AI-Enabled Rent-Seeking: How Generative AI Alters Market Transparency and Efficiency
Yukun Zhang, Tianyang Zhang
TL;DR
This paper develops a dynamic, AI-aware model of rent-seeking in which a regulator mitigates social welfare losses as generative AI simultaneously enhances information transparency and enables new manipulation-based rents. It shows a fundamental steady-state relation $R^* = \frac{\gamma}{\delta}$ and analyzes welfare $L(R,S) = \phi R^2 + \psi (1-S)^2$, highlighting the dual effect: AI can reduce traditional rents but may spawn novel rents via $\Delta B_{AI}$. Through theory and simulations, the authors demonstrate that policy tools—per-unit taxation $\tau_t$ and regulatory constraints—can steer the system toward high transparency and low rent-seeking, though the presence of AI-enabled manipulation raises new welfare challenges. The work provides a structured framework for policymakers to balance innovation with welfare, supported by experimental convergence to favorable equilibria under governance and a pathway for empirical validation and extension.
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.