Today, I\u2019m talking with Xinzhou Wu, who is the head of automotive at Nvidia.<\/p>\n<\/div>\n
Nvidia is obviously in the news constantly because of the AI boom \u2014 it\u2019s one of the most valuable companies in the world, because the AI industry can\u2019t get enough of the company\u2019s GPUs.<\/p>\n<\/div>\n
But Nvidia is also a key supplier to the auto industry. It\u2019s 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\u2019s already in newer Mercedes EVs, for example, as you\u2019ll hear him mention several times.<\/p>\n<\/div>\n
So I really wanted to get his perspective on how the auto industry is handling the big transition to self-driving EVs. That\u2019s 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<\/a>, 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.<\/p>\n<\/div>\n You\u2019ll hear Xinzhou say there\u2019s actually been startling progress in reinventing the fundamental nature of the car itself \u2014 something the industry calls the \u201csoftware-defined vehicle,\u201d controlled by just a handful of powerful computers instead of dozens or even hundreds of independent electronic control units, or ECUs. If you\u2019re a Decoder <\/em>listener, you have heard so many carmakers talk about the need to get away from ECUs; Xinzhou says that moment is basically here.<\/p>\n<\/div>\n We talked a lot about the Chinese car industry and how it\u2019s 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.<\/p>\n<\/div>\n We also talked about working at Nvidia itself. It\u2019s 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\u2019t shy away from the reality of needing to compete for resources and capacity against the company\u2019s booming AI business. His description of what wins those arguments, especially when his customers are as slow and cost-averse as automakers, was fascinating.<\/p>\n<\/div>\n Of course, we had to discuss AI and how Nvidia\u2019s approach to autonomy brings together what Xinzhou calls the \u201cclassical\u201d stack and the ability for reasoning models to operate the car. There\u2019s a lot here, including the idea that you\u2019ll 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.<\/p>\n<\/div>\n And, of course, you can\u2019t 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.<\/p>\n<\/div>\n Okay: Xinzhou Wu, head of automotive at Nvidia. Here we go.<\/p>\n<\/div>\n This interview has been lightly edited for length and clarity.<\/em><\/p>\n<\/div>\n Xinzhou Wu, you are the head of automotive at Nvidia. Welcome to <\/strong>Decoder<\/em><\/strong>.<\/strong><\/p>\n<\/div>\n I\u2019m 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.<\/strong><\/p>\n<\/div>\n 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\u2019s going on in the car industry, because you supply so many of the major automakers in virtually every country.<\/strong><\/p>\n<\/div>\n So let\u2019s just start there. What\u2019s your view of where the car industry is on this long, winding road to both autonomy and electrification?<\/strong><\/p>\n<\/div>\n That\u2019s an excellent question. I\u2019ve 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 \u201csoftware-defined vehicle.\u201d Right now with AI technology, it\u2019s getting to the next phase, what we call an \u201cAI-defined vehicle\u201d essentially.<\/p>\n<\/div>\n 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.<\/p>\n<\/div>\n Now I\u2019m 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\u2019s say, mostly mechanical, plus electrical machines, to some things that we can upgrade the capability through over-the-air (OTA) software pretty rapidly. That\u2019s what we call the \u201csoftware-defined vehicle\u201d era. Now, with the technology advancing towards generative AI, we are using AI to rewrite most of the software in the car. That\u2019s what we call the \u201cAI-defined vehicle.\u201d<\/p>\n<\/div>\n That has also, on one hand, accelerated the development pace of the vehicle capability. And on the other hand, it\u2019s also changed the way we define \u201cvehicle\u201d 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.<\/p>\n<\/div>\n 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\u2019s going to happen to cars. But I think some of these terms are a little bit fuzzy on the edges.<\/strong><\/p>\n<\/div>\n So you said \u201csoftware-defined vehicle.\u201d That\u2019s a pretty fuzzy term. I think the idea there is we\u2019re 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<\/a> talking about that.<\/strong><\/p>\n<\/div>\n Other legacy car makers have tried to do this. We had GM on the show<\/a>. They said, \u201cLook, we don\u2019t need to do that. We\u2019re fine. We\u2019ll do it our way.\u201d Ford tried to do this<\/a> in big ways. They had to set up a skunkworks and build an entirely new kind of way of making a car that they\u2019re very proud of. There\u2019ll be a truck coming out from that effort sometime soon, we\u2019re told.<\/strong><\/p>\n<\/div>\n I don\u2019t think the industry got there. That\u2019s basically what I\u2019m 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. <\/strong><\/p>\n<\/div>\n I\u2019ll put an asterisk on that. Maybe Ford will succeed with this new truck, but we don\u2019t 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?<\/strong><\/p>\n<\/div>\n 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\u2019s how you compete.<\/p>\n<\/div>\n In the rest of the world as well \u2014 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\u2019s 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.<\/p>\n<\/div>\n Some of them obviously will be slower. Some of them will be faster. That\u2019s the nature of this business. But I have no doubt that the world is basically evolving in that direction.