The Economics of Information Pollution in the Age of AI: A General Equilibrium Approach to Welfare, Measurement, and Policy

The advent of Large Language Models (LLMs) represents a fundamental shock to the economics of information production. By asymmetrically collapsing the marginal cost of generating low-quality, synthetic content while leaving high-quality production costly, AI systematically incentivizes information pollution. This paper develops a general equilibrium framework to analyze this challenge. We model the strategic interactions among a monopolistic platform, profit-maximizing producers, and utility-maximizing consumers in a three-stage game. The core of our model is a production technology with differential elasticities of substitution ($\sigma_L > 1 > \sigma_H$), which formalizes the insight that AI is a substitute for labor in low-quality production but a complement in high-quality creation. We prove the existence of a unique "Polluted Information Equilibrium" and demonstrate its inefficiency, which is driven by a threefold market failure: a production externality, a platform governance failure, and an information commons externality. Methodologically, we derive a theoretically-grounded Information Pollution Index (IPI) with endogenous welfare weights to measure ecosystem health. From a policy perspective, we show that a first-best outcome requires a portfolio of instruments targeting each failure. Finally, considering the challenges of deep uncertainty, we advocate for an adaptive governance framework where policy instruments are dynamically adjusted based on real-time IPI readings, offering a robust blueprint for regulating information markets in the age of AI.