A robotics startup called Generalist AI, based in Cambridge, Massachusetts, has demonstrated robot arms capable of learning new physical tasks almost instantly, after watching a single short instructional video rather than undergoing lengthy task-specific training. A Wired journalist witnessed the robots improvising solutions on the fly, such as using a dustpan alone to sweep a block into a bowl after its brush was removed, and switching grippers mid-task to get a better grip on banknotes inside a purse. This kind of adaptable, human-like physical reasoning is significant because robots have traditionally struggled to generalise beyond narrow training scenarios, often failing when conditions like lighting change.

Founded by former Google DeepMind and Boston Dynamics researchers Pete Florence, Andrew Barry and Andy Zeng, Generalist trains its models using specially built gripper-style gloves fitted with cameras, worn by human workers performing everyday chores; the company has shipped several hundred of these devices to contractors in Mexico and elsewhere to gather training data. Unlike some rivals, Generalist has built its AI models entirely from scratch rather than adapting an existing open-source language model, and the founders remain guarded about the precise training methods behind the system's apparent ability to reason intuitively about physics and transfer skills between tasks.