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Raquel Urtasun is the founder and CEO of self-driving truck startup Waabi as well as a computer science professor at the University of Toronto. Unlike some competitors, Waabis AI technology is designed to drive goods all the way to their destinations, rather than merely to autonomous vehicle hubs near highways. Urtasun, one of Fast Companys AI 20 honorees for 2025, spoke with us about the relationship between her academic and industry work, what sets Waabi apart from the competition, and the role augmented reality and simulation play in teaching computers to drive even in unusual road conditions. This Q&A is part of Fast Companys AI 20 for 2025, our roundup spotlighting 20 of AIs most innovative technologists, entrepreneurs, corporate leaders, and creative thinkers. It has been edited for length and clarity. Can you tell me a bit about your background and how Waabi got started? Ive been working in AI for the last 25 years, and I started in academia, because AI systems werent ready for the real world. There was a lot of innovation that needed to happen in order to enable the revolution that we see today. For the last 15 years, Ive been dedicated to building AI systems for self-driving. Eight years ago, I made a jump to industry: I was chief scientist and head of R&D for Ubers self-driving program, which gave me a lot of visibility in terms of what building a world-class program and bringing the technology to market would look like. One of the things that became clear was that there was a tremendous opportunity for a disrupter in the industry, because everybody was going with an approach that was extremely complex and brittle, where you needed to incorporate by hand all the knowledge that the system should have. It was not something that was going to provide a scalable solution. So a little bit over four years ago, I left Uber to go all in on a different generation of technology. I had deep conviction that we should build a system designed with AI-first principles, where its a single AI system end-to-end, but at the same time a system that is built for the physical world. It has to be verifiable and interpretable. It has to have the ability to prove the safety of the system, be very efficient, and run onboard the vehicle. The second core pillar was that the data is as important as the model. You will never be able to observe everything and fully test the system by deploying fleets of vehicles. So we built a best-in-class simulator, where we can actually prove its realism. And what differentiates your approach from the competition today? The big difference is that other players have a black-box architecture, where they train the system basically with imitation learning to imitate what humans do. Its very hard to validate and verify and impossible to trace a decision. If the system does something wrong, you cant really explain why that is the case, and its impossible to really have guarantees about the system. Thats okay for a level two system [where a human is expected to be able to take over], but when you want to deploy level four, without a human, that becomes a huge problem. We built something very different, where the system is forced to interpret and explain at every fraction of a second all the things it could do, and how good or bad those decisions are, and then it chooses the best maneuver. And then through the simulator, we can learn much better how to handle safety-critical situations, and much faster as well. How are you able to ensure the simulator works as well as real-world driving? The goal of the simulator is to expose the self-driving vehicles full stack to many different situations. You want to prove that under each specific situation, how the system drives is the same as if the situation happens in the real world. So we take all the situations where Waabi driver has driven in the real world, and clone them in simulation, and then we see, did the truck do the same thing. We also recently unveiled a really exciting breakthrough with mixed-reality testing. The way the industry does safety testing is they bring a self-driving vehicle to a closed course and they expose it to a dozen, maybe two dozen, scenarios that are very simple in order to say it has basic capabilities. Its very orchestrated, and they use dummies in order to test things that are safety critical. Its a very small number of non-repeatable tests. But you can actually do safety testing in a much better way if you can do augmented reality on the self-driving vehicle. With our truck driving around in a closed course, we can intercept the live sensor data and create a view where theres a mix of reality and simulation, so in real time, as its driving in the world, its seeing all kinds of simulated situations as though they were real. That way, you can have orders of magnitude more tests. You can test all kinds of things that are otherwise impossible, like accidents on the road, a traffic jam, construction, or motorbikes cutting in front of you. You can mix real vehicles with things that are not real, like an emergency vehicle in the opposite lane. Youre also a full professor. Are you still teaching and supervising graduate students? I do not teachI obviously do not have time to teach at all. I do have graduate students, but they do their studies at the company. We have this really interesting partnership with the University of Toronto. If you want to really learn and do research in self-driving, it is a must that you get access to a full product. And thats impossible in academia. So a few years ago, we designed this program where students can do research within the company. Its one of a kind, and to me, this is the future of education for physical AI. When did you realize the time was ripe for moving from academic research to industry work? That was about eight and a half years ago. We were at the forefront of innovation, and I saw companies were using our