Loading summary
Transcript141 lines
- [00:00]
Alex
We pioneered end to end learning when it was widely dismissed.
- [00:03]
Alex Kindle
Self driving in a way that economically scales the world is not solved.
- [00:07]
Raquel Uratzon
Our partnership is not up to 25,000 is over 25,000 or in other words a minimum of 25,000.
- [00:15]
Alex Kindle
Our volume is like double the cars Tesla builds a year 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.
- [00:23]
Alex
Every car is being intelligently driven by a machine that never blinks.
- [00:26]
Alex Kindle
You know you'll pay for your own private chauffeur that's in your car.
- [00:30]
Alex
Has Uber tried to buy you?
- [00:31]
Raquel Uratzon
Wabi is not for sale for anybody.
- [00:33]
Alex
This week in Startups is brought to you by Im8Health. Start feeling like your best self every day. Go to im8health.com twist and use the code Twist to get a free welcome kit, five 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, you 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 OpenCloud. No, today we're talking about sub 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 this 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 it's co founder and CEO Alex Kindle. Alex, how you doing?
- [01:53]
Alex Kindle
Awesome.
- [01:54]
Alex
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?
- [02:07]
Alex Kindle
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 in today's standards. It was like a 20,000 parameter world model. And we were all excited at the time of Intwin AI, hey, it was going to actually allow us to really, truly scale autonomy. And that picture stayed the same for the last decade. But it feels like the whole industry is really getting behind what we're doing now because this has been a contrarian approach for so many years. But in the last few months we've brought in investment from Nvidia, Qualcomm, arm, 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.
- [03:01]
Alex
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, et cetera, et cetera, et cetera, 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?
- [03:34]
Alex Kindle
A world model is a, I mean, it's at the basic core principle. It's a model that can understand the state of the world 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 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.
- [04:31]
Alex
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. 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.
- [05:03]
Alex Kindle
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 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 data sets, 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 dash cams to the automat makers 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 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, five, six, seven or how many of it radars. So it can understand all of these and then on top of that it's controllable so we can actually prompt or control it or re simulate something we've seen in the real world or adversarially test something and try and make our car learn or make mistakes in the world model so we can learn from that.
- [07:26]
Alex
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?
- [07:54]
Alex Kindle
Yeah, that's a great question. I think the censor debate is often a very heated one in the industry, but really probably not the is more nuance to it than, than what might. But 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. 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.
- [08:47]
Alex
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.
- [08:59]
Alex Kindle
Exactly. Alex, if you're in fog with camera only, you might struggle. If you've got a radar, you might, 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 custom spinning lidars on the vehicle. These are automotive grade, mass market, low cost sensing devices. So I guess there's a difference there.
- [09:45]
Alex
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. 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 curve here in that pronouncement?
- [10:10]
Sponsor Voice
Big cloud providers may offer you cheap compute, but you'll end up paying the difference in engineering costs and hiring extra developers. You don't want to waste time configuring virtual networks. None of us do we or your access policies. You want your team building your product. So it's time to look at Render. Render is the all in one cloud platform for developers that allows you to deploy, scale and secure your apps and agents with zero ops. Most cloud platforms ask you to split your focus between product and infrastructure or they force you into platform constraints. You know you're going to aggro in six months, but just connect your GitHub repo to Render and you are live web services, cron job, the whole stack in one platform. It's time to find out why 5 million developers are already using render.5 million. Go to render.com twist and apply for the Render startup program. You'll get anywhere from $500 to $100,000 in free credits depending on your stage and who your backers are. That's render.com/oh man.
- [11:14]
Alex Kindle
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 Shang High, despite what you see on the roads every day, self driving in a way that economically scales the world 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 a integration and product deployment phase. So what we're going to see with this capital is I mentioned 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, 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
- [12:24]
Alex
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.
- [12:34]
Alex Kindle
Exactly, exactly. And then two weeks ago, Nissan announced that they are going to bring this technology, bring our approach to 90% of their vehicles. They build about 3 million cars a year. So this is.
- [12:46]
Alex
That's 2.7 million.
- [12:47]
Alex Kindle
Yeah, 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 we're really excited about this and this business model. I mentioned how we had a contrarian technical strategy, but this is also a contrarian business model because there's three ways to bring autonomy to market, right? You can build your own cars, that's what Tesla's 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 AI driver. And so I think this is also interesting how it's enabling a different business model that might not know, might, might not be appreciated. At first thought we're going to get
- [13:41]
Alex
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, has wave gotten so Good at generalized 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?
