Jeffrey Ding, a leading scholar on China’s AI and professor at George Washington University, dives into the intricate relationship between technology and global power. He explains why long-term productivity relies on technology diffusion rather than just innovation. Historical examples highlight the cultural roadblocks faced by powers like the UK and Soviet Union, while emphasizing how China lags in this aspect. The importance of decentralized systems in the U.S., especially during the Cold War, and the need for strategic policymaking in AI development are also key discussion points.
Great powers must prioritize diffusion capacity, as long-term productivity growth relies on effectively adopting and spreading general-purpose technologies across their economies.
Historical case studies reveal that successful technological transitions are influenced by cultural and institutional readiness, highlighting the need for adaptive governance and education systems.
China's innovation capacity is hindered by its lagging diffusion capacity, emphasizing the importance of balanced investment in both technological development and infrastructure improvements.
Deep dives
The Importance of Long-Term Productivity Growth
Long-term productivity growth is crucial for great powers as it underpins their economic strength and, consequently, their military and geopolitical influence. Economic power is highly fungible, allowing nations to convert wealth into military might and enhance their global prestige. Historical patterns reveal that nations that can sustain higher productivity growth rates over extended periods dominate economically, leading to increased military strength and strategic advantage. Additionally, the ongoing shifts in China's demographics and its need to escape the middle-income trap emphasize the vital role of innovation and technology adoption in sustaining growth.
General Purpose Technologies (GPTs) as Growth Engines
General Purpose Technologies (GPTs) are foundational innovations that can drive extensive economic growth, transforming various sectors and fostering productivity improvements across the economy. Historical examples, such as the UK's industrialization, demonstrate how effective adoption and diffusion of GPTs, like mechanization, can lead to a competitive advantage over rivals. GPTs require not just initial innovation but a vast array of complementary technologies and organizational changes to maximize their impact. Effective implementation of these technologies is critical for nations aiming to maintain long-term economic dominance.
The Diffusion Capacity Challenge
Diffusion capacity, defined as a nation's ability to adopt and spread new technologies throughout its economy, is often overlooked when assessing technological capabilities. Historical examples show that countries may excel in innovation but struggle with implementing advances at scale, leading to stagnant productivity gains. A notable instance is the Soviet Union, which had significant scientific output but failed to effectively diffuse these innovations across its economy, resulting in long-term decline. Understanding the disparities in diffusion capacity, particularly between countries like the US and China, is vital for predicting future economic trajectories.
Policy Implications for AI and Future Technologies
As nations embrace emerging technologies like artificial intelligence (AI), a balanced approach is needed that prioritizes both innovation and diffusion efforts. Investment in human capital is essential for developing a broad base of AI engineers who can tailor existing technologies to specific industrial needs. Additionally, infrastructure improvements that facilitate technology transfer and access to computing resources are critical for maximizing the potential of GPTs. Policymakers must avoid locking into narrow technological paths and remain adaptable to the evolving landscape of technological advancements.
Lessons from Historical Case Studies
Examining historical case studies from past industrial revolutions reveals that the adoption of significant technologies often hinges on broader societal and institutional readiness. For instance, the US's rise during the Second Industrial Revolution was not merely due to groundbreaking inventions but also the successful diffusion of manufacturing techniques across various sectors. The importance of an adaptable educational system and decentralized governance structures has also been highlighted, allowing for more innovative responses to technological change. This multifaceted understanding of technology's role in national power can inform current strategies for harnessing AI and other transformative technologies.
Jeffrey Ding is a professor at George Washington University, leading US scholar on China’s AI, and the creator of the ChinAI Substack. In honor of the publication of his new book, Technology and the Rise of Great Powers, enjoy this interview with Jeff from the ChinaTalk archives.
Jeff Ding argues in a 2023 paper that great powers must harness general-purpose technologies if they want to achieve global dominance. That is, diffusion capacity (not just innovation capacity) is critical to economic growth — and China actually fares much worse in diffusion capacity than mainstream narratives imply.
Why long-term productivity growth is driven by the diffusion of general-purpose technology, and what makes this so crucial for great power competition;
Historical lessons from the UK, Soviet Union, US, and Germany illustrating the cultural and policy roadblocks to tech diffusion;
The importance of decentralized systems, and how this helped America win the Cold War
Why China’s diffusion capacity lags behind its innovation capacity, and how America should avoid getting locked into any one technological trajectory.
Co-hosting is Teddy Collins, formerly of DeepMind and the White House Office of Science and Technology Policy.