The AI Productivity Paradox: When Experience Becomes a Liability

The Becker et al. (2025) finding that AI tools increased completion time by 19% for experienced developers completely upends the narrative established by Peng et al. (2023) showing 55.8% speed improvements. This suggests we need to explore the conditions under which AI becomes counterproductive. My idea is to conduct a series of controlled experiments that systematically vary developer expertise, task complexity, and AI tool sophistication to map out the "productivity paradox zone." We could use eye-tracking and think-aloud protocols (building on Park's 2024 biometric approaches) to understand whether experienced developers are spending time fighting with AI suggestions, double-checking outputs, or struggling to maintain their mental models. This would challenge the core assumption that more AI always equals more productivity, potentially leading to adaptive AI systems that know when to step back for expert developers.

References:

1. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. Sida Peng, Eirini Kalliamvakou, Peter Cihon, Mert Demirer (2023). arXiv.org.
2. Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. Joel Becker, Nate Rush, Elizabeth Barnes, David Rein (2025). arXiv.org.
3. Assessing Software Developer Productivity and Emotional State Using Biometrics. Kang-il Park (2024). IEEE International Conference on Software Maintenance and Evolution.