<\/p>\n<\/div>\n I\u2019m 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<\/a> all that at huge rates.<\/strong><\/p>\n<\/div>\n You worked there. Was that your experience? Is that basically how it went that they got to start fresh?<\/strong><\/p>\n<\/div>\n I think that\u2019s 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.<\/p>\n<\/div>\n But as you said, the wave… Software-defined vehicles have been there for a long time and Tesla is the one that\u2019s really taking it to full production. I\u2019m not sure if they\u2019re 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.<\/p>\n<\/div>\n 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\u2019t heard people arguing against that recently. Maybe you heard something different, but I think that\u2019s a necessary step for everybody. At this stage, it\u2019s almost like a table stake for the next generation\u2019s architecture. Obviously we are talking to a lot of OEMs, but this is a consensus that the industry is moving towards.<\/p>\n<\/div>\n I\u2019m 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\u2019s 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\u2019t know, that the Trump administration doesn\u2019t 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\u2019t 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.<\/strong><\/p>\n<\/div>\n Or maybe it\u2019s 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\u2019re just going to hold off on it. There\u2019s 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\u2019m curious, from your perspective. You\u2019re the supplier, you\u2019re trying to sell the vision, you\u2019re trying to put the chips in all the cars. From your perspective, what has made that path harder?<\/strong><\/p>\n<\/div>\n Well, you have said quite a few things.<\/p>\n<\/div>\n 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\u2019s almost insane. But that\u2019s the nature of the auto business. The nature of the business is that it will slow things down a bit. And that\u2019s one thing.<\/p>\n<\/div>\n The other thing is, because the technology is changing so fast and so differently from, let\u2019s 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\u2019s why Nvidia can come in and help. Because we believe the technology is getting to \u2014 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.<\/p>\n<\/div>\n That\u2019s 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<\/a>, the safety operating system that helps the OEM to be able to adapt to this new world faster.<\/p>\n<\/div>\n 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.<\/p>\n<\/div>\n Let me ask about your part at Nvidia now, because I think this brings us to the Decoder <\/em>questions. I think everyone listening to the show is probably very familiar with the run Nvidia has been on with AI. It\u2019s one of the most valuable companies<\/a> in the world. Every GPU that Nvidia can make is accounted for. How many people work at Nvidia Automotive?<\/strong><\/p>\n<\/div>\n 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\u2019s hardware, software, model, and the infrastructure. It\u2019s a pretty sizable team. Nvidia also has a lot of things we can leverage from the other teams as well. For example, I\u2019m pretty sure you heard about Cosmos<\/a> and Nemotron<\/a>. These are our basic open-source foundation models. We are leveraging heavily from work from their side as well.<\/p>\n<\/div>\n How is your team organized? You mentioned you\u2019ve got hardware, software, and models. Is that the basic structure of the team or is it organized differently?<\/strong><\/p>\n<\/div>\n Yes. Well, on the engineering side, obviously we have product, we have strategy, we have something behind the scenes. Sometimes we call them unsung heroes. The map team, for example, which is still very critical for L3, L4, the high-level autonomy paths. And the data infrastructure.<\/p>\n<\/div>\n The literal navigation maps, that\u2019s what you\u2019re talking about.<\/strong><\/p>\n<\/div>\n Well, there\u2019s HD map as well. So roughly, I divide my team that way. Yes.<\/p>\n<\/div>\n And then is that all global? Is that mostly in the United States? Where\u2019s that located?<\/strong><\/p>\n<\/div>\n Mostly in the United States, but we do have a presence in China and Europe as well. Obviously, we are building a global product, a global platform, so we need a support team everywhere.<\/p>\n<\/div>\n 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?<\/strong><\/p>\n<\/div>\n Oh, that\u2019s a great question. At Nvidia, we have, let\u2019s say, a centralized hardware team, which is responsible for the hardware roadmap on our GPU, the CPU, all the chipset strategy, and the productization. We have a centralized software team as well. The automotive team is a separate organization, which is very much more automotive-focused, with the mission of building the automotive platform to leverage the work from our hardware team and the software team and adapt to automotive. We have the model team as well.<\/p>\n<\/div>\n Nvidia also has a culture of virtual teams. For example, our open-source model for Nemotron and Cosmos, they all sit across from our research team, the software team, and the hardware team. But they are virtual teams that work on these open-source foundation models. We can leverage that work and then in the automotive organization build up a model to help the AV industry have a powerful open-source model to work on.