SPEAKER_00: we pioneered end-to-end learning when it was widely dismissed self-driving in a way that

SPEAKER_01: economically scales the world is not is not solved our partnership is not up to 25 000 is over 25 000

SPEAKER_02: or in other words a minimum of 25 000. our volume is like double the cars tesla builds a year

SPEAKER_04: uh and that's just one of our partners and if you're a manufacturer selling a car that doesn't have this i think your demand is really going to fall off a cliff every car has been intelligently driven by a machine that never blinks yeah you'll pay for your own private chauffeur that's in your

SPEAKER_06: car has uber tried to buy you web is not for sale for anybody hello everybody and welcome back to

SPEAKER_10: 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 open clot no today we're talking about self-driving cars we're bringing

SPEAKER_12: 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 kendall alex how you doing awesome hey alex it's so good to have you back so late 2024 feels like 29 years ago in ai terms has the self-driving world been progressing as quickly as the kind of general

SPEAKER_14: ai landscape well you know if i go back to when we started in 2017 um one of our very first blog posts was about a world model that we put together back then it was i don't know today's standards it was like a 20 000 parameter world model uh and we're all excited at the time of in twin ai hey it was going to actually um you know allow us to to really truly scale autonomy and that picture stayed the same for the last decade but uh it feels like the whole industry is is really uh getting behind what we're doing now because um this has been a contrarian approach for so many years but in the last uh you know last few months we've brought an investment from um nvidia uh qualcomm arm amd all the the big chip companies and then uber nissan mercedes celantis uh 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

SPEAKER_17: uh it's it's a big privilege yeah so you guys wrote we pioneered end-to-end learning when it

SPEAKER_10: was widely dismissed we built world models years before they became fashionable we prioritized generalization across many environments over driverless optimization in single domain etc etc etc early and it seems to be correct but since we had you on uh you guys have released i think two new

SPEAKER_12: world models uh gaia 2 and 3 so i know it's a little bit basic but could you tell folks who are

SPEAKER_10: behind what a world model is in this context and then i'm really curious what improved between the generations of the world models that wave uses to power self-driving a world model is a i mean it's a

SPEAKER_14: at the basic core principle it's a it's a it's a model that can understand uh the state of the world a given action you take on the world and how the world evolves and so what that lets you do is i mean first of all it's a really powerful representation learning method it lets you uh you know learn a representation of the world that actually cares about what matters so if you're 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 um 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 it's firstly it's a really powerful representation learning method and then secondly uh it gives you benefits of it it can be a simulator it can actually allow you to simulate what's happening in the world um to to learn or

SPEAKER_21: to validate or to actually control what's in front of you and as far as i understand it i'm going to put

SPEAKER_10: this in super basic idiot terms but it's kind of a video game for your self-driving technology to

SPEAKER_12: 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 uh testing variants and then you can put your driver into this world this generated world and essentially do infinite

SPEAKER_10: miles in a virtual setting that would take um lots more time and money in the real world to do without

SPEAKER_14: the safety implications yeah that's right i mean we have an analogy in our own minds right now hippocampus we have world models that actually you know when we daydream or sleep you know we replay experiences a you know a gazillion times to actually uh reinforce how we you know how we act how we learn to swing a tennis racket or you know do any uh motor tasks that we have so we do the same thing but it's it's a lot more than that right it's a it's a representation a really rich representation of the world um but yes one of the best uses is a simulator and we know in in robotics and self-driving um it's not like uh you know a chat bot 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 uh if you have one you've solved the other and you solve the problem but the arms race between them we find that um 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 what 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 uh 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 uh the data sets as now at frontier scale for uh 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 um uh everything from internet scale data to dash cams to the the automakers that we partner with we've got over a dozen different companies now sharing data with us that we aggregate at scale and to train this this world model um so what's changed so we've scaled up data and compute uh parameter count but then we've also improved a number of things algorithmically uh so it's not only uh video but also understands radar and lidar uh it understands multiple sensors so um a typical self-driving car might have a dozen or so cameras might have you know five six seven or how many other radars so it can understand all of these um and then on top of that it's controllable so we can actually you know prompt or control it or re-simulate something we've seen in the real world or adversarially test something and try and um you know try and make our car learn or make mistakes in the

SPEAKER_10: world model so we can learn from that and when you talk about different sensors and different self driving cars and what they have equipped to them to me there is a a buffet of options you can have in your car i i presume that uh you know your your ai driver can work with what it's offered so if it

SPEAKER_12: has lidar and not radar radar not lidar visual blah blah uh it can take in i presume any type of

SPEAKER_31: information and use that to make its decisions is there a minimum level of ingestion required here

SPEAKER_02: yeah that's a great question i think the sensor debate is often a very heated one in the industry

SPEAKER_14: but really probably not the there's more nuance to it than than what might be seen 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 um now this is i think very natural to do with our approach because our model trains on very diverse data sensors in different locations different types and we can learn to understand which signals to represent and also which signals we can rely on and what a sensor architecture can or can't see because you can do that through a world model i mentioned it's a really powerful representation when you learn to predict the future if your sensor can't see part of the scene it can't predict the future in that way so you can learn this very naturally

SPEAKER_10: uh so you're not just simulating show me with rain show me with snow you're also simulating okay i'm

SPEAKER_12: in a smaller car with this sensor array in this weather environment so you can get super granular

SPEAKER_25: then or inside your world models exactly if you're in fog with camera only you might struggle if you've got a radar you might be right you can you can predict different things but yeah um to answer your

