The rapid expansion of artificial intelligence has become an important source of economic growth, but growing dependence on the technology boom is creating a difficult problem for central banks. Massive investments in data centres, advanced chips and computing infrastructure have supported business activity and helped offset some of the economic damage caused by higher energy costs. However, this contribution to growth may not be sustainable indefinitely. If investment slows sharply while inflation remains stubborn, monetary policymakers could find themselves dealing with weaker economic activity without gaining the relief they would normally expect from falling demand.

International Monetary Fund Managing Director Kristalina Georgieva has warned that central banks must account for the possibility of a slowdown in artificial intelligence-driven growth when deciding how to tackle inflation. Her comments come at a time when technology shares are experiencing considerable volatility and investors are increasingly questioning whether the enormous amounts being spent on AI infrastructure will generate adequate returns. The concern is no longer limited to the valuations of individual technology companies. It is becoming a broader question about the extent to which global economic growth and financial markets have become dependent on the continued expansion of the AI industry.

The timing is particularly significant because central banks are already facing competing pressures. Higher energy costs linked to the Middle East conflict have added to inflation risks, while the AI investment boom has provided an important source of economic activity in several major economies. A sudden weakening of that investment could remove one of the supports keeping growth resilient, complicating decisions on interest rates and the wider direction of monetary policy.

AI Investment Has Become a Significant Growth Driver

The scale of investment in artificial intelligence has transformed the technology sector into a major contributor to economic activity. Technology companies are spending enormous sums on data centres, high-performance computing systems, semiconductors and the electricity infrastructure required to operate them. These investments generate demand across several industries, including construction, engineering, energy, manufacturing and telecommunications.

The economic benefits extend beyond the companies directly developing AI systems. Semiconductor manufacturers receive orders for advanced processors, construction firms secure contracts for large infrastructure projects, and energy companies face growing demand from data-centre operators. The resulting investment activity supports employment, corporate revenues and economic growth, particularly in countries that manufacture advanced chips or host major technology businesses.

The United States has been one of the principal beneficiaries, while several Asian economies have also gained from their position in the semiconductor and hardware supply chain. The investment cycle has helped sustain economic activity at a time when other pressures, including higher oil prices and geopolitical uncertainty, could otherwise have weighed more heavily on growth.

However, there is an important distinction between investment-led growth and productivity-led growth. Building data centres and purchasing advanced chips generate immediate economic activity, but the longer-term benefits depend on whether businesses can use AI to improve productivity, reduce costs and develop commercially successful products. If companies continue spending heavily without achieving sufficient returns, the investment cycle could weaken before the promised productivity gains become widespread.

This is the uncertainty that increasingly concerns policymakers. The current expansion is supported partly by expectations about what artificial intelligence will deliver in the future. If those expectations prove excessive, investment decisions could change quickly, affecting not only technology companies but also the wider economy.

Market Volatility Exposes the Risks of Overdependence

The recent technology stock selloff has brought these concerns into sharper focus. Investors have become increasingly sensitive to high valuations, the cost of AI infrastructure and uncertainty over how quickly companies can turn their investments into revenue. The political consequences of the US midterm elections have added another layer of uncertainty, with expectations of greater regulatory scrutiny under the incoming Democratic-controlled Congress.

The market reaction reflects several concerns operating at the same time. Technology companies have committed substantial capital to infrastructure, but the commercial returns remain difficult to measure in many areas. Investors are also questioning whether the pace of spending can continue if companies begin to face pressure to improve profitability and control costs.

A sharp correction in technology shares could have consequences beyond the direct losses suffered by investors. Falling valuations may make it more expensive for companies to raise capital, weaken confidence among businesses and households, and encourage technology firms to postpone planned projects. If that happens across several major companies, the effects could spread through the supply chains and industries that have benefited from the AI investment cycle.

The IMF has previously warned that a sharp decline in AI investment could reduce global economic output by 1.1% by 2028, with US output falling by 1.8% in the scenario examined. These projections underline the potential scale of the exposure. They do not mean that such a decline is inevitable, but they demonstrate how a reversal in one major investment sector could affect broader economic performance.

