When AI Improves Answers but Slows Knowledge Creation: Matching and Dynamic Knowledge Creation in Digital Public Goods

TL;DR

Generative AI enhances individual problem-solving efficiency but may reduce public knowledge archives by decreasing platform contributions and resolutions, leading to potential long-term knowledge deterioration.

Contribution

This paper develops a dynamic model showing how AI impacts public good creation and identifies two distinct margins affecting knowledge archives.

Findings

AI reduces public archive creation via flow and resolution margins.

Private AI resolution leads to fewer public contributions and slower knowledge growth.

Encouraging public sharing of AI solutions can offset some decline but not all effects.

Abstract

Generative AI helps users solve problems more efficiently, but without leaving a public trace. Fewer discussions and solutions reach public platforms, and the archives that future problem-solvers depend on can shrink. We build a dynamic model of public good provision where agents contribute by solving problems that other agents posted on a public platform, and the accumulated solutions form a depreciating public archive. AI reduces archive creation through two margins that require different instruments. The flow margin: the posted volume of knowledge-enhancing queries declines as AI resolves more problems privately before they reach the platform. The resolution margin: the probability that posted queries are resolved declines as AI raises contributors' outside options, thinning the contributor pool and creating congestion on the platform. The two margins interact through a self-undermining feedback that can generate low-archive traps. The decomposition yields a diagnostic prediction: in the congested regime, a joint decline in posted volume and conditional resolution requires that supply-side pool thinning is quantitatively present, whereas volume decline with stable or rising resolution indicates that private diversion alone is the dominant force. Encouraging public sharing of AI-assisted solutions offsets the decline associated with private diversion but cannot repair participation-driven deterioration in conditional resolution, which requires maintaining contributor engagement directly.