Name: AI Driver: The Next Era of Transportation
Uploaded: 2025-05-06
Duration: 19 min 35 s
Description: What’s happening in the world of cars? We’ll explore how driver assistance and autonomous driving is reshaping the future—impacting everything from vehicle design and software to policy and infrastructure. Join us as we navigate the road ahead and uncover how cars, cities, and industries are adapting to an AI driven world.

What’s happening in the world of cars?  We’ll explore how driver assistance and autonomous driving is reshaping the future—impacting everything from vehicle design and software to policy and infrastructure. Join us as we navigate the road ahead and uncover how cars, cities, and industries are adapting to an AI driven world.

AI Driver: The Next Era of Transportation

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What do we think of that? Cool. Yeah, liked it. I, um. I'll confess that I recently signed up for an improv class back home in the US and This was like a much cooler way to do it, I think. So, second guessing it a little bit, um. As we can see, a lot is happening right now when AI leaves the screen and hits the real world, and in this next case actually hits the road. Joining us remotely is someone at the forefront of this transformation, uh, specifically. We have Alex Kendall, the co-founder and CEO at Wave, company pioneering, AI-driven, assisted, and automated driving technology, and soon we should be able to see Alex up here on this screen. Alex, how are you doing today? Hey Jason, thanks for having me. Thanks for joining us in in some way. So, um, the funny part here that's kind of funny and kind of not, is that Alex is actually based here, but as you can see he's not here next to me right now. He is in uh San Francisco Bay Area in the US. Alex, why don't why don't you fill fill us in on on what are you doing right now in uh in the US? Hey, sorry, everyone, I, I'm missing the party, but yeah, I'm also in our office in the Bay Area and it's been the most amazing global expansion year for Wave. Uh, just a year ago, our AI was only driving in central London, which is mostly 20 miles an hour speed limit. It's actually quite complex and the intelligence you need to negotiate with other road users is. quite extreme in London, uh, where we found out that it has 9 times more jaywalkers in San Francisco, if you want to start. Um, in the last year, we've been able to launch our D, uh, through Germany, uh, the US, and Japan, uh, and I started working with automakers around the world. So it's, it's been a really exciting year of seeing our AI scale to To cities around the world and of course get even closer to launching this into consumer vehicles. Um, Alex, we, uh, know your company raised a billion dollars last year, um, from SoftBank and others, and, um, we're just, I'm just gonna, I'm just gonna tell myself that that's what you're doing in the US right now. You're, you're raising another billion dollars, so, um. Uh, you, you don't have to, you don't have to comment. Perfect. Um, Alex, I think a lot of companies, uh, a lot of people here based in the UK will, will know way, but can you just help us understand how the technology your company is, uh, Has been innovating on is differentiated from others in the space, maybe a Waymo or a Tesla or competitors in China. Yeah, of course. When I think about the future of robotics, I, I imagine machines that have the intelligence to make their own decisions that they can deal with things they've never seen before and of course act in a way that's safe and, and something that we can trust to delegate tasks to. And for me, that's all about building an AI system that can understand the physical world and make these kind of decisions. When I started Wave about 8 years ago, autonomous driving was focusing on building a structure like high definition maps and hand coding how vehicles should drive. And to me, that wasn't the way the technology was going to scale. Uh, so we set off 8 years ago to go and build an end to end deep learning approach to autonomous driving. We were fortunate to be the first to build a reinforcement learning system that could learn to drive on the road. First to have a system that drove in urban um on public roads and in a city like London. Uh, and now, uh, in 2023, we were the first to put a large language model on the road that could understand and uh you could talk to it while it drive as well as build a generative world model that could simulate the complexities of, of urban and, and, and global driving. Uh, so it's really been a, a journey of building out this technology and we're at the point now where it's, it's remarkable you can jump in our car and all of these places around the world and it drives in a way that's, that's safe and human-like. Uh, and so now we have the opportunity to deploy this across many different, uh, fleets and automakers, and the, and the benefit of this approach is it really does generalize. It doesn't need high definition maps for each