AI 时代的人类劳动的代理驱动劳动理论

本文提出 Agency-Driven Labor Theory 作为一种 new theoretical framework 来 understanding human work 在 AI-augmented environments 中的作用。虽然 traditional labor theories 主要关注 task execution 和 labor time，但 ADLT 提议 human labor value 越来越 derived from agency ——即 make informed judgments、provide strategic direction 和 design operational frameworks for AI systems 的 capacity。论文 presents a mathematical framework expressing labor value as a function of agency quality、direction effectiveness 和 outcomes，提供 quantifiable approach 来分析 human value creation 在 AI-augmented workplaces 中的情况。drawing on recent work in organizational economics 和 knowledge worker productivity，ADLT 解释 how human workers create value by orchestrating 结合 human 和 artificial intelligence 的 complex systems。该理论对 job design、compensation structures、professional development 和 labor market dynamics 具有重大影响。通过 various sectors 的 applications，paper 展示 how ADLT 可 guide organizations 在 AI-augmented operations 过渡中 while maximize human value creation。该 framework 提供 practical tools 供 policymakers 和 educational institutions 使用，以 prepare workers for labor market where value creation 越来越 centers on agency 和 direction 而非 execution。