Developers often skip reading entire codebases before making changes. This article argues that AI-generated code should be treated similarly to code written by junior engineers or interns. The author draws parallels between self-driving cars and autonomous coding, noting our human bias for control despite proven safety.

The core message is that output matters more than the author. A personal experiment with Claude Sonnet 4.5 during a tight deadline proved that AI could clear backlogs effectively. While not perfect, the code was comparable to late-night human efforts.

The focus shifts from ownership to validation. Future discussions will cover how to validate AI output without reading every line, specifically for legacy code migrations.