new place you go to, and it can learn to drive different vehicles from cars to to vans, uh, without needing changing, uh, significant changes. So that is, so that is the advantage you you see, which is that, um, you might be, your technology might be able to help a car drive, um. With, you know, with assistance or maybe someday fully autonomously in London as well as Japan, as well as highways in the Bay Area, um, without much modification. Is it in are are we understanding that correctly? What on Jason. So whether it's a hands-off driver assistance system or a RoboTaxi, they're all going to leverage our foundation model that shares the learning from all of this experience. Now, here's, here's the real magic that 2 years ago, we couldn't even get conversations with uh automakers. For the last 2 years, we've seen this maturation of first the excitement in AI and uh I think conviction and, and what we've been able to build. But also we've seen software defined vehicles come to market where most of the high-end today and very soon mass market vehicles that are being produced have a GPU on board. They have surround cameras, surround radar, maybe a forward facing liar. They have the right hardware to operate a lean and efficient AI stack like ours. Let's, what I mean, let's just talk quickly about your Nissan, um, partnership, which was just signed or announced last month. Is that correct? And so can you explain briefly what what that partnership means? It's you're first with an automaker like you were alluded to, it's taken, I think 7 or 8 years since you're founding to get that first. Can you explain why, why it's taken that long and then what does this partnership actually mean in reality? Yeah, we, we've been on a deep tech journey which is um required us to solve a lot of frontier, uh, problems and embodied AI science. Um, but then there's also been this sort of, uh, joint collision of, of our automotive, uh, road maps where the technology is ready, but then also consumer vehicles have the right, uh, infrastructure and capabilities to be able to bring this to market. And with a partner like Nissan, uh, I'm super excited about the ambition they have. Uh, we announced that from 2027, Nissan vehicles will have our technology and then will enable a hands-off, uh, driving system. They can go through point to point complex urban environments, highways can help improve the safety and comfort of of those driving experiences. So cars, Nissan cars on the road in 2027, um. We'll have your technology in it. And this is, I, I think in the industry, this is called a level 2 technology. What does that mean exactly? You a driver will need to be alert. As your technology operates or something, so you can take your hands off the wheel, feet off the pedals and let the system drive, but you need to monitor it. Uh, and we could as possible want to leverage the experience from that to grow up eyes off where you can actually get time back. Uh, you could take a call, be on your phone, read a book, or sleep or something like this, uh, before, of course, robo taxes, which, uh, are going to allow you to, to not even need to take liability for driving at all. So you're in the San Francisco Bay Area right now and um a certain company named Waymo has been out on the road with, I think, um, fully autonomous uh ride hailing services. I I I I'm curious what kind of pressures, and I know they have a different model, but what kind of pressures that puts on you, um, to make either more of these auto deals happen or to just speed up sort of commercial progress overall. The Robotaxi vehicles that you see driving in San Francisco, Shanghai, and some of these places around the world are typically low volume vehicles with retrofitted sensors on them, um, whereas what we're talking about is natively working with mass produced vehicles through a software delivery. We, we license our AI to these vehicles. I mean, Nissan produced over 2 million cars a year. They're one of the 10 largest automakers, uh, and with partners like that. Our ambition is to see this, uh, we talk about being not not first to one city but first to 100 cities that way. We want to see autonomy truly reach scale, uh, and break through the kind of glass ceilings that AB 1.0 has had so far. I think with embodied AI you can build a system that is affordable, scalable, uh, and, and safe to drive a global setting on mass produced vehicles. I want to just make sure all of you know that I, I think we will still attempt to do some questions from the audience if you have them, so please raise your hand and um I'll get to you, um, but I, I have plenty more. Um, I'll come back, I'll come back to you all as you think of some. Uh, where are the, you know, the first places where you think your technology makes sense to operate? I know you talked about generalization and and the first to 100, but, um, 2027, 2028. What are the markets where car owners should be, you know, excited about the possibility of, of your technology being inside their their own