U.S.-China Artificial Intelligence Competition: A Conversation with Dr. Jeffrey Ding

Mar 27, 2025

Dr. Jeffrey Ding, an Assistant Professor at George Washington University and author of a book on technology and great power dynamics, dives into the U.S.-China AI competition. He argues that technological leadership hinges not on breakthrough inventions but on the diffusion of technology throughout society. While the U.S. boasts a stronger infrastructure for AI education and adoption, Ding emphasizes the importance of enhancing educational policies and public-private partnerships. He believes the U.S. should focus on accelerating AI diffusion, rather than restricting China's access to technology.

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INSIGHT

Diffusion Beats Breakthrough Monopoly

Technological leadership hinges on diffusing innovations widely, not just inventing breakthroughs.
Widespread diffusion across firms and sectors delivers the productivity gains that shift economic leadership.

INSIGHT

GPTs Diffuse Over Decades

General purpose technologies often take decades to diffuse and reshape economies.
Electricity took about four decades to produce economy-wide productivity impacts in the U.S.

INSIGHT

AI Is Broadly Enabling But Hard To Diffuse

AI shows signs of being a general purpose technology because it enables many other sectors.
Even so, diffusion still requires organizational change and human capital, so it can be slow.

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In this episode of the ChinaPower Podcast, Dr. Jeffrey Ding joins us to discuss U.S.-China artificial intelligence (AI) competition and his book, Technology and the Rise of Great Powers: How Diffusion Shapes Economic Competition. Dr. Ding describes the framework he uses to understand the competition between the US and China on AI and explains that while many assume leading a technological competition comes from developing the next breakthrough invention, it should actually be centered around the diffusion of these technologies throughout their population of users. Technological leadership, therefore, depends on which country can best transfer and spread innovation from its top firms to the entire economy more effectively. Dr. Ding notes that China prioritizes an innovation-centric approach while neglecting broad-based technical and STEM education. He finds that the United States is better positioned than China to adopt and diffuse AI across a broad spectrum of sectors, given that more U.S. training institutions meet a quality baseline compared to China’s. Dr. Ding advises that since the United States is better positioned to diffuse AI technologies throughout its economy, it should focus on “running fast” rather than restricting China’s access to advanced technologies. Finally, Dr. Ding recommends that Washington focus on education policy, widening the base of AI engineers by increasing training sites, supporting public-private partnerships, and helping SMEs develop their AI capabilities.

Dr. Jeffrey Ding is an Assistant Professor of Political Science at George Washington University, and the author of Technology and the Rise of Great Power: How Diffusion Shapes Economic Competition. Previously, he was a postdoctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence. His research has been published or is forthcoming at European Journal of International Security, Foreign Affairs, Review of International Political Economy, and Security Studies, and his work has been cited in The Washington Post, The Financial Times, and other outlets. He also writes a weekly "ChinAI" newsletter, which features translations of Chinese conversations about AI development, to 12,000+ subscribers including the field's leading policymakers, scholars, and journalists. Dr. Ding holds a Ph.D in international relations from Oxford University, where he studied as a Rhodes scholar.