next generation’s architecture. Obviously we are talking to a lot of OEMs, but this is a consensus that the industry is moving towards. I’m 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. Part of that is, I don’t know, that 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 now. 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 that the Chinese automakers are so competitive and so subsidized that the cost to do it for the legacy automakers is hard to overcome, 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. 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. I’m curious, from your perspective. 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? Well, you have said quite a few things. The auto industry is very heavy. It involves massive supply chains and lots of companies, lots of employees. And to make a change in the architecture and whenever you push out a car, you have to support it for 10-15 years. Nvidia, as a supplier, is also making a similar commitment to our customers for whatever technology we supply including chipsets, other platforms, and our AV technology. We will have the commitment to support the same generation for 10-15 years, even for the current generation of chips. If you think about it from a Silicon Valley provider kind of perspective, it’s almost insane. But that’s the nature of the auto business. The nature of the business is that it will slow things down a bit. And that’s one thing. The other thing is, because the technology is changing so fast and so differently from, let’s say, the automotive as we knew before and to the software-defined vehicle, to the AI-defined vehicle, you have to go through a different talent pool to be able to set up the company in a proper way and adapt to this new wave of technology innovations. That’s why Nvidia can come in and help. Because we believe the technology is getting to — we are mainly talking about the autonomous vehicle here, obviously. The technology is getting to a level of maturity. We are going to take this technology to mass production and a supplier can come in. That’s why we not only provide the AV technology, but we also are providing the whole platform starting from a chip, as well as to operating systems, an open source model, and what we call Halos, the safety operating system that helps the OEM to be able to adapt to this new world faster. The nature of the business is that not everybody can run at the same speed. So for sure, because of the heaviness of this industry, it will take some time for everybody to get to the finish line. But again, my job in Nvidia is to try to help everybody to get to everything that moves that will be autonomous, to get to this vision as soon as possible. Let me ask about your part at Nvidia now, because I think this brings us to the Decoder questions. I think everyone listening to the 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, in the order of thousands on the automotive team. Because we are working on the whole platform, there’s hardware, software, model, and the infrastructure. It’s a pretty sizable team. Nvidia also has a lot of things we can leverage from the other teams as well. For example, I’m pretty sure you heard about Cosmos and Nemotron. These are our basic open-source foundation models. We are leveraging heavily from work from their side as well. How is your team organized? You mentioned you’ve got hardware, softw