Waabi Raises $1B From Uber and VCs  ## Document Notes ## Highlights - **Big Bet: $1B Round And Uber Partnership** - Waabi raised $1B including $250M from Uber to deploy robo-taxis and scale beyond trucking. - The deal signals heavy industry backing and a big strategic bet on Waabi's AI-first approach. Transcript: Jaeden Schafer They partnered with Uber. They're getting into robo-taxis. It's an autonomous trucking company. So today on the podcast, I want to break down how they got into this $750 million over-subscribed Series C, a lot of other crazy stuff that happened, and they also got another $250 million From Uber. We're getting into all of that, how they're able to raise it what they're doing why they're different and what the competitive landscape looks like for some of these autonomous trucking And driving companies before we get into it i wanted to mention if you want to build tools without knowing how to code i'd love for you to go check out aibox.ai my own startup where you can Describe any tool or workflow that you do and have it link together different AI models, input prompts, and automatically build you a tool that you can use over and over again to help Automate your tasks. In addition, you get access to over 40 different AI models. If you want to chat with them, like ChatGPT, 11 Labs, Anthropic, Audio, Image, Text, tons of amazing models, it's 20 bucks a month, you get access to all of that so you can go check out aibox.ai All right let's talk about what's happening with wabi something that's interesting to me with the wabi story of course you know a billion dollars raised is absolutely incredible a Partnership with uber is incredible and i think lots of times you'll see like a partnership happen first and then you go raise the money after but they kind of did these things or at least The way they announced them that happened at the same time i'm sure there was probably talk that helped to raise the money but this is essentially a partnership with uber to deploy self-driving Cars on their their ride-sharing platform which is going to make wabi's first expansion beyond just autonomous trucking so they've kind of been working on so this is their series c It was co-led by Coachella Ventures, G2 Ventures, and then, you know, that and a bunch of others kind of put in $750 million. And alongside that, you have Uber that came in with $250 million, so they did a total of $1 billion. I think that Uber backing that $250 million is tied to deploying about 25,000 or more of Wabi driver-powered robo-taxis. And what's interesting is this is exclusively on Uber's platform, even though the company didn't share a timeline for, you know, when they're going to have all of this rolled out. And I think right now, when you're looking at it, it looks like a really big bet that Wabi's first, you know, kind of this AI-first approach is possibly going to succeed where a lot of others Have struggled in this. ([Time 0:00:06](https://share.snipd.com/snip/92e7177c-b6b4-4f1c-9259-78b5decfd9df)) - **One Stack To Serve Many Vehicles** - Waabi claims a generalized AI stack that runs across vehicle types rather than separate stacks per vertical. - That architecture could reduce duplication and speed deployment across trucks and passenger vehicles. Transcript: Jaeden Schafer I think everyone knows about Waymo and of course, Tesla. Waymo actually previously attempted to operate both robo-taxis and autonomous trucking before they got out of autonomous trucking altogether. They completely left that industry. So Wabi's founder and CEO is Raquel Ertsson, and she said that her company's capital-efficient strategy and generalized AI architecture give it a bit of a structural advantage. This is a direct quote. She said, our core technology enables for the first time a single solution that can operate across multiple verticals at scale. It's not about running two programs or maintaining two stacks. This is really interesting because, I mean, technically a car is a car and driving is driving. ([Time 0:02:39](https://share.snipd.com/snip/2a0a896d-fc77-442b-bb1e-ea4fdad4c4ef)) - **Founder’s Full-Circle Moment** - Raquel Ertsson previously served as chief scientist at Uber ATG before founding Waabi. - She then raised funding from Uber, closing a full-circle career moment with a $250M investment. Transcript: Jaeden Schafer This whole partnership is also bringing Yuritsu's career full circle, the CEO, because before founding Wabi, she was actually the chief scientist at Uber's autonomous vehicle division, Uber ATG, which Uber actually sold to Aurora Innovation in 2020. And so she already works with Uber Freight. And now this new agreement, I think, is going to really make that a lot deeper. It's kind of interesting, right? Like she was literally working in Uber, working on this. Uber sells off this kind of autonomous vehicle division to another company. She works there. She starts her own company. And Uber signs a deal with her and gives her $250 million. So, I mean, obviously, this is a problem that Uber's been trying to solve for a long time. They probably felt like they couldn't do it. And then when someone else did, they were willing to give them a big check. ([Time 0:04:03](https://share.snipd.com/snip/f6c000ed-b9fc-49ed-bf54-818aa0d10196)) - **Uber's Platform Data Network** - Uber's AV Labs will let platform partners collect shared real-world data from Uber vehicles. - That network effect could rival Tesla's fleet data advantage by aggregating multiple AV providers' data. Transcript: Jaeden Schafer So all of these companies that are on the platform also are going to get more data to help train their models to make them better. And it's kind of interesting because Tesla a lot of times kind of talks about how, look, we have like millions of vehicles on the road and this is what's making our AI, you know, autonomous Driving system so good, which is true. But it's not like other people can't replicate that because if you have this whole network over on Uber, now Waymo, Nervo, Avaride, WeRide, Momenta, all of these are also going to be Getting data and collecting and sharing and making