tl;dr: Despite AI coding tools promising higher productivity and quality, everyday software (banking apps, car infotainment, warranty forms) keeps getting buggier and more fragile as complexity grows and vendors prioritize KPI-driven features over stability. The author argues LLMs could fix bugs effectively, but companies won't dedicate cycles to it—leaving an opening for individual developers to build better alternatives as big-company software accrues AI debt.