China's Humanoid Boom: When Will Robots Get Their ChatGPT Moment?

Editor's note: this report was produced by TechNode reporter Lucia.

For many people, the first humanoid robot they ever saw was performing: dancing, flipping, or running through a rehearsed routine. The industry's central challenge now is turning those audience-pleasing demonstrations into machines that do useful work in ordinary settings.

That is the test facing China's fast-growing humanoid sector. As companies shift from stage shows toward commercial deployment, they are probing how these robots can take on practical roles in workplaces, services and other real-world environments.

Three forces give China an early edge: cost, capital and a large testing ground for commercialization. Its manufacturing base, reinforced by the EV supply chain for batteries and sensors, lets robot makers source hardware locally, iterate faster and cut prices. Counterpoint Research estimates that in the first half of 2026 five Chinese companies accounted for 86% of global humanoid shipments.

Funding is another factor. XPeng's robotics unit raised more than $900 million in August at a valuation above $6.3 billion. Galbot raised RMB 2.5 billion ($362 million) in March, after a round of more than $300 million in late 2025 valued it at roughly $3 billion.

China also offers a wide range of potential markets, with humanoids deployed or tested in automotive manufacturing, electronics, logistics, aerospace and energy. Commercialization remains early, though: deployments span purchases, preorders and pilots, while large-scale repeat orders are still limited.

The hard part of the robot boom

Chinese humanoid makers still face open questions over AI systems and integrated software. Vision-language-action models and world models are early-stage, and Nvidia leads with an end-to-end robotics software stack, leaving many Chinese startups reliant on its Orin chips even as domestic chipmakers develop alternatives.

The tougher test is converting physical capability into sustainable commercial demand. Jiang Han, a senior researcher at the Pangoal Institution, told TechNode that repeat orders and customer payback periods are key signs that robots are solving real problems rather than merely attracting trial use. China's longer-term advantage, he said, lies not in low manufacturing costs alone but in pairing supply-chain cost advantages with rapid hardware and algorithm iteration.

Data remains another bottleneck. Unlike large language models, robot developers cannot scrape the internet for examples of physical interaction. They are turning instead to synthetic data, simulation, reinforcement learning and real-world deployments. Harry Mellsop, co-founder of Antioch, a startup building simulation tools for physical AI, has described physical AI as being in its 'GPT-2 era' — referring to the OpenAI model that predated ChatGPT — with more data and computing power still needed.

Reliability can break an otherwise impressive demo. Speaking at BEYOND Expo, Fu Sheng, chairman and CEO of Cheetah Mobile and chairman of service robotics company OrionStar, said robotics' hardest challenge is 'the last 1%': a 99% success rate still means one failure in every 100 attempts. Commercial robots, he argued, must prove they can run reliably and efficiently over long periods before they become useful workers.

Safety and security add another hurdle. A high-profile accident could trigger public backlash as deployment accelerates. Jiang also points to supply-chain vulnerabilities and data-security compliance as gaps Chinese robot makers still need to close, particularly in European and U.S. markets.

Yuli Zhao, chief strategy officer at Galbot, a Chinese humanoid robotics startup focused on embodied intelligence and commercial deployment, expects demand to emerge first in manufacturing, warehouse logistics and retail — settings where tasks are repetitive and workflows clearly defined. Those conditions, he said, create real demand and give humanoid robots a better chance to deliver value at scale.

Fu makes a similar case for specialization. Rather than chasing a general-purpose robot, he argues that companies may find more commercial value in well-defined jobs such as agriculture, transport and sorting, then improving reliability, efficiency and deployment around those tasks.