A large-scale study of real-world repositories finds that AI-generated code differs from human-written code in complexity, structural traits, defect indicators, and commit-level activity patterns.
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A game-theoretic model shows that individually rational adoption of generative AI causes model collapse that reduces collective social welfare for important tasks, with habit formation creating spillovers from low-stakes to high-value domains.
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A Large-Scale Empirical Study of AI-Generated Code in Real-World Repositories
A large-scale study of real-world repositories finds that AI-generated code differs from human-written code in complexity, structural traits, defect indicators, and commit-level activity patterns.
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Generative artificial intelligence reduces social welfare through model collapse
A game-theoretic model shows that individually rational adoption of generative AI causes model collapse that reduces collective social welfare for important tasks, with habit formation creating spillovers from low-stakes to high-value domains.