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

Nvidia is obviously in the news constantly because of the AI boom — but it's also a major supplier to the entire auto industry As head of Nvidia's automotive division, Xinzhou Wu has a front-row seat to all the challenges EVs and autonomous vehicles are facing, especially in the US. 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. Read the ⁠full interview transcript on The Verge⁠. Links: Nvidia’s head of autonomous driving opens up about his plans | The Verge Hyundai, Nissan, BYD, and Geely Join Nvidia’s Level 4 | MotorTrend Nvidia, auto suppliers roll out partnerships to rekindle self-driving | Reuters Meet Alpamayo, Nvidia’s new AI model for autonomous cars | Forbes I tested Nvidia’s FSD competitor — Tesla should be worried | The Verge Subscribe to The Verge to access the ad-free version of Decoder! Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Decoder’s producers are Kate Cox and Nick Statt; this episode was edited by Xander Adams. Our editorial director is Kevin McShane. The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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• 1:27Speaker 02

  Hello and welcome to Decoder. I'm Neelai Patel, editor in chief of The Verge and Decoder is my show about big ideas and other problems. Today I'm talking about Jinju Woo, 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 in cars for years now and Jinju 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 mention several times. So I really wanted to get his perspective on how the auto industry is handling the

• 2:11Speaker 02

  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 Jinju 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.

• 2:54Speaker 02

  If you're a Decoder listener, you have heard so many carmakers talk about the need to get away from ECUs. Jinju 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. Jinju 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 Jinju 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

• 3:39Speaker 02

  and cost diverse as automakers, was fascinating. Of course, we also talked about AI and how Nvidia's approach to autonomy brings together what Jinju 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 Jinju 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. Tell me if you think his answer holds up. Okay, Jinju Woo, head of automotive at Nvidia. Here we go.

• 4:22Speaker 02

  Jinju Woo, you are the head of automotive at Nvidia. Welcome to Decoder.

• 4:39Speaker 03

  Sounds good. Having me.

• 4:41Speaker 02

  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 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.

• 5:20Speaker 02

  What's your view of where the car industry is on this kind of long winding road to both autonomy and electrification?

• 5:28Speaker 03

  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, sorry, software-defined vehicle. And then basically right now with the AI technology, it's really getting to the next phase, what do we call AI-defined vehicle essentially. With this massive technology innovations, as you said, the car, the auto industry is

• 6:11Speaker 03

  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. Now basically I'm in Nvidia. So what I've seen over my 15 years of careers 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 basically we can kind of upgrade the capability through OTA, software OTA pretty rapidly.

• 6:56Speaker 03

  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, accelerated the development pace of the vehicle capability. And in 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.

• 7:39Speaker 03

  So this is really exciting to see how the world will evolve from here with this new technology innovations.

• 7:47Speaker 02

  Let me pull apart some terms there. I hear them a lot from carmakers, 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.

• 8:00Speaker 03

  That's right.

• 8:01Speaker 02

  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 compute centers in a car. Tesla is very famous for having done this. Rivian is, they've made a huge bet on that. Wasim Benzayad from Rivian was just on the show talking about that. Other legacy carmakers 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 Skunk Works 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.

• 8:46Speaker 02

  That's basically what I'm saying. The startup carmakers 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. 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?

• 9:10Speaker 03

  A hundred percent. Again, I had the, let's say, opportunity to witness what happened in China from 2018 to 2023. And the whole industry 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 a single central computer kind of electrical architecture because that's how you compete. In rest of the world as well, obviously we have our partners as well through basically Drive and Drive AV basic collaboration, for example, our partner Mercedes.

• 9:54Speaker 03

  Their current generation is basically a central computer-based architecture. It's going to be in all their vehicles. And for the other basically OEMs, we're obviously working with all of them and trying to help them basically upgrade the architecture to one or two computers because there will be infotainment, there will be basically driving or ADAS, ECU. But I think definitely the world is actually moving pretty rapidly in that direction. Well, 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 basically the world is evolving in that direction.

• 10:30Speaker 02

  I'm actually curious about your history. You worked at Xpeng, which is a Chinese carmaker. It feels to me, sitting where I sit in the United States and being a car fan for a long time, that the Chinese automakers had a fairly unique advantage in that they were not big global automakers, they were not operating at 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 carmakers 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 legacy

• 11:14Speaker 02

  American carmakers 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?