technology, but it was hard for me to understand if we were working on the right things and if there was something that I hadnt thought of that is important when deploying a real product in the real world. And I decided at the time to join Uber, and I had an amazing almost four years. It blew my mind in terms of how the problem of self-driving is much bigger than I thought. I thought, Okay, autonomy is basically it, and then I learned about how you need to design the hardware, the software, the systems around safety, etc., in a way that everything is scalable and efficient. It was very clear to me that end-to-end systems and foundational models would be the thing. And four and a half years in, our rate of hitting milestones really speaks to this technology. Its amazingto give an example, the first time that we drove in rain, the system had never seen rain before. And it drove with no interventions in rain, even though it never saw the phenomenon before. That for me was the “aha” moment. I was actually [in the vehicle] with some investors on the track, so it was kind of nerve-racking. But it was amazing to see. I always have very, very high expectations, but it blew my mind what it could do.
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E-Commerce
Amid an uncertain economythe growth of AI, tariffs, rising costscompanies are pulling back on hiring. As layoffs increase, the labor market cools, and unemployment ticks up, were seeing fewer people quitting their jobs. The implication: Many workers will be job hugging and sitting tight in their roles through 2026. Put more pessimistically: Employees are going to feel stuck where they are for the foreseeable future. In many cases, that means staying in unsatisfying jobs. Gallups 2025 State of the Global Workforce report shows that employee engagement has fallen to 21%. And a March 2025 study of 1,000 U.S. workers by advisory and consulting firm Fractional Insights showed that 44% of employees reported feeling workplace angst, despite often showing intent to stay. So if these employees are hugging their current roles, its not an act of affection. Its often in desperation. Being a job hugger means youre feeling anxious, insecure, more likely to stay but also more likely to want to leave, says Erin Eatough, chief science officer and principal adviser at Fractional Insights, which applies organizational psychology insights to the workplace. You often see a self-protective response: Nothing to see here, Im doing a good job, Im not leaving. This performative behavior can be psychologically damaging, especially in a culture of layoffs. If I was scared of losing my job Id try everything to keep it: complimenting my boss, staying late, going to optional meetings, being a good organizational citizen, says Anthony Klotz, professor of organizational behavior at the UCL School of Management in London. But we know that when people arent loving their jobs but are still going above and beyond, that its a one-way trip to burnout. The tight squeeze In cases where jobs arent immediately under threat, the effects of hugging are more likely to be slow burning. When an employees only motivation is to collect a consistent paycheck, discretionary effort drops. Theyre less productive. Engagement takes a huge hit. Over time, that gradually chips away at their well-being. Humans want to feel useful, that they care about the work theyre doing, and that theyre investing their time well, Eatough says. When efforts are low, that can impact a persons sense of value. The effects stretch beyond the workplace, too. Frustrated and reluctant stayers can quickly end up in a vicious cycle, Klotz says, noting, When youre in a situation that feels like its sucking life out of you, you end up ruminating about how depleting it is, then end up so tired that you dont have energy for restorative activities outside of work. So its this downward spiralyou begin your workday even more depleted. Longer term, job hugging stunts growth. When youre looking out for yourself, rather than the team or organization, your investment in working relationships begins to break down, Eatough says. Over time, staying in that situation means youre more likely to become deeply cynical, which hurts the individual and their career trajectory. When hugging becomes clinging Feeling stuck is nothing new. At some point in their careers, most workers will be in a situation where if they could leave for a better role, they would, says Klotz, who predicted the Great Resignation. But what distinguishes job hugging is that its anxiously clinging to a role during unfavorable labor markets. Its not that employees dont want to quitits that they cant. Its human nature that when theres a threat of any sort that we move away from it and towards stability, Klotz says. Your job represents that stability. And currently, its not a great time to switch jobs. There are few options for job huggers. The first is speaking up and working with a manager to improve the situation. But this might be unlikely for employees who feel trapped or lack motivation in the first place. Klotz says cognitive reframing can helpfocusing purely on the positive aspects of a draining role, such as a friendly team, and tuning out the rest. Finally, slowly backing away from extra tasksin other words, quiet quittingcould mean workers can redraw work-life boundaries in the interim at least. Otherwise, beyond Stoic philosophy or a benevolent boss, there is little choice but to wait it out. In some cases, a job hugger may eventually turn it around, ease their grip, and become quietly content in their role. But more often, wanting to quit usually leads to actually quitting. In effect, job hugging is damage control: hanging on until the situation changes. I think well see some people be resilient, wait it out, and find another role, Klotz says. But therell be others in the quagmire of struggling with exhaustion of spending eight hours a day in a job they dont like.
