AI Can Lift Global Growth

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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.

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.

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.

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.

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.

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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