中文摘要
当机器能够执行的任务份额不断增长时，增长取决于两个边际：任务结构如何影响机器积累的回报，以及资本积累对这些回报的反应强度。我认为，在标准任务模型中，资本供给决定了与自动化相关的增长繁荣的规模，而任务结构则决定了这一增长繁荣的持续时间以及工资的演变。我记录了这样一个事实：当家庭最优选择储蓄率时，同一技术路径能够产生11.7%的年增长；而当储蓄率固定于校准的美国2025年水平23%时，该路径仅产生2.2%的增长。因此，增长缓慢可能反映了资本积累过少，而非技术瓶颈。此外，储蓄率上升主要是因为自动化提高了资本份额，而非因为家庭变得更节俭。长期收益率对增长繁荣进行了定价：冲击发生时，十年期实际收益率从5.1%跃升至13.6%。在我的基准校准中，手头拮据家庭的消费损失相当于其消费的五分之二至三分之二，而使其维持在趋势水平暂时最多耗费GDP的五分之一。
Abstract
When machines can perform a growing share of tasks, growth depends on two margins: how the task structure affects the returns to accumulating machines, and how strongly capital accumulation responds to those returns. I argue that, in a standard task model, capital supply governs the size of the growth boom associated with automation, while the task structure sets the duration of this growth boom and the evolution of wages. I document how the same technology path that generates 11.7% annual growth when households choose their saving rate optimally delivers only 2.2% when the saving rate is fixed at the calibrated U.S. 2025 level of 23%. Therefore, slow growth might reflect too little capital accumulation instead of a technological bottleneck. Also, the saving rate rises mainly because automation raises the capital share, not because households become thriftier. Long yields price the growth boom: the ten-year real yield jumps from 5.1% to 13.6% on impact. In my benchmark calibration, hand-to-mouth households lose the equivalent of two-fifths to two-thirds of their consumption, while keeping them on trend temporarily costs at most a fifth of GDP.