Task-interdependencies between Generative AI and Workers
构建了一个考虑任务相互依赖性的生产函数，分析生成式AI与工人互动如何产生学习网络外部性并影响生产率，为工作场所采用AI提供微观经济学基础。
Our paper formalizes a production function to give microeconomic foundations for the adoption of Generative AI (GAI) within workplaces. The production function accounts for task-interdependencies, the worker-GAI interaction and indistinguishability between human-created and AI-generated outputs. We show that workers and GAI represent two distinct but interdependent sides of the production, that jointly generate a network externality in learning that drives productivity. We find that in open learning organizations favoring the worker-GAI interaction, GAI should be matched to workers based on their ability to detect errors. We analyze configurations where the worker-GAI interaction is limited, referred as closed learning organizations, including firms banning the use of GAI, technological superclusters and emergence of small entrepreneurs innovating with GAI.