New geographies

AI ' s rise is also redrawing trade and capital-flow patterns. 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 re-regionalization is not de-globalization; it ' s a new geography of interdependence. The US and China remain dominant players, with Europe seeking to catch up through industrial policy and investment incentives. For many emerging markets, AI demand is already translating into exports and foreign direct investment-particularly in energy and component manufacturing-but also into vulnerability to technological and geopolitical shocks.

Capital flows increasingly follow the map of AI infrastructure. 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. As a result, a small group of tech giants now accounts for a disproportionate share of global AI-related capital expenditure and productivity expectations.

Research by the Institute of International Finance reveals a distinction between digital participation (the use of imported digital tools) and digital depth (the ability to produce and export digital goods and services 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 foreign direct investment linked to AI-era production. Their export profiles show rising shares of information and communications technology services, royalties, and digital content. Others remain primarily consumers of imported technologies and therefore rely more heavily on volatile portfolio flows driven by global liquidity cycles.

As AI becomes central to economic activity, digital depth may play a role in capital flow dynamics comparable to fiscal credibility or exchange-rate regimes-an underappreciated channel that global policymakers will need to monitor closely.

The scale of computing power required for AI training and inference has made electricity generation and grid capacity critical macroeconomic variables.

The macroeconomic implications are profound. 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.

Diffusion or concentration?

The deeper question is whether the AI boom will translate into broad-based productivity growth or remain confined to a narrow set of businesses and industries. History suggests that the payoff from general purpose technologies comes only after years of complementary investment-in skills, management practices, and institutional adaptation. Electricity and IT took decades to diffuse widely enough to raise aggregate productivity.

If AI adoption remains concentrated among hyperscalers and specialized service providers, the returns may plateau quickly, leaving the economy vulnerable once the investment cycle peaks. But if AI applications spread across industries, the potential for a sustained lift in potential output becomes real. Corporate surveys suggest diffusion is underway but uneven. While many firms are experimenting with AI, only a smaller group is implementing it at scale.

The risk is that diffusion will collide with inadequate infrastructure and outdated statistics. The mismatch between rapid technological change and slow policy adaptation could make the next few years unusually volatile. Growth could oscillate between bursts of investment and pauses for adjustment while policymakers struggle to interpret what the numbers mean.

Behind the numbers

The AI boom is unfolding against a backdrop of global uncertainty. Tariff wars, immigration restrictions, and fiscal imbalances have left the world economy more fragmented and less predictable. In this environment, AI stands out not just as a technological story but as a macroeconomic stabilizer-one of the few genuine sources of incremental demand and optimism.

Yet this narrow engine cannot carry the entire global economy indefinitely. The US expansion remains capital-heavy and employment-light. Europe risks missing out unless it retools its industrial and digital policy. Emerging markets must balance opportunity with prudence, ensuring that cheap energy or favorable regulation does not substitute for long-term competitiveness.

Policymakers and statisticians must move faster. Measurement frameworks must evolve to capture intangible capital; fiscal and monetary tools must account for sectoral divergence and new supply constraints; and international cooperation must ensure that the benefits of AI diffusion are not confined to a few economies.