The Self-Driving Startup Nobody Saw Coming | E2289

We pioneered end-to-end learning when it was widely dismissed.

Self-driving in a way that economically scales to the world is not, is not solved.

Our partnership is not up to 25,000, is over 25,000, or in other words, a minimum of 25,000.

Our volume is like double the cars Tesla builds a year, uh, and that's just one of our partners. And if you're a manufacturer selling a car that doesn't have this, I think your demand is really going to fall off a cliff.

Every car is being intelligently driven by a machine that never blinks.

You know, you'll pay for your own private chauffeur that's in your car.

Has Uber tried to buy you?

Wabi is not for sale for anybody.

This Week in Startups is brought to you by IM8 Health. Start feeling like your best self every day. Go to im8health.com/twist and use the code TWIST to get a free welcome kit, 5 free travel sachets, and 10% off your order. Squarespace. Turn your idea into a beautiful website. Go to squarespace.com/twist for a free trial. When you're ready to launch, Use offer code TWIST to save 10% off your first purchase of a website or domain. And Render. Find out why 5 million developers are already using the all-in-one cloud platform Render. Go to render.com/twist and apply for the Render Startup Program to get $500 to $100,000 in free credits depending on your stage and backers. Hello everybody and welcome back to TWIST. My name is Alex and today we're going deep on one of my absolute favorite topics in the world. And no, it's not about OpenClaw. No, today we're talking about self-driving cars. We're bringing back the CEO of a company that we had on the show back in late 2024 when Wave, a UK-based self-driving startup, was doing incredibly interesting things, working hard to bring technology to market. Since then, quite a lot has happened. We're going to dive into what Wave has done recently, how close it is to changing your life and my life. So please join me in welcoming back to the show its co-founder and CEO, Alex Kendall. Alex, how you doing?

Awesome.

Hey Alex, It's so good to have you back. So late 2024 feels like 29 years ago in AI terms. Has the self-driving world been progressing as quickly as the kind of general AI landscape?

Well, you know, if I go back to when we started in 2017, one of our very first blog posts was about a world model that we put together back then. It was, I don't know, not today's standards. It was like a 20,000-parameter world model. And we were all excited at the time of Intuin AI. Hey, it was going to actually, you know, allow us to really truly scale autonomy. And that pitch has stayed the same for the last decade. But it feels like the whole industry is, is really getting behind what we're doing now, because this has been a contrarian approach for so many years. But in the last, you know, last few months, we've brought in investment from NVIDIA, Qualcomm, AMD, all the big chip companies, and then Uber, Nissan, Mercedes, Stellantis, Microsoft. And it just feels like the industry is now believing that this once contrarian approach has the legs to go scale things for the industry. It's a big privilege.

Yeah. So you guys wrote, we pioneered end-to-end learning when it was widely dismissed. We built world models years before they became fashionable. We prioritized generalization across many environments over driverless optimization in single domain, etc., etc., etc. Early. And it seems to be correct. But since we had you on, You guys have released, I think, two new world models, Gaia 2 and 3. So I know it's a little bit basic, but could you tell folks who are behind what a world model is in this context? And then I'm really curious what improved between the generations of the world models that Wave uses to power self-driving.

A world model is a— I mean, it's a— at the basic core principle, it's a, it's a, it's a model that can understand the state of the world, a given action you take on the world and how the world evolves. And so what that lets you do is, I mean, first of all, it's a really powerful representation learning method. It lets you learn a representation of the world that actually cares about what matters. So if you're driving a car, you don't care about the clouds in the sky or the cars going the other way behind you. You care about the road lines, the curbs, the traffic signals in front of you, and anything that might intersect with you. And so by learning how to predict the world, you actually cause your machine learning model to represent what actually matters in the world in an unsupervised way. So firstly, it's a really powerful representation learning method. And then secondly, it gives you benefits of it. It can be a simulator. It can actually allow you to simulate what's happening in the world to learn or to validate or to actually control what's in front of you.

And as far as I understand it, I'm going to put this in super basic idiot terms, but it's kind of a video game for your self-driving technology to play. It creates a world with obstacles, traffic, weather, locations, rules, like Which side of the road do you drive on? And then you can create essentially an infinite number of testing variants, and then you can put your driver into this world, this generated world, and essentially do infinite miles in a virtual setting that would take lots more time and money in the real world to do without the safety implications.

Yeah, that's right. I mean, we have an analogy in our own minds, right? In our hippocampus, we have world models that actually, when we daydream or sleep, We replay experiences a gazillion times to actually reinforce how we act, how we learn to swing a tennis racket or do any motor task that we have. So we do the same thing, but it's a lot more than that, right? It's a representation, a really rich representation of the world. But yes, one of the best uses is a simulator. And we know in robotics and self-driving, It's not like a chatbot or something where you've got large-scale text on the internet, but getting the data and in particular getting the safety-critical data and then proving a system is safe is the hardest problem. And it's an arms race in our industry between learning a driving policy and learning a simulator. If you have one, you've solved the other and you solve the problem. But the arms race between them, we find that for simulation, end-to-end learning is not only the best approach in the world for learning driving policies, but it's also the best approach at learning to simulate. Because building a world model with an end-to-end deep learning model, what that allows you to do is it allows you to use data to model very complex and diverse scenes. It lets you learn very rich dynamics. So to answer your question, what's evolved? I mean, yes, of course, we've scaled up the parameter count, the datasets. It's now at frontier scale for the robotics industry. But our world model learns from— this is the advantage we have in self-driving is we have hundreds of petabytes of data across everything from internet-scale data to dashcams to the automations automakers that we partner with. We've got over a dozen different companies now sharing data with us that we aggregate at scale and to train this, this, this world model. So what's changed? So we've scaled up data and compute parameter count, but then we've also improved a number of things algorithmically. So it's not only video, but also understands radar and lidar. It understands multiple sensors. So a typical self-driving car might have a dozen or so cameras, might have you know, 5, 6, 7, or how many other radars. So it can understand these. And then on top of that, it's controllable. So we can actually, you know, prompt or control it or simulate something we've seen in the real world or adversarially test something and try and, you know, try and make our car learn or make mistakes in the world model so we can learn from that.

And when you talk about different sensors and different self-driving cars and what they have equipped to them, to me, there is a buffet of options. You can have in your car. I presume that, you know, your, your AI driver can work with what it's offered. So if it has LiDAR and not radar, radar, not LiDAR, visual, blah, blah, blah, it can take in, I presume, any type of information and use that to make its decisions. Is there a minimum level of ingestion required here?