• 11:27Speaker 03

  I think that's just one side of it. Definitely have a less legacy, basically, a burden to worry about is an advantage. But what I also see is not only, as I said, the new OEMs, but even the global player 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. And but again, you know, 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.

• 12:09Speaker 03

  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? This kind of architect, kind of software upgrade without have many, many, let's say, discrete ECUs. Actually, I haven't heard people arguing against that recently. Maybe you heard something different, but I think that's really a necessary step for everybody. At this stage, it's really almost like a table stake, you know, for the next generation architecture. Obviously, we're talking to a lot of OEMs. But this is, I think, to say the least, it's a consensus that the industry is moving towards.

• 12:48Speaker 02

  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 powered all these systems instead of having a 12-volt battery. Or maybe it's the Chinese automakers are so competitive and so subsidized

• 13:32Speaker 02

  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 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?

• 14:04Speaker 03

  The auto industry is very heavy. You know, it involves basically massive supply chain and lots of companies, lots of employers, 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 to have a technology with supply and 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, right? Even for the current generation of chip. If you think about it from, you know, Silicon Valley, from silicon provider kind of perspective, it's almost insane. But that's the nature of auto business.

• 14:49Speaker 03

  It has a, basically, 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 go 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, you know, we're talking about autonomous vehicle, obviously, you know, mainly here.

• 15:33Speaker 03

  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 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.

• 16:16Speaker 03

  But, you know, again, my job in Nvidia is to try to help everybody to get to this, everything that moves, that will be autonomous, this kind of vision as long as possible.

• 16:28Speaker 02

  Let me ask about your part of 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?

• 16:46Speaker 03

  We have actually quite a sizable team, somewhere between, basically, it's 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 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, pretty sure you heard about Cosmos and NemoTron. These are our basic open source foundation models. We're leveraging heavily from work from their side as well.

• 17:17Speaker 02

  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?

• 17:26Speaker 03

  Yes, I would, well, this is on the engineering side. Obviously, we have product, we have strategy, we have, you know, something kind of behind the thing. Sometimes we call them unsung heroes, right? The map team, for example, which is still very critical for L3, L4, the high-level autonomy pass and the data infrastructure.

• 17:46Speaker 02

  The literal navigation maps, that's what you're talking about.

• 17:49Speaker 03

  Well, there's HD map as well.

• 17:51Speaker 02

  Okay.

• 17:52Speaker 03

  So it's roughly that, you know, I divide my teams this way, yes.

• 17:57Speaker 02

  And then is that all global? Is that mostly United States? Where is that located?

• 18:00Speaker 03

  Mostly in the United States, but we do have a presence in China, in Europe as well. Obviously, we are building a global product, a global platform, so we need a support team everywhere.

• 18:10Speaker 02

  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?

• 18:22Speaker 03

  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 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 automotive. And then, basically, we have the model team as well, open source model team, actually part of the. Nvidia also have a culture of virtual teams. For example, our open source model for NemoTron and Cosmos,

• 19:07Speaker 03

  they all have, 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, try to build up, for example, as you, hopefully you have heard about it, to help the AV industry, basically, have a powerful open source model to work on.

• 19:34Speaker 02

  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. And 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 it's growing at?

• 19:51Speaker 03

  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 a gentleman to help, but yeah.

• 20:20Speaker 02

  How does that work? What does that debate look like? Is it a ROI debate? If we put this much money in, we'll get this much money out from our customers? Is it a market-sized debate? What are the parameters of the conversation?

• 20:33Speaker 03

  Well, I think it's all of the above, as you can imagine. Revenue is important, obviously, but also basically, you know, Nvidia, as you know, is a very strategic company. You know, we value what sometimes Jensen called the zero trillion dollar business. You know, we are looking for new opportunities which can create a trillion dollar you know, business all the time. So there need to be strategic kind of priorities we set inside the companies is the new direction we go. And you probably also know that we're not a market share company. So it's a balance between basically, you know, what's the current, what brings the money right now? And what can create the future, create the, you know, opportunity for the company in the future.

• 21:14Speaker 02

  Nvidia is a very uniquely run company, as you've mentioned. Jensen's deeply involved in everything. I've seen an interview with him where he said he doesn't have one-on-one meetings. He just meets with everyone all at once and everyone just hashes it out. What's that like?

