Measuring AI-generated code: focus on team impact, not individual developers

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Teams adopting AI coding tools struggle to tell whether the investment is paying off, and developers often distrust dashboards that might be used to compare them. The author argues that gut feeling is unreliable here, citing a 2025 controlled study in which experienced open-source developers were measurably slower with AI yet believed they were faster. Vendor metrics such as acceptance rates and lines of code show usage but say little about real impact. The article recommends four more telling measures, including follow-up defects after merge and review time in risky areas, collected at team level rather than per person. It also warns that per-person evaluation quickly turns metrics into a way for people to hide problems.

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Developer builds 87,094-line C++ GUI toolkit with no third-party dependencies

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