Yeah, that's a great question. I think the sensor debate is often a very heated one in the industry, but really probably not. There is more nuance to it than what might be seen. But at, at the core of what we do at Wave, we want to be the intelligence layer across any vehicle anywhere. And there's going to be some products that benefit from being camera only, some with radar, some with LiDAR. And so we support them all. Um, now this is, I think, very natural to do with our approach because our model trains on very diverse data sensors in different locations, different types, and we can learn to understand which signals to represent and also which signals we can rely on and what a sensor architecture can or can't see. Because you can do that through a world model. I mentioned it's a really powerful representation. When you learn to predict the future, if your sensor can't see part of the scene, it can't predict the future in that way. So you can learn this very naturally.

So you're not just simulating, show me with rain, show me with snow. You're also simulating, okay, I'm in a smaller car with this sensor array in this weather environment. So you can get super granular then. Or inside your world models.

Exactly. If you're in fog with camera only, you might struggle. If you've got a radar, you might do better. You can predict different things. But to answer your question, yes, there is a minimum bar of safety you need for a hands-off, eyes-off, or driverless system. Now, you can achieve all levels with a camera-only system if you're really good enough, but it might be faster and more efficient to get there with some radar or other sensing modality. So what we find in the industry today is that Most of our partners who are building, say, robotaxis, it's better to work with camera, radar, lidar. But crucially, these are not bespoke, you know, custom spinning lidars on the, on the vehicle. These are automotive-grade, mass-market, low-cost sensing devices. So there's, I guess, there's a difference there.

Absolutely. Now, you've had two new world models come out. You've also raised an enormous amount of money recently, $1.2, $1.5 billion, depending on kind of how you count in tranches and so forth. Uh, my read of that following your technological progress is that people are very impressed and you have cracked self-driving. I feel like we've gotten to the point where we can say we've figured it out. Is that fair, or am I a little bit ahead of the, uh, the curve here in that pronouncement?

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Oh man, self-driving is, I think, not only the hardest problem, but it's going to be a continued open problem for some time. I think the key thing to realize, and despite if you live in Silicon Valley or Shanghai, despite what you see on the roads every day, self-driving in a way that economically scales to the world is not, is not solved. And I think that what we bring is an approach that has demonstrated a path to that solution. And now we're entering an integration and product deployment phase. So what we're going to see with this capital is, I mentioned, you know, our mission is to bring intelligence to any vehicle anywhere. And so we're going to see that start to be deployed this year in supervised robotaxi trials. Starting in London, Tokyo, and 10 other cities on Uber. And from next year, in consumer vehicles, you know, we're supported by partners like Nissan, Mercedes, and Stellantis. Take Nissan, for example. Last year, they announced they're bringing us into their consumer vehicle lineup. Then earlier this year, we announced the Robotaxi, because what we find is automakers, they want to work with the same partner across L2, L3, and L4. It really helps speed and efficiency, and you can leverage data and integration.

And the Wave system can do different steps of the L1, 2, 3, 4, 5 ladder. So you can approach this kind of like whatever they need, you can offer.

Exactly. Exactly. And then 2 weeks ago, Nissan announced that they are going to bring this technology, bring our approach to 90% of their vehicles. You know, they build about 3 million cars a year.

So this is That's $2.7 million.

Yeah, this is a, this is an enormous, enormous volume. It's like double the cars Tesla builds a year. And that's just one of our partners. And so, you know, we're really excited about this and this, this, this business model. I mentioned how we had a contrarian technical strategy, but there's also a contrarian business model because, yes, there's, there's 3 ways to bring autonomy to market, right? You can build your own cars. That's what Tesla is doing. But then you're limited to just your own brand. You could build your own fleet city by city. That's what Waymo is doing, but it's a very expensive, high CapEx endeavor. Or what we're doing is we're licensing this to any fleet or automaker, and that's, I think, the largest business model. That's why we've chosen it. It's only possible because we've built a flexible and generalizable, like, AI driver. And so I think this is, this is also interesting how it's enabling a different business model that might not you know, might not be appreciated at first thought.

We're gonna get to that in just a second. But my question of have we cracked self-driving, you answered in a very interesting way, and I was being slightly puckish by asking it in that way. But I was curious, you know, with all the technological progress we've made, are we there? And then you said no, because we haven't sorted out the economics of bringing this to the world yet. Those are different points. So I guess the question, Alex, is has Wave gotten so good at, generalised self-driving now, that we're only left with the economic and manufacturing questions for bringing self-driving to mass market cars in the next 18 months? Or are there still technical— is there still science risk, I suppose? Or are we only talking about market risk?

I'll give a nuanced answer here. And I think what I try to appreciate in self-driving is firstly, to let our results do the talking and not sort of add undue hype and try to bring a bit of technical realism. I think these principles have served Wave well over the years, but I think so. I think we're through the scientific risk, certainly. So let's start with different levels of autonomy. So for hands-off driving, I think that we've now shown that, like last year, we drove in 500 cities around the world, Tesla system scales. Wave and Tesla built this, the end-to-end stack, and we've both shown that this scales globally. We've got the level of performance needed for a delightful product. People are willing to pay for it. You can see the amazing— Tesla announced they're doing $1.5 billion of revenue a year with this. Like, clearly there's product market fit with, with that kind of product, and the technology is performant to do that. Now, what is it going to take to get this from hands-off to eyes-off or driveless? That basically the same level of safety for L3 or for a point-to-point system. There is a gap in performance from the systems that, say, Tesla ourselves have today to get to general purpose driverless. You know, what Waymo has demonstrated in the geofenced areas they operate in to do that at a global scale, a way that economically scales with mass market hardware, no geofence to be able to do that. There is a gap there. But what I'm seeing is in front of us a very clear path to go do it. And so I'd argue we've moved on from the scientific risk and now it's engineering execution risk and like product integration and deployment risk ahead of us. Namely, what we need to do is we need to integrate this into vehicles that have the right infrastructure for these products. We've got programs underway with some of the biggest manufacturers, so that's underway from an engineering perspective. We need to scale up the AI model to reach that level of performance. And I think that's a very predictable scaling curve, a little bit like the— what we saw in the LLM scaling journeys. But that's a case of data compute, some algorithmic innovation along the way. But, but I think that's a predictable curve that we need to go run up. And then third, of course, to be able to validate it again, that's an engineering activity. We know how to do it. It's a case of now scaling the validation activities across the domain to prove that this is safer than a safe and competent human driver before we launch. We work through those three things. Then, of course, getting regulatory sign-off will allow the launch of these products. Even on the regulation piece, the amazing thing is that we've seen regulators put regulation in place ahead of the products being ready. And I think that's quite amazing to see. Of course, the US, in some states they allow it, some they don't, but there's a market there for it. Outside the US, the UN, 2 months ago— so we co-chair the industry committee for UN autonomy regulations, and the UN just put in place a legal pathway for L3 and L4 driving, which— and that covers basically every country except the US and China. So there is now a legal path to getting this deployed as well. So all in all, I think we moved from science risk and now it's an engineering and deployment risk.

