The AI coding hangover

The promise that LLMs could replace developers and produce free software is colliding with enterprise reality. Companies that used AI as a developer substitute rather than amplifier are now dealing with brittle, unmaintainable codebases, runaway cloud costs, security gaps, and technical debt without authorship. The pattern: AI-generated code passes tests but over-allocates resources, misses edge cases, and resists human understanding. Organizations are quietly rehiring developers to remediate systems that became mission-critical before anyone understood them. The real lesson is that AI automates tasks but cannot own outcomes—architecture, security, and operational judgment remain human responsibilities. Enterprises that pair developers with AI tools and enforce platform discipline will outperform those that tried to eliminate engineering headcount.