• 21:28Speaker 03

  Well, I've been in Nvidia for three years. I think it's very unique, honestly. It's not, obviously not everybody all at once, right? It's different groups. We all have, you know, a technical strategy, product, you know, different, you know, kind of a part of the business reviews with Jensen. It's a super exciting for me, actually, a learning experience, basically, to learn from his strategic thinking and how he think about the product, how he think about the strategy. He's also uniquely technically deep. So it's also quite inspiring, the basic experience as well to just also to see how much he's keep up to date on the technical side as well.

• 22:12Speaker 03

  Again, it's really, I would say, once in a lifetime experience and opportunity for me to be able to learn from Jensen. Yeah.

• 22:19Speaker 02

  When you describe the opportunity for autonomy, particularly in the future, because that seems like the big bet, right? We're gonna 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?

• 22:43Speaker 03

  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 13 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.

• 23:23Speaker 03

  So Nvidia's view is basically we'll help the ecosystem to get there, you know, as soon as possible by provide 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, it not only provides the SDK and the APIs to, for folks to develop models on our hardware, but also provide basically the safety guardrail for developer to put a model on it.

• 24:03Speaker 03

  And then basically we also define what we call the Hyperion, basically hardware platform. That's a production-ready platform which include both the computer resource, you know, the ECUs, and also the sensors we think it's necessary to achieve a different level of autonomy. And on top of that, we provide basically the admire, basically open source model, which we trained and open source, not only the model architecture, but also the parameter 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.

• 24:46Speaker 03

  For example, simulation right now is really important for developing AV. We usually call the AV problem is becoming a three computer problem, right? There's the training computer, as there's the simulation computer, and then there's the inference computing in the car. All these technology piece, you know, we want to provide to the ecosystem in a 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 autonomous in the future. This is where the trillion dollar, basically, opportunity can come from.

• 25:31Speaker 02

  We're just taking a quick break here. We'll be back in just a minute.

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• 27:20Speaker 02

  Welcome back. I'm talking with Nvidia's head of automotive, Shinji 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?

• 27:43Speaker 03

  That's right. Well, I think the world will, I think will embrace both models. One is basically robo taxi, you know, as you see, there's quite a few successful ones, basically, doing, you know, in China and in US and in the world. And we will see more, hopefully, going down this path. And basically, you know, we'll have like a taxi like a fleet where you can enjoy and 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,

• 28:28Speaker 03

  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, you know, 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, and obviously the AV software developer, you know, companies to help them, basically, to by supplying different technology piece from Nvidia to them.

• 29:01Speaker 02

  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, right? 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. I've heard this complaint dozens and dozens of times on the show. And they all kind of realized, oh, we need to take back the engineering of the car. We need to be more, much more firmly in control of the platform of the car.

• 29:46Speaker 02

  It sounds like in autonomy, for a variety of reasons, Nvidia sees an opportunity to become the main supplier to a wide variety of carmakers. 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, was in 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 it doesn't seem like every carmaker can stand up the technology bet and forward invest on the hope that the revenue will pay off. They they will need a supplier like Nvidia to show up with a ready-made platform and

• 30:31Speaker 02

  business model. Is that tilting more in your favor now? Have we have we gotten out of those woods? Or is it still up in the air?

• 30:38Speaker 03

  I think the beauty of the Nvidia business model in the automotive side is really our platform is is completely open. We provide a multiple layer of services. And depends on basically what OEM need or, you know, robotics company need, they can select what they want to, you know, work with us, you know, basically, you know, up to which layer. For, as you mentioned, basically Tesla, you know, some OEMs, they're so capable, they even want to build their own inference chip in the car.

• 31:08Speaker 02

  Yeah.

• 31:09Speaker 03

  Even for that, we're okay. You know, we'll still continue working with them. Actually, we are working with, you know, Tesla and 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 we still have the simulation computer and the training computing in the in the infrastructure we are working with them. And for for 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 in that case, we are going to go all the way.

• 31:50Speaker 03

  We are working like a tier one or tier 1.5, essentially, just to go hands by hand. This is our driver 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 driver AV stack to to to to work seamlessly in their vehicle. And actually, the engineers from both sides work pretty closely to make it really, let's say, adapt well into into 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 we try to help OEMs based on their capability at different levels.