The financial stability implications are also important. A prolonged market correction could expose weaknesses in investment portfolios and financing arrangements linked to the AI industry. If companies, funds and lenders have become too dependent on continued growth in technology valuations, a sudden reversal could create pressure across financial markets.

Central Banks Face a More Complicated Inflation Problem

Georgieva’s warning is particularly relevant because the relationship between economic growth and inflation is becoming less predictable. Traditionally, a slowdown in economic activity can reduce demand, ease pressure on prices and give central banks more room to lower interest rates. But the present situation is more complicated because the AI investment boom is supporting growth even as other factors are pushing inflation higher.

The Middle East conflict has disrupted oil supplies and contributed to higher energy costs. That creates an additional challenge for central banks because energy inflation can affect transport, manufacturing, electricity and the cost of essential goods. If AI investment begins to weaken at the same time, policymakers could face a combination of slower growth and persistent inflation.

This is not an easy environment for monetary tightening. Raising interest rates may help contain inflation expectations, but it can also discourage borrowing and investment. If the AI sector is already reducing spending, tighter financial conditions could intensify the slowdown. On the other hand, cutting rates too early could allow inflationary pressures to become more entrenched.

Central banks will therefore need to distinguish between different sources of inflation rather than respond only to headline economic growth figures. A weakening investment cycle may reduce demand in some sectors, but it will not automatically bring down energy prices or resolve supply disruptions. Policymakers must assess whether inflation is being driven by excess demand, supply constraints or a combination of both.

There is also a risk that financial market volatility could influence economic conditions before it becomes visible in conventional data. Falling share prices can affect household wealth, business confidence and corporate investment decisions. These developments may eventually weaken consumption and employment, creating additional pressure on central banks to reassess their policy outlook.

The Productivity Promise Has Yet to Be Fully Established

The long-term economic case for artificial intelligence remains substantial. Businesses are adopting AI tools for research, software development, customer services, data analysis and administrative work. The technology could improve efficiency across industries and allow companies to produce more with fewer resources.

However, the transition from technological capability to measurable productivity growth is not automatic. Businesses must invest in training, data systems, cybersecurity and organisational changes before AI tools can be integrated effectively into daily operations. Some companies may find that the cost of implementation is higher than expected, while others may struggle to identify applications that generate meaningful financial returns.

This gap between investment and productivity is important for the global economy. If AI spending continues to expand faster than its commercial benefits, financial markets may eventually demand stronger evidence of profitability. That could lead to more cautious investment decisions, even if the underlying technology continues to improve.

Regulation may also influence the pace of adoption. Governments are under pressure to address concerns about employment, data protection, competition, energy consumption and the use of AI in sensitive areas. Greater oversight may increase compliance costs, although it could also improve public confidence and encourage more sustainable adoption.

The central issue for policymakers is therefore not whether AI will remain important, but how quickly its economic benefits will materialise and whether the current level of investment can be justified.

Monetary Policy Must Account for a Changing Growth Structure

The AI boom has created a new source of economic dependence that central banks cannot afford to ignore. Investment in technology infrastructure is now large enough to influence national growth, corporate earnings and financial market valuations. A slowdown could therefore have consequences that extend well beyond the technology sector.

Central banks will need to monitor the pace of AI-related investment alongside traditional indicators such as employment, consumption, industrial production and inflation. They must also consider whether financial market corrections are likely to affect business confidence and spending. This does not mean monetary policy should be designed to protect technology valuations, but rather that policymakers need a clearer understanding of how changes in the sector could affect the wider economy.

The challenge is especially significant because the AI investment cycle is still evolving. Some of the spending may generate substantial productivity gains over time, while other projects may struggle to deliver acceptable returns. A correction would not necessarily mean the technology has failed, but it could expose the gap between financial expectations and actual commercial performance.

For central banks, the danger lies in assuming that growth will continue to receive the same support from AI investment. If that support weakens while energy-related inflation remains elevated, the policy choices become considerably more difficult. The next phase of the AI boom will therefore matter not only to technology companies and investors, but also to the institutions responsible for maintaining economic and financial stability.

(Adapted from Reuters.com)

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