Generative AI in robotics market to hit $11.9 billion by 2030

Microchip Technology has been selected by AnalogAI to supply its memBrain SAGE neuromorphic hardware IP for use in new edge AI processors. The collaboration centers on compute-in-memory technology targeting training and inference for adaptive robots, drones and vehicles, with AnalogAI planning to integrate memBrain SAGE into real-world, low-power AI systems designed for deployment at the network edge. Microchip Technology is a US-based semiconductor supplier with a roughly $38.5b market cap focused on smart, connected and secure embedded control solutions. The design win supports the edge-computing catalyst in the company's narrative, though analysts flag execution risk from competition, including edge-AI platforms from Nvidia and Qualcomm.

Humanoid robots are making meaningful progress in autonomy, dexterity and repetitive real-world tasks, but widespread commercial deployment remains a work in progress, according to a report published Wednesday by Citrini Research. After a week meeting robotics labs, engineers and investors across the San Francisco Bay Area, the firm found most humanoid robots remain in development, training and tele-operated demonstrations, with real-world deployments closer to pilots than clear return-on-investment opportunities. The report examined whether robotics is approaching its own ChatGPT moment and highlighted advances from GeneralistAI and Skild AI, along with Figure AI's demonstration involving 250,000 packages, suggesting the field may advance through a mosaic of signals rather than one defining breakthrough. Basic humanoid locomotion is effectively a solved problem while complex dexterity remains more difficult, and simple repetitive tasks are already trainable. Investment in the broader robotics infrastructure is keeping pace: Tesla Inc has been preparing production lines for Optimus, XPeng Inc has launched an automated production line for its IRON humanoid robot, and Figure AI is securing access to up to 100,000 Vera Rubin GPUs from NVIDIA Corp to train the models powering its humanoid robots. The report concluded robotic autonomy is developing along a curve rather than arriving through one decisive breakthrough, with broad generalizability remaining the longer-term goal.

Gao Yang, co-founder and chief scientist of Chinese startup Spirit AI, told Reuters that humanoid robots capable of handling most basic tasks on verbal command could become a reality as early as next year. "By mid-2027, you will be able to speak to a robot in natural language and it will complete a series of actions to carry out a task," he said, expressing confidence in the pace of software advances. The company has already achieved a roughly 90 percent success rate on simple tasks, but challenges remain with fine movements such as loosening a bottle cap and with handling unexpected situations, and adoption in ordinary households is still expected to take time. About 1,000 contract workers at the company wear data-collection devices at home and in factories, gathering movement data to train the robots. Spirit AI was founded in 2024 and has about 300 full-time employees. It has raised more than 670 million dollars to date and has deployed dozens of humanoid robots at battery giant CATL and online retail giant JD.com.