## AI Can Lift Global Growth

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## Bibliographic details
- Authors: MARCELLO ESTEVAO
- Published: February 17, 2026

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### AI as a driver of recent growth
- AI-related investment is accounting for a large share of GDP growth in the United States, fueling demand for servers, data centers, software, and power infrastructure.
- The US economy has entered a two-speed expansion: AI-intensive sectors are racing ahead while construction, manufacturing, and interest-rate-sensitive industries lag.
- Bureau of Economic Analysis: investment in information-processing equipment and software grew by 16.5 percent from a year earlier in the third quarter of 2025.
- Stripped of AI, GDP would have been markedly weaker.
- Global growth has not collapsed but is increasingly concentrated in narrow sectors and regions.

### Capital intensity and infrastructure requirements
- AI’s wave is unusually capital intensive: training large language models and deploying generative systems require vast computing power and physical infrastructure.
- Data centers worldwide may require $6.7 trillion in capital expenditure by 2030, estimates suggest.
- The scale of computing power required makes electricity generation and grid capacity critical macroeconomic variables.
- Energy bottlenecks could delay AI diffusion, anchor a higher level of core inflation, and generate localized overheating even as other sectors remain weak.
- Grid investment is becoming a central supply-side constraint, blurring the line between industrial and macroeconomic policy.

### Measurement challenges and statistical paradoxes
- National accounts and official statistics were designed for an industrial age and poorly capture intangible capital: data, algorithms, proprietary models, and cloud infrastructure.
- Some shifts are recorded (software and R&D), but many AI-related costs (training large models, refining datasets, creating new applications) are often expensed rather than capitalized.
- Semiconductors are treated as intermediate goods rather than carriers of embedded intellectual property.
- Consequence: GDP data can simultaneously overstate the immediate contribution of AI (by counting massive capital outlays) and understate its broader productivity spillovers.
- When measurement lags reality, policymakers risk misreading the economy—tightening too much or easing too soon.
- Example: The Federal Reserve faces a more complex policy landscape—AI adoption could raise potential output (making the economy less hot than headline data imply) while infrastructure constraints could set a new floor for inflation.

### New geographies, trade, and capital flows
- Imports and exports of computers, servers, and semiconductors have surged, signaling a global reallocation of supply chains.
- Manufacturing and assembly are shifting toward Southeast Asia, India, and specialized US hubs such as Texas and the Gulf Coast.
- This is a re-regionalization—not de-globalization—and leaves the US and China as dominant players while Europe seeks to catch up via industrial policy and investment incentives.
- Emerging markets have benefited from lower yields and a weaker dollar, with AI demand translating into exports and foreign direct investment—particularly in energy and component manufacturing—but increasing vulnerability to technological and geopolitical shocks.
- Equity markets have rewarded hyperscalers—the handful of firms building and financing the global computing backbone—with valuations and cash flows unseen since the dot-com era.
- Research distinguishes digital participation (use of imported digital tools) from digital depth (ability to produce/export digital goods and embed them in domestic value chains).
  - Emerging markets with digital depth—China, India, Korea, and a smaller group of specialized hubs—are attracting more stable FDI linked to AI-era production.
  - Others remain primarily consumers of imported technologies and rely more on volatile portfolio flows driven by global liquidity cycles.
- Digital depth may influence capital flow dynamics comparably to fiscal credibility or exchange-rate regimes.

### Diffusion versus concentration: growth scenarios and risks
- Historical precedent: general purpose technologies delivered broad payoffs only after years of complementary investment—in skills, management practices, and institutional adaptation.
- Two scenarios:
  - Diffusion scenario: AI applications spread across industries, enabling a sustained lift in potential output.
  - Concentration scenario: AI adoption remains confined to hyperscalers and specialized providers, returns plateau, and the economy becomes vulnerable after the investment cycle peaks.
- Corporate surveys indicate diffusion is underway but uneven—many firms experiment, fewer implement at scale.
- Risks include inadequate infrastructure, outdated statistics, and slow policy adaptation, which could produce volatile growth oscillating between investment bursts and adjustment pauses.

### Policy implications and recommendations
- Measurement:
  - Evolve measurement frameworks to capture intangible capital (data, algorithms, proprietary models, cloud infrastructure).
- Macro policy:
  - Fiscal and monetary tools must account for sectoral divergence and new supply constraints (notably energy and grid capacity).
  - Avoid policy errors driven by misinterpreting apparent slack or inflation signals when structural change is underway.
- Supply-side and industrial policy:
  - Invest in electricity generation and grid capacity to avoid bottlenecks that could delay diffusion and raise core inflation.
  - Support complementary investments in skills, management practices, and institutional adaptation to enable broader diffusion.
- International cooperation:
  - Coordinate to ensure AI diffusion benefits are not confined to a few economies.
- Emerging markets:
  - Balance seizing AI-linked opportunities (exports, FDI) with prudence to avoid overreliance on cheap energy or favorable regulation that undermines long-term competitiveness.

*Source: AI Can Lift Global Growth, F&D Magazine, Marcello Estevão*

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_Source: https://www.imf.org/en/publications/fandd/issues/2026/03/point-of-view-ai-can-lift-global-growth-marcello-estevao_