Nearly two years ago, Motional was at an autonomous vehicle crossroads. 

The company, born from a $4 billion joint venture between Hyundai Motor Group and Aptiv, had already missed a deadline to launch a driverless robotaxi service with partner Lyft. It had lost Aptiv as one of its financial backers, prompting Hyundai to step up with another $1 billion investment to keep it going. Several layoffs, including a 40% restructuring cut in May 2024, had whittled the company from its peak of about 1,400 employees to less than 600. Meanwhile, advancements in AI were changing how engineers were developing the technology. 

Motional was going to have to evolve or die. It paused everything and picked option No. 1.

Motional told TechCrunch it has rebooted its robotaxi plans with an AI-first approach to its self-driving system and a promise to launch a commercial driverless service in Las Vegas by the end of 2026. The company has already opened up a robotaxi service — with a human safety operator behind the wheel — to its employees. It plans to offer that service to the public with an unnamed ride-hailing partner later this year. (Motional has existing relationships with Lyft and Uber.) By the end of the year, the human safety operator will be pulled from the robotaxis and a true commercial driverless service will begin, the company said.

“We saw that there was tremendous potential with all the advancements that were happening within AI; and we also saw that while we had a safe, driverless system, there was a gap to getting to an affordable solution that could generalize and scale globally,” Motional president and CEO Laura Major said during a presentation at the company’s Las Vegas facilities. “And so we made the very hard decision to pause our commercial activities, to slow down in the near term so that we could speed up.”

This meant shifting away from its classic robotics approach to an AI foundation model-based one. Motional was never devoid of AI. Motional’s self-driving system used individual machine learning models to handle perception, tracking, and semantic reasoning. But it also used more rules-based programs for other operations within the software stack. And the individual ML models made it a complex web of software, Major said.

Meanwhile, AI models originally built for language began to be applied in robots and other physical AI systems, including the development of autonomous driving. That transformer architecture made it possible to build large and complex AI models, ultimately leading to the emergence, and skyrocketing use, of ChatGPT. 

Motional searched for ways to combine these smaller models and integrate them into a single backbone, allowing for an end-to-end architecture. It has also maintained the smaller models for developers, which Major explained gives Motional the best of both worlds.

“This is really critical for two things; One is for generalizing more easily to new cities, new environments, new scenarios,” she said. “And the other is to do this in a cost optimized way. So for example, the traffic lights might be different in the next city you go to, but you don’t have to redevelop or re-analyze those. You just collect some data, train the model, and it’s capable of operating safely in that new city.”

TechCrunch got a first-hand look at Motional’s new approach during a 30-minute autonomous drive around Las Vegas. One demo can’t provide an accurate assessment of a self-driving system. It can, however, pinpoint weaknesses and differences from previous iterations, and gauge progress. 

Progress is what I saw as the Hyundai Ioniq 5 I rode in autonomously navigated its way off Las Vegas Boulevard and into the pickup and drop-off area of the Aria Hotel. These bustling areas are notorious in La Vegas and my experience was no different as the autonomous vehicle slowly nudged its way around a stopped taxi and unloading passengers, changed lanes, then back again, passing dozens of people, giant flower pots, and cars along the way.

Motional previously operated a ride-hailing service in Las Vegas with partner Lyft using vehicles that would autonomously handle portions of a ride. Parking lots and hotel valet and app ride pickup areas were never part of those operations. A human safety operator, always behind the wheel, would take over to navigate parking lots or the busy pickup and drop-off points of hotel lobbies.

There is still more progress to be made. The graphics displayed to riders within the vehicle are still under development. And while there was never a disengagement during my demo ride — which means the human safety operator takes over — the vehicle did take its time to nudge itself around a double parked Amazon delivery van.

Still, Major argues Motional is on the right path to deploy safely and cost effectively. And its majority owner Hyundai is in it for the long haul, she said.

“I think the real long-term vision, you know, for all of this, is putting Level 4 on people’s personal cars,” Major said, referring to a term that mean the system handles all driving with no expectation of human intervention. “Robotaxis, that’s stop number one, and huge impact. But ultimately, I think any OEM would love to also integrate that into their cars.”

The nuclear industry is in the mist of a renaissance. Old plants are being refurbished, and investors are showering startups with cash. In the last several weeks of 2025 alone, nuclear startups raised $1.1 billion, largely on investor optimism that smaller nuclear reactors will succeed where the broader industry has recently stumbled.

Traditional nuclear reactors are massive pieces of infrastructure. The newest reactors built in the U.S. — Vogtle 3 and 4 in Georgia — contain tens of thousands of tons of concrete, are powered by fuel assemblies 14 feet tall, and generate over 1 gigawatt of electricity each. But they were also eight years late and more than $20 billion over budget.

The fresh crop of nuclear startups hopes that by shrinking the reactor, they’ll be able to sidestep both problems. Need more power? Just add more reactors. Smaller reactors, they argue, can be built using mass production techniques, and as companies produce more parts, they should get better at making them, which should drive down costs.

The magnitude of that benefit is something experts are still researching, but today’s nuclear startups are depending on it being greater than zero.

But manufacturing isn’t easy. Just look at Tesla’s experience: The company struggled mightily to profitably produce the Model 3 in large numbers — and it had the benefit of being in the automotive industry, where the U.S. still has significant expertise. U.S. nuclear startups don’t have that advantage.

“I have a number of friends who work in supply chain for nuclear, and they can rattle off like five to ten materials that we just don’t make in the United States,” Milo Werner, general partner at DCVC, told TechCrunch. “We have to buy them overseas. We’ve forgotten how to make them.”

Werner knows a thing or two about manufacturing. Before becoming an investor, she worked at Tesla leading new product introduction, and before that, she did the same at FitBit, launching four factories in China for the wearables company. Today, in addition to investing at DCVC, Werner has co-founded the NextGen Industry Group, which works to advance the adoption of new technologies in the manufacturing sector.

When companies of any size want to manufacture something, they face two main challenges, Werner said. One is capital, which is often the biggest constraint since factories aren’t cheap. Fortunately for the nuclear industry, that shouldn’t pose much of a problem. “They’re awash in capital right now,” she said.

But the nuclear industry isn’t immune from the other challenge all manufacturers face, which is a lack of human capital. “We haven’t really built any industrial facilities in 40 years in the United States,” Werner said. As a result, we’ve lost the muscle memory. “It’s like we’ve been sitting on the couch watching TV for 10 years and then getting up and trying to run a marathon the next day. It’s not good.”

After decades of offshoring, the U.S. lacks people experienced with both factory construction and operations. “There are for sure some people in the United States who have been doing this, but we don’t have the quantum of people that we need for everybody to have a full staff of seasoned manufacturing people.” She not just talking about machine operators, but everyone from factory floor supervisors all the way up to CFOs and board members.

The good news is that Werner sees a lot of startups, nuclear and otherwise, building early versions of their products in close proximity to their technical team. “That is pulling manufacturing in closer to the United States because it allows them to have that cycle of improvement.”

To reap the benefits of mass manufacturing, it’s helpful for startups of all stripes to start small and scale up. “Really leaning into modularity is very important for investors,” she said. The modular approach helps companies start producing small volumes early on so they can collect data on the manufacturing process. Ideally, that data will show improvement over time, which can put investors at ease. 

The benefits of mass manufacturing don’t happen overnight. Companies will often forecast cost reductions that can result from learning through manufacturing, but it might take longer than they expect. “Often it takes years, like a decade, to get there,” Werner said.