TLDR Tech / DevOps
Twelve Ways to Be Wrong About AI-Assisted Coding
Evaluating AI coding productivity is challenging; metrics like lines of code, adoption rates, ticket counts, self-reported productivity, and toy-task speedups often reflect activity over long-term value. Effective evaluation requires control groups, system-level metrics, sustained observation, and consideration of hidden costs like increased review burden, security risks, technical debt, and maintenance overhead.
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