36Kr's feature on Zhiyuan Robotics' Luo Jianlan argues that the "Scaling Law" for embodied intelligence is not "stack more parameters" but "deployment-data-iteration" — a flywheel where each deployment generates data, which trains better models, which enable more deployments. This is a contrarian view in a year dominated by "bigger models" narratives.
The "flywheel" mechanism: Zhiyuan has deployed ~5,000 robots across Chinese factories, each generating ~10 hours of real-world manipulation data per day. This data is fed back into the training pipeline, which produces better models, which are deployed to the next generation of robots. The flywheel compounds — each cycle produces more data, better models, and more deployments.
The "stacking parameters" critique: Luo argues that the "bigger model" approach is wrong for embodied intelligence. The bottleneck is not model capacity but real-world data — embodied AI needs to see real robots manipulating real objects, not synthetic data. The "flywheel" approach generates real data at scale, which is the real moat.
The benchmark: Zhiyuan's latest model, GO-1, hits 87% success rate on the in-house manipulation benchmark — a 22-point improvement over the previous generation. The improvement is attributed to the flywheel, not to parameter scaling (GO-1 has roughly the same parameter count as the previous generation).
The bigger takeaway: "embodied AI Scaling Law" is different from "LLM Scaling Law." The "more parameters" approach works for LLMs because text data is abundant. For embodied AI, real-world data is scarce, and the "flywheel" approach is the right way to scale. For the industry, this means "embodied AI" winners will be determined by who can deploy the most robots and build the strongest data flywheel, not who can train the biggest model.