vehicle? Well, I'll answer that question in a second, but let me just share one fun anecdote from when we scaled our systems. We started learning in London, of course, when we took it to the US in San Francisco, we had to learn things like to drive on the right side of the road and in the US you have a right turn at red light, uh, you're allowed to turn around at a red light. It's really interesting to see us then take our system to Germany. It, it's learned much more efficiently because it had already learned to drive on the right side of the road and it seemed more diverse experience, but it had to learn to unlearn the right turn of lights and ultimately end up learning all the different combinations around the world. So what we see today is the system performs fairly uniformly around everywhere we operate, learning from data in each different market that we're in. Uh, so from a technology perspective, Not necessarily really any difference around the world. Maybe when you think about snow or some more extreme weather conditions, there are more challenges, but again, it just becomes a data problem with AB 2.0. It's all about machine learning. Uh, so it really then comes down to, uh, market economics and regulation. Uh, I think how this is gonna go. I, I really can't wait for it to come to my home country in New Zealand, but I, I think naturally we will start in big markets, um, the US, Japan, Europe, a big automotive markets, and grow from there. Um, not, not here where I'm currently, uh. Currently sitting today? Europe and the UK. OK, all right. I, I, I probably missed that. Any, any questions from you all? Um. We have a couple of questions here, Alex, so we're going to try to make that happen. Um, could start in the back and then we'll work up here. Hi, Alex. Hi everybody. My name is Tom Whitaker. I'm a lawyer at Burges Salmon in the UK. You mentioned about the importance of regulations for your future success. What are you looking for from an effective regulatory framework and effective regulators? Oh, that's a great question. Uh, I, I've actually been, if you take a step back, it's really exciting to see how proactive regulation has been for autonomy. I mean, the technology is, is more nascent, yet we have, um, mature regulation in many places around the world. We were fortunate enough to help the government pass the Automated Vehicles Act 2024 in the UK last year. Um, but what we're really looking for is, is two things. One is clarity and the second is global harmonization. Uh, we do a lot of work with the UN ECE. Uh, we co-chair the industry body there, uh, and having that body essentially regulates every, most major markets outside the US and China for automotive regulations, and it's great to have that all harmonized. In the US, you currently have a state by state system. Again, we would look for ideally a federal system or even better, something aligned with the UN and global. Um, but as, as, as much as it can be harmonized and then secondly about setting clear expectations, this is really helpful on the marketing front to avoid when you think about building trust with robots and embodied AI, it's all about setting and meeting the right expectations. Um, so having that clearly laid out, um, their views on liability, I think these can be really helpful. But the good news is, uh, that the UN has made great progress for driver assistance, um, and soon Robotaxis, and the UK has put in place primary legislation for this last year, and within 2 years that should be fully enacted and operational. Um, we have another question here. Just tell us who you are. Hi, my name is Amrita. I work for Cine Media, and you spoke about market economics and regulation. In all of your learning, I like your anecdote about how your AI systems have learned in different countries. Do you see any need for a change in infrastructure, or is your system built that infrastructure isn't a dependency and it will work on roads and layouts as they are today, or is it a future city that is more AI autonomous vehicles first rather than what we have today? There are a couple of, uh, often, you know, mis misnomers like one of the big ones I often get is synthetic data can replace real world data, but of course, Today, the best synthetic data is generative AI which needs real data to train the systems. And so these things aren't true, but similarly with uh thoughts that you need 4 or 5G for autonomy, you need to change cities. No, that's, that's not the case. The technology that we're building learns to drive. Like, uh, just like we do in with the, the infrastructure that we currently have in our cities. It slows down the potholes, uh, it's, uh, can navigate in places it hasn't been to before and has everything on board the car needs for safety operation and so it does not need a live uh live data connection to operate. However, once autonomy is out there and becomes a prevalent part of, of, of, uh, vehicles, I think there are a bunch of opportunities to help improve cities beyond us and make