So to get us from hands-off to eyes-off, the path from here to there in the AI sense, in the technical sense, is solvable. We know how to do that. It's data compute and algorithm innovation. And if you're curious what we mean by that, just go read a paper from a major LLM lab. Talking about the latest model and how they changed the back functions of it to see how to make it better.

There are some interesting differences from LLMs though, right? Like we— there's the challenge of the real-time embodied inference you've got to do onboard the vehicle. It's much more constrained. There's a safety-critical challenge. There's different modalities. You've got much larger dimensional data, and then you've got to build a system that's safety-aware and uncertainty-aware because if you put out a— you can't hallucinate for a self-driving car. No, there's a lot of difference there, right?

No, no, for sure. But this actually brings me a question that I wanted to ask about the business model here, because I love taking the third approach, working with manufacturers who were already good at making lots of cars or working with demand providers like Uber who already have a lot of people. It just makes a lot of sense to me to take the technology to where there's already aggregated pools of demand that, that makes good sense. But let's, let's look forward a couple of years. I'm going to go buy a new Nissan. I have the option to get Wave built in. I click all the boxes. I would like L4, please. I don't want to even touch the steering wheel. Put it away. I just want to sit in the back and sleep because I'm a terrible driver. Let's be honest. How do I, the consumer, pay for that? Do I pay a fee to Nissan for the technology? Let's say it's a $5K add-on. Making up numbers here, not holding you to it. Or do I pay them some and then you guys some? Because to me, the compute side of this, can't be entirely local to the car. There probably is some data exchange, some inference costs. So to me, there's— it seems like it'd be something that I should pay you for on a regular basis. So it's good and it gets improved, but I'm not sure that's the plan.

Yeah, I think the journey— the industry is on a journey of figuring that out. So for consumer vehicles, you will pay the, the manufacturer who will then pass through economics to Wave. But there's different models that are being played out. Some manufacturers are looking to bundle this with the car and actually include it for free with all the cars they sell. Sell for a given model. Some are, you know, it's like a seatbelt. It's a feature that you should expect. Others are looking to have a one-time fee. Others are looking to have a recurring subscription. Some are a bit of both. Some may be a free trial. And then after a trial, then you subscribe to it. Of course, famously, Tesla charges $100 a month for these features. Others have got lower levels of subscription. So I think there's a bit of a price exploration that's, that's going to be done in the industry. But I think it's likely that we will see the industry move to a subscription model because, as you say, all the intelligence will run on the edge on the car. But there are going to be, you know, there's going to be an improving performance over time with over-the-air updates. And of course, for L3 or L4 driving, there'll be some ongoing insurance costs and things like this for the manufacturer to bear. So all in all, I do expect we will get to a subscription model for vehicles and you'll, you know, you'll pay for your own private chauffeur that's in your car.

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You just said something incredibly interesting that sounded very boring, but actually I think is very important. Insurance costs held by the OEM, in this case the car manufacturer. I thought that was also a pretty unsettled question as to who is liable for— okay, let's just be honest. A lot of people in the world, a lot of cars, even self-driving cars are going to hit people sometimes. Fewer. It's going to be safer, but it's going to happen. So do you expect that in our example, Nissan would hold the liability there for selling the system? The consumer for using the system or you for coding it?

Oh, look, this depends on both the level of autonomy, the regulatory environment, and of course, the commercial contracting between all the parties that bring together the product. So there's a lot of factors at play, but at a very high level, a hands-off system, if it's implemented correctly, as the driver, you should remain liable. And then an eyes-off or a driverless system, the manufacturer or operator will remain reliable. With, with some insured and contracted liability that flows through to, to the various parts of the ecosystem. So it really depends.

Are you going to build that, that financial backing, that infrastructure we need to handle the insurance element of this, or is that going to be handled by like Chubb or like Berkshire Hathaway?

We're staying focused. We're not going to build an insurance product, Alex, but if that changes, I'll let you know.

I mean, I kind of feel like it should be like an add-on offering to what, to what you're selling. All right. Let's talk about how big this market is. A lot of cars sold every year. Enormous market, key economic engine, ironically, for the world. How many cars do you think— what percentage of newly manufactured cars in 5 years do you think are made with the capacity to work with either Wave or a similar product in 5 years?

So today there are about 100 million vehicles produced each year. You know, call it 50-60 million consumer cars. Today, I think the number for advanced ADAS is what the industry calls it, is about 15%. But most of this is like highway lane keep assist or some very rudimentary systems. So the penetration of, call it, you know, outside of China, the full self-driving experience is just really Tesla, and that's a very small fraction of the market. So this is going to go from like nothing to everything over the next few years. What we're seeing is that today luxury manufacturers are bringing in the right level of compute on the cars, Nvidia or Qualcomm or something like that, GPU and surround sensing. And we're seeing more volume manufacturers like Nissan just announced that, you know, they're going to bring this kind of technology to the vehicles from, from financial year 2027. So over— you said 5 years, I think by 5 years we're going to see this level of hardware in a very significant portion of the market. And we'll see this improving experience over time. We'll start to see the introduction in the more premium end also of eyes-off technology and, and even driverless, consumer driverless technology. And I think we'll continue to see that flow down. But, but the steady state is that every vehicle is going to be capable of that. I mean, when you can, for, you know, a very low monthly subscription, get an eyes-off driving experience, I think this is going to completely change things. And actually, You know, yes, robotaxis are also transformational. But when you think about the scale, there's less than 10,000 robotaxis in the world today, but 100 million new cars a year. And so the scale of impact you can have through consumer vehicles is enormous. I think the advantage that Wayve brings, because we work on both robotaxis and consumer cars, means that firstly, the data we get from consumer cars will give us what we need to build general purpose robotaxis. Secondly, the manufacturing relationships with the OEMs is really important. Because an OEM really wants to focus on volume. And the only way they can have a business case to work on a robotaxi is if they can have a single partner that work across, across the spectrum of autonomy. So, you know, for these reasons, I think this will give us the ability to have native relationships where it's a vehicle built for as a robotaxi with us just as a software integration. It gives us this high margin software business coming across the spectrum of autonomy, gives us the data gives us the global supply chain and geography scale. And so I think for all these reasons, it's a, it's a, it's a very, very important opportunity for us that often, often goes unnoticed. But we're going to see this complete transformation of the consumer vehicle market with our AI in the coming years. And to answer your question, in 5 years, I think, you know, what if you're selling it, if you're a manufacturer selling a car that doesn't have this, I think your demand is really going to fall off a cliff.

