AI & LLMs
What is technical debt?
Technical debt is the accumulated future cost of shortcuts and expedient choices in a codebase: work that was deferred and now has to be paid back with interest in slower changes and more bugs.
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Technical definition
Technical debt is the implied cost of rework created when an implementation is chosen for short-term speed over long-term maintainability. It accumulates through duplicated logic, missing tests, unclear structure, outdated dependencies, undocumented decisions, and workarounds that were never revisited.
The debt metaphor is precise in one respect: the cost is not the shortcut itself but the interest, paid on every subsequent change that has to work around it.
In plain terms
Technical debt is the mess you agreed to clean up later. It does not stop the product working. It makes everything after it slower.
Why it matters
AI building accumulates technical debt faster than hand-written code, for a reason worth understanding.
When you build by prompting, each request is answered on its own. The AI does not know that the function it is writing already exists elsewhere, or that this is the fifth component doing the same job. It produces something that works, now, in isolation. Repeat that 40 times and the project has real structural debt, none of which was a mistake at the time.
What you notice is not the debt. It is the symptoms: changes take longer, edits break unrelated features, and the AI gets worse at requests it used to handle.
Some debt is the right call. A prototype you may throw away does not need clean architecture. Debt becomes a problem when the throwaway thing turns out to be the product, which happens constantly.