SPEAKER_14: question yes there is a there is a minimum bar of safety you need for a hands-off eyes off or driverless system um now you can achieve all levels with a camera only system if if you're really good enough but it might be faster and more efficient to get there with some radar or other sensing modalities so what we find in the industry today is that um most of our partners who are building say robotaxis uh it's better to work with camera radar lidar but crucially these are not bespoke you know custom spinning lidars on the on the vehicle these are automotive grade uh mass market low cost uh sensing devices so there's that there's i guess there's a difference there absolutely now you've

SPEAKER_10: had two new world models come out you've also raised a an enormous amount of money recently um 1.2 1.5

SPEAKER_12: billion dollars depending on kind of how you count in tranches and so forth uh my read of that following your technological progress is that people are very impressed and you have cracked self-driving i feel like we've gotten to the point where we can say we've figured it out is that fair or am i a little bit

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SPEAKER_14: self-driving is i think not only the hardest problem but it's it's gonna be a continued open problem for some time i think the key thing to realize and despite if you live in silicon valley or shanghai despite what you see on the roads every day um self-driving in a way that economically scales the world is not is not solved and i think that what we bring is an approach that has demonstrated a path to that solution and now we're entering an uh you know a integration and product deployment phase so what we're going to see with this capital is um i mentioned you know our mission is to bring intelligence to any vehicle anywhere and so we're going to see that start to be deployed uh this year in supervised robo taxi trials starting in london tokyo and 10 other cities on uber and from next year uh in consumer vehicles uh you know we're supported by partners like nissan mercedes and stelantis take nissan for example last year they announced they're bringing us into their consumer vehicle lineup um uh then earlier this year we announced the robotaxi uh because what we find is automakers they want to work with the same partner across l2 l3 and l4 it really helps speed and

SPEAKER_21: efficiency and you can leverage data and and integration and the wave system can do uh

SPEAKER_10: different steps of the l12345 ladder so you can approach this kind of like uh whatever they need

SPEAKER_25: you can offer exactly exactly and then two weeks ago nissan announced that uh they are going to bring

SPEAKER_14: this technology bring our approach to 90 of their vehicles you know they build about 3 million cars a year so this is that's 2.7 million yeah this is a this is a an enormous enormous um volume it's like

SPEAKER_02: double the cars tesla builds a year uh and that's just one of our partners and so um you know we're

SPEAKER_14: we're really excited about this and and this this this business model i mentioned how we had a contrarian technical strategy but there's also a contrarian business model because um yes there's 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 um 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 uh business model that's why we've chosen it it's only possible because we've built a flexible and generalizable um like ai driver and so i think this is this is also interesting how it's uh enabling a different business model that that might not you know might might not be appreciated at first thought we're gonna get to

SPEAKER_12: 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 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

SPEAKER_25: talking about market risk i'll give a nuanced answer here and i think um what i try to you know

SPEAKER_14: appreciate in self-driving is is firstly um to let our results do the talking and not sort of add undue hype and and and try bring a bit of uh technical realism i think these these principles have served wave well over the years but i i think um so i think we're through the scientific risk uh certainly so let's start let's start with different levels of autonomy so for um for hands-off driving uh i think that we've now shown that like last year we drove in 500 cities around the world uh tesla system scale said wave and tesla built this the n2n stack we're both showing that this scale is globally um 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 announcing a one and a half billion of revenue a year with this like uh clearly there's a product market fit with with that kind of product and the technology is performant to do that now what is it going to take to get this from uh hands off to eyes off or or drive or driverless that basically the same level of safety um uh for l3 or l4 a point-to-point system um there is a gap in performance from the systems that say tesla ourselves have today to get to general purpose driverless you know what waymo is has demonstrated in the geofenced areas they operate in to do that at a global scale a way that economically scales with mass market hardware no geofence to be able to do that there is a gap there but what i'm seeing is in front of us a very clear path to go do it and so i'd argue we've moved on from the scientific risk and now it's engineering execution risk and like product integration and deployment uh risk ahead of us namely uh 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 um the ai model to reach that level of performance and i think that's a very predictable scaling curve a little bit like the what we saw in the llm scaling journeys but that's a case of data compute um some algorithmic innovation along the way but but i think that's a predictable curve that we need to go run up and then third of course um to be able to validate it again that's an engineering activity we we know how to to do it it's a case of now um scaling the the validation activities across the domain to prove that this is safer than a safe and confident 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 the products being ready and i think that's um quite quite amazing to see you know 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 uh two months ago so we we co-chair the industry committee for u.n autonomy regulations and um the un just legal i put in place a legal pathway for l3 and l4 driving uh which that covers basically every country except the us and china so there is a now a legal path to getting this deployed as well so all in all i think we're moving from science risk and now it's an engineering and deployment risk

SPEAKER_47: so to get us from hands off to eyes off the path from here to there uh in the ai sense in the

SPEAKER_10: technical sense is solvable we know how to do that it's data compute and algorithm innovation and if you're curious what we mean by that just go read a paper from a major llm lab talking about the latest model and how they change the back functions of it to see how it could make it better

SPEAKER_50: there are some interesting differences from llms though right like we there's the um challenge of

SPEAKER_14: the real-time embodied inference you've got to do on board the vehicle it's much more constrained there's a safety critical challenge um there's the different modalities you've got much larger dimensional data um 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 for a self-driving car

