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.