Technical Debt Economics and AI: An Analysis of Cassanio's Concept

The article analyzes a paradox in modern software engineering: although AI drastically lowers the cost of generating code, it simultaneously increases the long-term cost of owning it. The author warns against the 'Hidden Feature Creep' phenomenon and the asymmetry of incentives in LLM models, which prefer creating new solutions over simplifying existing ones. The text emphasizes the difference between local code correctness and global stability of the delivery system, citing data from DORA and GitClear reports. The main conclusion is that an overproduction of changes may exceed an organization's capacity to metabolize them, leading to systemic paralysis. Therefore, the key quality metric in the AI era should not be the amount of generated code, but the durability of introduced changes and the minimization of technical debt.