定价智能：人工智能经济中基于任务的学习与劳动力替代
Pricing Intelligence: Task-Based Learning and Labor Displacement in the AI Economy
浏览论文内容
中文总结 AI 辅助
本文从微观经济学视角构建模型，探讨AI学习速度与方向的决定因素，分析垄断及竞争情形下AI提供商的策略对劳动力替代的影响，揭示相关张力引发的劳动力替代路径或学习陷阱的机制。
中文摘要 AI 辅助
人工智能学习的速度和方向由什么决定？本文从微观经济学视角，通过构建模型分析该问题：人工智能（AI）提供商向用户提供服务，用户需使用AI或劳动力完成任务，不同用户面临的复杂任务占比存在差异。当复杂任务被委托给AI时，AI会学习更好地执行这些任务；在AI相对优势在于简单任务的初始技术状态下，拥有大量复杂任务的用户对AI服务的支付意愿较低，这在利润最大化与复杂任务学习之间形成了张力。本文在垄断基准情形下，刻画了该张力何时会导致劳动力替代呈凸性路径或形成学习陷阱；AI提供商之间的竞争若足够激烈，会加快劳动力替代速度；不对称竞争可通过促进AI提供商的内生专业化，进一步提高劳动力替代速度。
英文摘要
What determines the speed of labor displacement through AI? I study this question in a microeconomic model in which AI providers sell access to users who must each complete a set of tasks using AI or labor. Users differ in the share of complex tasks they face. Using AI on complex tasks improves AI's ability to perform them. When AI initially has a comparative advantage in easy tasks, users with many complex tasks have low willingness to pay for AI. This creates a tension between profit maximization and complex-task learning. I characterize when this tension gives rise to a convex technological takeoff or a learning trap. Symmetric competition can slow adoption relative to monopoly when weak, but accelerates it when sufficiently intense by shifting usage toward more complex tasks. Asymmetric competition can further accelerate adoption by inducing endogenous specialization among providers.
