Beyond Benchmarks: Measuring the True Cost of AI-Generated Code

▤ Summary

This article examines the hidden costs of AI-generated code beyond raw productivity gains. It argues that large language models can increase technical debt by producing code smells, weak structure, and maintainability problems that raise long-term ownership costs. It also highlights serious security risks, including injection flaws and hard-coded secrets, and notes that some models generate high-severity vulnerabilities at alarming rates. The piece concludes that teams should treat AI as an amplifier, use human oversight, and evaluate code for security, reliability, and maintainability instead of only benchmark scores.

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Parasoft

$50M to $100M