Yeah. Apart from probably the most basic, you know, like Tata Nano style cars, like whatever is like, you know, very, very simple. Probably not everything.

But actually even, even regulatory requirements require every car to be sold today to have active braking systems. And over time, autonomy will be so important for road safety that even the most basic cars, like you say, I think will still have this technology. Otherwise, it's a moral imperative because of road safety.

I can't wait. It's gonna be so much— when my kids can just like walk out of our house and walk down or across the street and I know that every car is being intelligently driven by a machine that never blinks. It's gonna be so much better than the Yahoos who drive around my house currently at like 800 miles an hour at night. It's like, it's residential, dude. Break it down.

All right.

So 100 million cars a year. Going back to the Tesla example, $100 a month, $1,200 a year. Call it $1,000 for safety. 100 million times 1,000 is $100 billion. So clearly we're talking about a staggeringly large market. Do you need more capital to unlock it, or does the recent billion-dollar-plus raise give Wave enough runway to get all the way into production with an OEM and early volume?

Oh, we're in an awesome financial position. We've got over $2 billion—

I thought so—

capital right now. Amazing set of shareholders I mentioned earlier. And all the capital we need to go get this deployed and bring the business to a free cash flow positive and escape velocity. So, you know, these contracts we're signing are decade-long relationships with automakers. And so I think the great thing is we can give them the confidence of the security that, you know, we may not need to raise to get to that escape velocity. Of course, I wouldn't rule out any further raises because there's always opportunities to accelerate and grow into other verticals over time. But for now, yes, we've got everything we need to go run at this opportunity. And you feel the energy in our team now. It's, it's, it's such a, such a privilege now to get to go and go and build and deploy these products.

All right. One last question before I let you go. This has been tremendous. I love learning things. I was talking to Huobi. They've done something interesting. They started in the world of self-driving trucks, you know, 18-wheelers, and they've been moving towards cars. Now, today we've been talking about cars in various formats, be they in a robotaxi fleet or a vehicle at an OEM. Do you think that the technology that Wave has built with its world models is transferable to large commercial trucks and other forms of earth movers and, you know, construction equipment down the road? Or is that an entirely different dataset and therefore a different training question?

Oh, 100% it is. I've got a lot of data points I can share on this, actually. But we started with the hardest application, consumer vehicles. Because it would force us to build the most scalable technology. Consumer vehicles is the hardest because you've got to run on hundreds of dollars of hardware. You've got to work literally everywhere and you've got to deal with— I mean, Nissan has 60 different car lines. You've got to deal with a really diverse set of products.

And so 60, 60, yeah, that's a lot.

And even starting to learn in London, right? It's like one of the hardest environments to drive in. So you know, we've tackled the hardest problem first in our history to really build something that's scalable. But this is a stack that will work with any robotics application. We've done some proof of concepts in areas like sidewalk delivery, trucking, mining, warehouse logistics. I mean, all of these kind of applications, what we find is with a small amount of data put into our foundation model, it can learn behaviors in these domains as well. Even our simulator, our Gaia, we can adapt Gaia to these domains. So it's a small amount of data, but then the driving policy, the reinforcement learning, and the simulation stack, they all transfer with data.

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What I would say though is that 5 years ago we had an end-to-end learning demo. Like lots of people are getting excited by end-to-end AI for driving now. We had that 5 years ago. What we spent the last 5 years building is learning how to make this safety-qualified, compliant for the automotive industry, and to what it takes to actually make this safe and validatable and actually, you know, runnable in an embedded environment. That's an enormous amount of product work. And to be able to do that in Germany, Stuttgart, Tokyo, Detroit, and all the major automotive centers, like, that's really where the challenge is. And so I think this expertise is going to scale very nicely, and automotive will be the best launchpad for us because we want to become the intelligence layer across every robotic vertical. There is automotive first.

If you have the world models and you have the simulation experience and you can get your hands on the data, what can you not automate that has wheels? Is there any limit to this, or is it just a question of data and then investing the time to bring it to market?

Yeah, I think that's right. I, I mean, the other contrarian view I have, a lot of people getting excited about manipulation robotics today, but I think mobility is going to become so far before manipulation. It was interesting when I used to go to robotics conferences in my PhD, they used to divide the hall up into mobility, go this way, manipulation, go this way. They're two very different communities. They're going to be the same AI over time. But in mobility, there's a tech stack and platforms and automotive, you've got millions of cars being built. They're so far ahead of manipulation. And so I think we're going to see the Nvidia compute, the sensors, the software-defined vehicles. You can go put that on pick your, any vehicle you run from like luggage carousels in airports to totes in a warehouse to some Roomba in your, in your, you know, in your, in your house. And so I think we can, can scale mobility quite well. And then manipulation, look, there needs to be platforms at scale. There needs to be data. And I think, I think yes, we'll be able to adapt in a few-shot setting with the data we get from mobility. But I think manipulation will probably come second.

Wow.

Well, that's an incredibly bullish thing to leave on. I'm really excited about it. And I do want to just want to say thank you for agreeing that we can't have Waymo, Wabi, and Wave all starting with the same letter. And you're going to work on getting some other letters introduced to the self-driving world. Alex, an absolute treat. Where can people find your company on the internet? And then also, is there a job you're hiring for that you want to shout out into the void in case someone listening is the right candidate for you?

Yeah, thanks, Alex. So, I mean, we're on all the social platforms, the internet, just search WAVE, W-A-Y-V-E. If you want to come for a ride with us, we have our fleets, you know, in London, Tokyo, Stuttgart, Bay Area. You'll be able to call it on the Uber app soon. So come, come check it out. Come, come give the technology a go, see what it's like, or buy one of our cars from, from next year with partners like Nissan. So that's how you can really get— get—

yes, that's such a flex. Buy one of our cars next year.

Yeah.

How does it feel to finally be here, man? You've been working on this for a long time.

Well, it's been a decade, but still, still not there yet.

So let's, uh, but you can almost taste it. Like, you're starting to, like, you're starting to slowly reach for my credit card, you know? And that's a different feeling than a few years ago. It's exciting. Feels good.

