China's AI flywheel effect is massive
There's a very compact and clear explanation of how China is starting to get the edge on AI. It's published by the U.S.-China Economic and Security Review Commission in Washington (which is delightful in itself).
It clearly summarizes a point I was making just a few weeks ago: the U.S. and China are chasing AI supremacy in a very different way.
I quote the paper summary:
1️⃣ China has opted to go all in on an open-source approach to AI. Most Chinese labs publish model source code and weights. They also charge far less to use high-end products than their global competitors. This has resulted in the acceleration of global uptake of Chinese AI and created a feedback loop where widespread adoption drives iteration, then further adoption. As of publication, Alibaba’s Qwen models accounted for the largest model ecosystem on Hugging Face, with over 100,000 derivatives.
2️⃣ This open ecosystem enables China to innovate close to the frontier despite significant compute constraints. Chinese labs have narrowed performance gaps with top Western large language models. They have also developed key architectural and training advances that are now industry standards.
4️⃣ Open model proliferation creates alternative pathways to AI leadership. China’s strategy prioritizes data curation and refinement through the deployment of embodied AI in manufacturing, robotics, and research where specialized, real-world data from widespread use may compound into advantages that proprietary U.S. models cannot easily replicate, even if they maintain technical superiority on benchmarks.
5️⃣ China’s open AI model strategy and its manufacturing dominance are mutually reinforcing. As the Commission’s 2025 Annual Report documented, China’s industrial base generates “interlocking innovation flywheels” across adjacent sectors. Open models accelerate this dynamic by enabling low-cost AI deployment across factories, factories, logistics networks, and robotics—generating real world data that feeds back into model improvement. Beijing has built the institutional infrastructure to exploit this advantage, designating data as a formal factor of production and permitting enterprises to carry data assets on their balance sheets.
6️⃣ U.S. export controls primarily target the digital loop—restricting access to advanced chips used for frontier model training—but are not well suited to addressing the physical loop of deployment-driven data creation and accumulation across China’s manufacturing base. As open models reduce the compute required for effective deployment, China’s ability to generate proprietary industrial data at pace and scale becomes increasingly independent of access to cutting-edge hardware. This gap in the U.S. policy framework means that even successful controls on training compute may not prevent China from building AI advantages rooted in its physical economy.
One of my own takes at the time was exactly this:
(...) when you build monopolies around model access and subscription revenue, you optimize for a different outcome than when you integrate AI into the physical and administrative fabric of a society at scale and iterate from there. And with AI, the huge payoff of real-life applications at scale is an immense data trove.
For now, I'd suggest you keep the notion of Embodied AI in mind. And if you're an industrial or an AI startup, clearly focus on this part of the market as a solid move forward.
The full paper is here:
Ngor Luong
And my recent series on the importance of humanoid robots:
Philippe MEDA
