The rapid expansion of artificial intelligence investment is creating new risks for global financial stability as companies pour trillions of dollars into computing infrastructure, much of it increasingly financed through debt and private credit, according to Bank for International Settlements General Manager Pablo Hernández de Cos.
The scale of the AI buildout is already large enough to influence broader economic conditions, while its effects are reaching beyond technology companies into financial markets, trade, productivity and employment.
For central banks, AI does not alter their monetary policy mandates, but it makes economic conditions more difficult to assess because the technology can affect demand, supply and financial markets at the same time, Hernández de Cos said at a conference hosted by India’s central bank.
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The BIS estimates that the world’s five largest technology companies will invest more than $1 trillion in AI between 2025 and 2026. Industry forecasts indicate that global AI investment could rise from about $500 billion currently to as much as $4 trillion by 2030.
“The promise of AI is real,” Hernández de Cos said, while cautioning that its long-term economic impact would depend on policy decisions, investment in skills and infrastructure, and how broadly the gains from the technology are distributed.
The size of the investment cycle is becoming a central concern for policymakers because the financing behind the AI boom is increasingly extending beyond companies’ existing earnings.
Hernández de Cos said more of the investment was being financed through debt and private credit, with much of that funding remaining “opaque and interconnected.” That combination could become a source of vulnerability if expected AI revenues and profits fail to materialize quickly enough to justify current investment levels.
The concern is not that AI investment will necessarily produce a financial crisis. Rather, the scale of spending, high expectations surrounding future returns and increasingly complex financing structures could amplify the effects of a downturn if valuations or corporate earnings fall sharply.
“I do not say that this is where the AI boom must lead,” Hernández de Cos said. “But the scale and speed of the current investment boom, and the weight of expected commercial returns, do warrant some caution.”
He compared the current investment cycle with earlier periods of rapid technological expansion, including the railway boom and the dotcom surge, when expectations surrounding new technologies helped drive substantial investment before economic realities eventually forced valuations and spending patterns to adjust.
The AI boom is also reshaping international trade.
Countries deeply integrated into the technology supply chain, including South Korea, Singapore, Malaysia and Taiwan, have benefited from stronger export prices for AI chips and related equipment. That creates another channel through which AI can influence the global economy. A surge in demand for advanced chips and data-center equipment can boost exports, industrial production and investment in economies that supply the technology, while increasing their exposure to a potential reversal in AI-related demand.
The productivity impact could be substantial if companies successfully integrate AI into their operations.
Hernández de Cos pointed to studies showing productivity gains of between 10% and 65% for specific tasks, particularly in areas such as coding, consulting and professional writing.
Current estimates suggest AI could increase total factor productivity growth by about half a percentage point a year, depending on how quickly businesses adopt the technology and how effectively workers and capital are reallocated toward more productive uses.
The development is of the essence because higher productivity can increase economic output without requiring a proportional increase in labor and capital inputs. But those gains depend on companies redesigning processes, workers acquiring new skills, and economies moving resources away from activities made less productive by technological change.
Advanced economies are expected to benefit first because they have larger service sectors and greater capacity to deploy AI. Emerging markets face more varied prospects.
Hernández de Cos said India has a “genuine opportunity” to narrow the gap, helped by its digital public infrastructure.
The employment effects present a more complicated picture.
AI can increase the productivity of workers by helping them complete tasks more quickly, but it can also automate routine cognitive work. Hernández de Cos said job losses have so far been limited, although signs of disruption are emerging in customer service, programming and administrative roles. That puts greater pressure on governments and businesses to invest in retraining and reskilling as AI adoption expands.
The distribution of the productivity gains will be crucial. Companies that successfully deploy AI may reduce costs and increase output, while workers whose tasks are automated could face weaker demand for their skills. Economies that can move displaced workers into expanding areas of activity are more likely to capture the broader benefits of the technology.
For financial markets, the risks are concentrated in another area: the gap between expectations and earnings.
Hernández de Cos warned that lofty valuations, market concentration and opaque financing structures could create vulnerabilities if corporate profits fail to meet expectations.
The concern is considered relevant because AI investment is increasingly concentrated among a relatively small number of technology companies and infrastructure suppliers. Their spending decisions can affect semiconductor manufacturers, data-center operators, power producers and other parts of the global supply chain.
A slowdown in AI investment could therefore spread beyond the technology sector.
The opposite risk also exists. If AI adoption accelerates faster than expected and companies compete aggressively for computing capacity, electricity and specialized infrastructure, investment could remain elevated for longer, increasing demand pressures in already constrained parts of the economy.
The situation has resulted in a difficult environment for central banks. AI can increase productive capacity over time, potentially reducing inflationary pressure, while the investment boom can simultaneously increase demand for labor, equipment, electricity and construction.
The result is an economy in which the traditional relationship between demand, supply and prices becomes harder to interpret.
For policymakers, the central issue is therefore not whether AI will transform the economy. The technology is already affecting investment, trade and workplace productivity. The harder concern is whether the current pace of spending is supported by sustainable economic returns.
Analysts have noted that if productivity gains spread broadly through the economy, today’s infrastructure spending could ultimately support higher growth and stronger corporate earnings. But if investment runs ahead of commercial returns, the same financial structures supporting the AI buildout could amplify losses when expectations change.
Hernández de Cos’s warning leaves room for both outcomes. The BIS is not predicting an AI-led financial crisis. It is warning that the sheer scale of the boom means policymakers can no longer treat AI as a narrow technology-sector story.
The financial system, labor market and global economy are increasingly becoming part of the AI investment cycle. That makes the sustainability of the boom a macroeconomic issue, not simply a bet on which technology company develops the most capable model.