We are Changing our Developer Productivity Experiment Design
Hacker News
February 24, 2026
AI-Generated Deep Dive Summary
METR, known for its 2025 study revealing a 19% slowdown in task completion among experienced developers using AI tools, has encountered significant challenges in its latest experiment. Conducted from August to early 2026 with a larger, more diverse group of developers, the study faced issues like participant dropout due to reluctance to work without AI, lower pay rates affecting recruitment, and unreliable time measurements when multiple AI tools were used concurrently. These factors introduced selection biases, likely underestimating the productivity benefits of AI. While early results suggested speedups ranging from -18% to -4%, METR acknowledges that these figures may underestimate true gains due to self-selecting developers and tasks.
The evolving adoption of AI tools like Claude Code and Codex has complicated productivity measurements. Developers increasingly refuse to participate in tasks without AI, citing its perceived value. This trend has made it difficult to recruit a representative sample and led to task selection bias, with many participants choosing not to submit tasks expected to benefit from AI. As a result, METR's estimates likely represent lower bounds on AI's productivity impact.
This study underscores the growing challenges in accurately measuring AI's role in developer productivity amidst its widespread adoption. The difficulties faced by METR highlight the complexities of conducting such research as tools and practices evolve rapidly. For tech enthusiasts and professionals, understanding these nuances is crucial for assessing AI's true potential and implications for future development strategies.
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Originally published on Hacker News on 2/24/2026