Diffusion Over Innovation: Jeffrey Ding on the US China AI Race Diffusion Strategy
Jeffrey Ding explains why the US China AI race diffusion strategy will determine the next superpower. Learn why tech adoption matters more than invention.
Innovation gets the headlines, but diffusion wins the war. While the world's obsessed with who builds the fastest LLM, Jeffrey Ding, a professor at George Washington University, argues we're looking at the wrong metrics in the US-China AI rivalry.
US China AI Race Diffusion Strategy: Why Widespread Adoption Matters
In his award-winning book, Technology and the Rise of Great Powers, Ding posits that national power isn't derived from being the first to invent a tool. Instead, it's about how effectively a nation spreads that technology across its entire economy. It's the difference between a flashy prototype and a transformed manufacturing sector.
According to Ding's analysis in his ChinAI newsletter, the United States often leads in frontier innovation, but China is formidable in diffusion capacity. China's state-led approach can mandate AI integration into infrastructure, while the US relies on market forces that don't always reach traditional industries quickly.
The 2026 Competitive Landscape
As of January 2026, the focus is shifting. Leading researchers aren't just asking who has the most parameters, but whose GDP is seeing the productivity boost from these tools. Ding suggests that the winner won't be the one with the best lab, but the one with the best-equipped workforce.
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