This article examines what it means to use Large Language Models in everyday work. Drawing on a seven-month longitudinal qualitative study, we argue that LLMs do not straightforwardly automate or augment tasks. We propose the concept of configuration work to describe the labor through which workers make a generic system usable for a specific professional task. Configuration work materializes in four intertwined consequences. First, workers must discretize their activity, breaking it into units that the system can process. Second, operating the system generates cluttering, as prompting, evaluating, and correcting responses add scattered layers of work that get in the way of existing routines. Third, users gradually attune their practices and expectations to the machine's generic rigidity, making sense of the system's limits and finding space for it within their practices. Fourth, as LLMs absorb repetitive tasks, they desaturate the texture of work, shifting activity toward logistical manipulation of outputs and away from forms of engagement that sustain a sense of accomplishment. Taken together, these consequences suggest that LLMs reshape work through the individualized labor required to configure a universal, task-agnostic system within situated professional ecologies.

翻译：本文探讨了在日常工作中使用大型语言模型的意义。基于一项为期七个月的纵向定性研究，我们认为LLMs并非简单地自动化或增强任务。我们提出"配置工作"这一概念，用以描述工作者为使一个通用系统适用于特定专业任务而付出的劳动。配置工作具体体现在四个相互交织的后果中。首先，工作者必须将其活动离散化，分解为系统能够处理的单元。其次，操作该系统会产生杂乱化现象，因为提示、评估和修正响应增加了零散的工作层次，干扰了现有工作流程。第三，用户逐渐调整其实践和期望以适应机器的通用刚性，理解系统的局限性并在其实践中为其找到空间。第四，随着LLMs吸收重复性任务，它们使工作的质感去饱和化，将活动转向对输出的逻辑性操控，而远离那些维持成就感的工作参与形式。综上所述，这些后果表明，LLMs通过个体化的配置劳动重塑了工作形态，这种劳动旨在将通用的、任务无关的系统适配到具体的专业生态中。