Artificial Intelligence and Monetary Policy: A Framework and Perspective on Cyclical Transmission, Structural Transition, and Financial Stability | 국외연구자료

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Artificial Intelligence and Monetary Policy: A Framework and Perspective on Cyclical Transmission, Structural Transition, and Financial Stability

FRB of New York

2026.04.30

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I develop a framework analyzing how artificial intelligence (AI) reshapes monetary policy through three interrelated channels: cyclical transmission, structural transition, and financial stability. In the short run, AI can alter inflation dynamics by changing how supply and demand disturbances map into prices―through shifts in production technologies, pricing behavior, cost pass-through, and expectations―even when conventional measures of economic slack are unchanged. Over longer horizons, AI may shift the natural benchmarks around which policy is calibrated, including potential output and the natural rate of interest. For financial stability, AI may improve credit allocation and risk assessment, but can also heighten systemic vulnerabilities through inflated expectation-driven asset valuations and model monocultures. A particular risk arises at the intersection of these channels: if AI initially depresses realized efficiency through adoption frictions while simultaneously fueling elevated asset valuations, the economy may face cost-push inflation and financial fragility at once―an AI-specific stagflation risk that the interest rate instrument alone is ill-suited to address. I argue that AI does not call for a redefinition of central banks’ objectives, but it does require a recalibration of existing frameworks: its diffusion blurs the distinction between cyclical fluctuations and structural shifts, raising the value of cost-side diagnostics and robust policy strategies over exclusive reliance on reduced-form inflation-gap relationships.

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