- [14:21]
Alex Kindle
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 bringing a bit of technical realism. I think these principles have served wave well over the years, but 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 scale. So Wave and Tesla built this, the end to end stack and we're both showing 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 announcer doing one and a half billion of revenue a year with this. Clearly there's product market fit 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 driverless that basically the same level of safety for L3 or L 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. 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 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 what we saw in the LLM scaling journeys, but that's a case of data compute, some algorithmic innovation along the way. 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 two 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 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.
- [17:33]
Alex
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 algorithmic innovation. And if you're curious what we mean by that, just go read a paper from a major LLN lab talking about the latest model and how they changed the back functions of it to see how make it better.
- [17:57]
Alex Kindle
There are some variants from LLMs though, right. 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 the 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.
- [18:20]
Raquel Uratzon
No.
- [18:20]
Alex Kindle
There's a lot of differences there, right?
- [18:23]
Alex
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 are 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 makes good sense. But let's look forward a couple 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 and 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 if that's the plan.
- [19:24]
Alex Kindle
Yeah, I think the industry's on a journey of figuring that out. So for consumer vehicles, you'll pay 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 a car and actually include it for free with all the cars they sell for a given model. Some are, 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 a bit of both, some maybe 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 a improving performance over time with, over their 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.
- [20:38]
Sponsor Voice
AI tools are making it easier than ever to run your own business even as a solo founder. But you still need a beautiful attention grabbing website to help your new company stand out in a very crowded field. And you don't want AI slop Nope. You want to use Squarespace. That's the easiest and fastest way to turn your idea into a real business. Because the team at Squarespace cares deeply about design and functionality. And a plain looking or generic or AI slop website, man, that's going to be a red flag for your customers, for your investors and people who want to come work for you and people join your team. But Squarespace will take all the guesswork out of designing your first website with the blueprint AI builder, which has been finely tuned to make beautiful websites. Squarespace isn't just going to help you make a new web page either. They're going to be your all in one platform for launching your business. They're also going to help you set up your email. They're going to handle invoicing, paperwork, all your needs. Go to squarespace.com twist for a free trial and when you're ready to Launch, go to squarespace.com twist for 10% off your first website or domain purchase.
- [21:39]
Alex
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's 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?
- [22:12]
Alex Kindle
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 driver system, the manufacturer or operator will remain reliable with some insured and contracted liability that flows through to the various parts of the ecosystem. So it really depends.
- [22:45]
Alex
Are you going to build that financial backing, that infrastructure we need to handle the insurance element to this or is that going to be handled by Chubb or Berkshire Hathaway?
- [22:54]
Alex Kindle
We're staying focused. We're not going to build an insurance product, Alex, but if that changes, I'll let you know.
- [23:00]
Alex
I mean, I kind of feel like it should be like an add on offering 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 five years do you think are made with the capacity to work with either wave or a similar product in five years?
- [23:29]
Alex Kindle
So today there are about 100 million vehicles produced each year. You know, 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 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 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 they're going to bring this kind of technology to the vehicles from financial year 2027. So over you said five years. I think by five 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 even driverless consumer driverless technology. And I think we'll continue to see that flow down. But the steady state is that every vehicle is going to be capable of that. I mean when you can, for a very low monthly subscription, get a eyes off driving experience, I think this is going to completely change things. And actually yes, robo taxis 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 WAVES 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 the spectrum of autonomy. So for these reasons, I think this will give us the ability to have native relationships where it's a vehicle built 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 very, very important opportunity for us that 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 five years, I think 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.
- [26:23]
Alex
Yeah, apart from probably the most basic like Tata Nano style cars, like whatever is very, very simple, probably not everything
- [26:32]
Alex Kindle
else, but actually 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.
- [26:50]
Alex
I can't wait. It's going to be some one of 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 going to be so much better than the yahoos who drive around my house currently. I think 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 bucks a month, 1200 bucks a year, call it a thousand for safety. 100 million times 1000 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?
- [27:37]
Alex Kindle
We're in an awesome financial position. We've got over $2 billion 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 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 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 get to go and go and build and deploy these products.
- [28:24]
Alex
All right, one last question before I let you go. This 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, 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 Aviam. 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 data set and therefore a different training question?
- [29:01]
Alex Kindle
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 really diverse set of products.
- [29:26]
Alex
60, 60, yeah, that's a lot.
- [29:29]
Alex Kindle
And even starting to learn in London. Right. It's like one of the hardest environments to drive in. So we've tackled the hardest problem first in our history to really build something as 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, 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.
