Everyone knows AI is increasingly integrated into everyday life, but its rapid development raises questions about its broader economic impact. It has the potential to boost economic growth, yet also raises concerns about job displacement and the uneven distribution of gains across workers, firms and countries. The integration of AI into the economy also adds a layer of complexity around monetary policy, particularly inflation, the labour market and the neutral rate.
AI can influence economic growth in various ways. Most notably, boosting long-term growth by increasing productivity, although the extent and timing of these gains are uncertain. In parallel, AI may accelerate capital deepening (when the capital per worker increases) by stimulating investment in digital infrastructure, while also partially offsetting the drag from demographic ageing.
In the US, we have raised our real GDP growth forecast for the next decade by 0.4 percentage points, with higher productivity growth offsetting the diminishing contribution from labour supply. In the euro area, we expect a smaller uplift, reflecting the lower AI adoption rates. The UK sits between the US and the euro area, given higher services intensity and faster adoption in financial and professional services.
AI is often portrayed as a force for job destruction, but research points to a more nuanced picture: limited broad-based disruption so far, with changes primarily at the task level rather than wholesale job elimination. Several studies show modest changes in hiring for AI-exposed roles, with effects concentrated in specific segments and mainly among younger or entry-level workers. To date, the impact has manifested more as a slowdown in hiring demand than as large-scale layoffs. For example, one study found that openings for routine, automation-prone roles fell by 13% after the release of ChatGPT, while demand for more analytical, technical and creative jobs grew by 20%.
In terms of labour supply, there is no clear evidence yet of significant AI-driven shifts in aggregate participation or willingness to work. AI could influence labour supply by reducing the time it takes to do household chores (potentially increasing participation), improving job search or lowering barriers to skill acquisition through tutoring tools. Conversely, it might reduce labour supply if AI-enhanced leisure becomes more appealing or if job insecurity discourages workforce entry.
AI affects inflation through multiple, and sometimes opposing, channels. In the near term, AI capital expenditure in the US has already created acute price pressures in electricity, memory chips, transformers and construction materials – key inputs for the data centre buildout. Since this capex is not labour-intensive, it is not creating wage pressures. We estimate AI is contributing around 0.3 percentage points to core personal consumption expenditure inflation in the US.
AI’s disinflationary impact is likely to be particularly strong in services – the most persistent component of inflation. Since many service activities rely on cognitive tasks, they are highly exposed to AI-driven productivity gains. Evidence from firm-level studies, including work summarised by the International Monetary Fund, points to clear cost savings in finance, healthcare, and customer services. Conversely, AI may have ambiguous effects on competition: it can increase price transparency and reduce search costs (lowering markups), but it may also reinforce market concentration as firms with superior data and computational resources gain pricing power. In such cases, cost savings may not be fully passed on to consumers.
AI poses several challenges for monetary policy. The first is measurement. AI-driven improvements in product quality, customisation and digital services are imperfectly captured in the consumer price index and GDP statistics. As highlighted by the IMF (2024), this could understate real output and overstate inflation by 10–30 basis points annually, complicating output-gap and natural rate estimates.
Second is the natural rate. The direction is contested and is an active debate inside the Federal Reserve. Former Federal Reserve Chair Jerome Powell argued that AI likely raises neutral rates in the near term, because the demand side – the massive physical buildout required to power AI – is running ahead of any productivity payoff, and the disinflationary benefits of AI remain theoretical for now. Federal Reserve Chair Kevin Warsh suggested instead that stronger productivity from AI can support non-inflationary growth, giving room for rate cuts without sparking inflation. Our base case sits closer to Powell’s framing: a modest 25 basis point increase in neutral rates over the next decade, front-loaded as capital expenditure runs ahead of productivity gains.
There is no doubt however, that AI is one of the most consequential macroeconomic forces of the coming decades. Its effects will be neither uniform nor immediate; they will unfold gradually. We believe AI will gradually lift productivity and exert disinflationary pressure – particularly in services – while raising prices for electricity and advanced semiconductors. However, the transmission will be slow. We therefore forecast 0.5 percentage-point annual productivity boost in the US over the next decade thanks to AI, with smaller uplifts of 0.1 to 0.3 percentage points in Europe and Japan, respectively.