It's, no, it's awesome. I mean, tell you what's been the most incredible experience is I've been living on a plane for the last, uh, you know, last year, flying around Germany, Japan, and the US, uh, and being able to sell this technology when the market has shifted from not even giving me a meeting to now loving it. That's the biggest privilege. But in terms of growth, absolutely, we're hiring, we're growing. There's so much demand from the automotive sector. Every car manufacturer wants this tech. What I think we've built at Wave is unique at the intersection of frontier embodied AI and automotive. Bringing together these cultures, typically chalk and cheese, we've built a company that has both. And what this means is that if you work on frontier AI and want to see your work deployed in a consumer product at millions of units scale in the near term, we're the place to do it. Or if you want to work on automotive and production-grade technology, but with frontier AI, again, this is the culture. And so that's the environment we built together. And of course, across the full stack, machine learning, data, software, all the way through to product and application and validation for sure. And then interesting roles in operations, public policy, and all of the enabling functions to unlock this, this future. So yeah, if you're interested, come ride the wave.

That $2 billion won't spend itself. Come help Alex. All right. Thanks, man. And we'll have you on a lot sooner than a year and a half because that was way too long. So I'll talk to you in Q3. Thanks a lot, Alex.

See you next time, Alex.

We're going to sit down with one of the most interesting companies in the world. Just raised a bunch of money, has an interesting take on how to bring self-driving not just to trucking, but also to cars. So please join me in welcoming to the show, it's Wabi founder and CEO Raquel Ratzen. Raquel, how you doing?

I'm doing fantastic, Alex, and really a pleasure to be here with you today.

Oh, an absolute treat. Now I want to start with world models because when I was learning about self-driving way back in the day, no one talked about them. But when you founded Wabi, some of the first publications you did as a company were discussing the Wabi Driver and Wabi World, essentially putting world models at the very core of your company. So for folks out there who are a little bit behind, what are world models and particularly why have you selected them as one of the core technologies at Wabi?

Yeah, so when building Wabi, you know, we identified that there were, you know, two very big, important kind of pieces of technology that, you know, were going to be fundamental in terms of bringing self-driving, a scalable solution to self-driving. On one side was, can you build autonomy systems that can truly generalize? And have human-like capabilities of reasoning. And the second big piece was about, yeah, in the era of AI, data is as important as the model itself, right? So can we build representations of the world that can enable us to build simulation systems that are as realistic as the real world so that we can expose the system with no consequences to all the safety-critical situations, et cetera, right? And that kind of drove that innovation required to bring this, you know, these two pieces to market.

Is a world model in the context of self-driving a very high-end specific video game for your AI to drive around in and to be stress tested? Is that a reasonable way to think of it?

I think it's important to maybe make the distinction is about, What are the things or the characteristics that you need a world model to have in the context of self-driving or physical AI? It can be generalized a little bit, which I think will help with, you know, some of the viewers and listeners today, which is that it's not just about creating interactive worlds where what is interacting is actually the self-driving vehicle or the robot in the physical AI case, but also it's very important to, and those have to be be, you know, super realistic, right? But it's very important that you also have controllability of what they are generating. And that has actually been, or is one of the big differentiations in terms of building world models for physical AI versus for, you know, creating pretty, you know, I would say pretty movies or, you know, cool video games, et cetera.

Sure. I didn't mean to imply that the world models are a video game, but from the perspective of the AI model that's doing the self-driving, and they're put into virtual situations, I presume in sequence, many thousands, millions of times, and they're forced to kind of react to the environment that is created for them. So from maybe from the AI model's perspective, it might feel video game-ish. I'm just trying to give people something to stand on to understand.

Yeah, yeah, yeah. So they are, you know, alternate representations of the world that's where the self-driving vehicle interacts with that world. And you know, the key there is that you wanna, create those world models so that they truly represent all the things that might happen when you're driving in the physical world for self-driving, right? And yeah, so, and you are, you know, the self-driving vehicle is acting on them as if it was a video game for the self-driving vehicle. Yes, correct.

And the reason why this matters, going back to your point about data being so important, is that if you have a world model that is a good representation of the physical world, you can stress test your driving systems, the wobbly drivers, you put it, and therefore you can take a quicker approach to market because you've already understood the world versus just mapping a single city. And that seems to be the distinction point between certain self-driving technologies, world models, or high-definition mapping. Is that fair?

So I would say that those are, yes, two different maybe debates that we can have. One is about how do you train and test the autonomy system? And world models are an absolute key in order to allow you to, you know, in parallel in the cloud, you know, test, you know, systems at this scale and train the systems to do the right thing, right? And it can bypass, you know, many years or, you know, or centuries of experimentation in the real world, right? And that's, you know, that's big. And then there is the debate about, Well, what is the information that the autonomy system should have in order to make the right decisions?

I see. Okay.

And that's where HD maps, and we, you know, happy to talk about, you know, all the beauty and, you know, behind, you know, high-definition maps, et cetera.

Does use of a world model reduce the need for on-car sensors or mapping, or is it more of an underlying framework that takes mapping and sensors essentially to the next level of safety and reliability?

Yeah, so I will say that, you know, regardless of the autonomy system that you deliver or you're trying to build, world models really enable you to train and test that autonomy system to the next level. Now, what that means is that it's going to cut down significantly two things, which is the time to market. Right. It's going to increase the safety of that system, right? It's going to increase your understanding of the safety of your system, which is tremendously important. And it's also going to cut down, if your world model is very efficient, your spend that otherwise you will do by integrating out the thousands of engineers that you need over time for delivering your technology. So that's one side, but they are very useful regardless of whether you use high-definition maps or whether you use different sensors. And I think the, you know, in the debate of the camera only versus multiple sensors, maps versus not, to me it's a question about safety versus BOM cost and together with cost of, you know, creating high definition maps. So what we have, for example, done is create a way to build high definition maps that is super efficient. And super robust. So it's not anymore a debate about, is it scalable? Well, yes, it is scalable and provides you with an additional layer of safety. So it's a no-brainer that you should use that because you have a safer product. At the same time, the self-driving vehicle, if those maps are wrong or if those maps are not up to date, it has the ability to react and drive regardless, right? So you know, I will say that, You know, we should debate less about high-definition maps versus not. It's about, do you have technology that can build those maps really in a scalable manner? And you need AI for that, right? To build those maps in a scalable manner. And if the answer is yes, of course you should use them because then you're gonna be safer.

Going back to something you said, BOM cost is BOM, um, uh, bill of materials, essentially, like the hardware cost.

Yeah, yeah, yeah.

Okay, cool. I just wanted to, for folks out there who thought we changed subject to war very quickly, not that kind of BOM cost.

Very— Not that BOM, yeah, yeah.

