Technology is at the center of the emerging competition between the United States and China, with far-reaching consequences for democratic societies. At stake in this competition are the prestige and reach of liberal values, as well as the economic competitiveness and national security of the United States and its allies and partners. Fortunately, there are steps that the United States government, working with the private sector and other democratic governments, can take to sharpen America’s edge across four dimensions of the technology competition: talent; norms and standards; research and development; and trade, investment, and industrial policy.
Technology is perhaps the most intense realm of competition between the United States and China today, and artificial intelligence (AI) is central to that contest. By developing state-of-the-art capabilities in AI, China seeks to achieve a strategic advantage over the United States and its allies. It also aims to leverage new forms of AI-enabled surveillance and repression in ways that strengthen its illiberal model of governance – both within China and around the world. Democratic countries have started to push back, with rising calls for the development of robust AI norms, and the United States and EU each passing major semiconductor bills. Nonetheless, China still threatens to outpace the United States and its allies in AI research and standards-setting.
Ultimately, the United States’ and China’s competition over AI and emerging technology will create ripple effects that go far beyond the digital domain. The values that underpin free and open societies are at stake, and the countries and coalitions that gain a sustainable advantage will be rewarded with economic benefits and a national security edge. Luckily, there are steps that the United States can take, working with democratic allies and partners, to protect democracy and liberal values in an age of AI.
Acknowledgements and disclosures
The authors are grateful to Patricia Kim and Ryan Hass for their insights and suggestions; to Alexandra Dimsdale, Emilie Kimball, and Rachel Slattery for their work on the production of this paper; and to Valerie Wirtschafter for producing the data visualizations that accompany it. Dylan Hanson provided invaluable research assistance on both products.