The Self-Driving Startup Nobody Saw Coming | E2289

27:50So, you know, these contracts were signing a 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, we've got everything we need to go run at this opportunity. and you feel the energy in our team. Now it's such a privilege now to get to go and build and deploy these products. All right, one last question before I let you go.

28:26This has been tremendous. I love learning things. I was talking to Wabi. 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 AVNOM. 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 construction equipment down the road? Or is that an entirely different data set and therefore a different training question? Oh, 100 % it is.

29:02I'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. 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.

29:39But 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, we can learn behaviors in these domains as well. Even our simulator, 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. When you think of middle-aged guys who remain really vibrant, virile, healthy, a couple of names come to mind, right?

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31:04five free travel satchets and 10 % off your order. These statements have not been evaluated by the FDA. This product is not intended to diagnose, treat, cure, or prevent any diseases. What I would say though, is that five years ago, we had an end-to-end learning demo. Like lots of people are getting excited about end-to-end AI for driving. Now we had that five years ago. What we spent the last five 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 runnable in an embedded environment, that's an enormous amount of product work.

31:40And to be able to do that in Germany, Stuttgart, Tokyo, Detroit, and all the major automotive centers, 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?

32:12Yeah, I think that's right. 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 whole up into like 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 an 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 you know nvidia compute the sensors the software to find vehicles you know 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 house.

33:00And so I think we can scale mobility quite well. And then manipulation, look, there needs to be platforms at scale. There needs to be data. And I think, yes, we'll be able to adapt in a few shots 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 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.

33:32Where 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 of 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 check it out come give the technology a go, see what it's like or buy one of our cars from next year with partners like Nissan so that's how you can really get That's such a flex, buy one of our cars next year How does it feel to finally be here man?

34:12You've been working on this for a long time Well, it's been a decade, but still not there yet. So let's... But you can almost taste it. Like you're starting to like, I'm starting to slowly reach for my credit card, you know, and that's a different feeling than a few years ago. It's exciting. It feels good. It's, no, it's awesome. I mean, tell you what's been the most incredible experiences. I've been living on a plane for the last, you know, last year, flying around Germany, Japan and the US and being able to sell this technology when the market has shifted from not even like giving me a meeting to now loving it.

34:46Like 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.

35:27And so that's the environment we build 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 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.

36:08So please join me in welcoming to the show. It's Wabi founder and CEO, Raquel Ratson. Raquel, how are 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 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?

36:42Yeah. So when building WABI, we identified that they were two very big, important pieces of technology that were going to be fundamental in terms of bringing cell driving, a scalable solution to cell 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, you know, in the era of AI, data is as important as, you know, 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.

37:31Right. And that kind of drove that innovation required to bring 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. It's about what are the things or the characteristics that you need a world model to have? in the context of cell driving or physical AI, it can be generalized a little bit, which I think will help with 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 cell driving vehicle or the robot in the physical AI case, but also it's very important to, and those have to be super realistic, right?

38:23But 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 creating pretty, I would say, pretty movies or cool video games, etc. 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, 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 maybe from the AI model's perspective, it might feel video game-ish.

39:06I'm just trying to give people something to stand on to understand. Yeah, yeah. So there 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 want to create those world models so that they truly represent all the things that might happen when you're driving in the physical world for cell driving. Right. And yeah. So and you are, you know, the cell driving vehicle is acting on them as if it was a video game for the cell 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 Wabi driver, as you put it.

39:51And 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, I guess, 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, in parallel, in the cloud, test systems at this scale and train the systems to do the right thing. And it can bypass many years or centuries of experimentation in the real world.

40:38And 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 it's the maps. And we're happy to talk about all the beauty behind 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 that you're trying to build, world models really enable you to train and test that autonomy system to the next level.

41:26Now, what that means is that it's going to cut down significantly two things, which is the time to market. It's going to increase the safety of that system. 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.

42:05And I think in the debate of the camera only versus multiple sensors, maps versus not, to me it's a question about safety versus bomb cost, and together with cost of 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 cell 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.

42:53Right? 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 going to be safer. Going back to something you said, bomb cost is BOM, bill of materials, essentially, like the hardware cost. Okay, cool. I just wanted to, for folks out there who thought we changed subject to war very quickly, not that kind of bomb cost.

43:26Not that bomb, yeah, yeah. Very different. Now, on the generalization point, you guys started off with self-driving trucks on highways. Then you expanded into surface streets. And now, with your latest Series C announcement and the Uber deal, which we will get to in a second, 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.

44:02Yeah, 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 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, generisable technology that will enable Wabi to actually capture many of these multi-trillion dollar markets. And it has been fundamental, both the type of autonomy system that we have, which is verifiable end-to-end technology.

44:43And I'm happy to go into what that means and why it's very different from the traditional AB 1.0 or what has become more traditional now, AB 2.0. But yeah, it has been a massive accelerator. And what is very exciting about the technology that we have is that for the first time, is 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, robot taxis versus trucks. On the contrary, it's the same brain and the same, you know, simulator and 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.