Very different. Uh, now on the, on the, on the generalization point, you guys started off with self-driving trucks on highways, then you expanded into surface streets, and now, uh, with your latest Series C announcement and the Uber deal, which we will get to in a second, uh, moving into robo-taxis. Does the original world model foundation of the company make it easier for you guys to expand from like one segment of the road world into surface streets and then into I presume residential as well. I'm just trying to understand if the world model itself has accelerated your ability to move from one major area of automation into others.

Yeah, 100%, 100%. That I think it's worth mentioning that, you know, the physical AI platform that we built from day one that is composed of the world model, the simulator together with the autonomy system was built from day one for being utilized for multiple, you know, physical AI use cases. So we had, you know, in mind from day one that can we build that really next generation, you know, generalizable technology that will enable Wabi to actually capture many of these, you know, multi-trillion dollar markets, right? And it has been fundamental, both the type of autonomy system that we have, which is verifiable end-to-end technology. And I'm happy to go into what that means and why it's very different from the traditional AV 1.0 or the, what has become more traditional now, AV 2.0, right? But yeah, has been a massive accelerator. And what is very exciting about the technology that we have is that for the first time, it's not anymore a compromise between this use case and that use case. You don't need to fork, you know, build two teams, fork the stack into, you know, robotaxis versus trucks. On the contrary, it's the same brain and the same, you know, simulator-on-world model that actually does both use cases. The same as for humans, we don't change our brain every time that we actually drive a different vehicle. For the first time, this technology enables us to do so. So you actually, it's additive, you accelerate each program with the other program. Yeah. Which is a totally different, you know, mindset compared to what it was in the past.

On one hand, I absolutely agree with you that we humans use one brain for all of our driving needs, no matter what car type, road condition, weather, etc. And so having a single intelligent mind to handle driving for machines makes a lot of sense to me. On the other hand, human brains are not very specialized. And so is there a place in the future for specialized driving systems that are better at, say, trucking than driving cars in a city? Or does the single brain get so smart that we don't need to really differentiate between use case when I guess literally rubber meets the road?

Yeah, so in the case of self-driving, you know, the brain is aware of what is driving, which is important, right? Because you don't wanna have the same style driving an 18-wheeler, right? 80,000 pounds, you know, cargo truck versus a robotaxi. But that's an example where we don't need to be like super specialized in terms of technology. Now, when you go to other types of skills that are more different, then is where maybe specialization makes sense. But a lot of the core characteristics of perceiving and understanding the world in 4D, not 3D, 4D, which is, you know, we live in a 3D world that changes over time. Those capabilities and reason and action, those are core and common to everything.

So at the end of 2025, it seems that Anthropic and OpenAI released a couple of AI models, especially in the coding context, that really changed how people felt about AI, how they used it, and it has led to a flowering of new products, features, capabilities. It's been a really tremendous last 6 months, I would say, in AI generally. It feels like we've had that same explosion of capability in self-driving in the last 2 or 3 years, and especially I would say in the last year. So Raquel, I'm curious, has there, has something fundamentally changed in the AI models and intelligence more generally that has impacted Wabi and your competitors in a similar way? Or am I over-analogizing general AI versus the more specific stuff that you guys are using?

Yeah, yeah. So, and it's very interesting to see and I've been fortunate to be working at the forefront of innovation in AI for 27 years now. Okay, so I'm gonna give you the 27 years view of what has happened. I love it, yeah, appreciate it. And I'm aging myself here on, you know, life. But the, you know, what is, what has been very interesting is that for the physical world and in particular for self-driving, there is 3 things that are converging at the same time, like call it tectonic plates that you need because it's more than just AI. On one side is, you know, is the hardware and the OEMs, the platforms, the redundant platforms ready? So that you can truly build a scalable, safe product. This is the time where all the investment over the last decade by both, you know, trucking OEMs and passenger car OEMs, this is actually converging and it's ready now. So that's a big piece of the puzzle in terms of why now deployment and scale, you know, '26+ is the year for this, or the set of years for this. On the, you know, One other piece that is important as well is the regulatory frameworks are evolving in order to really enable this deployment. When you look at the consumers of this technology, both in the robotaxi side, humans want to use, you know, self-driving technology. It was a question mark before whether people will trust. And what we see with Waymo deployments is that Yes, people understand that actually this technology is making roads safer. And in many ways, this is a better product than if it's a human driving.

Raquel, I don't want to get you off topic here, so get to your third point in a second. But I've been blown away by how quickly normies have taken up Waymo. Like, I thought it was going to take them much more time to get comfortable with it. But no, it was—

It's experience. And then seeing is believing. That's, I think, you know, in many ways, and, you know, for humans, it's fascinating, I would say. And the last bit, sorry, the last, I guess the fourth, and you know, for trucking, it's a no-brainer, right? Driver shortage, the cost of human drivers, the pervasive safety issues, you know, et cetera, make a very clear case of why everybody wants to adopt this technology. If you build the product that is important for them, right? Or that will solve their pain points. And then the last bit is why you asked me the question, sorry to go around, you know, in a circle, right? Also, there is, you know, massive changes in terms of what AI can do today. And what we see really is these next generation companies that, you know, second mover advantage in many ways of, you know, the AV 1.0, maybe you can deploy, scaling is extremely complex, et cetera, right? Smaller DDs with this next generation of AI technology. Is so much more powerful and you can truly build through reasoning, as I was saying, capabilities to really generalize from almost no example. And that changes the equation totally in terms of the product that you can build, the ability to really solve all the long tail and how quickly you can expand geographically and across use cases, right? As we were talking about before.

So market preparedness and demand, having the right regulatory structures in place, willingness of people on the consumer side to uptake this, obvious market fit on the trucking side, and improvements to AI together are really driving the success.

Make everything like, now is the moment. But for physical AI, you need more than just the AI piece. It's all of these things together that are ready now, and it makes this an extremely exciting time for, for self-driving. And, and for, you know, it's gonna change really the way Because transportation is at the, you know, the middle of everything, right? It's going to change the way that this world works.

Yeah. And I think it's going to change it for the better. Now, one thing we've talked about is the cost of all of this. And one thing that I was really impressed to see reading through coverage of your recent Series C was how asset light and efficient your company is, which contrasted a little bit with the amount of money that you raised, Raquel. And normally when I hear asset light, highly efficient, I don't think this is the company that needs between $750 million and $1 billion to get to the next step of its progress. So what am I missing there? And very politely, apart from the fact that you could, why did you raise so much money?