SPEAKER_12: no there's a lot of difference out there right no no for sure uh 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 that makes good sense but let's let's look forward a couple years i'm gonna go buy a new nissan uh 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 um how do i the consumer pay for that do i pay a a fee to nissan for the technology let's say it's a 5k add-on making up numbers here not holding you to it or do i pay them some and then you guys some because to me the compute side of this can't be entirely local to the car there probably is some data exchange some inference costs so to me there's it seems like it'd be something that i should pay

SPEAKER_55: you for on a regular basis so it's good and gets improved but i'm not sure that's the plan yeah i

SPEAKER_02: think the journey the the industry is on a on a journey of figuring that out so um uh for consumer

SPEAKER_14: vehicles you'll pay the uh the manufacturer who will then pass through economics to to wave but there's different models that are being played out some manufacturers are looking to uh bundle this with the car and actually include it for free with all the cars they sell for a given model some uh uh you know it's like a seat belt 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 of it are both some maybe a free trial and then after a trial then you subscribe to it um of course famously tesla charges a hundred dollars a month for these features others have got um 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 uh it's likely that we will see the industry move to a subscription model because as you say all the all the intelligence will run on the edge on the car but there are going to be um you know there's going to be a improving uh performance over time with over there 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 i do expect we will get to a subscription model for vehicles and you'll um you know you'll pay for your own private chauffeur that that's in your car

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SPEAKER_12: incredibly interesting that sounded very boring but actually i think it's very important uh insurance costs held by the oem in this case of the car manufacturer i thought that was also a pretty unsettled question as to who is liable for okay let's just be honest a lot of people in the world a lot of cars even self-driving cars are going to hit people sometimes fewer it's going to be safer but it's going to happen so do you expect that in our example nissan would hold the liability there for selling the system the consumer for using the system or you for coding it oh look this depends on uh both

SPEAKER_00: the level of autonomy the regulatory environment and of course the commercial contracting between

SPEAKER_14: 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 um uh if it's implemented correctly uh as the driver you should remain liable and then an eyes off or a driver system the manufacturer or operator will remain reliable with uh with some insured and contracted liability that flows through to to the various

SPEAKER_13: parts of the ecosystem so it really depends are you going to build that that financial back backing that infrastructure we need to handle the insurance element to this or is that going to be handled by like chubb or like yeah berkshire hathaway we're staying focused we're not going to build an insurance

SPEAKER_00: product alex but uh if that changes i'll i'll let you know i i mean i i kind of feel like it should

SPEAKER_64: be like an add-on offering to what to what you're selling all right uh let's talk about how big this

SPEAKER_12: market is um a lot of cars sold every year enormous market key economic engine ironically um for the world

SPEAKER_10: how many cars do you think in what what percentage of newly manufactured cars in five years do you think are are are made with uh the capacity to work with either wave or a similar product uh in five years

SPEAKER_02: so today there are about 100 million vehicles produced each year you know um uh 50 60 million uh

SPEAKER_14: consumer cars um 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 um call it uh you know outside of china the full self-driving experience is just really tesla and that's a very small fraction of the market uh so this is going to go from like nothing to everything over the the next few years what we're seeing is that today luxury manufacturers are bringing in um the right level of compute on the cars a nvidia or qualcomm or something like that gpu and surround sensing and uh we're seeing more volume manufacturers like nissan just announced that uh you know they're going to bring this kind of technology to the vehicles from uh from financial year 2027. so over you said five years i think by five years we're going to see uh this level of hardware in a very um significant portion of the market and we'll see this improving experience over time we'll start to see the introduction and the uh more premium and also of eyes off technology um uh and uh and and even driverless consumer driverless technology i mean i think we'll continue to see that that flow down but but the steady state is that every vehicle is going to be capable of that i mean when you can for you know a very low monthly subscription get a eyes off driving experience i think this is going to completely change things uh and and actually you know yes robo taxis are also transformational but when you think about the scale there's less than 10 000 robo taxis in the world today but 100 million new cars a year and so the the scale of impact you can have through consumer vehicles is enormous um i think the advantage that waves brings because we work on both robo taxis and consumer cars means that um firstly the data we get from consumer cars will give us what we need to build general purpose robo taxis uh secondly um 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 robot taxi is if they can have a single partner that work across um across the spectrum of autonomy so uh you know for these reasons i think um this will give us the ability to have uh native relationships where it's a vehicle built for as a robot taxi 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 um and so i think for all these reasons it's a it's a it's a very very important opportunity for us um that often uh often gone it goes unnoticed but we're going to see this complete transformation of the consumer vehicle market with our ai in the in the coming years and to answer your question in in five years i think uh you know what if you're selling if

SPEAKER_04: you're a manufacturer selling a car that doesn't have this i think your demand is really going to

SPEAKER_10: fall off a cliff yeah apart from probably the the most basic you know like um tata nano style cars like whatever is like you know very very simple probably not everything but actually even even

SPEAKER_14: 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

David Friedberg: still have this technology um otherwise it's it's it's a moral imperative because of road safety i can't

SPEAKER_10: wait it's gonna 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 gonna be so much better than the yahoo's who drive around my house currently at like 800 miles an hour at night it's like it's residential dude break it down all right so 100 million cars a year

SPEAKER_12: going back to the tesla example 100 bucks a month 1200 bucks a year call it a thousand for safety uh 100 million times a thousand is a hundred billion dollars so clearly we're talking about a staggeringly large market do you need more capital to unlock it or does the recent billion

SPEAKER_10: dollar plus raise give wave enough runway to get all the way into production with an oem and early

