Why Your AI Coding Metrics Are Probably Wrong

Companies adopting AI coding tools often rely on misleading metrics to prove their value—such as lines of code generated or completion speed on artificial benchmark tasks. This article identifies critical methodological flaws in common evaluation approaches: the lack of control groups, the misuse of proxy metrics that don't capture real-world productivity, and the conflation of correlation with causation. For teams to accurately assess whether AI tools genuinely improve their development practices, they need rigorous, scientifically sound measurement approaches.