Discussions

There’s a huge debate happening in engineering circles right now — is an ai code assistant a long-term quality booster, or is it secretly piling up technical debt while making things look faster?

On one hand, AI tools can draft functions, generate boilerplate, and even auto-suggest tests in seconds. Tasks that took developers 30 minutes can become 30 seconds. That’s hard to ignore. Faster delivery means more features shipped, more sprint goals achieved, and more time saved for humans to think about architecture instead of repetitive tasks.

But speed doesn’t always equal correctness.

If AI suggests code that works today, but isn't aligned with established architectural patterns, naming conventions, or business domain rules — this can create hidden complexity. That hidden complexity becomes tomorrow’s refactoring nightmare. We’ve already seen teams ship “AI-generated code” quickly, only to debug strange behavior later because a model didn’t understand the actual context.

So the real question becomes: are we using these tools as assistants or replacements?

When AI is guided by strong coding standards, code reviews, and architectural oversight, it reduces tech debt — because it accelerates the boring parts. It becomes like a smart autocomplete.

But when teams let AI “auto-write everything,” especially without reviewing the logic deeply, tech debt grows — silently — like mold.

There’s also a hybrid future emerging: where AI doesn’t just generate code but also validates behavior from real traffic. Tools like Keploy already do this by turning live API calls into test cases. That means AI helps both sides: create faster but also verify correctness.