📄 中文摘要
大型语言模型（LLMs）能够自动化许多小任务，但为何没有显著的生产力提升？研究提出，劳动通常被打包成多任务工作，而不是单独交易，可能是原因之一。这种多任务的工作方式使得劳动的效率和效益在不同任务之间得以优化，从而影响整体生产力的提升。跨任务生产力的概念强调了在不同任务之间的协同作用，可能是理解当前生产力现象的关键。
跨任务生产力的重要性
出处: The import of cross-task productivity
发布: 2026年2月13日
📄 English Summary
The import of cross-task productivity
Large language models (LLMs) have the capability to automate numerous small tasks, yet significant productivity effects are not observed. This research suggests that one reason for this phenomenon may be that labor is typically bundled into multi-task jobs rather than transacted individually. This multi-tasking approach allows for the optimization of efficiency and effectiveness across different tasks, thereby influencing overall productivity gains. The concept of cross-task productivity highlights the synergistic effects between various tasks, which could be crucial for understanding current productivity trends.