SPEAKER_14: volume oh we're in an awesome financial position we've got over two billion dollars right now amazing set of shareholders i mentioned earlier um 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 um you know the the industry these these contracts were signing a decade-long relationships with automakers and so um i think the great thing is we can give them the confidence of of the security that that you know we may not need to to raise to to get to that escape velocity of course i wouldn't rule out any further raises because there's always uh opportunities to accelerate and grow into other verticals over time but for now yes uh we've got everything we need to go run at this opportunity and uh you feel the energy in our team uh now it's uh it's it's such a such a privilege now get to go and go and build and deploy

SPEAKER_10: these products all right one last question before i let you go this has been tremendous i love learning things um 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

SPEAKER_12: that the technology that wave has built with its world models is transferable to large commercial

SPEAKER_10: 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 oh 100 it is uh i've

SPEAKER_14: got a lot of data points i can share on this actually but um 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 um 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 um and so 60 60 yeah and that's a lot and even starting to learn in london right it's like one of the hardest uh environments to drive in so you know we've tackled the hardest problem first is in in our history to really build something as scalable but this is a stack that that will work with any robotics application um we've done some proof of concepts in areas like uh sidewalk delivery um trucking mining uh warehouse logistics i mean all of these kind of applications what we find is with a small amount of data uh put into our foundation model we can learn behaviors in these domains as well um even our simulator uh gaia we can um adapt gaia to these domains so it's a small amount of data but then the the driving policy the reinforcement learning and the simulation stack they all transfer with uh with data when you think of

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SPEAKER_14: treat cure or prevent any diseases 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 into an ai for driving now we had that five years ago um what we spent the last five years building is learning how to make this safety qualified compliant for the automotive industry uh and to what it takes to actually make this um you know safe and validatable and actually you know runnable in an embedded environment that's an enormous amount of product work and to be able to do that in germany uh stuka tokyo detroit and all the major automotive centers um like that's really where the 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 if you have the

SPEAKER_10: 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

SPEAKER_14: investing the time to bring it to market yeah i think that's right i i um and 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 like 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 there's so far ahead of manipulation and so i think we're going to see the you know nvidia compute the sensors the software defined vehicles you know you can go put that on pick your any vehicle you run from like luggage carousels and airports to um totes in a in a warehouse to some rumba in your in your um uh you know in your in your house and so i think we can can scale mobility quite well and then manipulation look there needs to be platforms at scale there needs to be data and i think uh um i think yes we'll be able to adapt in a few shots setting with the

SPEAKER_04: data we get from mobility um but i think manipulation will will probably come second wow

SPEAKER_10: well that's uh an incredibly bullish thing to leave on i'm really excited about it and i do want to just want to say thank you uh for agreeing that we can't have waymo wabi and wave all starting with

SPEAKER_12: the same letter and you're going to work on getting some other letters introduced to the self-driving world alex an absolute treat um 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

SPEAKER_14: someone listening is the right candidate for you yeah thanks alex uh so i mean we're we're on all the social platforms of internet just search wave w-a-y-v-e um if you want to come for a ride with us uh we have our fleets um you know in london tokyo stuttgart bay area you'll be able to call it on the uber app soon um so come come check it out uh come come give the technology go see what it's like or we'll buy uh one of our cars from from next year uh with partners like nissan so that's how you

SPEAKER_53: can really get get yes that's such a flex buy one of our cars next year yeah how does it feel to

SPEAKER_02: finally be here man you've been working on well it's been a decade but still still still not there yet

SPEAKER_100: so let's uh i i but you can almost taste it like you're starting to like you're starting to slowly reach for my my credit card you know and that's that's a different feeling than a few years ago

SPEAKER_02: it's it's exciting it feels good it's no it's it's awesome i mean tell you what's been the most

SPEAKER_14: incredible experiences i've been living on a plane for the last uh you know last year flying around germany japan in the us uh and being able to sell this technology when the market has shifted from not even like giving me a meeting to now loving it like that's the that's the biggest privilege um but in terms of uh growth absolutely we're we're hiring we're growing there's so much demand from the automotive sector every every car manufacturer wants this tech um what i think we've built at wave is unique at the intersection of um uh 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 an automotive and production grade technology but but with frontier uh ai again this is the culture and so um that's the environment we're built together and of course across the full stack um machine learning data software all the way through to product and application and validation for sure and then interesting roles and operations public policy uh and all of the uh enabling functions to unlock this

SPEAKER_88: this future so um yeah if you're interested come ride the wave about two billion dollars won't spend

SPEAKER_10: 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

SPEAKER_46: we're gonna sit down with one of the most interesting companies in the world just raised a bunch of money has an interesting take on how to bring self-driving not just to trucking but also to cars so please join me in welcoming to the show it's wabi founder and ceo raquel ratzen raquel how

SPEAKER_105: you doing i'm doing fantastic alex and really a pleasure to be here with you today oh an absolute

SPEAKER_46: 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 we're 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 uh what are world models and particularly why have you selected them as one of the core technologies at wabi yeah so

SPEAKER_01: so when building wabi you know we identified that they were you know two very big important kind of pieces of technology that uh uh you know we're going to be fundamental in terms of bringing cell driving a scalable uh solution to cell driving one on one side was can you build autonomy systems that can truly generalize um and have you know human-like capabilities of reasoning and the second big piece was about yeah you know in the era of ai uh data is as important as uh you know the model itself right so can we build representations of the world that can enable us to build simulation systems that

