Twelve Ways to Be Wrong About AI-Assisted Coding
Summary
An in-depth critique of common metrics used to evaluate AI-assisted coding, arguing that lines of code, task speed on toy problems, and adoption rates can misrepresent true productivity. It emphasizes the need for credible counterfactuals, consideration of code security, and long-term technical debt, supported by a wide range of research.