Raquel Urtasun has spent 16 years in the self-driving space, long enough to navigate every metaphorical glorious hill and plunging valley. She took the trip from the early “pipe dream” dismissals, to the “we’re this close” certainty, and back again.

The industry is now riding a new wave of optimism and investment, including at Waabi Innovation Inc., the autonomous trucking company that Urtasun founded in 2021. The Spanish-Canadian professor at the University of Toronto, and former chief scientist of Uber’s Advanced Technologies Group, has helped make Waabi a key player. Beginning in fall 2023, theToronto-based startup has been running geofenced cargo routes from Dallas to Houston in a fleet of retrofitted Peterbilt semis, navigating even residential streets in loaded, 36,000-kilogram (80,000-pound) behemoths with a human “safety observer” on board.

In October, the company reached a milestone by integrating its “Waabi Driver” physical-AI system in Volvo’s new VNL Autonomous truck, which the Swedish automaker is building in Virginia. That self-driving solution uses Nvidia’s Drive AGX Thor, an AI-based platform for autonomous and software-defined vehicles.

In January, the Toronto-based startup raised $750 million in its latest funding round to accelerate commercial development in autonomous trucking, and expand its system into the fiercely competitive robotaxi space. Backers include Khosla Ventures, Nvidia, and Volvo.

Urtasun says the Waabi Driver can scale across a full range of vehicles, geographies and environments—although snowstorms can still create a no-go zone for now. It’s powered by what Urtasun calls the industry’s most advanced neural simulator. The verifiable, end-to-end AI model will be a “shared brain” that partners can transplant into cars, trucks, and pretty much anything on wheels. The idea is to grab a chunk of a global autonomous trucking business that McKinsey estimates could be worth more than $600 billion a year by 2035; with autonomous haulers responsible for 15 percent of total U.S. trucking miles as early as 2030.

Backed by an additional $250 million from Uber, Waabi plans to deploy at least 25,000 autonomous taxis through Uber’s ride-hailing service, whose world-dominating reach encompasses 70 countries, about 15,000 cities and more than 200 million monthly users.

Urtasun spoke with IEEE Spectrum about how Waabi is counting on sensors and simulation to prove real-world safety; and why the move to autonomy is a moral imperative that outweighs the disruption for human drivers—whether they’re driving trucks or family sedans. Our conversation was edited for length and clarity.

IEEE Spectrum: Until quite recently, autonomous tech seemed to have hit a wall, at least in the public’s mind. Now investors are flooding the zone again, and companies are all-in. What happened?

Raquel Urtasun: There were a lot of empty promises, or [people] not realizing the complexity of the problem. There was a realization that actually, this problem is harder than people anticipated. It’s also because of the type of technology that was developed at the time, what we call “AV 1.0”. These are hand-engineered systems that need to be brute-forced by humans. You need lots of capital and a massive amount of miles on the road just to get to the first deployment.

What you see with the next generation—AV 2.0 and systems that can reason—is that you finally have a solution that scales. When we started the company, this was a very contrarian view. But today, the breakthroughs in AI have made it clear that this is the next big revolution. It’s not just about more compute; it’s about building a brain that can generalize. That is the “aha moment” the industry is having now.

Even for someone who believes in the tech, seeing a driverless semi-trailer in your rear-view mirror might be unsettling. Now you’ve integrated your tech into the aerodynamic, diesel-powered Volvo VNL Autonomous truck. How do you convince regulators and the public that these trucks belong on the street?

Urtasun: Safety, when you think about carrying 80,000 pounds on this massive rig, is definitely top of mind. We believe the only way to do this safely is with a redundant platform that is fully developed and validated by the OEM, not with a retrofit. The OEM does a special type of truck that has all the redundant steering, power, and braking, so that no matter what happens, there is always a way we can interface and activate that truck in a safe manner. Then we are responsible for the sensors, the compute, and obviously the brain that drives those trucks.

AI’s Impact on Trucking Jobs

One of the biggest points of contention is the displacement of human drivers. As AI disrupts a range of workplaces, how do respond to people who say this will eliminate good-paying, blue-collar jobs?

Urtasun: The way we see this is that everybody who’s a truck driver today, and wants to retire as a truck driver, will be able to do so. This is physical AI; this is not like the digital world where suddenly you can switch immediately to this technology. That adoption and scaling is going to take time. There will also be many jobs created with this technology; remote operations, terminal operations, and other things. You have time to change the form of labor of being on the road, which is for weeks at a time—and it’s a really difficult and dehumanized job, let’s be honest—to something you can do locally. There was an interesting [U.S.] Department of Transportation study that showed because of this gradual adoption, there will be more jobs created than actually removed.