<\/p>\n<\/div>\n As I said, basically every GPU Nvidia can manufacture is accounted for in some way. It\u2019s the nature of the AI industry right now. They\u2019re going to go into some neocloud somewhere. Do you have to fight for resources, and attention against that business, which is growing at the speed and the scale it\u2019s growing at?<\/strong><\/p>\n<\/div>\n Yes, believe it or not. [Laughs] <\/em>Even Nvidia has a limited supply of GPU for compute. We have an internal priority, and I\u2019m working with my colleagues basically almost on a weekly basis to decide how to set aside the different compute, sometimes for training, sometimes for testing, or resources for a different thread of work in the company. And sometimes we need Jensen [Huang, Nvidia CEO] to help, but yeah.<\/p>\n<\/div>\n How does that work? What does that debate look like? Is it an ROI debate? \u201cIf we put this much money in, we\u2019ll get this much money out from our customers\u201d? Is it a market size debate? What are the parameters of the conversation?<\/strong><\/p>\n<\/div>\n It\u2019s all of the above, as you can imagine. Revenue is important obviously, but also Nvidia, as you know, is a very strategic company. We value what Jensen sometimes calls the zero trillion dollar business. We are looking for new opportunities which can create a trillion-dollar business all the time. So, there need to be strategic priorities we set inside the companies in the new direction we go. You probably also know that we are not a market share company. It\u2019s a balance between basically what brings the money right now and what can create the future, what can create opportunity for the company in the future.<\/p>\n<\/div>\n Nvidia is a very uniquely run company. As you\u2019ve mentioned, Jensen\u2019s deeply involved in everything. I\u2019ve seen an interview<\/a> with him where he said he doesn\u2019t have one-on-one meetings. He just meets with everyone all at once and everyone just hashes it out. What\u2019s that like?<\/strong><\/p>\n<\/div>\n I\u2019ve been at Nvidia for three years. It\u2019s very unique, honestly. And it\u2019s obviously not everybody all at once. It\u2019s different groups. We all have a technical strategy product, a different part of the business reviews with Jensen. It\u2019s super exciting for me, actually, an experience to learn from his strategic thinking and how he thinks about a product, how he thinks about a strategy. He\u2019s also uniquely technically deep. It\u2019s quite an inspiring experience as well to just see how much he\u2019s keep up to date on the technical side. It\u2019s, I would say, a once-in-a-lifetime experience and opportunity for me to be able to learn from Jensen.<\/p>\n<\/div>\n When you describe the opportunity for autonomy, particularly in the future, that seems like the big bet. \u201cWe\u2019re going to bring to bear Nvidia\u2019s compute excellence, and the power of AI to cars, and have them drive themselves.\u201d What does that revenue model look like? Does it look like you\u2019re 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?<\/strong><\/p>\n<\/div>\n That\u2019s also an excellent question. Right now we firmly believe that everything that moves will be autonomous. Every mile driven by the car in the future will be autonomous. If you look at it, among all the cars, we drive 13 trillion miles per year. Right now the percentage of autonomous miles among all the mileage driven is probably negligible. I think it\u2019s 0.006%, or something like that. So this is the opportunity in front of us. Nvidia\u2019s view is that we\u2019ll help the ecosystem together as soon as possible by providing all the foundation technology pieces again, starting from chips to operating systems, and then to what we call Halos. The Halos operating system is really important because it not only provides the SDK and the APIs for folks to develop models on our hardware, it also provides the safety guardrails for developers to put a model on it.<\/p>\n<\/div>\n We also define what we call Hyperion<\/a> as a hardware platform. That\u2019s a production-ready platform, which includes the computer resource, the ECUs, and also the sensor suite. We think it\u2019s necessary to achieve a different level of autonomy. On top of that, we provide Alpamayo<\/a>, an open-source model, which we trained. It\u2019s open-source not only in the model architecture, but also in the parameters and the data that you can use to fine-tune the model on our platform. On top of that, we also provide all the infrastructure needed. For example, simulation is really important for developing AV right now. We usually say that the AV problem is becoming a three-computer problem. There\u2019s the training computer, there\u2019s the simulation computer, and then there\u2019s the inference computer in the car. All these technology pieces we want to provide to the ecosystem in a platform we call Nvidia Drive, so that folks can develop that technology on top of our platform.<\/p>\n<\/div>\n We hope that we can get a percentage of the revenue that the ecosystem can get from every mile that is driven autonomously in the future. This is where the trillion-dollar opportunity can come from.<\/p>\n<\/div>\n So, revenue per mile. That sounds like the core metrics that you\u2019re 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\u2019s robotaxis everywhere, and they\u2019re being monetized per ride? Where does revenue per mile come from, and how does that number go up?<\/strong><\/p>\n<\/div>\n That\u2019s right. Well, I think the world will embrace both models. One is robotaxis. As you see, there are quite a few successful ones in China, in the US, and in the world. We\u2019ll see more hopefully going down this path. We\u2019ll have a taxi-like fleet where you can enjoy taking you from place A to place B without a driver in the car.