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Federal Reserve Chairman Kevin Warsh said on August 28, 2026, that artificial intelligence will change not only economic growth but also the way the Fed sets interest rates, interprets data and communicates with markets. Speaking at the Federal Reserve Bank of Kansas City’s Jackson Hole Economic Policy Symposium, Warsh called AI “a new variable—potentially a new factor of production” with consequences for both the economy and monetary-policy conduct.
His message was methodological rather than a promise of an immediate rate change: historical relationships, forecasting models and fixed policy habits may become less dependable if AI changes productivity, labor markets, prices, expectations and financial conditions faster than policymakers can observe.
What Warsh said about AI and the Fed
Warsh argued that the factors relevant to sound monetary policy change over time. Because AI could alter the economy’s supply capacity and the behavior of households, businesses and investors, the Fed should avoid treating past relationships as permanent rules.
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— Kevin Warsh, Jackson Hole keynote, August 28, 2026
That does not mean AI automatically requires lower or higher interest rates. It means policymakers may need to reassess the benchmarks used to judge inflation, employment and economic slack as adoption develops.
Why AI could make inflation harder to read
The timing of AI’s effects is the central uncertainty. Investment and demand can increase before new systems deliver broad productivity and supply gains. Warsh told Congress on July 15, 2026, that AI investment would probably raise measured prices over the following 12 months. He also stressed that a one-time price-level increase is not necessarily ongoing inflation if supply subsequently expands.
The New York Fed’s Staff Report 1192, published in April 2026 by economist Simone Lenzu, identifies three channels through which AI could affect monetary policy:
| Channel | What can change | Policy question |
|---|---|---|
| Cyclical transmission | AI can change how supply and demand shocks pass through to prices and output. | Is a price increase temporary, or is it becoming persistent inflation? |
| Structural transition | AI can shift potential output and the natural rate of interest as production methods and labor demand change. | Are current interest rates restrictive, neutral or stimulative under the new structure? |
| Financial stability | High asset valuations and concentration around a small number of models or providers can create vulnerabilities. | Could an AI-related financial correction damage demand or credit conditions? |
The report also warns of an AI-specific stagflation risk: adoption bottlenecks could create cost-push inflation while valuations rise. Conventional rate increases may cool demand but cannot by themselves remove infrastructure shortages, implementation delays or other supply constraints.
Could AI change interest-rate policy?
Potentially, but not according to a preset AI rule. If AI produces sustained productivity gains, the economy may be able to grow faster without generating the same inflation pressure. A stronger supply side could also affect the natural rate of interest—the rate consistent with stable inflation and full employment. If that rate rises, a policy rate that previously seemed neutral could become comparatively accommodative.
The opposite is also possible in the near term. Large investment programs can lift demand for computing equipment, electricity, water, construction and skilled labor before productivity gains appear. Policymakers therefore have to separate an initial relative-price adjustment from a broad, continuing rise in prices.
Governor Lisa Cook’s September 28, 2026 speech illustrated the issue. She referred to announced AI investment plans totaling $2 trillion and said electricity and water costs were increasing by around 5% year over year, potentially partly because of AI-related demand. She also reaffirmed the Fed’s 2% inflation target. Those figures do not establish how much inflation AI will ultimately cause; they show why officials are monitoring both demand pressure and future supply capacity.
Why Warsh questioned routine forward guidance
Warsh said forward guidance introduced during the Global Financial Crisis had “overstayed its welcome.” His concern is that detailed promises about the Fed’s next move can make markets focus too heavily on the communication itself and can slow the central bank’s response when conditions change.
In an economy altered rapidly by AI, guidance based on an outdated forecast could become misleading. Warsh instead favored more reliable models and robust policy rules while acknowledging that perfect forecasting remains an aspiration. The practical approach is adaptive: update assumptions as evidence on productivity, prices, employment and financial conditions arrives rather than treating a previous path as a commitment.
What the Fed will need to monitor
- Productivity: whether AI improves economy-wide output, not just the performance of early-adopting firms.
- Prices and supply: whether investment-related increases fade as capacity expands or spread into persistent inflation.
- Employment: whether AI substitutes for tasks, complements workers or changes the pace and composition of hiring.
- Expectations: whether households and businesses alter spending, pricing and wage decisions in response to AI forecasts.
- Financial stability: whether elevated valuations, leverage or concentration create risks that could disrupt credit and demand.
- Policy operations: how AI affects supervision, internal analysis and the reliability of models used by the central bank and financial institutions.
Fed officials are studying these effects across inflation, employment, financial stability, supervision and the central bank’s own operations. The challenge is that the same technology can be disinflationary in the long run and inflationary during a capacity-constrained rollout.
What this means for readers and markets
Warsh’s remarks do not announce a new AI-adjusted interest-rate formula or predict a specific Federal Open Market Committee decision. They signal a willingness to revise models and communication practices as the economy’s structure changes.
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For markets, the implication is greater sensitivity to incoming data and less certainty that old relationships will hold. For businesses and households, AI investment may bring near-term cost and demand pressures before any broad productivity dividend appears. For policymakers, the task is to distinguish temporary price-level changes from persistent inflation while estimating a moving potential-growth rate and natural interest rate.
The immediate lesson is timing: investment and demand effects may arrive first, while productivity and supply benefits take longer and remain uncertain. Monetary policy will have to respond to that sequence without assuming that either the risks or the gains are permanent.
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