45:29For the first time, this technology enables us to do so. So you actually, it's additive. You accelerate each program with the other program, which is a totally different 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, et cetera. 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?

46:08Or 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 want to have the same style driving an 18-wheeler, right? 80 ,000 pounds, you know, cargo truck versus a robot taxi. 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.

46:47But 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 six months, I would say, in AI generally. It feels like we've had that same explosion of capability in self-driving in the last two or three years, and especially I would say in the last year.

47:36So Raquel, I'm curious, 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. And it's very interesting to see. And, you know, I've been fortunate to be working at the forefront of innovation in AI for 27 years now. Okay, so I'm going to give you the 27 years view of what has happened. And I'm itching myself here on, you know, life. But, you know, what has been very interesting is that for the physical world, and in particular for self-driving, there is three things that are converging at the same time.

48:22Like, call it tectonic plates that you need, because it's more than just AI. On one side 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 trucking OEMs and passenger caring OEMs, this is actually converging and is ready now. So that's a big piece of the puzzle in terms of why now deployment on a scale, you know, 26 plus 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.

49:09When you look at the consumers of this technology, both in the robot taxi side, humans want to use cell-driving technology. It was a question mark before whether people will trust. And what we see with way more 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. 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. I thought it was going to take them much more time to get comfortable with it, but no.

49:47It's experience and then seeing is believing. That's, I think, in many ways for humans, it's fascinating, I would say. And the last bit, sorry, I guess the fourth, for trucking, it's a no-brainer, right? Driver shortage, the cost of human drivers, the pervasive safety issues, etc. 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 while you asked me the question, sorry to go around, you know, in a circle, right? But also there is, you know, massive changes in terms of what AI can do today.

50:31And what we see really is these next generation companies that, you know, second mover advantage in many ways of, you know, maybe 1.0. maybe you can deploy, scaling is extremely complex, etc. With this next generation of AI technology it's 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 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.

51:16So 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 this acceleration. 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 self-driving. It's going to change really the way, because transportation is at the middle of everything, right? It's going to change the way that this world works.

51:52I 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 a billion dollars 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?

52:35I have a great 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 the future for a company like Wabi, we raised actually over a billion dollars in this last round. And what that means is that 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, both the right bets, the right investments and think about not just about what we need for the next two years, but how is this market going to play?

53:32if there is any delays or anything that happened in the ecosystem, whether it's in adoption, whether it's in certain scaling, et cetera, by some of our partners, being fully robust to anything and being able to go all in in terms of, no, yes, I were tracking, 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, robot taxes, right? Without compromising or thinking that we can actually do that because we are so capital efficient that a billion is infinite money for us, right?

54:12So it's, you know, and it really sets us in a very different place than anybody else in the industry where there is going to 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. Which has seemingly arrived, back to our point. Correct. The strategy has been absolutely spot on, right? And in terms of, you know, we invested heavily on foundational technology, right? And at the beginning, it was all about building this technology that didn't exist.

54:59That really, you invest more, you take maybe a bit longer to go to on-road for the first time. But when you do, suddenly you are placed in a very different position than everybody else. And now it's about that next level of investment for the widespread adoption of this technology. So that's why the billion dollars, why to do this. I really appreciate that in-depth answer. But at no point did you say investing in building lots of rolling hardware. And so I'm taking it that you're still going to stay very focused on the autonomy layer and leave the car and truck manufacturing to other people. Correct.

55:37We 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. We believe that partnering with folks that really have, you know, excelled at this over the last century is actually the right path to really bring that safe cell driving technology. And again, we are not, you know, since we are, you know, we have the disability and we can really think long term, right? We are not pressured to do things compromising that that are just short term.

56:23let 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 good foundational technology, slower to road, but also better long term. And I would say, Alex, maybe one thing that people didn't necessarily or criticize 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 modeled, right?

57:21Which 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 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 want to roll out this to market as soon as possible and then simplify your 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 tradeage, 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.

58:02And nobody wants this product. So instead, we invested really through this, next generation AI technology, building. For the first time, truly technology can drive in generalized rough streets. Now we can go to the end customer. We can go to the 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. Because I couldn't actually chase this down before our chat to level of confidence. Where is Wabi today in the commercialization of its self-driving technology in the trucking space?

58:43Are there lots of trucks on the roads that you guys are powering today? Is there one? I couldn't quite figure out where you are now. So, Raquel, tell me. Yeah, yeah, fantastic. So, there's definitely more than one truck. So we have, you know, since 2023, we've been doing commercial operations with, you know, some of the best of the top, you know, shippers, carriers, 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. And we have, you know, a decent sized fleet of cell driving vehicles, I will say.

