Technical Debt Persists in AI Era, Just Paid in Compute Tokens Instead of Headcount

A growing argument in software engineering holds that LLMs have not eliminated technical debt but merely changed how organizations pay for it. Where companies once hired more engineers to manage complex, poorly structured codebases, they now spend more on inference costs, larger context windows, and repeated AI-generated attempts. LLMs perform well in clean, modular codebases but struggle with enterprise systems riddled with circular dependencies, hidden side effects, and years of architectural compromises. Because AI produces far more code per unit of time than humans, it can introduce regressions faster in poorly structured systems, meaning faster code generation does not automatically translate to faster software delivery. The core issue, according to this analysis, is architectural entropy — a problem that scales of compute alone cannot solve.