My latest series of posts are driven by what I believe are two of the biggest forces that ultimately determine our future: General Purpose Technologies and geopolitical dynamics. In a previous post, I described the role of Necessity, Invention and Convergence in driving the diffusion of general purpose technologies. Necessity drives invention, but true trasitions occur when necessity and invention converge across industries, economies, and societies. However, as technological competition accelerates, particularly in artificial intelligence (AI), a deeper question emerges: What determines the diffusion of transformative technologies, and how does that shape global power dynamics?

Jeffrey Ding’s book, Technology and the Rise of Great Powers (2024), provides a compelling perspective on this question. His General Purpose Technology diffusion theory argues that economic and geopolitical power does not simply belong to the nation that invents new technologies – it belongs to the nation that successfully diffuses them across industries, infrastructure, and society. This contrasts with Leading Sectors (LS) theory, which focuses on dominance in key industries. These theories provide a clearer view of how power transitions unfold in the modern world.

For centuries, shifts in productive capacity, technological diffusion, and economic dominance have determined the rise and fall of great powers. As Ding argues, many power transitions occur not because of invention alone, but because of how well emerging technologies are integrated, scaled, and leveraged across industries and societies. LS theory suggests that power belongs to nations that dominate key industries and profit from monopolizing innovation, while general purpose technology diffusion theory suggests that power shifts toward nations that successfully scale transformative technologies. The NIC framework builds upon these ideas, highlighting that necessity drives invention, but convergence accelerates diffusion, leading to system-wide transformation.

This has direct implications for today’s U.S.-China competition in AI. While the U.S. currently leads in foundational AI research, China excels in scaling and diffusing AI across its economy. The question is: does leadership belong to the country that innovates first, or to the one that integrates and applies AI most effectively? Historically, power transitions have been shaped by who scales technology most effectively, rather than just who invents it. The Industrial Revolution rewarded not just Britain’s early innovations, but also the U.S. and Germany, who scaled industrialization faster. Similarly, AI’s economic impact will be determined by who embeds it into real-world applications at scale.

A key insight is that convergence across domains – not just technology – drives diffusion. AI alone does not determine power shifts; rather, it is how AI converges with economic policy, military strategy, industrial shifts, and global alliances that truly defines outcomes. A relevant example is how large-scale infrastructure initiatives integrate economic expansion with digital infrastructure, supply chain influence, and financial systems. As AI-driven infrastructure, digital finance, and supply chain networks continue to scale, they are reshaping technological and geopolitical landscapes. The effectiveness of different regions and organizations in this transformation will depend on how well they integrate AI and other transformative technologies into broader economic and strategic frameworks.

If history tells us anything, it’s that great power shifts happen when technological diffusion aligns with economic necessity and geopolitical convergence. AI is no exception. If the U.S. accelerates AI diffusion across industries and strengthens alliances, it can maintain leadership. If China closes the gap on foundational AI research while continuing to dominate diffusion, it could overtake the U.S. in AI-driven economic power. If AI converges with military, financial, and industrial power faster in one country, it will accelerate the balance of power shift.

The discussion around AI leadership must move beyond just who creates the best models and focus on who integrates AI into their national strategy most effectively. As Ding’s book highlights, past power transitions were determined not just by invention, but by how well great powers scaled and applied new technologies. The fusion of general purpose technology diffusion theory and LS theory reveals a clear pattern: technological leadership is necessary, but not sufficient; economic and industrial diffusion determines long-term power; and geopolitical convergence accelerates the impact of diffusion.

Global leadership in AI is not just a technological race – it is a systemic, geopolitical, and economic transformation. The countries that align AI diffusion with global strategy, alliances, and industrial policy will determine the future of the 21st century.

Given how closely Ding’s work aligns with this broader discussion, I’ll be reviewing Technology and the Rise of Great Powers in a follow-up post. In the meantime, I have added it to my library. Understanding the historical lessons of technological diffusion and power shifts is critical for making sense of the future we are now entering. Stay tuned.