Agibot moves toward a ten-billion-yuan revenue target; humanoid robot scale-up will depend on orders and task data
Agibot has set a revenue target of 10 billion yuan for 2027, ten times the 1 billion yuan achieved in 2025. Xiangcai Securities is optimistic about humanoid robots as a practical application of physical AI. In the near term, it is focused on mass production of components; long-term competition will depend on whether model capabilities and real-world operational data can reinforce each other.
Humanoid robots are moving from demonstrations of mobility to deployment on real tasks. Whether Agibot’s expansion targets translate into opportunities across the supply chain will depend on mass-production orders and the continuous iteration of models and data from physical robots.
From 1 billion yuan to 10 billion yuan: what expansion targets has Agibot set?
At its partner conference on 17 April 2026, Agibot disclosed that revenue had reached 1 billion yuan in 2025. It also set targets of 10 billion yuan in revenue for 2027 and more than 100 billion yuan for 2030. Using actual 2025 revenue as the baseline, the 2027 target implies a tenfold increase in scale over two years.
The plan gives the supply chain a direction for capacity expansion, but mass production and orders stand between the targets and revenue for suppliers. The material describes commercialization as being in the early “1–10” scale-up stage; Xiangcai Securities also identifies the pace of mass production and order validation as the most direct variables for the supply chain.
As robots move from being able to move to being able to work, how are the bottlenecks changing?
Xiangcai Securities sees humanoid robots as an important practical application of physical AI, with the key challenge being to turn visual understanding, spatial reasoning and language instructions into physical actions and the ability to perform tasks. The industry’s focus has therefore expanded from mechanical structures, mobility and hardware costs to whether sufficient real-task data is available and how robots can complete work autonomously.
Model iteration and the feedback of data from physical robots are mutually dependent: deployment across more productive-use scenarios requires breakthroughs in intelligence as well as real-task data to support training. Under the industry development path outlined by Agibot, “moving like a human” is only an initial trial stage; expanding deployment thereafter will depend on improvements in intelligence driven by a data flywheel.
Why should supply-chain opportunities be assessed separately over the short and long term?
Xiangcai Securities believes that segments with greater certainty of benefiting in the near term include dexterous hands, reducers, lead screws, joint modules, perception hardware and data collection equipment. The link is demand for this hardware from mass production of robots; realizing the opportunity will still depend on actual production scale-up and orders.
Over the medium to long term, the firm is more optimistic about robot manufacturers that lead in embodied foundation-model capabilities. Midstream companies must advance hardware iteration while improving software capabilities in parallel; the material suggests that companies capable of continuous mass production are better placed to meet demand as volumes increase.
Among the partnerships cited in the material, Longcheer Technology participated in Agibot’s Series B financing and is working with Agibot on industrial applications. Its internal teams are also researching the use of robotics and AI to upgrade production lines. The current collaboration involves jointly exploring applications at scale, with the specific business connection in factory settings; the material provides no figure for orders or revenue already generated.