SPEAKER_110: are as realistic as the real world so that we can expose the system with no consequences to all the

SPEAKER_01: safety critical situations etc right and that kind of drove that uh innovation required to bring this

SPEAKER_46: uh you know these two pieces uh 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

SPEAKER_01: is that a reasonable way to think of it i think it's important to uh maybe make the distinction is about what are the things or the characteristics that you need a world model to uh to have in the context of self-driving or physical ai it can be generalized a little bit which i think will help with you know some of the viewers and listeners today uh 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 you know super realistic right but it's very important that you also have controllability of

SPEAKER_105: what they are generating and that's that has actually been uh or it's one of the big differentiations

SPEAKER_01: in terms of uh building world models for physical ai versus for you know creating pretty uh you know i would say pretty movies or uh you know cool video games etc sure i i didn't mean to imply that

SPEAKER_46: the world models are a video game but from the perspective of the the ai model that's doing this 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 from maybe from the ai model's perspective it might feel video game-ish i'm just trying to give people um something to stand on to understand yeah yeah so they are you know alternative representations

SPEAKER_01: of the world that's where the driving vehicle interacts with that world and you know the key there is that you wanna uh create those world models so that they truly represents represent all the things that might happen when you're driving in the physical world for self-driving right and

SPEAKER_120: yeah so and you are you know the self-driving vehicle is acting on them as if it was a video game for the self-driving vehicle yes correct and and the reason why this matter is going back to your

SPEAKER_46: point about data being so important is that if you have a world model that is a good representation of

SPEAKER_126: the physical world you can stress test your your your driving systems the wabi driver as you put it

SPEAKER_46: um and therefore you can take a a quicker approach to to market because you've already understood the world versus just mapping a single city and that seems to be the the distinction point between certain

SPEAKER_127: self-driving technologies world models or high definition mapping is that fair so i would say that

SPEAKER_105: those are yes two different maybe debates that we can have uh one is about how do you train and test the autonomy system and world models are an absolute key in order to allow allow you to you know in parallel in the cloud you know test uh you know systems at this scale and and train the systems to do the right thing right and it can bypass you know many years or you know or centuries of experimentation in the real world right and that's you know that's big and then there is the debate about well what is the information that the autonomy system should have in order to make the right

SPEAKER_01: decisions i see okay and that's where it's the maps uh and we you know happy to talk about you know uh all the beauty and and uh you know uh behind you know high definition maps etc does use of a world

SPEAKER_126: world model reduce the the need for um on car sensors or mapping or is it more of a a underlying framework

SPEAKER_46: that takes mapping and sensors essentially to the next level of safety and reliability yeah so i will

SPEAKER_01: say that um you know regardless of the autonomy system that you uh deliver or that you're trying to build uh world models really uh enable you to train and test that autonomy system to the next level now what that means is that it's gonna cut down significantly two things uh which is the time to market right uh it's gonna increase the safety of that system right it's gonna increase your understanding of the safety safety of your system which is you know tremendously important

SPEAKER_105: um and it's also gonna cut down if your world model is very efficient your spend that otherwise you will do by integrating out you know the thousands of engineers that you need over time you know for delivering your your technology um so that's one side but they are very useful regardless of whether you use high definition maps or whether you you use different sensors and i think the you know in the debate of the camera only versus multiple sensors maps versus not to me it's a question about safety versus bomb cost and uh and uh together with cost of uh you know creating high definition maps so what what 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 you know 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 uh it uh it has the ability to react and drive regardless right so so you you know is um i will say that you know we should debate less about high definition maps versus not is about do you have technology that can build those maps really is in a scalable

SPEAKER_03: manner and you need ai for that right to build those maps in an 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

SPEAKER_140: bomb cost is bom um uh bill of materials essentially like the hardware okay cool i just want to for

SPEAKER_46: folks out there who thought we changed subject to war very quickly not that kind of bomb cost very different uh now on the on the on the generalization point you guys started off with self-driving trucks on highways then you expanded into surface streets and now uh with your latest series c announcement and the uber deal which we will get to in a second uh moving into robo taxis does the original world model foundation of the company make it easier for you guys to expand from like one segment of the road world into surface streets and then into i i presume residential as well i'm just trying to understand if the world model itself has accelerated your ability to move

SPEAKER_144: from one major area of automation into others yeah 100 100 uh that i think it's worth mentioning that

SPEAKER_01: uh you know the physical ai platform that we built from day one that is composed of the world model the simulator together with the autonomy system was built from day one for uh being utilized for multiple uh you know physical ai use cases uh so we had uh you know in mind from day one that can we build that really next generation you know generalizable technology that will enable wabi to actually capture many of these you know multi-trillion dollar markets right and 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 into what that means and why it's very different from the traditional ab 1.0 or the what uh has become more

SPEAKER_105: traditional now ab 2.0 right uh 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 uh you know build two teams fork the stack uh into you know robot taxis versus versus trucks uh on the contrary is the same brain and the same uh you know simulator on world model that actually does both use cases the same as for humans we don't change our brain every time that we actually drive a different vehicle for the first time this technology enable us to do so so you actually is additive you accelerate each program with the other program which is a

SPEAKER_01: totally different you know mindset compared to what it was in the past on one hand i absolutely agree

SPEAKER_126: with you that we humans use one brain for all of our driving needs no matter what car type road condition weather etc and so having a single intelligent mind to handle driving for machines makes a lot of sense to me on the other hand human brains are not very specialized and so is there a place in the future for specialized driving systems that are better at say trucking than driving cars in a city or does the the single brain get so smart that we don't need to really differentiate between use case

