Robot brain builders are pushing out of their GPT-2 era

Physical AI is one of the hottest sectors in venture investing, with companies raising billions to apply the tools that gave us Large Language Models to robotics.

That excitement helped deliver a big IPO for Unitree, China’s leading robot maker, which saw the company valued at $66 billion after its arrival on China’s equivalent of the NASDAQ. This week, however, the bottom fell out, and the company lost nearly half of its value. Analysts point to one obvious issue: While the robots’ physical capabilities are improving, they still lack the know-how to actually do value-creating work.

At last week’s Actuate conference, a gathering of developers building AI brains for robots, the excitement was clear. The event has tripled in size since it kicked off in 2023, and had 1500 attendees, according to the organizer, Foxglove, a company that helps physical AI model builders manage and visualize their data.

The risk was also evident: A sign on a booth for Avala, another physical AI infrastructure player, promised to solve “the robotics data crisis.”

That crisis is the lack of high-quality training data for AI models. Attempts to build generalized robots that can do any task are still far off, and using end-to-end learning for specific tasks still hasn’t delivered products with reliable, commercial performance. For developers, the answer is to better mimic the advances of the frontier AI labs—find or create more diverse data sets, mess with different training regimes, and figure out better reinforcement learning scenarios.