AI-Enabled Rent-Seeking: How Generative AI Alters Market Transparency and Efficiency

TL;DR

This paper examines how generative AI impacts market transparency and rent-seeking, revealing both benefits in reducing traditional rents and new challenges like information manipulation that can harm social welfare.

Contribution

It introduces a dynamic economic model analyzing AI's dual role in decreasing traditional rents while enabling new rent-seeking behaviors, and proposes policy solutions.

Findings

Generative AI increases market transparency and reduces traditional information rents.

AI also facilitates new rent-seeking behaviors such as information manipulation.

Policy interventions can mitigate negative effects on social welfare.

Abstract

The rapid advancement of generative artificial intelligence (AI) has transformed the information environment, creating both opportunities and challenges. This paper explores how generative AI influences economic rent-seeking behavior and its broader impact on social welfare. We develop a dynamic economic model involving multiple agents who may engage in rent-seeking activities and a regulator aiming to mitigate social welfare losses. Our analysis reveals a dual effect of generative AI: while it reduces traditional information rents by increasing transparency, it also introduces new forms of rent-seeking, such as information manipulation and algorithmic interference. These behaviors can lead to decreased social welfare by exacerbating information asymmetries and misallocating resources. To address these challenges, we propose policy interventions, including taxation and regulatory measures. This study provides a new perspective on the economic implications of generative AI, offering valuable insights for policymakers and laying a foundation for future research on regulating AI-driven economic behaviors.