AI助益未必提效，反而可能降低生产力，关键看使用者能力与AI可靠性。
Human-AI Productivity Paradoxes: Modeling the Interplay of Skill, Effort, and AI Assistance
- 构建人机协作模型，分析技能、努力与AI辅助的动态关系。
- 高依赖AI反致效率下降，存在显著短期产出损失风险。
- 使用者对AI错误的识别适应力决定长期技能分化趋势。
生成式人工智能工具在职场和教育中快速普及，但其实际影响仍存争议。本文提出一个模拟人机交互的模型，研究多种机制下AI如何影响生产力。模型设定不同技能水平的人类代理在获得AI协助时，以效用最大化为目标投入努力以完成任务。结果表明，若考虑技能发展或AI不可靠性的内生性，都会引发生产力悖论：增加AI使用反而导致生产力下降，造成显著短期产出缺口。此外，我们考察了长期中AI对技能分布的影响，发现当个体对AI输出错误的识别与适应能力（即AI素养）存在差异时，稳态下会出现技能极化现象。研究揭示了人机生产力悖论与技能分化的多重机制，并提出了可识别其出现条件的简洁指标。
原文摘要 · Abstract (English)
Generative Artificial Intelligence (AI) tools are rapidly adopted in the workplace and in education, yet the empirical evidence on AI's impact remains mixed. We propose a model of human-AI interaction to better understand and analyze several mechanisms by which AI affects productivity. In our setup, human agents with varying skill levels exert utility-maximizing effort to produce certain task outcomes with AI assistance. We find that incorporating either endogeneity in skill development or in AI unreliability can induce a productivity paradox: increased levels of AI assistance may degrade productivity, leading to potentially significant shortfalls. Moreover, we examine the long-term distributional effect of AI on skill, and demonstrate that skill polarization can emerge in steady state when accounting for heterogeneity in AI literacy -- the agent's capability to identify and adapt to inaccurate AI outputs. Our results elucidate several mechanisms that may explain the emergence of human-AI productivity paradoxes and skill polarization, and identify simple measures that characterize when they arise.
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