SPEAKER_03: 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 uh 80 000 pounds you know cargo truck versus a robot taxi but that's an

SPEAKER_105: example where we don't need to be like super specialized uh in terms of technology now when you go to other types of skills uh that are more different uh then is where uh maybe specialization makes sense uh but a lot of the core characteristics of perceiving and understanding the world in 4d uh not 3d 4d which is you know we live in a 3d world that changes over time yep those capabilities and and reason and

SPEAKER_149: action uh those are core and and common to to everything so at the end of 2025 uh it seems that

SPEAKER_126: anthropic and open ai 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 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 has something fundamentally changed in the ai models and uh intelligence more generally that has impacted wabi and and your competitors in a similar way or am i over analogizing um general ai versus the more specific stuff that you

SPEAKER_156: guys are using yeah yeah so so and and it's it's very interesting to see and you know i've been

SPEAKER_01: fortunate to be working at the forefront of innovation in ai for 27 years now okay so i'm gonna give you the 27 years view of what has happened and i'm etching myself here on uh you know life but um the you know what is what has been very interesting is that for the physical world and in particularly for cell

SPEAKER_105: driving there is three things that are converging at the same time like call it tectonic plates that

SPEAKER_01: you need because it's more than yes ai on one side i is uh you know is the hardware and the oem is 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 uh by both you know trucking oems and passenger kind oems this is actually converging and is reading now so that's a big piece of the password in terms of why now deployment and scale you know 26 plus is is the year for uh for this or the the set of years for this on the you know one other piece that is important as well is the regulatory

SPEAKER_105: frameworks are evolving uh in order to really enable this uh this deployment when you look at the consumers of this technology both uh in the robot taxi side humans want to use uh you know cell driving technology was a question mark before whether people will with trust and then what we see with way more deployments is that yes people understand that it's actually this technology is making roads

SPEAKER_03: safer and and in many ways this is a better product than if it's a human driving right i don't want to

SPEAKER_153: get you off topic here so get your third point in a second but i've been blown away by how quickly

SPEAKER_144: normies have have taken up uh waymo like it i thought it was gonna take them much more time to get

SPEAKER_162: comfortable with it but but no it was experience and then and then seeing is believing that's that's i think

SPEAKER_01: uh um you know in many ways and uh you know for humans uh is it's fascinating i would say and the last bit sorry the last i guess the fourth uh fourth and you know for trucking is a no-brainer right driver searches shortage the costs of human drivers the uh pervasive safety issues uh you know etc make a a very clear case of why everybody wants to adopt this technology if you build the product that is important for them right uh or that will solve their their their pain points and then the last

SPEAKER_105: bit is what you asked me the question sorry to go around you know in a circle right but uh also there is you know massive changes in terms of what ai can do today and what we see really is these next generation companies that you know second mover advantage in many ways of you know maybe 1.0 maybe you can deploy scaling is you know it's extremely complex etc right small of these uh with this next generation of of ai technology is so much more powerful and you can truly build through through reasoning as as i was saying capabilities to really generalize from almost no example and that that

SPEAKER_01: changes the equation totally in terms of the product you can build the ability to really solve all the

SPEAKER_105: long tail and how quickly you can expand geographically and across use cases right as we were talking about

SPEAKER_126: before so market preparedness and demand having the right regulatory structures in place willingness of people on the consumer side to uptake this obvious market fit on their trucking side and improvements to ai together are really driving this make everything like now is the moment but for physically

SPEAKER_01: i you need more than just the ips is all of these things together that are ready now and is it makes this a extremely exciting time for for cell driving and and for you know it's going to change really the way because transportation is at the middle of everything right uh it's going to change the way that

SPEAKER_126: this this world works yeah and i think it's going to change it for the better now one thing we we've talked about is uh 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 assets light and efficient your company is which contrasted a little bit with the amount of money that you raised raquel and normally when i 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 to to you know get to the next step of its progress so what am i missing there and and uh very politely apart from the fact that you could why did you raise so much money

SPEAKER_176: i have a great question yeah yeah and many many people have asked me the this question is like

SPEAKER_01: you don't need them you know that amount of money why did you raise so much money so so the uh and it's not yes because we could um when you think about um you know the you know the future for a

SPEAKER_105: company like wabi you you know we raised actually over a billion dollars um and in this last last round and what uh what that means is that uh you know we are the most stable company in the market and that means that uh and that was you know it was important um i thought uh for really being able to uh make you know both the right bets the right investments and and and and and think about not just about what we need for the next two years but how is this market gonna play if there is any uh you know delays or anything that happened in the ecosystem whether it's in adoption whether it is you know in certain scaling etc by you know some of our partners uh you know being fully robust to improve anything and being able to go all in in terms of noise our trucking you know leadership positioning and a scale and deployment right which is now is the time but also be able to um you know go into the additional vertical that we are adding now robot taxes right without you know compromising or thinking uh that you know we can actually do that because we are so capital efficient that a billion is infinite money for us right so it's you know in and and it really sets us in a very different place than anybody else in the industry where uh there is gonna be uh or there is you know a lot of pressure

SPEAKER_01: in a quarterly basis for them to actually show progress to continue their journey right versus for

SPEAKER_105: us from day one everything was about building for the scale moment uh and which has seemingly

