AI推理成本如何影响通胀与货币政策，这篇论文给出了量化分析框架。
The Economics of AI Inference: Inflation Dynamics, Welfare Costs, and Optimal Monetary Policy under the Inference-Cost Phillips Curve
- 构建了包含AI推理成本的菲利普斯曲线（ICPC），揭示其对通胀的传导机制。
- 实证发现美国通胀对推理成本弹性接近1，且多国数据具一致性。
- 为央行应对生成式AI引发的通胀提供了最优政策响应公式。
本文构建了一个统一的微观经济与货币理论，研究人工智能推理成本向通胀、福利及最优货币政策的传导机制。提出推理成本菲利普斯曲线（ICPC），在新凯恩斯模型中引入企业边际成本中的非平凡AI推理成分λ̄，并推导出闭式结构斜率κ*_inf = λ̄ * κ，其中κ为标准Calvo-Yun斜率。推导出受推理成本冲击下的消费者福利的希克斯-卡尔多分解，证明了改进型泰勒原则，并刻画了承诺下的最优货币政策响应系数ψ*_inf = (1 + φρ) * λ̄ * κ。通过二阶福利损失公式实现模型闭式求解。使用2022年1月至2026年4月美国月度数据，采用两步GMM估计器与Newey-West HAC标准误，估计出经验斜率κ̂_inf = 0.087（HAC标准误0.021），与结构预测在一个标准误内。50个滚动窗口的缩放回归得到b̂ = 0.987（R² = 0.998），表明近单位弹性传导。对G7国家的简化形式面板回归得b̂^G7 = 0.094（标准误0.026），沃尔德检验不拒绝跨国同质性（p = 0.78）。该框架为生成式AI冲击下推理成本动态、货币政策响应与福利代价的联合研究提供统一均衡基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。