# Evolving with AI: A Longitudinal Analysis of Developer Logs

arXiv: https://arxiv.org/abs/2601.10258 · HTML: https://mlsanity.com/paper/2601.10258/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2026.

## Built on by (most cited first, 4 of 4)

- [RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions](https://mlsanity.com/paper/2605.01104/) (2026)
- [An Empirical Study of Proactive Coding Assistants in Real-World Software Development](https://mlsanity.com/paper/2605.05700/) (2026)
- [The Work Behind Delegation: A Framework for Supervising AI Coding Agents](https://mlsanity.com/paper/2609.24234/) (2026)
- [ParallelPilot: Supporting Coordination and Monitoring in Parallel AI Coding](https://mlsanity.com/paper/2609.33113/) (2026)

## Abstract (arXiv)

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.