ISBN: 978-0691260341, Princeton University Press, 2024, 280 pages, $29.95 (Hardcover)
Reviewed by: Anthony Canevello, Embry-Riddle Aeronautical University, Daytona Beach, Florida, United States
In Technology and the Rise of Great Powers, Jeffrey Ding argues that a nation’s rise during technological revolutions depends less on inventing new technologies and more on its ability to diffuse general-purpose technologies (GPTs) throughout its economy and institutions. Using historical analysis of the British, American, and Japanese experiences across the first three industrial revolutions, as well as statistical models of technology diffusion, Ding demonstrates that the widespread adoption of GPTs is more strongly correlated with economic leadership than dominance in specific leading sectors. Extending this framework to the Fourth Industrial Revolution, he predicts that U.S.-China competition in artificial intelligence will hinge on each state’s capacity to embed AI across industrial sectors.
The book’s depth and rigor of analysis, along with its integration of historical and contemporary evidence, make it an important text for scholars of international political economy and technology policy. Jeffrey Ding, an Assistant Professor of Political Science at George Washington University’s Columbian College of Arts & Sciences, researches emerging technologies and international politics. This background gives him a distinctive vantage point from which to examine the topic, offering readers a rigorous and nuanced analysis that weaves together historical and contemporary evidence. While the depth of his analysis and the rigor of his methods suggest that the book is primarily intended for academic audiences, it remains an essential work for scholars of international political economy and technology policy.
In his book, Ding presents a novel theory on the drivers of global economic power transitions during industrial revolutions. Using statistical analysis and historical case studies, he demonstrates that technological diffusion—rather than initial invention or even dominance in a given sector—is the key determinant of which states emerge dominant after periods of technological upheaval. The book tests the plausibility of Ding’s General-Purpose Technology (GPT) model and compares its explanatory power to that of the more traditional Leading Sector (LS) model. The LS model assumes that the state which first innovates within a leading industrial sector gains a relative advantage. In contrast, the GPT model shifts the emphasis from invention to implementation. Ding theorizes that it is a state’s success in diffusing GPTs—foundational technologies with wide applicability across many sectors—that enables it to achieve and maintain dominance after a period of industrial revolution. He then extends this analysis to predict outcomes for the Fourth Industrial Revolution (IR-4), centered on U.S.-China competition in artificial intelligence (AI).
Ding begins with the First Industrial Revolution (IR-1), comparing Britain, France, and the Netherlands through both LS and GPT lenses. While the LS model attributes Britain’s rise to dominance in cotton textiles, iron, and steam engines, Ding demonstrates that the timeline of Britain’s productivity growth aligns more closely with the diffusion of GPTs such as the factory system, mechanization, and steam power. For the Second Industrial Revolution (IR-2), Ding evaluates the United States, Germany, and Great Britain. While the LS model would predict German dominance based on its strength in steel, chemicals, and electrical equipment, the GPT model better explains the U.S. rise. The United States broadly applied GPTs such as interchangeable manufacturing, electrification, and chemicalization across multiple industrial sectors and invested heavily in mechanical engineering education, enabling widespread diffusion.
The Third Industrial Revolution (IR-3) provides a partial anomaly for the LS model. Although the United States pioneered the leading sectors of computing, consumer electronics, and semiconductors, Japan temporarily overtook it in productivity. However, Ding demonstrates that the United States retained overall dominance because it more effectively embraced the GPT of computerization. He supports this claim through quantitative analysis of diffusion patterns and the development of software engineering infrastructure. Finally, Ding applies the GPT framework to IR-4, suggesting that U.S.-China competition in artificial intelligence will mirror earlier patterns. While China has made significant progress in AI research, the United States retains a comparative advantage due to its higher ratio of AI practitioners, institutional capacity for technological diffusion, and historically greater success in embedding GPTs into diverse industrial sectors.
Ding’s work offers a refreshing and empirically grounded reinterpretation of how technology drives shifts in global power. His diffusion-based framework challenges the long-standing emphasis on invention and industrial leadership while providing a more nuanced understanding of why some states sustain technological leadership over time. The historical case studies are meticulously researched and well integrated with quantitative evidence. However, the book’s dense theoretical structure and methodological detail make it more suitable for scholars than general readers. For policymakers, Ding’s insights into institutional capacity and skill diffusion offer valuable implications, though the presentation assumes some familiarity with international political economy and innovation theory. Overall, the book represents a significant scholarly contribution to the study of technology, power transitions, and international competition.