all of these autonomous riding better as well. So you're going to see kind of everyone else share their data. Tesla seems to be the one that has the most of it. ([Time 0:05:21](https://share.snipd.com/snip/0ffb7052-01b1-4b38-a0cc-20b961c73c83)) - **Simulation-First Training With Digital Twins** - Waabi trains its Waabi Driver inside a closed-loop simulator called Waabi World that builds digital twins and edge cases. - This simulation-first approach aims to generalize from fewer real-world examples and reduce data needs. Transcript: Jaeden Schafer Unlike a lot of different AV development that is going to rely on a whole bunch of kind of real-world data sets, a really massive real-world data set, Wabi says that their system is less Data-hungry. And the way they've done this is actually really fascinating to me. So the Wabi driver is essentially trained and validated inside of what is called Wabi World. This is a closed-loop simulation environment that automatically builds digital twins of real environments. So it's, you know, simulating all of the sensors in real time. It's generating edge case scenarios. It's allowing the system to learn from its mistakes without human intervention. So, you know, they kind of have this whole simulated world they've built and the AI is learning to drive inside of it. But the simulated world is, you know, very similar to the real world, which we can do these types of things when we have, you know, technology like Google Maps and Street View and all this Other stuff. And that technology behind it, really, because I don't think Google's licensing that data, but you could go drive a car around and get real information and then use that to replicate These kind of digital worlds that it's used to simulate with. So according to Year at Sun, this like approach that they're taking is going to make their system and it's going to allow it to reason about its surroundings more like a human driver and Generalize from fewer examples than traditional AV systems. ([Time 0:06:03](https://share.snipd.com/snip/5449f361-5c3e-4816-8765-6a68950499a2)) - **Domain Differences Shape Strategy** - Different driving domains (freeway vs back roads; trucks vs cars) present distinct challenges that determine go-to-market paths. - Waabi focuses on freight on freeways and passenger vehicles to target high-value, scalable routes. Transcript: Jaeden Schafer I would drive around there to get from the airport to my podcast studio all the time. But the problem with Waymo's, and I know this will get solved at some point, but Waymo's, what I realized when I first did my first Waymo ride was usually it's a 15 minute drive from the Airport to my podcast studio and it was 35 minutes. And I was like, what the heck? Why is it taking 35 minutes? And it was because it wouldn't go on the freeway. It was going on back roads. And that's kind of where Waymo was at at the time. And maybe these things are changing. But the freeway, you know, so like we talked about the difference between regular vehicles and semi-trucks or freight. Now there's also a difference between back roads and freeway. And so it feels like they have to do, like Wabi has to do some of the hardest things. They have to do freight on freeways. ([Time 0:07:31](https://share.snipd.com/snip/71fcd54e-f666-4f71-bddb-2afddd6c5423)) - **Embed Autonomy With OEMs** - Do integrate sensors and autonomy at the factory floor with OEMs rather than retrofitting vehicles. - Waabi argues fully integrated, redundant platforms built with OEMs yield safer, scalable deployments. Transcript: Jaeden Schafer From kind of the get-go, they're collecting and simulating passenger vehicle data alongside the trucking work, which I think shows that robo taxis were kind of always part of the long Term roadmap for them. And that strategy, according to her, has basically allowed Wabi to build a lot faster with a lot less capital than, you know, maybe like some other first generation AV companies. She said, we don't need massive fleets, enormous data centers or endless numbers of people and chips. ([Time 0:08:38](https://share.snipd.com/snip/a26da60c-0139-40b4-990f-1595a79a09ac)) - **Funding Position And Strategic Investors** - The $1B round brings Waabi's total funding to about $1.28B, positioning it between smaller rivals and giants like Aurora. - Strategic investors (Volvo, Porsche, BlackRock) tie capital to manufacturing and distribution advantages. Transcript: Jaeden Schafer So, with this current billion-dollar round, WAPI's total funding is now at about $1.28 billion. They did a $200 million Series B in June of 2024. So if you're just kind of comparing that, Aurora Innovations has raised about $3.4 billion. ([Time 0:09:19](https://share.snipd.com/snip/abc2e173-e4d9-495f-a620-ca83907c1b17)) - **Pilots Now; Driverless Later** - Waabi has active commercial trucking pilots with safety drivers and plans for driverless trucks pending vehicle validation. - Software may be ready but vehicle validation and timelines still slip due to safety requirements. Transcript: Jaeden Schafer So Wabi's already launched a whole bunch of commercial trucking pilots. They're mostly they're doing this in Texas and they have human drivers in the cab. They've already planned to deploy a full driverless truck on public highways by the end of last year. That was kind of the plan, but their timeline got pushed into somewhere in the next future coming quarters. I think anyone that's been following autonomous driving knows that that kind of deadline push is something that happens. We've seen that a lot with Tesla over the years. And it's essentially because, I mean, you want to be optimistic, but at the same time, if the safety rating isn't there, if it's not perfect, you just can't deploy these systems until It's completely ready. So according to them, they're also working with Volvo to build some purpose-built autonomous trucks, which they showed off last October. Ayratsu says that software is ready, but that the vehicles themselves still need full validation before launch. ([Time 0:09:43](https://share.snipd.com/snip/1be99faf-bde0-42ee-92f8-b535af3109a1))