<\/p>\n<\/div>\n I think the passenger fleet will also continue to exist for a long time because there are many people who still prefer a private space during travel. It\u2019s like many people still prefer to own their house as compared to renting an apartment. There\u2019s an economy behind this as well. We think both models will thrive. That\u2019s why we are working with both the robotaxi companies and the other OEMs, as well as the AV software developer companies to help them by supplying different technology pieces from Nvidia.<\/p>\n<\/div>\n One of the interesting dynamics through at least the electrification portion, over 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. They had lost control of car design in a big way. The tier-one suppliers to the big automakers were in many ways in charge of big subsystems of the cars. When they wanted to do an over-the-air update, they had to go talk to 15 different suppliers to get that done. I\u2019ve heard this complaint dozens and dozens of times on the show. And they all kind of realized, \u201cWe need to take back the engineering of the car. We need to be much more firmly in control of the platform of the car.\u201d<\/strong><\/p>\n<\/div>\n 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\u2019s obviously the intention with them thinking, \u201cWe need to take control of the car.\u201d Tesla might use 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\u2019ve made their technology bets. Rivian, I think Wassym is very proud of the fact that he is in charge of that platform company, and he\u2019s going to build that platform. RJ [Scaringe, Rivian CEO] is certainly very proud<\/a> of the fact that Rivian is that kind of company.<\/strong><\/p>\n<\/div>\n What\u2019s the dynamic there? Because it doesn\u2019t seem like every car maker can stand up the technology bet and forward invest on the hope that the revenue will 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?<\/strong><\/p>\n<\/div>\n I think the beauty of the Nvidia business model on the automotive side is that our platform is completely open. We provide multiple layers of services, and depend on what OEM or robotics companies need. They can select what they want to work with us up to which layer for, as you mentioned, Tesla. Some OEMs are so capable. They even want to build their own inference to paint the car. Even for that, we\u2019re okay. We\u2019ll still continue working with them. Actually, we are working with Tesla and many OEMs who are building using their own inference chip by collaborating with them in the cloud. We even try to help optimize their models. With different OEMs, we have different collaborations, because we still have the simulation computer and the training computing in the infrastructure we are working with them.<\/p>\n<\/div>\n For some of the OEMs, they would like to have a more turnkey solution. We are very happy to work with them as well. In that case, we are going to go all the way. We are working like a tier one, or tier 1.5, just to go hand by hand. This is our driver AV kind of partners, for example, Mercedes. We work very closely with them to define the products they want, and then also adapt our driver AV stack to work seamlessly in their vehicle. The engineers from both sides work pretty closely to make it adapt well with the Mercedes design DNA and the customer experience they would like to offer.<\/p>\n<\/div>\n This is really important for us. We are not picking winners per se. We try to help OEMs based on their capability at different levels. The openness is really important for our engagement model with OEMs.<\/p>\n<\/div>\n One of the reasons I\u2019m so curious about this is that you mentioned training models. You mentioned in other interviews that you\u2019re doing synthetic data to train autonomy in different ways. I\u2019m very curious about that. It just strikes me, looking at the industry. Waymo has this gigantic lead in autonomous miles driven, and they\u2019re very proud of it, and that\u2019s helped make their cars as successful as they are in the markets they\u2019re in. Tesla has a huge number as well because they\u2019re training on the actual cars that are being driven. Not every automaker can figure out how to get to a billion autonomous miles driven.<\/strong><\/p>\n<\/div>\n They\u2019re going to have to rely on some third party to get them to at least the status quo, if not beyond. It feels like Nvidia is sitting there ready to be that third party. Is that a lot of the sales pitch to the automakers? \u201cYou can just buy our technology in whatever open capacity that you want, and we will quickly get you to a competitive state\u201d?<\/strong><\/p>\n<\/div>\n I would say this is the one compelling point for OEMs to engage with Nvidia in the Hyperion ecosystem, in the Drive ecosystem. Because one of the key defining things for Hyperion is the compute architecture, and also the sensor architecture is the data sharing. For anybody who engages and becomes an Nvidia Drive partner, we share data through our existing program, through which we collect millions of hours of data. Through the different car programs, we are also accumulating that data from different OEMs. And then we can build a model, first of all, which is trained with all this data. We make sure at least the data collected in our different car programs is shared with the OEM. That\u2019s number one. Number two is in the new era, we strongly believe compute is data as well. So, as you mentioned, there\u2019s a lot of synthetic data.<\/p>\n<\/div>\n