• 32:32Speaker 03

  So, as I said, the openness is really important for for our basically kind of engagement model with OEMs.

• 32:40Speaker 02

  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 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 as 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 is 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 at the

• 33:25Speaker 02

  sell for in whatever open capacity that you want, and we will just quickly get you to a competitive state?

• 33:32Speaker 03

  I would say this is one of the compelling point of basically OEM to engage with Nvidia in the higher-end ecosystem, in the drive ecosystem. Because one of the key thing in for high period, basically, again, which define the compute architecture and also the sensor architecture, is the data sharing. For anybody who who engage, become a drive partner, Nvidia drive partner, we not only we share data through our kind of existing program, which we collect and 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.

• 34:15Speaker 03

  And, you know, and then, basically, you know, we can build a model, first of all, basically, which can work with all the, you know, which 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 your OEM. That's number one. Number two is, in the new era, we we strongly believe computers data as well. As you mentioned, there's a lot of synthetic data. And and also, there's a neural reconstruct data, which we call Neuralack. This is a very important piece of technology in simulation, where we have collected data

• 34:54Speaker 03

  from the field, but we can use neural reconstruction to sometime to fuzz the data to to to, you know, change the background or change the car trajectory. We can basically generate a lot of variance of the same data. And all these data, again, they're they're, 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 engaged with the drive ecosystem, we can catch up on the data gap, which is very important.

• 35:33Speaker 02

  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 it make that person come out later, and the car will have to react to it as though it's real. And you're going to 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 carmakers 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 is it cheaper? Like, why would they participate in their competitors in that kind of data sharing arrangement?

• 36:17Speaker 03

  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 for anybody who wants to do that. And also, it's kind of repetitive as well, you know, you know, if you can find, you know, for example, what we provide in the drive platform or the drive ecosystem, it's it's it can save a lot of effort and basically money from from our customers.

• 36:52Speaker 02

  I'm curious about that, because the idea that you'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 have mapped all the roads on top of everything. Right? So you're going to, you know, 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?

• 37:37Speaker 02

  Is that the future of the platform? Or are you in the middle?

• 37:39Speaker 03

  Well, the approach we take right now for what we call the L two plus plus, essentially, is mapless. As you said, you know, correctly, so basically, the model would definitely need more data and to cover that more corner case. And the model is obviously getting bigger as we speak as well, basically, you know, for this generation, next generation. We are going to use a much big model with with with with more parameters. And also, a foundation models will make a play a big role here. And, you know, being able to make this model very capable, essentially, you know, more data is very, very critical. But on the other hand, though, the trend of using foundation model, which is already

• 38:23Speaker 03

  trained with internet data, that can help, 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 leverage from the, let's say, the frontier model perspective, and the leverage internet, basically, kind of a 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, is especially level four, right?

• 39:07Speaker 03

  This is one of the main, main work thread, you know, we are focusing on right now. And back to OEM, I think, being able to leverage, basically, you know, what we have built upon through our collaborations with the existing, basically, engagement, and our massive capability of basically data generation using synthetic data set and the neural reconstruction, and also being able to leverage the foundation model capability, 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.

• 39:51Speaker 02

  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 the car crashes, or one of those horrible long latency loops where the model goes off in the wrong direction and realizes it. And then, like, you can look at the chain of thought, and it's like, oh, it got it totally wrong. It's it feels bad, you know, in the way that, like Anthropic believes that Claude feels bad. None of that seems compatible with the very real-time nature of driving a car. How do you bridge that gap? Latency?

• 40:37Speaker 02

  The need to have one of those big models in the background, the sort of reasoning tangents that the models can go on. How is that compatible with driving a car?

• 40:47Speaker 03

  Safety is so important to us and obviously so important for the AV industry. So let me answer your question from, you know, our kind of approach in different layers of our offering. So to address safety, this is obviously not new for the auto industry and we have developed very sophisticated, basically even development protocol and also validation protocol to be able to prove the software is safe. All right, that's called ISO 26262. And, you know, we actually developed our hardware and operating, you know, system, OS level software and the application level software, you know, to the highest standard,

• 41:29Speaker 03

  which is very important, which is very critical to be able to deploy anything, you know, to drive the car.

• 41:35Speaker 02

  Sure.

• 41:37Speaker 03

  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 redundancy stack even for our L2 plus or Ada's basically function.

