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Illustration by OpenAI’s DALL·E 3)
Today’s edition opens with an introduction by Jordan Schneider, founder and editor-in-chief of the indispensable ChinaTalk, a leading podcast and newsletter on US-China tech relations. I’m forever grateful for his support a couple of years ago, which enabled Sinification to secure funding when it needed it most. — Thomas
In this article, Liu Shaoshan — a leading figure in China’s embodied AI research and a state-designated “high-end overseas talent”— proposes a roadmap for Chinese AI dominance cueing off America’s successful diffusion of TCP/IP protocols in the late 20th century. Just as influence over the internet afforded the USA “a truly global mechanism of discursive control”, Liu argues, AI diffusion and the standards exported along with AI systems will be key to power projection in the 21st century.
He outlines four strategic levers for achieving open-source dominance — technological competitiveness, open-source ecosystem development, international standard-setting and talent internationalisation. Acknowledging that China’s engagement in open source AI still has a long way to go, he advocates for the creation of a comprehensive “China HuggingFace” that maximises market share by publishing toolkits for model training, embodied AI implementation, and everything in between. Finally, he argues that Beijing should encourage Chinese AI talent to live and work abroad, especially in Belt and Road participant countries, rather than encouraging them to come back to China.
This piece is particularly resonant at a time when leading White House AI advisors are tweeting stuff like this:
For the policy answer to the challenges Liu raises, check out Nathan Lambert’s call for action to replicate Deepseek in America.
— Jordan Schneider
Key Points
US tariffs and export controls heighten global uncertainty but create a strategic opening for China’s AI industry to expand internationally and reshape “the global technological order”.
Global adoption of US or Chinese technology — not domestic technological prowess alone — is becoming the key battleground for great-power status in AI.
America’s success with TCP/IP’s global rise in the 1980s shows centrally-led government policies, open-source, mandatory standards and talent “exports” can turn national tech into the global default.
Rogers’ diffusion model suggests four steps for China: woo “innovators” with cutting-edge tech, attract “early adopters” through open-source, secure an “early majority” by setting international standards, and reach late adopters through Chinese talent going global.
Thus, China’s first objective should be to match US-level capabilities so that its AI-related technologies are credible and attractive to global “innovators” and “early adopters”.
China’s DeepSeek-R1 and other LLMs now rival OpenAI in maths, coding and/or reasoning, demonstrating their technical credibility for such global uptake.
Moreover, China’s embodied AI sector shows strong international competitiveness, with robust upstream manufacturing, rapidly improving midstream technologies and world-leading pilot deployments, forming a highly competitive end-to-end ecosystem that supports rapid technological advancement.
Nevertheless, China still trails the US in the maturity of its open-source community reach and its influence over international standards systems.
Recommended actions:
Back talent-going-abroad schemes that help place Chinese experts in key positions in emerging markets such as in universities, labs and start-ups, thereby supporting the spread of Chinese technologies and standards internationally.
Establish an “Open-Source Co-construction Fund” to boost China's influence in global technical governance and standards-setting.
Shift from ISO-centric competition to a “code-as-standard” strategy: export products pre-loaded with open-source standards, ensuring that adoption effectively becomes standardisation.
Promote collaborative hardware-software stacks that embed Chinese standards directly in already established code repositories like GitHub and Hugging Face, thereby easing global adoption.
Build a “Chinese Hugging Face”: an integrated and globally influential open-source hub for models, middleware and applications, covering the full pipeline from model development to deployment.
The Author
Name: Liu Shaoshan (刘少山)
Year of birth: est. 1984 (age: 40/41)
Position: Director of the Embodied Intelligence Centre at the Shenzhen Institute of Artificial Intelligence and Robotics (AIRS); Founder and CEO of intelligent robotics company PerceptIn.
Previously: Technology Leadership Panel Advisory Group Member, National Academy of Public Administration (2023-4); Senior Autonomous Driving Architect, Baidu USA (2014-6); Senior Software Engineer, LinkedIn (2013-4); Software Development Engineer, Microsoft (2010-3)
Other: IEEE Senior Member; Recognised as a “national high-level overseas talent” (国家高层次海外人才) by the Chinese government (under its broader Overseas High-Level Talent Recruitment Programme, historically known as the Thousand Talents Plan)
Research focus: Embodied AI; Autonomous driving; Computing systems; Technology policy
Education: BSc, MSc and PhD (2010), UC Irvine; MPA, Harvard University.
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