SPEAKER_176: arrived back to our point yeah correct the strategy has been absolutely spot on right uh and in terms

SPEAKER_105: of you know we invested heavily on uh foundational technology right and and at the beginning was all about building this technology that didn't exist that really you know you invest more you take maybe a bit longer to go to on road for the first time but when you do suddenly you are placed in a very

SPEAKER_01: different position than everybody else right and now it's about that next level of investment for the why the spread adoption of this technology so that's that's why the billion dollars uh you know why to

SPEAKER_126: do this i really appreciate that in-depth answer but at no point did you say investing in building lots of 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 to other people correct we

SPEAKER_105: continue to be a technology provider we are not an oem and and this is very important for us which is you know we don't believe that uh you know retrofitting or suddenly becoming an oem is a path for for uh you know for us we don't believe that this is a safe path as well to market yeah we believe

SPEAKER_01: that partnering with folks that really have you know uh excelled at this over the last century

SPEAKER_105: uh is actually the right path to to really bring that safe cell driving technology and and again we are not um you know since we are you know we have the disability and we can really think long term

SPEAKER_01: right uh we are not pressured to do things compromising that that are just short term uh let me show you

SPEAKER_03: progress on the short term but that's not really the part that anybody wants for for the future yeah

SPEAKER_186: back to your point about you know good foundational technology slower to road but also better long term

SPEAKER_105: so that and i would say alex maybe one one thing that um you know people didn't necessarily or criticize wabi and uh in the past was about uh why not to start with quite a lot of you know uh i will say operations and you know commercial operations and what we focus really is build a product uh and and you know get ready a product that really um solves the pain points and really addresses what the customers want you know you mentioned before surface streets and i and i just want to maybe um add one one uh one note there which is you know the industry went with this have to have uh which is a model right which is you have hubs close to the highway and then you drive autonomously between the hubs and then a human will do the end of the of the trip in both sides right and that was a reason 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 you know 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 drainage which in the economics actually can be depending on your length of whole massive like 0.6 uh you know dollars 2.8 dollars per mile which just basically breaks the whole thing yeah and and nobody wants this product right so instead you know we invested uh really through this next generation ai technology building for the first time truly technology can drive in generalized surface streets now we can go to the end customer we can go to the door and then suddenly you have a better product now that you

SPEAKER_195: have a better product roll out your product and that's the face that we are right now okay so actually

SPEAKER_06: let's i really want to get to the uber thing in a second but let's just stay on this what because

SPEAKER_126: i i couldn't actually chase this down before our chat to my to to level of confidence where is wabi today in the commercialization of its self-driving technology in the trucking space um are there lots of trucks on the roads that you guys are powering today are is there one i i just i couldn't quite figure out where you are now so raquel yeah yeah yeah fantastic so so it's definitely more than one

SPEAKER_01: track so so we have you know since 2023 uh since 2023 we've been doing uh commercial operations uh with you know some of the best uh of the top you know shippers carriers uh north america uh we have a massive partnership with uber freight for billions of miles of deployment on the uber freight network uh which really is it's really nicely you know sitting between supply and demand um we have you

SPEAKER_105: know a decent size fleet of cellular vehicles um i will say and is that is that double digits

SPEAKER_156: triple digits it's double digits double digits double digits double digits um of uh of of tracks and where we are is you know our commercialization true commercialization path is really through the oem

SPEAKER_01: uh and i i want to make sure that uh i represent our partner with what they feel comfortable or what they have said publicly okay but uh as they say uh last year they are quarters away from that uh the volvo so volvo is our oem partner yes uh for those that don't know um they're fully redundant uh fully validated platform yeah last year was quarters away that can give you a sense very and yes soon yeah very soon so it's very soon right and 2027 they have also uh say publicly so you know that will be hundreds of trucks which is you know pretty a very nice number already for a 27 deployment right so so that's that's where you know if you want to know where what is so that's

SPEAKER_126: that's where that's exactly what i wanted yeah 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 you could charge for on a recurring basis as a business to

SPEAKER_105: your oem partners for example yeah so um i can tell you that wavish technology both the world model

SPEAKER_01: and the autonomy system is super efficient and you can see that by how advanced we are in terms of the technology right about to you know uh driver's launch with the oem etc yeah um and prior to this round it's also public how much money we have raised right so if you put all this together you can see how efficient we are 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 how we do this yeah she's bragging right now that was a brag i'm not bragging yes i just i think this is important because it's at the at the core

SPEAKER_105: of wabi is all about sustainable efficient solutions through next generation technology that's that's really at the core of our dna we are innovators we have been you know for the last you know i said before two more than two decades in terms of building this technology but sets us apart from yeah

SPEAKER_01: saying the more it's more philosophy of yes bigger data centers more uh uh you know more data and then just you know uh expand everything in the cloud uh to your point about how efficient is this uh this thing

SPEAKER_105: uh but as it relates to the business model going back to your question um so we are a you know it's driver as a service both on the tracking 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 not robotaxis and that's where our partnerships our customers are tremendously important for us right uber plays a fundamental role on that uh go to market right for robotaxis and uh you know it's very obvious that they are the market so that and they're very incentivized to grow and continue growing that market so so that's uh you know that's very exciting right and the second bit uh for trucking uh so it depends on the oem also like who um who will operate uh those trucks uh and uh this is also publicly known that uh uh volvo plans to also

