## Tomorrow’s Electricity

## Source details

**Canonical URL:** [Tomorrow’s Electricity](https://www.imf.org/-/media/files/publications/fandd/article/2024/12/books-wellisz.pdf)

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### Core thesis
- The leading sectors theory (popularized by historians such as Paul Kennedy) holds that the country that is first to adopt a disruptive technology in key industries will rise to global economic dominance.
- Jeffrey Ding disputes the leading sectors theory and proposes a GPT diffusion theory: long-term economic rise depends on the broad diffusion of general-purpose technologies (GPTs) across many sectors over decades, not first-mover advantage in isolated leading industries.
- Key claim: “GPT infrastructure, not the flashy efforts to secure the high ground in innovation, will decide which nation owns the future.”

### Historical evidence and reinterpretation
- Britain: Although breakthrough inventions (spinning jenny, water frame) increased cotton production, the textile industry accounted for only a small portion of subsequent increases in productivity, industrial production, and per capita GDP.
- United States: The invention of the electrical generator in 1871 did not bring widespread electrification of industry until decades later.
- Japan: Despite technological front-runner status in the 1980s, Japan experienced economic stagnation in the 1990s because it was slow to adopt widespread computerization; this counters the notion that technological pacesetters automatically win the race.
- Ding attributes US and British success to “GPT infrastructure”—systems of education and training that cultivate deep pools of engineering skills—rather than dominance in a few leading industries.

### Policy implications and recommendations
- Countries focused on securing a monopoly over the next GPT (for example, artificial intelligence) may be misreading history if they concentrate primarily on research and development.
- Ding suggests that policymakers should:
  - Emphasize GPT infrastructure: expand education, training, and workforce development to build deep engineering skill pools.
  - Offer technology training to less-skilled workers so they can embed the next GPT throughout the economy.
- Specific critique: Neither China nor the United States can gain a monopoly on artificial intelligence; both focus too much on investments in research and development and not enough on diffusion and skills.

### Takeaway
- Broad adoption of technology across the economy drives long-run growth, not leadership in a few pioneering industries.
- Diffusion of GPTs through workforce skills, education, and institutional infrastructure over decades determines which nations rise to dominance.

*chris wellisz manages communications for the World Bank’s trade team.*

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_Source: https://www.imf.org/-/media/files/publications/fandd/article/2024/12/books-wellisz.pdf_