Generative artificial intelligence reduces social welfare through model collapse
Fabian Baumann, Erol Ak\c{c}ay, Joshua B. Plotkin

TL;DR
This paper models how generative AI can lead to social welfare decline due to model collapse, especially when adoption spreads from low to high-stakes tasks, despite short-term individual benefits.
Contribution
It introduces a simple behavioral model showing how genAI adoption can harm social welfare through model collapse and habit formation effects.
Findings
GenAI adoption initially benefits individuals but reduces social welfare in key tasks.
Habit formation causes spillover from low-stakes to high-stakes tasks, amplifying welfare losses.
Without intervention, rational genAI use can significantly decrease collective welfare.
Abstract
Generative artificial intelligence (genAI) is rapidly reshaping how knowledge and culture are produced and consumed. Yet generative models are vulnerable to model collapse: when trained on data generated by earlier versions of themselves, their outputs can lose diversity and accuracy. This creates a social dilemma, because delegating tasks to genAI can be individually beneficial in the short term even as widespread adoption degrades future model performance. Here we develop a parsimonious model of behavior in collaborative interactions in which individuals can either exert human effort, rely on genAI, or refrain from work altogether. The welfare consequences of genAI are organized by a simple two-dimensional taxonomy: the strength of the incentive to perform the task without AI, and the severity of model collapse. Within this framework, the introduction of genAI -- while initially…
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