SPEAKER_01: operate some of the cell driving vehicles so through building a transportation as a service uh i would say business unit which is uh volvo autonomous solutions and that's different than some of the other oems for us it's transparent whether you know that's a matter whether it is through the oem or is direct to customer uh is the same business model as it relates to to wabi it

SPEAKER_03: will just depend who pays us directly whether it's the oem or whether it is you know say a walmart

SPEAKER_224: for example but is it is it a recurring fee or is it a one-time payment yeah so it's per mile so

SPEAKER_191: it's very cool okay cool that's what i was trying to just chase down to make sure that yes it's a

SPEAKER_01: very very well uh there i mean you can do a blend of things like this right so it's a bit more uh so you know uh sophisticated but uh the big piece is always the uh per mile basis yeah that makes

SPEAKER_126: 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 um sorry that's that was terrible uh okay uh before i let you go i have to ask more about the uber uh robo taxi deal um you guys said and i'm pulling into my notes here to find the quote um up to 25 000 robo taxis with uber i believe so uber has a lot of partners on the self-driving side uh including neuro and lucid and that deal was demand network uber uh 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 uh so who are you going to work with on the uh making

SPEAKER_108: cars side for that partnership yeah yeah so so let me maybe address the the uber partnership a little

SPEAKER_01: bit of how wabi plays plays a role in the uber ecosystem so what is what is very interesting is that

SPEAKER_03: our partnership is not up to 25 000 is over 25 000 or in other words a minimum of 25 000.

SPEAKER_105: uh okay so greater than or equal to not up to i would say so that that already tells you a little bit about the scale of the of the of the partnership and and um and in the ecosystem is the same as tracking right in the in the ecosystem for us since we are the technology provider you know uber plays the you know the market components right and then there is the oem to your point uh um that uh will

SPEAKER_01: provide the rhythm platform where we vertically integrate with and that's again we believe that's the safe path uh save and scalable and only scalable path path to market um we haven't announced

SPEAKER_236: yet the oem uh what would you like would you like to do that today on the show i know that you will

SPEAKER_01: love that um what i can tell you is uh that uh you know there is is is we love again the coming at the

SPEAKER_105: right time second mover advantage of the ocean has been boiled there is a few oems that have that but on the platform ready now and and it's very exciting you know uh how excited the ecosystem is

SPEAKER_01: about partnering with us and we are very excited about partnering with them so more details to come in other words but uh uh we are yeah as i said very excited about uh you know our our entering to

SPEAKER_105: robot taxis and and in a in a swift and and really exciting manner yeah i i'm really i think wabi and

SPEAKER_126: wave are the two most exciting companies in the self-driving world today i think uh apart from the headlines that waymo grabbed so i'm very optimistic to learn more as the year goes on uh 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 a taxi service side of things for robotaxis um has uber tried to buy you

SPEAKER_100: because it feels like you guys are like best friends who live together like why you know like

SPEAKER_105: at some point why can't you formalize it i would say that through the through the years since the inception of the company many people have tried to buy uh to buy wabi uh what i can tell you is that you know my goal here is really to build a physically i power house that is transforming the world

SPEAKER_11: so you know what is not for sale for anybody um all right dara you need to add a zero to that offer

SPEAKER_46: try again no i i'm 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 lobby program to everything from delivery

SPEAKER_126: bots to possibly even robots inside of factories because i can see the world and humanoids your name is yeah yeah to me there's a big generalization of the world model approach in solving autonomy to actually bringing it inside of i don't know why it wouldn't work inside of buildings once you've built out the right systems for that so there's a lot of stuff coming down the road um am i going to be able to to buy a self-driving car like like l4 l5 in the next three years do you think in the next three

SPEAKER_156: years um a level four level five yeah um that's uh that would be hard okay well in that case experiencing

SPEAKER_110: robotaxis at the scale yes um uh personally on vehicles on that time frame is harder okay well

SPEAKER_126: can you go uh back to work and get on that for me because i would like to uh to buy one because i hate

SPEAKER_01: driving and i keep and i i can tell you maybe i can tell you why you say that i think it's um it's

SPEAKER_105: important which is for a decade people thought that you know um level two will go first then it would be level three and then it will be level four and it makes total sense because it's okay it's just adding plus one to it right 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 where they 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

SPEAKER_153: 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 back

SPEAKER_246: you need to either you build level four technology or you build level two technology and that's you

SPEAKER_105: know separates us from some other end-to-end companies yeah okay and i think this is very important to understand and that's you know at the core of why i say three years is difficult because i truly believe because it's a totally different safety problem that you need to solve that is not just about i drive well i don't have many interventions that's you know a metric that matters for level two whatever that is not a level four metric and and and what people don't necessarily realize is that this is a gigantic difference between yeah animal two plus pro that

SPEAKER_01: 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

SPEAKER_126: for coming on an absolute treat um 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 um thank you so much uh what's the website people

SPEAKER_105: want to go and learn more so wabi.ai please come and come and uh you know check us out and we are massively expanding as well so and it's you know the most exciting innovative technology you know company and physical ai and you know is it's an amazing place to to work and it's an amazing place

SPEAKER_01: to partner with and we yeah looking forward to tell more and more about a story and but more importantly for people to actually really see our deployment in the real world everywhere well as we

SPEAKER_144: say here in the states keep on trucking thanks for coming thank you thanks for watching this week in

SPEAKER_265: 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 at launch accelerator.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 table with top ai founders and operators every week find it this week in ai.ai check out the twist ticker our daily newsletter at this week in startups.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 5 p.m central time you can submit an audio or video file question at thisweekin.com