- [30:15]
Sponsor Voice
When you think of middle aged guys who remain really vibrant, virile, healthy, couple of names come to mind, right? David Beckham and Jason Gallaghanis. That's right. No coincidence that both of us are drinking IMAIDS. It's delicious daily Ultimate Essentials which has 16 different supplements all in one delicious drink. This thing's got 92 nutrient rich ingredients including all the vitamins, all the minerals and adaptogens, pre pro and postbiotics. Since I've started drinking this, I feel more alert, I feel less fatigued in the afternoon and hey calms my digestion down. As well. This stuff tastes great, makes you feel great and I'm a huge fan. So start feeling like your best self every day with I Am 8 go to I'm8health.com twist and use the code twist to get a free welcome kit, five free travel sachets 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.
- [31:15]
Alex Kindle
What I would say though is that five years ago we had a, 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. And 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 launch pad for us because we want to become the intelligence layer across every robotic vertical there is. Automotive first.
- [32:00]
Alex
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?
- [32:13]
Alex Kindle
Yeah, 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 mobility, go this way, manipulation, go this way. And 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 Nvidia compute the sensors, the software defined vehicles. You can go put that on, pick any vehicle you run from luggage carousels in airports, to totes in a warehouse, to some Roomba in your house. And 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 shot setting with the data we get from mobility, but I think manipulation will probably come second. Wow.
- [33:18]
Alex
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?
- [33:41]
Alex Kindle
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 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 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 stuff.
- [34:08]
Alex
That's such a flex. Buy one of our cars next year. How does it feel to finally be here, man? You've been working on this for a long time.
- [34:15]
Alex Kindle
Well, it's been a decade, but still not there yet. So let's.
- [34:19]
Alex
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. Feels good.
- [34:30]
Alex Kindle
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 year, flying around Germany, Japan and the US 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 unit 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 future. So, yeah, if you're interested, come ride
- [35:49]
Alex
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.
- [35:59]
Alex Kindle
See you next time.
- [35:59]
Alex
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 but also to cars. So please join me in welcoming to the show. It's WABI founder and CEO Raquel Uratzon. Raquel, how you doing?
- [36:15]
Raquel Uratzon
I'm doing fantastic, Alex, and really a pleasure to be here with you today.
- [36:18]
Alex
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?
- [36:43]
Raquel Uratzon
Yeah, so when building wabi, you know, we identified that they were two very big, important kind of pieces of technology that 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 in the era of AI, data is as important as the model itself. 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 these two pieces to market?
- [37:37]
Alex
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?
- [37:50]
Raquel Uratzon
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 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 super realistic. 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 models for physical AI versus for creating pretty, I would say pretty movies or cool video games, et cetera.
- [38:45]
Alex
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. I'm just trying to give people something to stand on, to understand.
- [39:10]
Raquel Uratzon
Yeah, yeah. So there are alternate representations of the world where the self driving vehicle interacts with that world. And 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 self driving. Right. And the self driving vehicle is acting on them as if it was a video game for the self driving vehicle. Yes, correct.
- [39:37]
Alex
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, 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?
- [40:07]
Raquel Uratzon
So I would say that those are just 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. And 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?
- [40:47]
Alex
I see, okay.
- [40:47]
Raquel Uratzon
And that's where HD maps and we happy to talk about all the beauty behind high definition maps, et cetera.
- [40:57]
Alex
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 in sensors essentially to the next level of safety and reliability?
- [41:11]
Raquel Uratzon
Yeah. So I will say that 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. 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 in the debate of the camera only versus multiple sensors maps versus not to me it's a question about safety versus bomb cost 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 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. I will say that we should debate less about high definition maps versus not is about do you have technology that can build those maps really in a scalable manner and you need AI for that 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.
- [43:14]
Alex
Going back to something you said bomb cost is bom bill of materials essentially
- [43:19]
Raquel Uratzon
like the hardware cost. Okay, cool.
- [43:21]
Alex
I just wanted to for folks out there who thought we changed subject to war very quickly, not that kind of bomb cost very much.
- [43:27]
Raquel Uratzon
No, that bomb, yeah, yeah, very different.
- [43:29]
Alex
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 with your latest series 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.
- [44:03]
Raquel Uratzon
Yeah, 100%. I think it's worth mentioning that 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 physical AI use cases. So we had in mind from day one that can we build that really next generation generalizable 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. And I'm happy to go into what that means and why it's very different from the traditional AV1.0 or what has become more traditional now, AV 2.0. Right. 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 it's not anymore a compromise between this use case and that use case. You don't need to fork build to teams fork the stack into robot taxis versus trucks. On the contrary, is the same brain and the same simulated 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 enable 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.
- [45:42]
Alex
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? 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.
- [46:17]
Raquel Uratzon
Yeah. So in the case of self driving, the brain is aware of what is driving, which is important because you don't want to have the same style driving an 18 wheeler, £80,000 cargo truck versus a robotaxi. But that's an example where we don't need to be 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.
