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Yes, potentially—but AI is not a proven way to bring down inflation. If its productivity gains expand the supply of goods and services and lower costs faster than investment, spending, and energy or computing needs push demand and input prices up, AI could ease inflationary pressure. The balance depends on timing, expectations, labor-market effects, and which industries adopt it.
How AI could push inflation down—or up
Inflation reflects the balance between demand and the economy’s ability to supply goods and services, as well as changes in production costs. AI can affect both sides of that balance, which is why its overall price effect is uncertain.
The disinflationary channel: more output at lower cost
When AI helps workers or businesses produce more with the same resources, productivity rises. If those gains reduce the labor or other costs per unit of output, firms may be able to increase supply without raising prices as much. AI could also help manage energy use and electricity grids, potentially reducing some costs.
The inflationary channel: investment and demand arrive first
Building and deploying AI requires investment. Expectations of higher future income can also lead households and firms to spend or invest sooner. Meanwhile, demand for computing power and electricity can add pressure to input costs. If this spending grows before productivity gains translate into extra supply, AI adoption can add to inflationary pressure.
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A 2025 Bank for International Settlements speech describes these competing channels: productivity can lower unit labor costs, while computational demand can raise electricity use and pressure energy prices. It also notes that expected future gains can bring consumption forward (BIS, 13 June 2025).
What economic models say about the timing
A 2024 BIS working paper models AI adoption across industries and finds that the inflation effect depends partly on whether households and firms anticipate future productivity gains. When they do not, adoption is initially disinflationary in the model; as higher output, consumption, and investment feed through the economy, inflation rises moderately later. When they do anticipate the gains, inflation rises immediately as spending responds ahead of productivity.
The paper is a model result, not evidence that AI has already lowered inflation or a forecast that it will do so. It also finds that where AI improves productivity matters: in the model, a given aggregate productivity increase has twice the output effect when AI affects sectors producing consumption goods rather than investment goods. The propagation through suppliers and prices therefore matters, not just the size of the productivity gain (BIS Working Paper 1179, 17 April 2024).
Productivity estimates are not inflation forecasts
The OECD estimates that AI could add 0.25–0.6 percentage points to annual aggregate total-factor productivity growth over a 10-year horizon, and 0.4–0.9 percentage points to labor productivity. These are modeled estimates, not measured outcomes or predicted percentage-point reductions in inflation. They depend on assumptions about adoption, which tasks are exposed to AI, and how effects spread across industries and suppliers (OECD, 2024).
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Productivity growth can make lower costs and stronger supply possible, but it does not translate mechanically into lower consumer-price inflation. The result depends on how quickly firms adopt AI, whether they pass cost savings through to customers, and how demand and input prices change at the same time.
Why AI investment and its relationship to workers matter
AI-related investment can itself boost economic demand. An IMF working paper models U.S. ICT investment and finds that the macroeconomic effects depend on whether ICT capital complements or substitutes for labor. In its scenarios, complementary investment can raise output and inflation and increase the natural rate, while substitution can imply a looser policy stance. These are results from a particular model, not a universal forecast for AI or every economy (IMF Working Paper 2025/224, October 2025).
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This distinction helps explain why a productivity story alone is incomplete. If AI works alongside employees and supports more production, the resulting demand and output may grow together. If it replaces some tasks, the effects on wages, spending, and prices may differ. The eventual outcome also depends on how quickly new supply becomes available.
AI’s own costs and market conditions can constrain the gains
AI services are inputs to production, and their cost and availability affect how broadly businesses can adopt them. OECD market indicators report falling quality-adjusted prices and increasing numbers of providers and model offerings, which could make adoption less expensive. The same analysis identifies data, computing power, and skills as potential bottlenecks that can constrain diffusion or raise costs (OECD, 17 June 2025).
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Lower prices for AI models do not by themselves mean lower consumer-price inflation. They may reduce one business input cost, but the effect on final prices depends on adoption, competition, other production expenses, and whether savings are passed on.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can AI help central banks forecast inflation?
AI may be useful to central banks and analysts as a forecasting aid, but better forecasts are different from lower inflation. A St. Louis Fed study used Google’s PaLM to make in-sample conditional inflation forecasts for 2019–23 and compared them with the Survey of Professional Forecasters. The authors reported lower mean-squared errors overall in most years and at almost all horizons, but PaLM’s forecasts returned more slowly to the 2% inflation anchor.
This is a comparison for one model, a specific period, and an in-sample forecasting exercise—not proof that generative AI consistently outperforms professional forecasters in other settings. Nor does a forecast itself control prices (Federal Reserve Bank of St. Louis Review, 29 November 2024).
What to watch to judge AI’s inflation effect
There is no single AI adoption figure that answers whether inflationary pressure is easing. The relevant evidence is how productivity, spending, costs, and supply evolve together. Useful indicators include:
- Timing: Are output and productivity gains arriving before or after AI investment and related spending increase?
- Expectations: Are households and firms spending or investing ahead of gains they expect in the future?
- Labor relationship: Is technology complementing workers and supporting more production, or substituting for labor in ways that change employment and spending?
- Industry effects: Is adoption improving production of consumer goods, investment goods, or both, and how are the gains spreading through suppliers?
- Input constraints: Are computing power, electricity, data, and skilled workers readily available, or are shortages pushing costs up?
- Pass-through: Are firms passing lower unit costs through to prices, or retaining the savings?
- Measurement: Can policymakers separate structural improvements in productive capacity from cyclical changes in demand?
The broader evidence remains unsettled. An IMF literature review in March 2024 said empirical findings on AI’s productivity and employment effects were inconclusive at that time. A BIS speech in November 2025 likewise described the labor and price effects as still developing and difficult to separate from cyclical factors, with differences across industries and regions (IMF, 22 March 2024; BIS, 14 November 2025).
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