Chapter 1: What significant funding did Waabi secure and what role did Uber play?
Welcome to the podcast. I'm your host, Jaden Schaefer. Today on the podcast, we're talking about a company called Wabi that has just raised a billion dollars. They partnered with Uber.
Chapter 2: How does Waabi's AI-first approach differ from traditional methods?
They're getting into RoboTaxis. 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 see a lot of other crazy stuff that happened and they also got another 250 million dollars 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
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Chapter 3: What competitive landscape does Waabi face in autonomous trucking?
It's $20 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.
Chapter 4: How does Waabi plan to expand into the robo-taxi market?
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 ride-sharing platform, which is going to make Wabi's first expansion beyond just autonomous trucking. This is what 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, how 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.
This is not an easy thing. Scaling a single autonomous driving system across a whole bunch of different vehicle types and then commercial use, I think, adds a whole nother level of complexity. to all of this.
Chapter 5: What technological innovations are driving Waabi's success?
So there's a bunch of competitors doing this, right? 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 Ertzson, and she said that her company's capital efficient strategy and generalized AI architecture give it a bit of a structural advantage.
Chapter 6: How does Waabi's simulation environment enhance AI learning?
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.
But if anyone has ever tried to drive a semi truck before, you know that, I mean, this is very different. You have to get a different type of license. These are massive vehicles. The way that it backs up, the way that, you know, everything's happening on that truck is very different than a regular car. You know, they're saying, look, a car is a car.
These are, you know, it's using the exact same system. But we have seen other players like Waymo try to get into both just vehicles and freight and exit the freight because it was kind of maybe too risky or too hard or they felt like they could come to market with the cars sooner. And maybe Waymo gets into it.
But usually when you see a company try something and exit out of it, it just feels like that's not the vertical they're going to go after. So here it appears that this is something they're actually going to attack, which is going to be interesting.
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.
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Chapter 7: What are the future implications for autonomous driving with Waabi's approach?
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.
Chapter 8: What partnerships are crucial for Waabi's growth and scaling strategy?
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. So Wabi is now joining a whole bunch of different companies that are doing this and also other ones that are deploying on Uber's platform.
So Waymo, Neuro, Avride, Wave, WeRide, Momenta, all of these are getting onto the Uber platform. And and so they're kind of coming alongside them. The deal also is coming at the same time that Uber has launched this new internal group called Uber AV Labs, which is going to let Uber vehicles collect real world data for all of their autonomous partners.
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, you know, 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. And so it's going to be interesting to see which of these two models win. 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 the 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 Yuritsan, 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. Wabi has spent the past four and a half years developing that technology. And they've done it for highways and surface street tracking.
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