Very good question. Yeah, yeah. And many people have asked me this question. It's like, you don't need that amount of money. Why did you raise so much money? And it's not just because we could. When you think about, you know, the future for a company like Huobi, you know, we raised actually over $1 billion in this last round. And what that means is that, you know, we are the most stable company in the market. And that means that, and that was, you know, it was important, I thought, for really being able to make, you know, put the right bets, the right investments, and think about not just about what we need for the next 2 years, but how is this market gonna play? If there is any, you know, delays or anything that happened in the ecosystem, whether it's in adoption, whether it is, you know, in certain scaling, et cetera, by, you know, some of our partners, being fully robust to anything and being able to go all in, in terms of not just our trucking, you know, leadership positioning and scale and deployment, right? Which is now is the time, but also be able to, you know, go into the additional vertical that we are adding now, robotaxis, right? Without compromising or thinking that, you know, we can actually do that because we are so capital efficient that a billion is, infinite money for us, right? So it's, you know, and it really sets us in a very different place than anybody else in the industry where there is gonna be, or there is, you know, a lot of pressure in a quarterly basis for them to actually show progress to continue their journey, right? Versus for us, from day one, everything was about building for the scale moment. Uh, and which has seemingly—

which has, you know, back to our point.

Yeah, correct. The strategy has been absolutely spot on, right? Uh, and in terms of, you know, we invested heavily on, uh, foundational technology, right? And, and at the beginning was all about building this technology that didn't exist that really, you know, you invest more, you take maybe a bit longer to go to on-road for the first time, but but when you do, suddenly you are placed in a very different position than everybody else, right? And now it's about that next level of investment for the widespread adoption of this technology. So that's, that's why the, the billion dollars, uh, you know, why, why to do this.

I really appreciate that in-depth answer, but, uh, at no point did you say investing in building lots of rolling hardware. And so I'm taking it that you're still gonna stay very focused on the autonomy layer, and leave the car and truck manufacturing to other people.

Correct. We continue to be a technology provider. We are not an OEM. And this is very important for us, which is, you know, we don't believe that, you know, retrofitting or suddenly becoming an OEM is a path for, you know, for us. We don't believe that this is a safe path as well to market. Yeah, we believe that partnering with folks that really have, you know, excel at this over the last century, is actually the right path to really bring that safe self-driving technology. And again, we are not, you know, since we are, you know, we have that stability and we can really think long-term, right? We are not pressured to do things compromising that, that are just short-term, let me show you progress on the short-term, but that's not really the path that anybody wants for the future.

Yeah, back to your point about, you know, good foundational technology, slower to road, but also better long-term. So that makes sense.

And I would say, Alex, maybe one thing that, you know, people didn't necessarily, or criticized Wabi in the past was about why not to start with quite a lot of, you know, I will say operations and, you know, commercial operations. And what we focus really is build a product and, you know, get ready a product that really solves the pain points and really addresses what the customers want. You know, you mentioned before Selfish Streets, and I just want to maybe add one note there, which is, you know, the industry went with this hub-to-hub, which is a model, right? Which is you have hubs close to the highway and then you drive autonomously between the hubs and then a human will do the end of the trip in both sides, right? And that was a risk. The reason that they did this is that, oh, for trucks with technology, it's too difficult to drive on surface streets, you know, like surface streets, and we wanna roll out this to market, you know, as soon as possible and then simplify the autonomy problem. And when you end up with that approach is that this is not the product that customers want. Nobody wants to pay for that drayage, which in the economics actually can be, depending on your length of haul, massive, like $0.6 to $0.8 per mile, which just basically breaks the whole thing. Yeah. And nobody wants this product, right? So instead, you know, we invested really through this next generation AI technology building for the first time truly technology can drive in generalized surface streets. Now we can go to the end customer, we can go to their door, and then suddenly you have a better product. Now that you have a better product, roll out your product. And that's the phase that we are right now.

Okay, so actually let's— I really want to get to the Uber thing in a second, but let's just stay on this. What— because I couldn't actually chase this down before our chat to my level of confidence. Where is Huobi today in the commercialization of its self-driving technology in the trucking space? Are there lots of trucks on the roads that you guys are powering today? Or is there one? I just, I couldn't quite figure out where you are now. So Raquel, tell me.

Yeah, yeah, fantastic question. So there's definitely more than one track. So we have, you know, since 2023, since 2023, we've been doing commercial operations with, you know, some of the best of the top, you know, shippers, carriers in North America. We have a massive partnership with Uber Freight for billions of miles of deployment on the Uber Freight network, which really is really nicely, you know, sitting between supply and demand. We have, you know, a decent-sized fleet of self-driving vehicles, I will say.

Decent? Is that double digits? Triple digits?

It's double digits.

Double digits.

Double digits. That's still a lot of trucks. Of trucks. And where we are is, you know, our commercialization, true commercialization path is really through the OEM. And I wanna make sure that I represent our partner with what they feel comfortable or what they have said publicly, okay? But as they say, last year they are quarters away from that. Volvo is our OEM partner, yes, for those that don't know. Their fully redundant, fully validated platform last year was quarters away. That can give you a sense. Very—

and yes, soon. Yeah, very soon.

So it's very soon, right? And 2027, they have also said publicly, so you know, that will be hundreds of trucks, which is, you know, pretty— a very nice number already for a 2027 deployment. Right. So, so that's, that's where, you know, if you want to know where Wavi is, so that's, that's where our path to commercialization is.

That's exactly what I wanted. Now, I can see two ways to charge for this, just in the case of trucking, just in the case of your current OEM partner. You could sell them, sell them the system, be it the hardware, software, whatever you want to call it, and then let them have it. Or you could offer it effectively as a service. And what I'm not sure about is for world model trained AI drivers, how compute intensive the actual operation of driving a truck is. Is that very compute heavy? Is it remote? Is it local? And is that a thing that you could charge for on a recurring basis as a business to your OEM partners, for example?

Yeah, so I can tell you that Wabi's technology, both the world model and the autonomous system, is super efficient. And you can see that by how advanced we are in terms of the technology, right? About to, you know, driverless launch with the OEM, et cetera. Yeah. And prior to this round, it's also public how much money we have raised, right? So if you put all this together, you can see how efficient we actually are compared to also other world models companies that just do world models, right? There's a lot of secret sauce also in how we do this.

Yeah, she's bragging right now. That was a brag. I'm not bragging.