• 41:53Speaker 03

  Other than the end plan 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 stack, basically kind of run in parallel, this, the classical stack is, is acting like a, sometime we call it the big brother. But essentially it's, it's a safety guard rail.

• 42:35Speaker 03

  Try to verify all the trajectories from the end to end model and use it, you know, use the, 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 a sensor setup, but at a software architecture set. So this is, I would say the second point I want to make to answer your safety question.

• 43:18Speaker 03

  And the number three, also basically when we develop, developing a 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, as I said, for every model we, we, we release. Right now, we're looking at, in our program right now, we are running five million basically test every day. And obviously roughly every day we have a 10 iteration of the model, the end to end model. So we're doing really massive validation to make sure, you know, all, in all these scenarios,

• 44:03Speaker 03

  you can think of that, you know, every tested test scenario, you know, the model is, is, is generated right trajectory. So that's also super critical for us. So, so this is what we do to, to, to make sure our product is safe.

• 44:18Speaker 02

  Let me ask you a really dumb question. I'm really curious about. You've talked a lot about the model and how we'll operate the car. And yes, there's, you know, the classical stack is the safety guard rail. 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 gonna go hit the brakes, the way that any sort of general model reasons in, in language in the background?

• 44:43Speaker 03

  Sure. The 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 R1, which has a, you know, more or less more limited compute. The next generation is Sora 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, make, make a lane change, for example.

• 45:17Speaker 02

  But as it's, 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, in a couple of miles. And it's doing that in language to operate the car?

• 45:28Speaker 03

  I think it's a combination of things. Language is already embedded in the model, but the vision signal is also super important, as you know. So it's, it's, I would say it's multi-model, but language is part of it. Obviously, it's, as you know, the model is black box. We don't exactly know basically what, what, what, what is exactly doing, but you can ask about it. And then, you know, the model will answer what's, what it's trying to do and you can reason.

• 45:52Speaker 02

  I just have this vision of a chatbot model just like freaking out as it reams down the highway at 55 miles an hour.

• 45:59Speaker 03

  The recent, basically GTC, I think Jensen did, GTC Taiwan released a, a video that the model is talking constantly. It's explaining what it'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.

• 46:15Speaker 02

  What's the latency on that? Is that, I mean, obviously you're deploying the systems, 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.

• 46:31Speaker 03

  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 advantage of deploying, you know, drive the car with a 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 millisecond. 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, which, which is pretty fast. And regarding the language reasoning, obviously if you think about it, well, that's human brain, right?

• 47:13Speaker 03

  But if you think about the, the language, basically, I would say the, the information rate is, is, it's 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, is well under control. Let me put it that way.

• 47:32Speaker 03

  And, and, and again, you're not driving the car with language only. That's the key thing, as, as I said, usually the reasoning part is, is, I believe it's slower. Again, we don't know exactly what the model is doing, but the, the pixel part is, is, is, that's, that's what drives the basic instantaneous kind of reaction of the vehicle.

• 47:50Speaker 02

  If you ask Anthropic, they will tell you that Claude has feelings and emotions and it can get scared. Do you, do you think about that? Do you think your models have emotions when they're driving the car?

• 48:02Speaker 03

  We'll, we'll use the guard rail to make sure it doesn't get too moody.

• 48:07Speaker 02

  I'm just curious. I mean, it, like you said, we don't know how the models are working. I just, 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.

• 48:18Speaker 03

  Yeah.

• 48:21Speaker 02

  We have to take it on a short break here. We'll be right back.

• 48:33Speaker 04

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• 50:03Speaker 02

  Welcome back. I'm talking with Jinji Wu, Nvidia's Head of Automotive, about how all these AI systems very literally work. Is all this running locally in the car?

• 50:11Speaker 03

  No, no, no, no. All these validation offline. But the second part with the safety guardrail, when we run two stack in parallel, that's definitely in the car. And in the car, at every frame, the software in our ADAS ECU, we are comparing basically the trajectory from both the classical stack and the end-to-end model to make sure the model is outputting a safe basic trajectory.

• 50:38Speaker 02

  So do the cars require fast connectivity to be autonomous with your approach?

• 50:43Speaker 03

  Not necessarily, but we do require some connectivity to get navigation information and some mapping information. Mostly, most of these are a navigation map. So not only the model side and also the classical stack, which we do use some of the navigation mapping information to help us understand the world better, essentially.

