AI’s Promise for the Global Economy

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If properly used, AI could significantly accelerate economic growth and help productivity growth rebound.
Meaningful impacts in labor productivity may begin to appear "by the end of this decade" (author’s best guess).
Roy Amara’s law: likelihood of overestimating short-run impacts and underestimating long-run impacts of technological transformation.

Colliding shocks reducing supply elasticity and raising costs:
- War, pandemic, climate change, geopolitical tensions, resurgent nationalism, and national-security–focused economic policy.
- Rapid postpandemic fragmentation of global supply networks driven by diversification and resilience priorities, and policy initiatives to bring supply chains home or to friendly countries.
Examples and structural consequences:
- India now produces 15 percent of iPhones.
- Only South Korea and Taiwan Province of China make (as opposed to design) the most advanced semiconductors.
- Tradeoffs: cannot maximize resilience and minimize costs simultaneously; structural shift has contributed to inflationary pressures.

Key secular forces reducing supply elasticity:
- Declining productivity, especially in advanced economies.
- Aging populations in economies that account for more than 75 percent of global output.
- Rising sovereign debt following the pandemic; global sovereign debt now exceeds global gross domestic product.
Exact sovereign debt ratios cited:
- United States: 120 percent.
- Europe: 88.6 percent (with Greece, Italy, Spain, France, Belgium, and Portugal above this average; Greece and Italy "by a lot").
US productivity statistics (exact figures preserved):
- US productivity growth averaged 1.68 percent from 1998 to 2007.
- Productivity growth slowed to 0.38 percent from 2010 to 2019.
- Tradable goods and services sectors: fell from 4.27 percent to 1.23 percent.
- Nontradable services sectors: declined from 0.73 percent to effectively zero.
Measured productivity edged up during the pandemic due to partial shuttering of less productive industries and shift to remote work in higher-productivity sectors.
Structural outcome: relatively rapid shift from demand-constrained to supply-constrained growth — subdued growth, enduring inflation, elevated real interest rates, and likely higher borrowing costs than the decade following the global financial crisis.

Three revolutionary transformations:
- Multidecade digital transformation accelerated by breakthroughs in AI.
- Revolution in biomedical and life sciences.
- Technologies underpinning the transition to sustainable energy.
Characteristics of generative AI:
- First AI with humanlike capacity to operate in multiple domains and detect/switch domains based on conversational prompts.
- Capable of tasks such as discussing inflation, writing computer code, and doing some mathematics (noted as work in progress).
- Better framed as machine-human collaboration ("augmentation") rather than full automation.
General-purpose nature:
- AI as a general-purpose technology with applications across the economy; only general-purpose technologies can produce economy-wide productivity surges.
Potential sectoral impacts:
- High-impact sectors already investing heavily (technology, finance).
- Need diffusion to large employment sectors that tend to lag: government, health care, construction, hospitality.

Key barriers:
- Talent, computing power, and rapidly expanding electricity demand for training increasingly powerful generative AI models.
- Regulatory need to prevent misuse of technology and data; risk-mitigation regulatory agenda is underway globally.
- Automation bias (the "Turing Trap"): tendency to view AI as full automation and replacement for humans.
- Concentration of powerful training systems in private-sector cloud computing (mostly in the US and China) and competition for talent disadvantaging science and academia.
Data availability:
- Internet provides ample training data; personalized and sensitive data are not required to train large language models.
- Specialized applications (e.g., AlphaFold) require domain-specific data and expert input.
Geographic and policy risks:
- Europe risks falling behind the United States and China for three reasons:
- Relative underfunding of basic research.
- Lag in computing power to support research.
- Failure to fully leverage the large scale of the European economy (fragmented capital markets and fragmented regulation).
- China characterized as an AI powerhouse.
- India likely a growing force given digital roots, large internal market, and engineering human capital.
- Most other emerging market economies will be largely consumers of advanced AI technology in the near term.

Rebalance policy emphasis:
- Strengthen policies for accessibility, diffusion, and skills acquisition alongside risk-mitigation and misuse prevention.
- Avoid government "picking winners"; effective competition policy should be part of the portfolio.
- Focus on diffusion to lagging sectors and support for small and medium enterprises.
- Prioritize retraining and new skills acquisition as jobs change with AI collaborators.
Democratize infrastructure and research:
- Expand computing infrastructure to a broad community of researchers and innovators to balance academic and private innovation and support widespread diffusion.
Anticipated macroeconomic benefits with supportive policy:
- With policy support to accelerate diffusion across the entire economy, AI could significantly accelerate economic growth and help productivity rebound.
- If AI relaxes supply-side constraints, it could indirectly lower real interest rates and the cost of capital over time, aiding the energy transition and supporting aging populations.

Timing:
- Author’s best guess: meaningful impacts in labor productivity may begin "by the end of this decade."
Diffusion scenarios:
- If AI adoption remains concentrated in tech-intensive sectors, economy-wide gains are unlikely to be fully realized.
- If AI is broadly accessible and diffused to lagging sectors with supportive policy, AI could produce a major sustained surge in productivity over time.

AI’s Promise for the Global Economy — Michael Spence, F&D Magazine, September 2024.