Key Findings
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.
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.
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.
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.
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.