You’ve spoken about a personal motivation behind this. Why do you believe the advantages of autonomy outweigh any growing pains, including the potential for unexpected accidents or even deaths?

Urtasun: There are 2 million deaths on the road globally per year, and nobody’s questioning that. That’s the status quo. If you think the machines have to be perfect to deploy, you are actually sacrificing many humans along the way that you could have saved. Human error in accidents is between 90 percent and 96 percent. Those could be preventable accidents. Some accidents will always be unavoidable; a tire could blow for a machine the same as it could for a human. But the important comparison is how much safer we are. This technology is the answer to many, many things.

Most of the industry is focused on “hub-to-hub” highway driving. But you’ve argued that Waabi’s AI can handle the complexity of local streets.

Urtasun: The rest of the industry has gone with this business model where you need hubs next to the highway. This adds a lot of friction and cost. Thanks to our verifiable end-to-end AI system, we can drive in surface [local] streets. We can do unprotected lefts, traffic lights, and tight turns. These core capabilities enable us to drive all the way to the end customer. We are already hauling commercial loads for customers like Samsung through our Uber Freight partnership.

You’ve mentioned that Waabi doesn’t like to talk about “number of miles” driven as a metric. For an engineering audience, that sounds counterintuitive. How does your “simulation-first” approach replace the need for real-world road time?

Urtasun: In the industry, miles have been used as a proxy for advancement. How many miles does Tesla need to drive to see any of these situations? But we are a simulation-first company. Waabi World can simulate all the sensors, the behaviors of humans, everything. It is the only simulator where you can mathematically prove that testing and driving in simulation is the same as driving in the real world. You can expose the system to billions of simulations in the cloud. This is what allows us to be so capital efficient and fast.

Verifiable AI vs. Black Box Systems

What is the difference between your “interpretable” AI and the “black box” systems we see elsewhere?

Urtasun: We’ve seen an evolution on passenger cars for level– 2+ systems to end-to-end, black box architectures. But those are not verifiable. You cannot validate and verify those systems, which is a massive problem when you think about regulators and OEMs trusting that technology.

What Waabi has built is end-to-end, but fully verifiable. The system is forced to interpret what it is perceiving and use those interpretations for reasoning, so that it can understand the consequences of every action. It is much more akin to how our brain actually works; your “Type 2” thinking, where you start thinking about cause and effect and consequences, and then you typically do a much better choice in your maneuver.

Tesla is famously, and controversially, relying on camera data almost exclusively to run and improve its self-driving systems. You’re not a fan of that approach?

Urtasun: We use multiple sensors: lidar, camera, and radar. That’s very important because failure modes of those sensors are very different and they’re very complementary. We don’t compromise safety to reduce the bill- of- materials cost today.

Those (passenger car) level-2+ systems are not architected for level 4, where there’s no human on board. People don’t necessarily realize there is a huge difference in terms of the bar when there is no human to rely on. It’s not, “Well, if I don’t have a lot of system interventions, I’m almost there.” That’s not a metric. We are native level 4. We decide which areas the system can drive in, and in what conditions. We are building technology that can drive different form factors—trucks or robotaxis—with the same brain.

Editor’s note: This article was updated on 13 March to correct an error in the original post. Contrary to what was stated in the original post, the trucks being driven from Dallas to Houston do have a human observer on board.

In October 2025, Atlassian co-founder and CEO Mike Cannon-Brookes appeared on the 20VC podcast and said something that now reads rather differently. Technology creation, he argued, is ‘not output-bound.’ Atlassian would employ more engineers in five years, not fewer. They would just be more efficient.

On 11 March 2026, Mike Cannon-Brookes sent a memo to staff announcing 1,600 redundancies, approximately 10% of the company’s global workforce, framed as a necessary adaptation to the AI era.

The same week, the company’s shares had lost more than half their value since January, swept up in a sector-wide rout that traders have been calling the ‘SaaSpocalypse’: a sustained selloff in enterprise software stocks driven by investor fears that AI agents could make conventional SaaS tools obsolete.

The stated rationale in the memo was future-facing. Atlassian said the cuts would allow it to ‘self-fund further investment in AI and enterprise sales, while strengthening our financial profile.’ Cannon-Brookes was careful in his framing.

“Our approach is not ‘AI replaces people.’ But it would be disingenuous to pretend AI doesn’t change the mix of skills we need or the number of roles required in certain areas. It does. This is primarily about adaptation. We are reshaping our skill mix and changing how we work to build for the future.”