• 51:07Speaker 02

  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.

• 51:25Speaker 03

  Well, which is, this is not wrong. But on the other hand, basically, the car have to drive autonomously in completely blind spot as well.

• 51:38Speaker 02

  Yeah.

• 51:39Speaker 03

  Real time, basically low latency, I would say content dependency, have that dependency in the cloud, at least for the ADAS kind of

• 51:52Speaker 03

  application L2 plus, which is what we call that, which is meant to work everywhere. Building that dependency is not a good idea.

• 51:59Speaker 02

  When you get to a level four, level five, that's when you have the connectivity dependency.

• 52:04Speaker 03

  That's right. Yes. Yeah.

• 52:06Speaker 02

  What happens when you lose the connectivity at level four autonomy? When you're at level five and you don't have a steering wheel anymore and you lose connectivity, what happens?

• 52:13Speaker 03

  You can think of connectivity is kind of a sensor. And again, the basic driving capability cannot have huge dependency on that. One of the core concept of developing level for technologies, you have a 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 lost your GPS. But the car with a local perception, it need to be able to get to a safe point and pull over. That's a minimum requirement an L4 system need to have. So this is just the L4

• 52:55Speaker 03

  basic principle to be able to develop such a system.

• 53:00Speaker 02

  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 a radio. Right? Like, that's just a battery and wheels. That's that whole car. It doesn't even have paint job. Like, 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.

• 53:44Speaker 02

  How does that play out? Is that push you more into that robotaxi model? Or do you think people are just going to buy expensive self-driving cars?

• 53:54Speaker 03

  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 become 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 bigger than basically the cost. I see, have witnessed basically the drop of both actual camera sensor price drop 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. Right now in ADAS and all the components

• 54:38Speaker 03

  are much, become much more and more basically mature and to some level commodity. And on the computer side, as you know, the computer is growing at such a rapid pace. So we talked about Moore's Law in the semiconductor industry some times ago. But in the auto, basically, segment, in the autonomous driving segment, the compute has, the computer 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.

• 55:21Speaker 02

  But in the cloud or in the car?

• 55:24Speaker 03

  In the car.

• 55:25Speaker 02

  In the car.

• 55:27Speaker 02

  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.

• 55:35Speaker 03

  Yes, of course. Yes.

• 55:38Speaker 02

  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 or anyone demands?

• 55:51Speaker 03

  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 allocating personal computer, but from fab capacity as well. So, but that's definitely one of the things we are looking into. Actually, most likely, even the chip price might need to go up essentially

• 56:36Speaker 03

  because of this intense kind of demand for every chip, everybody can grab on essentially. But the positive side is basically

• 56:48Speaker 03

  the technology is really getting, 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

• 57:14Speaker 03

  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, what we call the Hyperion high, we provide basically 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

• 57:58Speaker 03

  to be able to drive L4. We also provide, you need ECU redundancy as well. You need to basically kind of our next generation, actually, to be more present, current generation saw 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.

• 58:28Speaker 02

  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?

• 58:47Speaker 03

  Short answer is yes.

• 58:50Speaker 03

  We believe that LiDAR is the important sensor to provide the safety and 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, theoretically, people can prove out with massive mileage essentially

• 59:32Speaker 03

  to say that LiDAR may not be necessary. But it will comes with ODD limitation essentially.

• 59:42Speaker 02

  Sorry, what's an ODD limitation?

• 59:43Speaker 03

  ODD is basically applicable, basically domain. You can deploy the technology. Obviously, we have done quite a bit of analysis on this. Based on our current understanding and the framework 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.

• 1:00:13Speaker 02

  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?

• 1:00:26Speaker 03

  So, you know, there's two levels of the answer, I guess, to this question. And obviously, basically, for the basic L2++ basically technology, Elon is probably ahead of everybody, essentially. You know, he has a vision a long time ago, and he has sticked 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, and sorry, ADAS kind of market. And everybody is playing a catch-up game, essentially. And we are very happy, actually, Elon is so successful.

• 1:01:10Speaker 03

  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 4, essentially, I think it's more open, I would say, because obviously, there's established players that have proven to the, who are already basically like Waymo, who are doing basically already, 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.

