When AI Improves Answers but Slows Knowledge Creation: Matching and Dynamic Knowledge Creation in Digital Public Goods
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914436656136192 |
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| author | Sun, Keh-Kuan |
| author_facet | Sun, Keh-Kuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00468 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | When AI Improves Answers but Slows Knowledge Creation: Matching and Dynamic Knowledge Creation in Digital Public Goods Sun, Keh-Kuan General Economics Economics 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. |
| title | When AI Improves Answers but Slows Knowledge Creation: Matching and Dynamic Knowledge Creation in Digital Public Goods |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2604.00468 |