Research

When scholars and policymakers consider how technological advances affect the rise and fall of great powers, they draw on theories that center the moment of innovation—the “Eureka” moment that sparks astonishing technological feats. In this book, Jeffrey Ding offers a different explanation of how technological revolutions affect competition among great powers. Rather than focusing on which state first introduced major innovations, he instead investigates why some states were more successful than others at adapting and embracing new technologies at scale. Drawing on historical case studies of past industrial revolutions as well as statistical analysis, Ding develops a theory that emphasizes institutional adaptations oriented around diffusing technological advances throughout the entire economy. Examining Britain’s rise to preeminence in the first industrial revolution, America and Germany’s overtaking of Britain in the second industrial revolution, and Japan’s challenge to America’s technological dominance in the third industrial revolution (also known as the “information revolution”), Ding illuminates the pathway by which these technological revolutions influenced the global distribution of power and explores the generalizability of his theory beyond the given set of great powers. His findings bear directly on current concerns about how emerging technologies such as AI could influence the US-China power balance.

Even during the fiercest periods of the Cold War, the U.S. and the Soviet Union cooperated on nuclear safety and security. Since an accidental or unauthorized nuclear detonation by another nation would threaten peace everywhere, it seems straightforward that states more experienced in developing nuclear safety and security technologies would transfer such methods to other states. Yet, the historical record is mixed. Why? Existing explanations focus on the motivations of the transferring state, emphasizing the political costs and proliferation risks of sharing nuclear safety and security technologies. This article argues that specific technological features condition the feasibility of assistance. For more complex nuclear safety and security technologies, robust technical cooperation is crucial to build the necessary trust for scientists to transfer tacit knowledge without divulging sensitive information. Leveraging a wealth of elite interviews and archival evidence, my theory is supported by four case studies: U.S. sharing of basic nuclear safety and security technologies with the Soviet Union (1961-1963); U.S. withholding of complex nuclear safety and security technologies from China (1990-1999) as well as from Pakistan (1998-2003); and U.S. sharing of complex nuclear safety and security technologies with Russia in the Warhead Safety and Security Exchange (1994-2007). My findings suggest the need to examine not only the motivations behind sharing nuclear safety and security technologies but also the process by which nuclear assistance occurs and the features of the technologies involved. These findings also bear on how states cooperate to manage the global risks of emerging technologies, especially as policymakers and scholars reference nuclear assistance as a historical template.

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.

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.

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.

What resources and technologies are strategic? Policy and theoretical debates often focus on this question, since the “strategic” designation yields valuable resources and elevated attention. The ambiguity of the very concept, however, frustrates these conversations. We offer a theory of when decision makers should designate assets as strategic based on the presence of important rivalrous externalities for which firms or military organizations will not produce socially optimal behavior on their own. We distill three forms of these externalities, which involve cumulative-, infrastructure-, and dependency-strategic logics. Although our framework cannot resolve debates about strategic assets, it provides a theoretically grounded conceptual vocabulary to make these debates more productive. To illustrate the analytic value of our framework for thinking about strategic technologies, we examine the US-Japan technology rivalry in the late 1980s and current policy discussions about artificial intelligence.

How does software contribute to military accidents? The stakes are high. In the present, software systems have been partly responsible for naval accidents, and such incidents could trigger larger conflicts. In the past, computerized early warning systems produced “near-miss” nuclear crises during the Cold War. In the future, military systems that incorporate new advances in artificial intelligence could fail with devastating consequences. Existing studies apply “normal accidents” and “high reliability organizations” theory to shed light on the causes of military accidents. While these approaches are helpful, they neglect the military’s system acquisition process, which involves outsourcing software development to prime contractors such that input from military operators is limited to the late stages of development. By expanding the causal timeline of military accidents beyond decisions made on the battlefield to those made decades earlier in software design and development, this article presents an alternative way to explain how software contributes to military accidents. It tests the explanatory power of this theory against “normal accidents” and “high reliability organizations” theory across a range of case studies, including: i) the 1988 Vincennes incident, in which a U.S. naval ship accidentally shot down an Iran Air civilian airliner; ii) the Patriot fratricides at the beginning of the Second Gulf War; iii) the 2017 USS McCain collision; and iv) software upgrades in the 2021 Kabul airlift.

How do states assess technological self-sufficiency in a globalizing world? To sustain long-term growth and limit foreign dependency, rising powers pursue domestic sources of technological innovation. In recent decades, however, the hybridization of innovation — marked by increased cross-border financial flows and expanded mobility of high-skilled workers — has fostered hybrid firms that challenge emerging economies’ ability to assess “independent” innovation. Borrowing Robert Reich’s notation, the grounds for debate over “who is us” have fundamentally shifted. This article posits that, compared to their predecessors, rising powers today adopt more malleable boundaries for the corporate actors included within indigenous innovation because their technology ecosystems are more reliant on transnational technical communities and foreign direct investment. Case studies of how policymakers evaluated independent innovation in China, India, and Japan provide empirical support for the theory. These comparisons, across time and between states, illustrate how structural changes in the global economy have made it more difficult for rising powers to draw lines between “domestic” and “foreign” companies, resulting in unsettled assessments of independent innovation. This article contributes to academic and policy debates about the consequences of economic dependence, the efficacy of high-profile industrial policies, and how developing states manage the challenges of globalization.

It is generally recognized that the United States and China are the two leading states in AI development. In recent years, U.S.–China competition in AI has escalated, reflecting a bilateral relationship that has turned increasingly contentious. Unsurprisingly, analysis about the potential effects of AI on U.S.–China relations is strongly rooted in technonationalism, which emphasizes interstate competition over technological assets. By decentering the nation-state as the key unit of analysis for technological change, this chapter presents an alternative framework for comprehending the implications of AI for U.S.–China relations. It first articulates how transnational networks of both firms and individuals complicate the calculation of the U.S. and China’s “national interests” in AI. It then probes how AI advances, such as those in machine translation, could bind the two countries even closer together. Taking technoglobalism seriously is essential to rebalancing discussions about how AI could transform U.S.–China relations.