• 1:01:53Speaker 03

  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 AV ecosystem get to this vision of, you know, every my or everything moves that need to be, will be autonomous. This kind of vision becomes a reality.

• 1:02:16Speaker 02

  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?

• 1:02:33Speaker 03

  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 science and basically reasoning. So it's good to hear their view as well.

• 1:02:55Speaker 02

  I want to wrap up by talking about something that maybe is the least in your control. Models are going to keep getting better, and Nvidia is going to keep making chips. Maybe customers are going to 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 furious. 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.

• 1:03:37Speaker 02

  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?

• 1:04:05Speaker 03

  Well, basically, well, I certainly believe the policymakers, they have their reasoning and 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.

• 1:04:48Speaker 03

  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 are working with them on open-source models, Cosmos and Amayo. 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, or Nvidia, basically, platforms

• 1:05:30Speaker 03

  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.

• 1:05:52Speaker 02

  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?

• 1:06:06Speaker 03

  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 conform to all the kind of local kind of regulation to make sure we are complying to all the basic regulations we need to be complying to at different regions.

• 1:06:26Speaker 02

  Does that mean the regional variance 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.

• 1:06:36Speaker 03

  Absolutely. That's, 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 regional kind of a difference. So the model will behave differently in different regions based on the input. And some of the things are, what do we call the country coded? So you have to, obviously, the rules are quite different in different regions, like in Europe as compared to US. 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.

• 1:07:19Speaker 02

  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 4 as a mainstream self-driving experience first? Because if I had to look at it, I would bet that Level 4 self-driving will happen in China way before it happens in the United States as a mainstream experience.

• 1:07:46Speaker 03

  I actually don't think that's true. As you know, basically, Waymo is already getting to everybody to L4 experience, at least in certain ODDs in San Francisco. And they're 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 in all of them has get to the maturity of Waymo, at least in San Francisco. But again, we're trying to help everybody in the ecosystem. So from OEM perspective, it's a different competition landscape. But even basically on the OEM side,

• 1:08:30Speaker 03

  I think, you know, different regions have different kind of, well, one side is probably, you know, the China streets is also much more competitive, much more challenging as compared to US streets. So, you know, to be able to, and Level 4, I would sometimes call it a 0-1 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 can be safely deployable to every customer without driver in a kind of city, kind of size region without any limitation is still in US, not in China.

• 1:09:09Speaker 02

  Yeah, that's Waymo. I think Waymo would, is it going to be very flattering 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 somebody living in Chicago for a long time.

• 1:09:34Speaker 03

  Yep.

• 1:09:34Speaker 02

  I'm very curious how that goes in Chicago, New York City, right? The question I have is, the mainstream experience feels like you just buy a car, and just like Level 2 ADAS is kind of a commodity in cars now, Level 4 will be a mainstream commodity in cars. You push the button and it starts driving itself. How far away do you think we are from that?

• 1:09:54Speaker 03

  Well, first of all, that's exactly my mission, you know, trying 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.

• 1:10:05Speaker 02

  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, Junzhu. 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?

• 1:10:21Speaker 03

  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 of the 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, 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, a ecosystem player.

• 1:11:05Speaker 03

  We are working, you know, with almost like all OEMs. Right now, I would say 80% of the mass production OEMs are in Nvidia's high period, basically ecosystem for L4. So we are really building with this future with everybody. So this is, hopefully, you'll see more exciting announcements from us somewhere down the road.

• 1:11:27Speaker 02

  Yeah, well, I guess I will have to have you back soon. Thank you so much for being on Decoder.

• 1:11:31Speaker 03

  Thanks for having me, Neelai. It's very nice chatting with you.

• 1:11:35Speaker 02

  I'd like to thank Junzhu 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 [email protected]. We really do read all the emails. Or you can hit me up directly on Threads or Blue Sky. We're also on YouTube. You can watch full episodes at DecoderPod. It's the same handle on Instagram and TikTok. Check out those platforms. They're a lot of fun. If you like Decoder, please share it with your friends and subscribe wherever you get your podcasts. Decoder is a production of The Verge and part of the Vox Media Podcast Network. The show is produced by Kate Cox and Nick Stat. This episode was edited by Xander Adams. Our editorial director is Kevin McShane, and the Decoder music is by Breakmaster Cylinder. We'll see you next time.

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