- [47:06]
Alex
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. So Raquel, I'm curious, has there been, 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?
- [47:53]
Raquel Uratzon
Yeah, yeah. 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 going to give you the 27 years view of what has happened and I'm itching myself here live. But 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, like 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 that 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 OEMs, this is actually converging and it's really now. So that's a big piece of the puzzle in terms of why now deployment and scale 26 is the year for this or the set of years for this. On the 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 robotaxis side, humans want to use self driving technology 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.
- [49:35]
Alex
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
- [49:46]
Raquel Uratzon
with it, but no, it's experience and then seeing is believing. That's I think in many ways. And for humans it's fascinating, I would say. And the last bit, sorry, the last, I guess the fourth for tracking is a no brainer, right? Driver shortage, the cost of human drivers, the pervasive safety issues, 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 or that will solve their pain points. And then the last bit is why you asked me the question. Sorry to go around in a circle, but also there is massive changes in terms of what AI can do today. And what we see really is these next generation companies that second mover advantage in many ways of the AV 1.0. Maybe you can deploy Skadden. It's extremely complex, et cetera, small of this. 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 as we were talking about before.
- [51:17]
Alex
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.
- [51:31]
Raquel Uratzon
Make everything like now is the moment. But for physically you need more than just the AI piece is all of these things together that are ready now and it makes this extremely exciting time for self driving. And 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.
- [51:53]
Alex
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 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?
- [52:35]
Raquel Uratzon
I have a great question. Yeah. And many people have asked me this question is like, you don't need that amount of money. Why did you raise so much money? So, and it's not just because we could. When you think about the future for a company like Huawei, 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, it was important, I thought, for, 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 if there is any delays or anything that happen in the ecosystem, whether it's in adoption, whether it is 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 leadership, positioning and scale and deployment. Right. Which is now is the time, but also be able to go into the additional vertical that we are adding now, robot taxis, 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. So 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, versus for us from day one, everything was about building for the scale moment,
- [54:39]
Alex
which has seemingly, to our point, correct.
- [54:43]
Raquel Uratzon
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 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 onroad for the first time, 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 why the billion dollars, why to do this.
- [55:22]
Alex
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.
- [55:37]
Raquel Uratzon
Correct. We continue to be a technology provider. We are not an oem. And this is very important for us, which is we don't believe that retrofitting or suddenly becoming an OEM is a path 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 excelled at this over the last century is actually the right path to really bring that safe self driving technology. And again, we are not, since we have the stability and we can really think long term, 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 pattern anybody wants for the future.
- [56:29]
Alex
Yeah, back to your point about good foundational technology, slower to road, but also better long term.
- [56:35]
Raquel Uratzon
And I would say, Alex, maybe one thing that people didn't necessarily or criticize Huobi in the past was about why not to start with quite a lot of I will say operations and commercial operations. And what we focus really is build a product and get ready a product that really solves the pain points and really addresses what the customers want. You mentioned before selfish streets. And I just want to maybe add one note there, which is the industry went with this hub to have model, 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. And that was the reason, the reason that I did this is that oh for trucks with technology it's too difficult to drive on surface streets, generalized surface streets. And we want to roll out this to market 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 drainage, 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. And 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 roughly 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.
- [58:25]
Alex
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 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? Is there one? I just, I couldn't quite figure out where you are now. So Raquel, tell me.
- [58:54]
Raquel Uratzon
Yeah, yeah, fantastic. 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 selenium vehicles, I will say decent.
- [59:28]
Alex
And is that, is that double digits, triple digits?
- [59:31]
Raquel Uratzon
It's double digits, double digits, double digits. 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 what they have said publicly. 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 of very.
- [60:14]
Alex
Yes, soon, very soon.
- [60:15]
Raquel Uratzon
So it's very soon. Right. And 2027, they have also say publicly, so that you know, that will be hundreds of trucks, which is pretty, a very nice number already for a 27 deployment. Right. So that's where, you know, if you want to know where Huawei is. So that's where our path to go to.
- [60:34]
Alex
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?
- [61:13]
Raquel Uratzon
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 the driver is launched with the oem et cetera. And prior to this round it's also public how much money we have raised. 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.
- [61:48]
Alex
Yeah, she's bragging right now. That was a brag. I'm not bragging.
- [61:51]
Raquel Uratzon
Yes, I think this is important because 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 sets us apart from just saying the more it's 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, is driver as a service, both on the trucking side as well as on the robotaxi 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. Right. For robotaxis and you know, it's very obvious that they are the market so that they're very incentivized to grow, continue growing that market. 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 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 it doesn't matter whether it's through the OEM or is direct to customer is the same business model as it relates to Huawei. It will just depend who pays us directly whether it's the OEM or whether it's say a Walmart for example.