our streets even more healthy, but there's gonna be quite a long, um, uh, period of penetration of this technology, uh, before you start to see some of those kind of changes, and you can imagine, you know, when you think about the way that junctions work, traffic lights work, when you can communicate with vehicles more reliably, which you optimize those. Uh, uh, or speed limits, which are largely design with safety in mind. When we, uh, vastly improve road safety by orders of magnitude, how can you look at changes there? Um, and in fact, most congestion is due to road accidents today. And again, when we are able to, um, uh, vastly improve road safety, how does this change, um, uh, road patterns and, and transportation, and there's a bunch of secondary opportunities, but first we need to focus on actually getting this technology out so the world can benefit from it, and that will happen with the existing structure. Alex, I have another one on my end. Um, there's a pretty large US based uh automaker named Tesla, and um, a certain tech leader running that company and making promises about autonomous robotaxis in the next year or so. Uh, I'm curious, at a high level, there are some comparisons between your technology approach and theirs, and I'm curious, what, if anything, have you learned from. Tesla's approach and or observing their evolution in this field, and where have you decided, um, is it different or better path? Well, look, it was You know, gratifying to see Tesla pivot to the technical strategy we're taking after many years ago, Elon told me this would never work, uh, but we, we do share today a lot in common in our strategy of using end when learning, uh, for driving. Uh, it's really the two of us that have been pushing this forward, uh, and driving without any high definition maps. Some of the, uh. Also, the fact that we think that it's important, critical, in fact, to have consumer vehicles at scale, uh, to be able to leverage their data to actually build a safe and generalizable autonomy. These are the things we share. Um, one of the key differences is that we are much more sensor agnostic, uh, and so while I think a camera, predominantly camera approach is right way to go forward. Uh, having redundant sensing, mostly radar, maybe a forward facing liar, I think can really help feeding quality of, of, of the system, particularly as you go from hands off to eyes off for Robotech. Uh, and so we are, uh, working on solutions that have redundant sensing capabilities and because of that, I think I will be able to offer a much safer experience. Have you told Elon I told you so yet, or are you saving that for? A rainy, uh, day on when when you're acquired or public on the market. That'll be a fun Twitter one day, but look, I, I think this is such a hard problem. One of the key values we have at our company is, is humility, uh, and really not assuming we know the solution because it always surprises you. Uh, it's, it's one of the greatest engineering problems there is today, and, uh, I think this movement into the age of autonomy where we have intelligent machines that We can delegate so many is gonna be one of the profound shifts we go through and uh uh there's, there's certainly gonna be more challenges on the road to make it make it a reality, so we're gonna stay humble for now. For now, OK, that's good. Stay humble. I have one question and then we'll get you in the audience up here real quick. Um, the Nissan deal, is it exclusive in any way? And either way, what, what is the biggest challenge to um more deals in the future? Our, our, uh, intention in forward is to really see the embodied AI as a platform across automakers and fleets and so we've designed it in a way that's generalizable, flexible, we can work with different SOCs, different vehicles in different countries. Having it out there at scale, uh, will enable automakers to build the driving experience and, uh, uh, products they want to on top of that. And we're really excited about seeing that go out broadly across the market. And, uh, in general, I think that this will become more safe in performance than what anyone automaker can build vertically because the scale is just going to be so much larger. Um, uh, so, so, so that's really exciting to us. What's, what's our rate limiting things today, uh, you know, look, uh. Automotive integration is a reason why uh the industry prides itself on quality and reliability, um, because the scale of validation that you need to do in these products and that we and our partners are doing before launching it to, uh, to, to make sure we can trust the results, uh, is, is, is of course vast. And so we're working through that with our partners, but, uh, you know, it'll be gradually and then suddenly all at once. This will be a predominant technology across fleets in the next couple of years. Alex, unfortunately, we're all out of time. We'll have to do this again, um, sitting next to each other. Thank you for making it work. See you soon. Thanks, Jason. Thanks Aron.