More than 900 of the affected roles are in software research and development, according to reporting from Implicator.ai.

By geography, North America bears the largest share, 40% of cuts, or around 640 people, followed by Australia at 30% (roughly 480), and India at 16% (around 250). The remainder is spread across Japan, the Philippines, and offices across Europe, the Middle East, and Africa. Atlassian filed a WARN notice in Washington state showing 63 affected workers, the majority of them remote employees in engineering and data science roles.

Departing employees will receive a minimum 16-week separation package, with one additional week per year of service, a pro-rated FY26 bonus, a $1,000 technology payment, and six months of extended healthcare coverage.

The restructuring is expected to cost between $225 million and $236 million in total charges, split between severance ($169–174 million) and office space reductions ($56–62 million), with most costs landing in the company’s third fiscal quarter. Atlassian said the cuts should be largely complete by the end of June 2026.

Alongside the layoffs, Atlassian confirmed that Rajeev Rajan will step down as chief technology officer on 31 March, after nearly four years in the role. Rajan, previously a VP of engineering at Meta and a two-decade veteran of Microsoft, has not publicly commented on the departure.

The company described it as a generational transition: two executives, Taroon Mandhana and Vikram Rao, will split the CTO responsibilities going forward. Mandhana, formerly Atlassian’s head of engineering for AI and products, takes the title of CTO Teamwork; Rao, previously chief trust officer, becomes CTO Enterprise and Chief Trust Officer. Atlassian described both as ‘next generation AI talent.’

The restructuring sits in some tension with Atlassian’s underlying trading performance. The company reported cloud revenue of approximately $1.067 billion in its most recent quarter, up 26% year on year. Remaining performance obligations, a forward indicator of contracted future revenue, stood at roughly $3.814 billion, up 44%.

Its Rovo AI assistant crossed five million monthly active users in February, and the company now counts more than 600 customers generating over $1 million in annual recurring revenue.

Atlassian reaffirmed its full-year financial guidance for the fiscal year ending June 2026 in the same SEC filing that disclosed the cuts.

That combination, strong operational metrics, healthy forward revenue, and 1,600 redundancies, has sharpened scrutiny of what is actually driving these decisions across the industry.

OpenAI CEO Sam Altman described the practice of using AI as justification for cuts made for other reasons as ‘AI washing’ in February, noting that fewer than 1% of 2025 job losses could be directly attributed to artificial intelligence.

Atlassian is not alone in the pattern.

Block cut roughly 4,000 employees last month as Jack Dorsey declared a shift to an ‘intelligence-native’ model, and the stock jumped the day after the announcement. WiseTech Global, another Sydney-based software firm, announced 2,000 cuts over two years. Oracle said AI was enabling it to shrink some development teams.

By early March 2026, tech layoffs globally had surpassed 45,000, according to RationalFX, with AI among the most frequently cited justifications. Whether the technology is genuinely driving workforce changes or serving as a convenient explanation for restructuring decisions shaped primarily by investor pressure is a question that has no clean industry-wide answer, and that Atlassian’s own numbers make harder, not easier, to resolve.

Atlassian has been unprofitable since 2017. Its shares were down around 33% for 2025 before the SaaSpocalypse selloff, and have fallen more than 84% from the peak they reached in 2021 during the pandemic-era surge in cloud-based collaboration tools.

The stock rose around 2% in after-hours trading following the restructuring announcement, the same market logic that rewarded Block, and that Cannon-Brookes might have had in mind when he wrote, with notable precision, about the bar for what ‘great’ looks like for software companies having gone up.

Google is rolling out one of the biggest updates to Google Maps in years, powered by its Gemini AI models.

The changes introduce a new conversational feature called Ask Maps. There is also a redesigned navigation experience that Google says is its largest driving upgrade in more than a decade.

Ask Maps turns the app into something closer to a travel assistant than a traditional map. Instead of searching for individual places, users can ask natural questions such as where to charge a phone nearby without waiting in line. Or, for genuine convenience, users can ask where to find a public tennis court with lights at night. Maps then responds conversationally, showing recommendations directly on the map.

Ever had one of those mornings where your hair simply refuses to cooperate, leaving you wrestling with frizz or uneven strands while the clock keeps ticking before work, school, or a night out?

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The ghd Original drops 30%, making it an easy deal-of-the-day pick

The ghd Original is now over $45 off, ideal for shoppers who want salon‑quality styling without overspending.

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Although many people buy this ghd primarily to straighten hair, its rounded barrel design also allows the straightener to twist slightly while styling, making it easy to create soft curls or loose waves.

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