Production is a sequence of steps that can be executed (1) manually, (2) augmented with AI, or (3) fully automated within contiguous AI-executed steps called ''chains.'' Firms optimally bundle steps into tasks and then jobs, trading off specialization gains against coordination costs. We characterize the optimal assignment of humans and AI to steps and the firm's resulting job structure, showing that comparative advantage logic can fail with AI chaining. The model implies non-linear productivity gains from AI quality improvements and admits a CES representation at the macro level. Empirical evidence supports the model's key predictions that (1) AI-executed steps co-occur in chains, (2) dispersion of AI-exposed steps lowers AI execution at the job level, and (3) adjacency to AI-executed steps increases the likelihood that a step is AI-executed.

翻译：生产是由一系列步骤构成的链条，这些步骤可 (1) 人工执行，(2) 通过AI增强，或 (3) 在被称为“链”的连续AI执行步骤中实现完全自动化。企业优化地将步骤捆绑为任务，进而组合成工作，在专业化收益与协调成本之间权衡取舍。我们刻画了人类与AI在步骤上的最优分配及由此形成的企业工作结构，并揭示在AI任务链化情境下，比较优势逻辑可能失效。该模型表明，AI质量改进带来的生产率提升呈非线性特征，并在宏观层面可纳入CES（常替代弹性）函数形式。实证证据支持模型的关键预测：(1) AI执行的步骤在链中共同出现；(2) 工作中AI暴露步骤的分散程度会降低AI在岗位层面的使用率；(3) 与AI执行步骤的邻近性会增加该步骤被AI执行的概率。