- [63:54]
Alex
But is it a recurring fee or is it a one time payment?
- [63:58]
Raquel Uratzon
Yeah, so it's per mile. So it's recurring fee per mile.
- [64:01]
Alex
Okay, cool. That's what I was trying to just chase down to make sure that yes,
- [64:03]
Raquel Uratzon
there's a variable 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 perma basis.
- [64:13]
Alex
Yeah, 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.
- [64:19]
Raquel Uratzon
It incentivizes everybody to be on the same page.
- [64:22]
Alex
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. So you guys said. And I'm pulling to my notes here to find the quote, up to 25,000 robo taxis 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, you know, 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?
- [65:05]
Raquel Uratzon
Yeah, yeah. So let me maybe address the Uber partnership a little bit and how Huawei 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.
- [65:25]
Alex
Ah, okay. So greater than or equal to, not up to, I would say.
- [65:28]
Raquel Uratzon
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, Uber plays the market component 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. Path to market. We haven't announced yet the oem.
- [66:04]
Alex
Would you like to do that today on the show?
- [66:07]
Raquel Uratzon
I know that you will love that. What I can tell you is that there is, we love again, the coming at the right time. Second moving advantage of the ocean has been boiled. There is a few OEMs that have that redundant platform ready now and it's very exciting how excited the ecosystem is about partnering with us. And we are very excited about partnering with them. So more details to come, in other words. But we are, as I said, very excited about our entry into Robotaxis
- [66:47]
Alex
in
- [66:47]
Raquel Uratzon
a swift and really exciting manner.
- [66:49]
Alex
Yeah, 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 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 four years. Your company has a partnership with Uber Freight and you now have partnership with Uber's taxi service side of things. Free Robotaxis. 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?
- [67:31]
Raquel Uratzon
I would say that through the, through the years since the inception of the company, many people have tried to buy, 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 Wabi is not for sale for anybody.
- [67:52]
Alex
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,
- [68:08]
Raquel Uratzon
humanoids, you name it.
- [68:10]
Alex
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, like L4, L5 in the next three years, do you think?
- [68:30]
Raquel Uratzon
In the next three years? Level four, level five?
- [68:35]
Alex
Yeah,
- [68:39]
Raquel Uratzon
that, that would be hard.
- [68:43]
Alex
Okay, well in that case, experience in
- [68:44]
Raquel Uratzon
robotaxis at scale, yes, personally, on vehicles on that time frame is harder.
- [68:52]
Alex
Okay, well, can you go back to work and get on that for me? Because I would like to, to buy one because I hate driving and I keep.
- [69:01]
Raquel Uratzon
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 level two will go first, then it will be level three, and then it will be level four. And it makes total sense because it's just adding plus one to it as 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 way more. 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.
- [69:40]
Alex
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?
- [69:49]
Raquel Uratzon
You need to either you build level four technology or you build level two technology and that separates us from some other end to end companies. Okay. And I think this is very important to understand and that's at the core of why I say three years is difficult. Because I truly believe because it's a totally different safety problem that you need to solve that it'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 product 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.
- [70:42]
Alex
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 robo taxis onto the market and the streets. Thank you so much. What's the website? If people want to go and learn
- [70:58]
Raquel Uratzon
more so wabi AI, please come and check us out. And we are massively expanding as well and it's the most exciting, innovative company in physical AI and it's an amazing place to work and it's an amazing place to partner with and yeah, 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.
- [71:26]
Alex
Well, as we say here in the states, keep on trucking. Thanks for coming.
- [71:30]
Raquel Uratzon
Thank you.
- [71:30]
Jason
Thanks for watching this week in startups. If you liked this episode, check out more if you're a startup founder founder university cohort 13 kicks off this fall. It's a 12 week program that provides guidance on building your product, launching to real customers and pitching to investors. Top startups receive $25,000 or $125,000 in investment. Apply now at Founder University Twist Already have traction. The Launch Accelerator invests $125,000 and connects you with 500 plus investors. To help you raise your next round. Apply@LaunchAccelerator Co if you're an accredited investor looking to gain access to quality deal flow, apply for Jason's Angel Syndicate at the syndicate.com we find two to three deals a month and check out this week in AI Jason's experts only round with top AI founders and operators every week. Find it this week in AI AI. Check out the Twist Ticker, our daily newsletter at thisweekinstartups.com 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 5pm Central Time. You can submit an audio or video file question@thisweekin.com.