Nobody is going to read the code

AI coding tools currently lag behind humans in generating correct code, with error rates significantly higher than human-generated code, as indicated by the CodeRabbit's report that highlights increased logic and security issues within AI-produced code. The traditional method of human code review, already strained, is becoming untenable as AI accelerates code production without improving review capacity, leading to a mismatch where human reviewers struggle to catch complex logic and security flaws. Instead, the future of code verification is shifting towards validating the intent and behavior of code outputs rather than traditional line-by-line reviews, with organizations increasingly adopting automated reviews, static analysis, and validation pipelines to ensure outputs meet expected functionality. Despite improvements in speed and efficiency, the challenges of AI-generated code require robust infrastructure to catch errors before reaching production, as the discrepancy between AI's rapid output and the validation capabilities of development teams continues to grow.