💻 Reviewing AI Code: Why the 'Like an Intern' Argument Fails
Thomas Depierre challenges the idea that using LLM assistants is safe because a developer can simply review the code as they would an intern's code. Empirical data shows that effective auditing is limited by volume (no more than 400 lines per hour) and time (no more than 1 hour). Under such load, the gains in coding speed are entirely consumed by the time required for verification.
🌍 There is a critical risk of "false confidence": developers find fewer errors in AI-generated code but feel more confident in it. This calls into question the scalability of AI coding without the implementation of fundamentally new verification methods.
👤 It is important to understand that using AI does not exempt one from deep verification. At high code generation speeds, the human factor becomes a bottleneck that reduces the overall reliability of the system.
Source 1: https://www.softwaremaxims.com/blog/reviewing-ai-code