Editorial 2 : Diffusion is Destiny
Context: How technology affects balance of power and the lesson for India.
Introduction: Jeffrey Ding’s much-discussed book Technology and the Rise of Great Powers (Princeton University Press) is upending a lot of conventional wisdom. Transitions in the balance of power in the international system are often driven by technology.
Conventional Wisdom vs. Ding’s Thesis
- Traditional Leading Sectors Theory
- Dominance in leading sectors (e.g. textiles, chemicals, electronics) drives economic and geopolitical power.
- Examples
- Britain’s textile dominance during the Industrial Revolution.
- Germany’s chemical industry in the Second Industrial Revolution.
- Japan’s 1980s edge in consumer electronics and automobiles.
- Ding’s General Purpose Technologies (GPT) Framework
- GPTs are technologies with broad applicability that drive productivity gains across multiple sectors (e.g. steam engines, electricity, AI).
- According to Ding, national power stems from diffusion of GPTs, not sector-specific dominance.
- Examples
- Britain’s Industrial Revolution success relied on iron-based machinery diffused across industries, not just textiles.
- The U.S. overtook Germany by institutionalizing electricity adoption and engineering standards.
Historical Case Studies
- First Industrial Revolution (Britain)
- Conventional View: Textile innovation as the driver.
- Ding’s View: Power came from iron-based machines and widespread engineering skills.
- Second Industrial Revolution (Germany vs. U.S.)
- Germany: Led in sectors like chemicals but lacked GPT diffusion.
- U.S.: Surpassed Germany by standardizing electricity adoption and fostering engineering adaptability.
- Third Industrial Revolution (Japan vs. U.S.)
- Japan: Dominated consumer electronics but lagged in computerization diffusion.
- U.S.: Leveraged GPTs (e.g. computing) to transform multiple sectors simultaneously.
Implications for Development
- Lessons for Countries like India
- Prioritize Systemic Change:
- Invest in human capital (widespread education, not sector-specific training).
- Build institutional adaptability and interoperability of technologies.
- Avoid Overemphasis on Leading Sectors: Sectoral gains (e.g. exports) are transient. GPT diffusion ensures long-term growth.
- Challenge: GPT diffusion lacks headline appeal and requires long-term, foundational investments.
Geopolitical Implications
- U.S. vs. China
- China’s Strengths: Dominance in leading sectors (e.g. electric vehicles).
- U.S. Edge: Superior GPT diffusion (e.g. AI, engineering ecosystems) if institutions remain intact.
- Ding’s Prediction
- Critical Factor: Not who invents technologies (e.g. AI), but who diffuses them widely.
- Risk: U.S. advantage could erode if policy undermines institutional frameworks (e.g. Trump-era reforms).
Conclusion and Way Forward
- Diffusion is Destiny, nations must prioritize systemic GPT adoption over sectoral dominance.
- Foster institutional flexibility, human capital, and foundational investments.
- Shift focus from "mission-mode" innovations to economy-wide technological integration.