Artificial Intelligence reshapes central bank orthodoxy: monetary authorities grapple with inflation dynamics in the age of technological transformation

The accelerating deployment of artificial intelligence systems in recent years introduces an unprecedented variable into the economic analysis models that monetary authorities have relied upon for decades. Unlike previous technological cycles, whose impacts on prices and productivity could be reasonably forecasted, AI presents simultaneous and contradictory effects across the inflationary structure of global economies. While some automation technologies reduce production costs and consequently inflationary pressures, other applications accelerate demand for specialized inputs, creating bottlenecks that elevate prices in specific sectors. This complexity compels central banks to reassess their theoretical frameworks and their strategies for setting interest rates.

The absence of consensus among major monetary authorities worldwide reflects the genuine difficulty in quantifying these effects in real time. While some policymakers emphasize AI's disinflationary potential—arguing that productivity gains will offset demand pressures—others adopt a more cautious stance, fearing that the predictive capacity of their conventional models proves inadequate in the face of this transformation. The central question is not merely whether AI reduces or amplifies inflation, but rather on what time horizon these effects materialize and how they distribute across different economic segments.

This impasse places central banks in a delicate position: maintaining rigor in inflation control criteria may mean choking off the adoption of beneficial technologies; conversely, prematurely relaxing monetary policy under the promise of future productivity gains exposes economies to risks of disanchoring inflation expectations. Parametric uncertainty—that is, the inability to precisely estimate the coefficients relating AI innovation to price behavior—introduces an element of decision-making under uncertainty that challenges traditional policy formulation processes grounded in established econometric models.

Progressive convergence of perspectives will likely occur only when sufficient empirical data accumulate and clearer patterns emerge from economic reality. Until then, central banks are expected to maintain vigilance over multiple indicators—from service sector inflation to real wages and long-term expectations—to calibrate responses that acknowledge both the risks and opportunities that artificial intelligence presents. Clear communication about the sources of this methodological uncertainty also becomes instrumental for maintaining institutional credibility during a period of paradigmatic transition.

Original source: Euronews Business