Abstract:We develop a unified microeconomic and monetary theory of artificial intelligence inference costs and their pass-through to inflation, welfare, and optimal monetary policy. We introduce the Inference-Cost Phillips Curve (ICPC), an augmented New Keynesian Phillips curve in which firm-level marginal costs of producing differentiated goods include a non-trivial AI inference component lambda-bar, and prove a closed-form structural slope kappa*_inf = lambda-bar * kappa, where kappa is the standard Calvo-Yun slope. We derive a welfare-relevant Hicks-Kaldor decomposition of consumer welfare under inference-cost shocks, prove a generalized Taylor principle for the inference-augmented economy, and characterize the optimal monetary policy response coefficient psi*_inf = (1 + phi*rho) * lambda-bar * kappa under commitment. A second-order welfare loss formula closes the model in closed form. We confront the theory with U.S. monthly data 2022:M01-2026:M04 using a two-step GMM estimator with Newey-West HAC standard errors and Hansen J-test, recovering an empirical slope kappa-hat_inf = 0.087 (HAC s.e. 0.021) which lies within one standard error of the structural prediction. A scaling regression over 50 rolling-window subwindows yields b-hat = 0.987 (R^2 = 0.998), consistent with a near-unit-elasticity pass-through. A G7 reduced-form panel with Driscoll-Kraay HAC standard errors yields b-hat^G7 = 0.094 (s.e. 0.026), and a Wald test fails to reject cross-country homogeneity (p = 0.78). The framework provides a single equilibrium scaffold for the joint study of AI inference cost dynamics, monetary policy under generative-AI shocks, and the welfare cost of inference-driven inflation.
| Comments: | 6 pages, 5 tables |
| Subjects: | General Economics (econ.GN); Machine Learning (cs.LG) |
| MSC classes: | 91B64, 62P20, 62M10 |
| ACM classes: | J.4; I.2.11 |
| Cite as: | arXiv:2605.20281 [econ.GN] |
| (or arXiv:2605.20281v1 [econ.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20281 arXiv-issued DOI via DataCite |
From: Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov [view email]
[v1]
Tue, 19 May 2026 05:26:11 UTC (80 KB)
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