Research

My research focuses on how scientific and technological advances interact with international politics. My main interests are in the technological causes of power transitions, the effect of general-purpose technologies like artificial intelligence (AI) on military affairs, and China's scientific and technological capabilities. Though situated in political science and international relations, my work draws from a wide range of disciplines, including economics, history, and science and technology studies.

Publications and Technical Reports

1. Jeffrey Ding and Allan Dafoe, 2021. "The Logic of Strategic Assets: From Oil to Artificial Intelligence." Security Studies.

*Analysis in Washington Post Monkey Cage Blog

What resources and technologies are strategic? This question is often the focus of policy and theoretical debates, where the label “strategic” designates those assets that warrant the attention of the highest levels of the state. But these conversations are plagued by analytical confusion, flawed heuristics, and the rhetorical use of “strategic” to advance particular agendas. We aim to improve these conversations through conceptual clarification, introducing a theory based on important rivalrous externalities for which socially optimal behavior will not be produced alone by markets or individual national security entities. We distill and theorize the most important three forms of these externalities, which involve cumulative-, infrastructure-, and dependency-strategic logics. We then employ these logics to clarify three important cases: the Avon 2 engine in the 1950s, the U.S.-Japan technology rivalry in the late 1980s, and contemporary conversations about artificial intelligence.

2. Jeffrey Ding, 2019. "China's Current Capabilities, Policies, and Industrial Ecosystem in AI," testimony before U.S.-China Economic and Security Review Commission.

China has been hyped as an AI superpower poised to overtake the U.S. in the strategic technology domain of AI. This section compares the current AI capabilities of China and the U.S. by slicing up the fuzzy concept of “national AI capabilities” into three cross-sections: 1) scientific and technological (S&T) inputs and outputs, 2) different layers of the AI value chain (foundation, technology, and application), and 3) different subdomains of AI (e.g. computer vision, predictive intelligence, and natural language processing). This approach reveals that China is not poised to overtake the U.S. in the technology domain of AI; rather, the U.S. maintains structural advantages in the quality of S&T inputs and outputs, the fundamental layers of the AI value chain, and key subdomains of AI.

3. Jeffrey Ding, 2018. "Deciphering China's AI Dream." Oxford, UK: Center for the Governance of AI, Future of Humanity Institute, University of Oxford.

This report examines the intersection of two subjects, China and artificial intelligence, both of which are already difficult enough to comprehend on their own. It provides context for China’s AI strategy with respect to past science and technology plans, and it also connects the consistent and new features of China’s AI approach to the drivers of AI development (e.g. hardware, data, and talented scientists). In addition, it benchmarks China’s current AI capabilities by developing a novel index to measure any country’s AI potential and highlights the potential implications of China’s AI dream for issues of AI safety, national security, economic development, and social governance.

4. Remco Zwetsloot, Helen Toner, and Jeffrey Ding, 2018. "Beyond the AI Arms Race." Foreign Affairs.

The idea of an "artificial intelligence (AI) arms race" between China and the United States is gaining a momentum of its own. This essay reviews AI Superpowers by Kai-fu Lee, one of the most in-depth discussions of U.S.-China AI competition. It argues that Lee's reliance on flawed analogies and discounting of the importance of medium-sized innovations in deep learning leads to an overestimation of China's AI capabilities. It also calls for a reset away from the AI arms race narrative.

Working Papers

1. Jeffrey Ding and Allan Dafoe. "Engines of Power: Electricity, AI, and General Purpose Military Transformations." Under Review. [pdf]

Major theories of military innovation focus on relatively narrow technological developments, such as nuclear weapons or aircraft carriers. Arguably the most profound military implications of technological change, however, come from more fundamental advances arising from “general purpose technologies” (GPTs), such as the steam engine, electricity, and the computer. With few exceptions, political scientists have not theorized about GPTs. Drawing from the economics literature on GPTs, we distill several propositions on how and when GPTs affect military affairs. We call these effects “general-purpose military transformations” (GMTs). In particular, we argue that the impact pathway of GMTs is broad, delayed, and shaped by indirect productivity spillovers. Additionally, GMTs differentially advantage those militaries that can draw from a robust industrial base in the GPT. To illustrate the explanatory value of our theory, we conduct a case study of the military consequences of electricity, the prototypical GPT. Finally, we apply our findings to artificial intelligence, which will plausibly cause a profound general-purpose military transformation.

2. Jeffrey Ding. "The Diffusion Deficit in Scientific and Technological Power: Re-assessing China's Rise." Under Review.

Virtually all scholars recognize that scientific and technological (S&T) capabilities are becoming increasingly important factors in a nation’s overall power. Unsurprisingly, debates over a possible U.S.-China power transition have highlighted China’s rise as a “science and technology superpower.” These discussions have overwhelmingly centered on national innovation capabilities, reflective of the bias in assessments of S&T power toward the generation of novel advances. This paper argues that these assessments should, instead, place greater weight on a state’s capacity to diffuse, or widely adopt, innovations. The latter, according to a growing body of econometric and historical evidence, is a better indicator of a state’s long-term economic growth. In two historical case studies — the U.S. in the Second Industrial Revolution and the Soviet Union in the early postwar period — I demonstrate the relative importance of diffusion capacity versus innovation capacity in assessing the S&T capabilities of rising powers. Finally, a diffusion-centric approach reveals that China is far from being an S&T superpower.

3. Jeffrey Ding. "The Rise and Fall of Great Technologies and Powers." Job Market Paper. Under Review. [pdf] [supplementary appendix]

How do technological revolutions affect the rise and fall of great powers? Scholars have long observed that major technological breakthroughs disrupt the economic balance of power, bringing about a power transition. However, there has been surprisingly limited investigation into how this process occurs. Existing studies establish that a nation’s success in adapting to revolutionary technologies is determined by the fit between its institutions and the demands of these technologies. The standard explanation emphasizes institutional factors best suited for monopolizing innovation in new, fast-growing industries (leading sectors). I propose an alternative mechanism based on the diffusion of general-purpose technologies (GPTs), which presents a different trajectory for countries to leapfrog the industrial leader. Characterized by their potential for continuous improvement, pervasiveness, and synergies with complementary innovations, GPTs only make an economy-wide impact after a drawn-out process of diffusion across many sectors. The demands of GPT diffusion shape the institutional adaptations crucial to success in technological revolutions. Specifically, I emphasize the role of education systems and technical associations that broaden the base of engineering skills associated with a GPT. To test this argument, I set the leading-sector mechanism against the GPT diffusion mechanism across three historical case studies, which correspond to historical industrial revolutions: Britain’s rise to preeminence in the early 19th century; the U.S.’s overtaking of Britain before World War I; Japan’s challenge to U.S. technological dominance in the late 20th century. Evidence from these case studies support the GPT diffusion explanation, shedding new insights into how emerging technologies like AI, which some regard as driving a fourth industrial revolution, will affect a possible U.S.-China power transition.