Jesse Levinson explains why Level-5 autonomous driving requires deep vertical integration of hardware, software, and AI. Zoox builds its own AI stack rather than licensing from Mobileye, designs a fail-operational architecture that survives component failures, and operates its fleet as a ride-hailing service instead of selling cars, which changes every engineering trade-off.
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I think, you know, one of the things that makes Zoox challenging is that nobody has solved this type of problem before, right? There's a big gap between autonomous sort of features that you can get on a car today and then true Level 5 mobility, which means you get in a vehicle and it takes you where you want to go and you really don't have to do anything. And in fact, you can't even do anything other than say where you want to go. Or hey, I wanna get out of the vehicle. From a startup's perspective, one of the really exciting things is that we get to do the hardware and the software and the service jointly and holistically. And I think people sometimes don't appreciate how big of an advantage that is. If you look at what Apple did with the iPhone, they didn't just write a new operating system and put it on a Motorola Razr or a BlackBerry, and they also didn't make a new hardware device that was running Windows Mobile Phone from 2006. They could have done, either of those things, but I don't think it would have made nearly the impact on the market that they were able to achieve with the iPhone. Now, I think with autonomous vehicles, especially fully autonomous vehicles, I think that integration between hardware and software and the user experience is even more important than it was with cell phones, right? And so one of the fun things that we get to do, but also the very difficult things, is we get to think holistically about, you know, where do we put sensors? What type of sensors do we need? How much computing power do we need? It's probably more sensors and more computers than you're gonna be able to buy on a car in the next couple years, because our business model is very different. We don't actually sell these vehicles to customers. We own and operate this fleet, and then you can use them the way you use Uber and Lyft, to just hopefully be much better, and even cheaper to operate, because you're not paying the driver. So, we do need to develop our own software, our own AI, our own algorithms. We're not using Mobileye or anybody else's AI technology. It's completely developed in-house at Zoox. When it comes to computers and sensors, you know, we, as much as possible, buy things off the shelf or work with technology partners to, you know, get access to their upcoming products. And then when it comes to the sort of physical mechatronic layer of the vehicle, it's also really powerful that we get to design a level of robustness and some amount of fail-operational into that design that cars today are not built for. Cars today are not built to drive 100,000 miles a year, for 4 years, and they're also not designed to be necessarily safe when a whole bunch of components might fail. The good news is we don't have to reinvent, for example, the entire concept of an electric car the way Tesla had to do. What Tesla did 10 years ago from a vehicle engineering perspective was quite amazing, very, very difficult, and there was really no supply chain for EV technology then. In the last decade, Tesla, and now a bunch of other companies, make electric cars at quite some scale. So if we're looking for batteries, and motors, and steering columns, and these sort of things, sometimes we might need to innovate a little bit with the supplier. But for the most part, it's about building a new architecture, but not reinventing every single hardware component in the vehicle.
