Working Paper: CEPR ID: DP17221
Authors: Hans Gersbach; Evgenij Komarov; Richard von Maydell
Abstract: We model Artificial Intelligence (AI) as self-learning capital: Its productivity rises by its use and by training with data. In a three-sector model, an AI sector and an applied research (AR) sector produce intermediates for a final good firm and compete for high-skilled workers. AR development benefits from inter-temporal spillovers and knowledge spillovers of agents working in AI, and AI benefits from application gains through its use in AR. The economy converges to a steady state and displays a sequence of four tipping points in the transition: First, entrepreneurs and second, high-skilled workers drive the accumulation of self-learning AI, which will later be re-balanced by reverse movements to the AR sector (third and fourth). In the steady state, AI accumulates autonomously due to application gains from AR. We show that suitable tax policies induce socially optimal movements of workers between sectors. In particular, we provide a macroeconomic rationale for an AI-tax on AI-producing firms, once the accumulation of AI has sufficiently progressed.
Keywords: Applied Research; Artificial Intelligence; Growth; Labor Market Transitions; Learning Capital; Tech Giants
JEL Codes: E13; E24; O33; O41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Application of AI (C45) | AI accumulation (C45) |
| Entrepreneurs and high-skilled workers (J61) | Accumulation of self-learning AI (C45) |
| Suitable tax policies (H29) | Socially optimal movements of workers between AI and AR sectors (J68) |
| Knowledge spillovers (O36) | Earlier transitions in labor allocation (J29) |
| Tipping points (E32) | Labor transitions between sectors (J63) |