The cost of generating code with AI is trivial, shifting the primary expense to its maintenance, validation, and deployment. This inverts the traditional software engineering model where human code production was the main bottleneck, making code's complexity a liability.

Classic software engineering warns against full rewrites due to risk and time ("second-system syndrome"). However, AI's ability to rebuild an entire product in days, not years, makes rewriting a powerful and low-cost tool for correcting over-complicated early versions or flawed core assumptions.

AI coding's true enterprise value is limited because models struggle with legacy systems. Companies run on trillions of lines of mediocre code in old languages like COBOL—a problem that requires human intervention over decades, not a simple AI solution, which limits immediate, real-world impact.

AI coding tools dramatically accelerate development, but this speed amplifies technical debt creation exponentially. A small team can now generate a massive, fragile codebase with inconsistent patterns and sparse documentation, creating maintenance burdens previously seen only in large, legacy organizations.

Meredith Whittaker warns that while AI coding agents can boost productivity, they may create massive technical debt. Systems built by AI but not fully understood by human developers will be brittle and difficult to maintain, as engineers struggle to fix code they didn't write and don't comprehend.

With AI commoditizing code creation, the sustainable value for software companies shifts. Customers pay for reliability, support, compliance, and security patches—the 'never ending maintenance commitment'—which becomes the key differentiator when anyone can build an initial app quickly.

The idea that building with AI is cheap is a dangerous oversimplification. While initial creation is fast, leaders are realizing the immense long-term costs of maintenance, unwinding mistakes, and integrating with legacy systems are substantial and often dangerously overlooked.

AI coding assistants have recently crossed a critical threshold. They are no longer just for building new features but are now highly effective at refactoring legacy code. This dramatically changes the economics of modernizing established software companies by accelerating the notoriously slow process of paying down technical debt.

As generative AI collapses the cost of producing code, software development risks drowning in unmaintainable spaghetti code. Aishwarya Srinivasan notes that while anyone can build a prototype in ten minutes, engineering moats remain anchored in traditional fundamentals, intentional architecture, access security, and knowing what problem is actually being solved.