Raquel Urtasun, scientistFounder and CEO of autonomous vehicle technology company Waabi, launched her company in June 2021, at a time when it seemed the AV industry was about to consolidate.
Urtasun and her 40-strong team in Toronto and California came out of the gate with an $83.5 million raise from a number of high-profile investors including Uber, Aurora and Khosla Ventures.
Waabi uses an AI-first approach to commercialize autonomous cargo faster and more efficiently than its competitors, Urtasun told TechCrunch. As a professor in the Department of Computer Science at the University of Toronto, co-founder of the Vector Institute for AI, and former chief scientist at Uber ATG, the self-driving unit that Uber sold to Aurora, she has gained some insights that support both industry and academia it. After all, despite consolidation and gains by some key players, no one had really figured it out yet.
So what does an AI-first approach really look like?
In February 2022, Waabi launched Waabi World, a high-fidelity closed-loop simulator that not only virtually tests Waabi’s self-driving software, but can also teach it how to drive. Waabi World automatically creates digital twins of the world from data, runs near real-time sensor simulations, creates scenarios to stress test the Waabi driver, and teaches the driver to learn from their mistakes without human intervention. This, Urtasun said, saves countless hours of human labor to train the Waabi driver both in simulation and on the road.
The entire Waabi world is powered by AI in a way that other companies’ simulators don’t, as they rely more heavily on deep neural networks, AI algorithms that allow the computer to learn using a series of connected networks, to identify patterns in data. In the past, developers couldn’t figure out the how and why behind an AI’s decision-making when using deep neural networks, which is very important when bringing self-driving vehicles onto public roads, so they resorted to machine learning and rule-based algorithms for integration into a broader system.
Urtasun said she found a way to solve the problem of the “black box” effect behind deep neural networks by combining them with probabilistic inference and complex optimization. The result? The developer can understand the decision-making process of the AI system and incorporate previous knowledge so that he does not have to teach the AI system everything again.
We sat down with Urtasun to discuss the ins and outs of starting a business after working for a larger company, the surprises of being a founder and why freight transport will be the first AV industry to be commercialized at scale.
The following interview, part of an ongoing series featuring founders building transportation companies, has been edited for length and clarity.
After working for Uber and being an academic, what are your takeaways on what it’s like to be a first-time founder?
When I decided to start Waabi, I didn’t really know what it meant to be a founder. I’ve worked in industry and this space and stuff like that, but as a founder you have to wear so many hats and there’s so much going on. I was not expecting that. And Waabi is very different now than when it started, so there’s something that surprised me.
But it was an incredible ride. I have to say, there’s nothing like building what you truly believe in with a team you enjoy working with. There is nothing that cannot be done.
You’re wearing a lot of hats now, but how did it compare to working under someone at Uber and not controlling the whole show?
I was part of the leadership team at Uber, so I had a lot of influence and, you know, a lot of say on a lot of things. But building is different – and it’s not just Uber, it’s general. When you’re in a large company with 1,000+ people going in one direction, it’s so difficult and slow to actually execute that process, even when you all agree that you have something else to direct.
From that point of view, it’s very exciting to be in a startup that’s much more dynamic, but it’s not Uber versus not Uber. I think any major company would be similar. But I had a great time at Uber. I learned so many things and really discovered what it means to really be a part of a big problem and prepared myself really well for what I’m doing today.