Yes, I just, I think this is important because it's at the core of Wabi is all about sustainable, efficient solutions through next generation technology. That's really at the core of our DNA. We are innovators. We have been, you know, for the last I said before, more than two decades in terms of building this technology, but sets us apart from, yeah, saying the more is more philosophy of, yes, bigger data centers, more data, and then just expand everything in the cloud, to your point about how efficient is this technology. But as it relates to the business model, going back to your question, so we are, you know, it's driver as a service, both on the trucking side as well as on the robotaxi side for Huabi. We are a technology provider. We don't plan to own and operate neither trucks nor robotaxis. And that's where our partnerships, our customers are tremendously important for us, right? Uber plays a fundamental role on that go-to-market, right, for robotaxis. And, you know, it's very obvious that they are the market, so that they're very incentivized to grow and continue growing that market. So, so that's, you know, that's very exciting, right? And the second bit for trucking, so it depends on the OEM also, like who, who will operate those trucks. And this is also publicly known that Volvo plans to also operate some of these self-driving vehicles through building a transportation as a service, I would say, business unit, which is Volvo Autonomous Solutions. And that's different than some of the other OEMs. For us, it's transparent whether, you know, doesn't matter whether it's through the OEM or is direct to customer, is the same business model as it relates to Wavve. It will just depend who pays us directly, whether it's the OEM or whether it's, you know, say a Walmart, for example.

But is it a recurring fee or is it a one-time payment?

Yeah, so it's per mile, so it's recurring fee.

Per mile, got it, okay, cool. That's what I was trying to just chase down to make sure that I understood.

Yes, they separate very well, Dave Erickson. I mean, you can do a blend of things like this, right? It's a bit more, you know, sophisticated, but the big piece is always the per mile basis. Yeah.

That makes great sense, 'cause that means the more they're using it, the more value they're getting, the more money you make. So it seems very aligned.

It incentivizes everybody to be on the same page.

Yeah, yeah, yeah. Or perhaps driving in the same direction. Sorry, that was terrible. Okay, before I let you go, I have to ask more about the Uber Robotaxi deal. You guys said, and I'm pulling through my notes here to find the quote, up to 25,000 robotaxis with Uber, I believe. So Uber has a lot of partners on the self-driving side, including Nuro and Lucid. And that deal was demand network Uber, Lucid cars, Nuro self-driving tech. You guys have announced, you know, your technology, their network, but not as far as I know, an OEM. So who are you gonna work with on the making cars side for that partnership?

Yeah, yeah, so let me maybe address the Uber partnership a little bit and how Huobi plays a role in the Uber ecosystem. So what is very interesting is that our partnership is not up to $25,000, is over $25,000, or in other words, a minimum of $25,000.

Ah, okay.

So greater than or equal to, not up to.

I would say. So that already tells you a little bit about the scale of the partnership. And in the ecosystem, it's the same as tracking, right? In the ecosystem for us, since we are the technology provider, you know, Uber plays the market component, right? And then there is the OEM, to your point, that will provide the redundant platform where we vertically integrate with. And that's again, We believe that's the safe path, safe and scalable and only scalable path to market. We haven't announced yet the OEM.

Would you like to do that today on the show?

I know that you would love that. I would. What I can tell you is that, you know, there is, we love again the coming at the right time, second mover advantage of The ocean has been boiled. There is a few OEMs that have that redundant platform ready now. And it's very exciting, you know, how excited the ecosystem is about partnering with us. And we are very excited about partnering with them. So more details to come, I know that, of course, but we are, yeah, as I said, very excited about, you know, our entry into robotaxis and in a in a swift and really exciting manner.

Yeah, I'm really— I think Huobi and Wave are the two most exciting companies in the self-driving world today, I think, apart from the headlines that Waymo grabs. So I'm very optimistic to learn more as the year goes on. Raquel, one last question before I let you go. You worked for Uber's ATG, you told me before the show, for 4 years. Your company has a partnership with Uber Freight and you now have a partnership with Uber's taxi service side of things for robo taxis. Has Uber tried to buy you? Because it feels like you guys are like best friends who live together. Like, why, you know, like at some point, why can't you formalize it?

I would say that through the years since the inception of the company, many people have tried to buy Wabi. What I can tell you is that, you know, my goal here is really to build a physical AI powerhouse that is transforming the world. So, you know, Wabi is not for sale for anybody.

All right, Dara, you need to add a zero to that offer. Try again. No, I'm optimistic and we don't have time to get to it today, but I want to have you back on to talk about physical AI in general and how to take the Wabi program to everything from delivery bots to possibly even robots inside of factories, because I can see the world model—

And humanoids, you name it. Yeah, yeah.

To me, there's a big generalization of the world model approach in solving autonomy to actually bringing it inside of— I don't know why it wouldn't work inside of buildings once you've built out the right systems for that. So there's a lot of stuff coming down the road. Am I going to be able to buy a self-driving car like L4, L5 in the next 3 years, do you think?

In the next 3 years? A Level 4, Level 5? Yeah. That's, that would be hard. Okay, well, in that case, experiencing Robotaxis at scale, yes. Personally owned vehicles on that timeframe is harder.

Okay, well, can you go back to work and get on that for me? Because I would like to buy one because I hate driving and I keep—

And I can tell you, maybe I can tell you why I say that. I think it's important, which is for a decade people thought that, you know, level 2 will go first, then it will be level 3, and then there will be level 4. And it makes total sense because it's okay, it's just adding +1 to it, right? It's humans. Okay, that makes sense. But what we've seen and what I learned through my career as well is what we've seen with Waymo, what I learned through my career as well is that that is not the fastest path and it's not even clear that that's actually a path.

So you don't wanna go from L0, no help, to L1, lane assist, to L2, L3, L4. You wanna, do you skip or do you go backwards?

You, you need to either you build Level 4 technology. Yeah. Or you leave, build Level 2 technology. And that's, you know, separates us from some other end-to-end companies. Yeah. Okay. And I think this is very important to understand. And that's, you know, at the core of why I say 3 years is difficult because I truly believe, because it's a totally different safety problem that you need to solve. That is not just about, I drive well, I don't have many interventions. That's, you know, a metric that matters for level 2, whatever. That is not a level 4 metric. And what people don't necessarily realize is that this is a gigantic difference between a level 2+ product that is performant to a level 4 system where there is no more human. And you need to go for a level, you need to be the level 4 native technology. And that's what we have done.

Raquel, thank you so much for coming on. An absolute treat. When you do announce your future OEM provider, please come back on the show and tell me all about it because I want to know the timeline to get that more than 225,000 robotaxis onto the market and the streets. Thank you so much. What's the website if people want to go and learn more?

So Wabido AI, please come and, you know, check us out. And we are massively expanding as well. So, and it's, you know, the most exciting innovative technology, you know, company in physical AI. And, you know, it's an amazing place to work and it's an amazing place to partner with. And we, yeah, looking forward to tell more and more our story and, but more importantly for people to actually really see our deployment in the real world everywhere.

Well, as we say here in the States, keep on trucking. Thanks for coming.

Thank you.

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