AI-powered coding assistants are rapidly becoming fixtures in professional IDEs, yet their sustained influence on everyday development remains poorly understood. Prior research has focused on short-term use or self-reported perceptions, leaving open questions about how sustained AI use reshapes actual daily coding practices in the long term. We address this gap with a mixed-method study of AI adoption in IDEs, combining longitudinal two-year fine-grained telemetry from 800 developers with a survey of 62 professionals. We analyze five dimensions of workflow change: productivity, code quality, code editing, code reuse, and context switching. Telemetry reveals that AI users produce substantially more code but also delete significantly more. Meanwhile, survey respondents report productivity gains and perceive minimal changes in other dimensions. Our results offer empirical insights into the silent restructuring of software workflows and provide implications for designing future AI-augmented tooling.

翻译：AI驱动的编程助手正迅速成为专业集成开发环境（IDE）中的常驻工具，但其对日常开发工作的持续影响仍缺乏深入理解。现有研究聚焦于短期使用或自我报告感知，未能解答长期AI使用如何重塑实际日常编码实践的问题。我们通过混合方法研究填补这一空白，结合800名开发者两年间的细粒度遥测数据与62位专业人士的问卷调查，分析工作流变化的五个维度：生产力、代码质量、代码编辑、代码复用与上下文切换。遥测数据显示，AI用户生成了显著更多的代码，但同时也删除更多代码；而问卷调查受访者报告了生产力提升，并在其他维度感知到最小变化。我们的研究结果为软件工作流的静默重构提供了实证洞察，并为设计未来AI增强工具提供了启示。