Robot Brains Could Have Their ‘ChatGPT Moment’ by 2027, ACE Robotics Chairman Says

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In brief ACE Robotics Chairman Wang Xiaogang predicts embodied AI could reach its “ChatGPT moment” by the end of 2027.

A shortage of real-world training data remains a major obstacle, with ACE aiming to collect tens of millions of hours within two years.

The Chinese startup plans to deploy its technology in 1,000 stores over the next year and eventually pursue an IPO.

Humanoid robots can walk, dance, and box, but getting them to perform useful work in the unpredictable physical world reliably remains one of artificial intelligence’s biggest challenges.

ACE Robotics Chairman Wang Xiaogang believes advances in AI models and real-world training data could soon give robots the intelligence needed to move beyond demonstrations and into commercial use, according to a report by Reuters.

“We expect to reach the ‘ChatGPT moment’ for embodied intelligence by the end of next year, driven by world models and environmental data capture,” Wang told Reuters.

Founded in July 2025, ACE Robotics is a Chinese startup developing AI models for humanoid robots. Backed by Ant Group and SenseTime, it raised more than $100 million in the first half of 2026 and, according to Reuters, plans to pursue an IPO “as early as permitted.”

While large language models such as ChatGPT and DeepSeek have spread rapidly, robots still struggle to perform a wide range of tasks in unfamiliar environments, and a lack of training data remains a major obstacle.

Embodied AI enables robots and other physical agents to perceive their environment, reason about it, and convert decisions into actions through sensors and actuators. At the same time, world models help AI understand how the physical world works by learning how objects and environments behave.

For robots, that means anticipating what might happen when they move, pick something up, or interact with their surroundings before taking an action.

“Over the past few years, the entire industry has accumulated data of roughly 100,000 hours, which is far from enough to train embodied foundation models,” Wang said.

Researchers elsewhere are experimenting with similar approaches. In October, researchers unveiled HumanoidExo, a wearable exoskeleton that captures human movements to train humanoid robots.

Other companies are developing their own AI models for robots.

In January, Boston Dynamics unveiled the production version of its Atlas humanoid, saying advances in AI helped bring the robot closer to commercial deployment. In June, Alibaba introduced its Qwen-Robot Suite, a set of AI models designed to help robots navigate, perform physical tasks, and simulate real-world environments.