Cost–Benefit of AI Regulation in the United States:Innovation, Competition, and Consumer Harm Under Alternative Legal Regimes

Abstract

The rapid diffusion of artificial intelligence (AI) technologies has generated unprecedented economic opportunities alongside significant risks to consumers, competition, and social welfare. Policymakers in the United States face increasing pressure to regulate AI systems to mitigate potential harms, including algorithmic discrimination, market concentration, privacy violations, and safety failures. At the same time, concerns persist that overly restrictive regulation may stifle innovation, reduce competitive entry, and slow productivity growth.This paper conducts a comprehensive cost–benefit analysis of alternative AI regulatory regimes in the United States. It examines how different legal approaches—ranging from light-touch governance and sector-specific rules to comprehensive ex ante regulation—affect innovation incentives, market structure, and consumer harm. Drawing on economic theory, legal analysis, and emerging empirical evidence, the study evaluates the trade-offs inherent in AI regulation and identifies conditions under which regulatory intervention enhances social welfare.The analysis highlights that the welfare effects of AI regulation depend critically on design features, enforcement capacity, and market context. While targeted regulation can reduce consumer harm and promote fair competition, poorly calibrated rules risk entrenching incumbent firms and discouraging entry. The findings underscore the importance of adaptive, evidence-based regulatory frameworks that balance innovation with accountability in the evolving AI economy.