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E-Commerce
The rapid expansion of artificial intelligence and cloud services has led to a massive demand for computing power. The surge has strained data infrastructure, which requires lots of electricity to operate. A single, midsize data center here on Earth can consume enough electricity to power about 16,500 homes, with even larger facilities using as much as a small city. Over the past few years, tech leaders have increasingly advocated for space-based AI infrastructure as a way to address the power requirements of data centers. In space, sunshinewhich solar panels can convert into electricityis abundant and reliable. On November 4, 2025, Google unveiled Project Suncatcher, a bold proposal to launch an 81-satellite constellation into low Earth orbit. It plans to use the constellation to harvest sunlight to power the next generation of AI data centers in space. So instead of beaming power back to Earth, the constellation would beam data back to Earth. For example, if you asked a chatbot how to bake sourdough bread, instead of firing up a data center in Virginia to craft a response, your query would be beamed up to the constellation in space, processed by chips running purely on solar energy, and the recipe sent back down to your device. Doing so would mean leaving the substantial heat generated behind in the cold vacuum of space. As a technology entrepreneur, I applaud Googles ambitious plan. But as a space scientist, I predict that the company will soon have to reckon with a growing problem: space debris. The mathematics of disaster Space debristhe collection of defunct human-made objects in Earths orbitis already affecting space agencies, companies, and astronauts. This debris includes large pieces, such as spent rocket stages and dead satellites, as well as tiny flecks of paint and other fragments from discontinued satellites. Space debris travels at hypersonic speeds of approximately 17,500 mph in low Earth orbit. At this speed, colliding with a piece of debris the size of a blueberry would feel like being hit by a falling anvil. Satellite breakups and anti-satellite tests have created an alarming amount of debris, a crisis now exacerbated by the rapid expansion of commercial constellations such as SpaceXs Starlink. The Starlink network has more than 7,500 satellites providing global high-speed internet. The U.S. Space Force actively tracks more than 40,000 objects larger than a softball using ground-based radar and optical telescopes. However, this number represents less than 1% of the lethal objects in orbit. The majority are too small for these telescopes to identify and track reliably. In November 2025, three Chinese astronauts aboard the Tiangong space station were forced to delay their return to Earth because their capsule had been struck by a piece of space debris. Back in 2018, a similar incident on the International Space Station challenged relations between the U.S. and Russia, as Russian media speculated that a NASA astronaut may have deliberately sabotaged the station. The orbital shell Googles project targetsa sun-synchronous orbit approximately 400 miles above Earthis a prime location for uninterrupted solar energy. At this orbit, the spacecrafts solar arrays will always be in direct sunshine, where they can generate electricity to power the onboard AI payload. But for this reason, sun-synchronous orbit is also the single most congested highway in low Earth orbit, and objects in this orbit are the most likely to collide with other satellites or debris. As new objects arrive and existing objects break apart, low Earth orbit could approach Kessler syndrome. In this theory, once the number of objects in low Earth orbit exceeds a critical threshold, collisions between objects generate a cascade of new debris. Eventually, this cascade of collisions could render certain orbits entirely unusable. Implications for Project Suncatcher Project Suncatcher proposes a cluster of satellites carrying large solar panels. They would fly with a radius of just 1 kilometer, each node spaced less than 200 meters apart. To put that in perspective, imagine a racetrack roughly the size of the Daytona International Speedway, where 81 cars race at 17,500 mph while separated by gaps about the distance you need to safely brake on the highway. This ultradense formation is necessary for the