Technical Debt: AI makes code cheap, but what about the old stuff?

It's like patching a leaky roof with duct tape to finish quickly: you solve it now, but you know you'll have to fix it properly someday, spending much more. In software, these are the shortcuts taken to deliver a product fast.

These quick choices accumulate, making the code increasingly difficult to modify or extend. Each "patch" adds complexity, turning the system into a tangled puzzle. The concept of technical debt was introduced by Ward Cunningham in 1992, not yesterday.

If AI can generate brand new code in seconds, why bother fixing an old mess full of "technical debt"? The temptation is strong: "let's just throw everything away and rewrite it from scratch!".

But rewriting complex systems, perhaps built over ten years, is never a trivial undertaking. There are intricate business logics and forgotten use cases that AI doesn't know. A 2022 Stripe report estimated that development teams spend about 33% of their time on technical debt. Quite a drain, isn't it?

The challenge is no longer writing code, but understanding what's already there, often poorly documented and layered. AI is great at generating new text and code, but it's not a computer science historian who knows every single decision made in the past.

Generating new code without understanding the old risks creating even greater, invisible technical debt. We might find ourselves in endless rewrite cycles, never solving the core problem. Managing legacy code represents a significant expense for many companies, often exceeding the initial development cost.