59:26Decent? Is that double digits? Triple digits? It's double digits. Double digits. Double digits. It's still a lot of trucks. Of trucks. Of trucks. And where we are is, you know, our commercialization, true commercialization path is really through the OEM. and I want to make sure that I represent our partner with what they feel comfortable or 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, they're fully redundant, fully validated platform. Last year was quarters away. That can give you a sense.

1:00:13Very. Yes. Soon. Yeah. Very soon. It's very soon, right? And 2027, they have also, say publicly, so you know, that will be hundreds of trucks, which is, you know, pretty, a very nice number already for a 2070 planet, right? So that's where, you know, if you want to know where WAVI is, so that's where our path to commercialization is. 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 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.

1:00:52And 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 you could charge for on a recurring basis as a business to your OEM partners, for example? Yeah. So I can tell you that Wabish 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 the driver's launch with the OEM, et cetera. Yeah. And prior to this round, it's also public how much money we have raised.

1:01:37Right? 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 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 for the last, I said before, more than two decades in terms of building this technology. But it sets us apart from, you know, saying the more it's more philosophy of, you know, bigger data centers, more, you know, more data, and then just, you know, expand everything in the cloud, to your point about how efficient is this technology.

1:02:30But 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 robot taxi side for Wabi. 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 for robotaxis. And it's very obvious that they are the market, so they're very incentivized to continue growing that market. So that's very exciting. And the second bit for trucking, so it depends on the OEM also, who will operate those trucks.

1:03:18And this is also publicly known that Volvo plans to also operate some of the cell-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, it doesn't matter whether it is through the OEM or it's direct to customer. It's the same business model as it relates to WABID. It will just depend who pays us directly, whether it's the OEM or whether it is, you know, say, at Walmart, for example. But is it a recurring fee or is it a one-time payment?

1:03:58Yeah, so it's per mile, so it's a recurring fee. Per mile, got it. Okay, cool. That's what I was trying to just chase down to make sure that I understand. Yes, it's very well there. I mean, you can do a blend of things like this, right? It's a bit more sophisticated, but the big piece is always the per mile basis. That makes great sense because 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.

1:04:27Okay, before I let you go, I have to ask more about the Uber robotaxi deal. You guys said, and I'm pulling into 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 Neuro and Lucid. And that deal was demand network, Uber, Lucid Cars, Neuro, self-driving tech. You guys have announced your technology, their network, but not, as far as I know, an OEM. So who are you going to work with on the making cars side for that partnership? Yeah, yeah. So let me maybe address the Uber partnership a little bit, how Wabi plays a role in the Uber ecosystem.

1:05:11So 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 skill 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, 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.

1:06:00We haven't announced yet the OEM. Would you like to do that today on the show? I know that you will love that. But 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 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 it was, but we are, yeah, as I said, very excited about, you know, our entering to robot taxis and in a swift and really exciting manner.

1:06:48Yeah. I'm really, I think Wabi and Wave are the two most exciting companies in the self-driving world today, I think, apart from the headlines that Waymo grabbed. 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 four years your company has a partnership with uber freight and you now have a partnership with uber's uh taxi service side of things for robo taxis um 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.

1:07:37What I can tell you is that my goal here is really to build a physically high-powered house that is transforming the world. So 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. again, we don't have time to get to 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. 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.

1:08:20So 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 three years, do you think? In the next three years, a level four, level five? Yeah.

1:08:40That would be hard. Okay. Well, in that case - Experiencing robotaxis at a scale, yes. Personally on 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 reading. And I can tell you why you say that. I think it's important, which is for a decade, people thought that, you know, level two would go first, then it would be level three, and then it would be level four. And it makes total sense because it's, okay, it's just adding plus one to it. right? It's humans. Okay, that makes sense.

1:09:25But 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 want to go from L0, no help to L1, lane assist to L2, L3, L4. You want to, do you skip or do you go backwards? You need to either you build level four technology or your bill level 2 technology. That separates us from some other end-to-end companies. I think this is very important to understand. That's the core of why I say three years is difficult. I truly believe because it's a totally different safety problem that you need to solve.

1:10:15It's not just about I drive well, I don't have many interventions. That's a metric that matters for level two, whatever, that is not a level four metric. And what people don't necessarily realize is that this is a gigantic difference between a level two plus pro that is performant to a level four system where there is no more human. And you need to go for a level, you need to build a level four 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.

1:10:54Thank you so much. What's the website if people want to go and learn more? So Wabi.ai, please come and 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're looking forward to tell more and more our story 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.

1:12:10Thank you. Check out This Week in AI, Jason's experts-only roundtable with top AI founders and operators every week. Find it thisweekina.ai. Check out The Twist Ticker, our daily newsletter, at thisweekinstartups.com slash ticker. Thanks again to our sponsors for making today's show possible. Follow the show on Instagram, follow the show on x.com. This Week in Startups publishes three days a week, Monday, Wednesday, and Friday at 5 p.m. Central Time. You can submit an audio or video file question at thisweekin.com.