satellites to transmit data to each other. The constellation splits complex AI workloads across all its 81 units, enabling them to think and process data simultaneously as a single, massive, distributed brain. Google is partnering with a space company to launch two prototypesatellites by early 2027 to validate the hardware. But in the vacuum of space, flying in formation is a constant battle against physics. While the atmosphere in low Earth orbit is incredibly thin, it is not empty. Sparse air particles create orbital drag on satellites; this force pushes against the spacecraft, slowing it down and forcing it to drop in altitude. Satellites with large surface areas have more issues with drag, as they can act like a sail catching the wind. To add to this complexity, streams of particles and magnetic fields from the sunknown as space weathercan cause the density of air particles in low Earth orbit to fluctuate in unpredictable ways. These fluctuations directly affect orbital drag. When satellites are spaced less than 200 meters apart, the margin for error evaporates. A single impact could not only destroy one satellite but also send it blasting into its neighbors, triggering a cascade that could wipe out the entire cluster and randomly scatter millions of new pieces of debris into an orbit that is already a minefield. The importance of active avoidance To prevent crashes and cascades, satellite companies could adopt a leave no trace standard, which means designing satellites that do not fragment, release debris, or endanger their neighbors, and that can be safely removed from orbit. For a constellation as dense and intricate as Suncatcher, meeting this standard might require equipping the satellites with reflexes that autonomously detect and dance through a debris field. Suncatchers current design doesnt include these active avoidance capabilities. In the first six months of 2025 alone, SpaceXs Starlink constellation performed a staggering 144,404 collision-avoidance maneuvers to dodge debris and other spacecraft. Similarly, Suncatcher would likely encounter debris larger than a grain of sand every five seconds. Todays object-tracking infrastructure is generally limited to debris larger than a softball, leaving millions of smaller debris pieces effectively invisible to satellite operators. Future constellations will need an onboard detection system that can actively spot these smaller threats and maneuver the satellite autonomously in real time. Equipping Suncatcher with active collision-avoidance capabilities would be an engineering feat. Because of the tight spacing, the constellation would need to respond as a single entity. Satellites would need to reposition in concert, similar to a synchronized flock of birds. Each satellite would need to react to the slightest shift of its neighbor. Paying rent for the orbit Technological solutions, however, can go only so far. In September 2022, the Federal Communications Commission created a rule requiring satellite operators to remove their spacecraft from orbit within five years of the missions completion. This typically involves a controlled de-orbit maneuver. Operators must now reserve enough fuel to fire the thrusters at the end of the mission to lower the satellites altitude, until atmospheric drag takes over and the spacecraft burns up in the atmosphere. However, the rule does not address the debris already in space, nor any future debris, from accidents or mishaps. To tackle these issues, some policymakers have proposed a use tax for space debris removal. A use tax or orbital-use fee would charge satellite operators a levy based on the orbital stress their constellation imposes, much like larger or heavier vehicles paying greater fees to use public roads. These funds would finance active debris-removal missions, which capture and remove the most dangerous pieces of junk. Avoiding collisions is a temporary technical fix, not a long-term solution to the space debris problem. As some companies look to space as a new home for data centers, and others continue to send satellite constellations into orbit, new policies and active debris-removal programs can help keep low Earth orbit open for business. Mojtaba Akhavan-Tafti is an associate research scientist at the University of Michigan. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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