Habr AI: why code written by AI turns into dangerous debt for teams

AI-generated code often looks like a gift to a team: tasks get closed quickly, tests pass, the release doesn't slip. The problem surfaces later, when the module needs to be expanded, fixed, or simply understood — and it turns out that no one actually read it properly.

Where the debt comes from

AI accelerates feature delivery but transmits almost no context. A developer asks the model to create a service, hook, or integration layer, gets a working result, and moves on. A couple of weeks later, no one remembers why those particular abstractions were chosen, why there are three layers of wrappers here, or where the strange conditional branches came from. The code formally lives in the repository, but the logic behind its creation stayed in the chat, a temporary prompt, and the head of the person who was in a hurry to close the task.

"Tasks get closed, metrics turn green."

At this stage, everything really does look fine: CI passes, the product team is happy, the backlog shrinks. But AI optimizes the answer for the local request, not for the long life of the system. That's why the project gets code that solves the problem here and now but is poorly connected to the rest of the architecture, repeats existing patterns, and often masks complexity with neat function names.

Why it's scary to touch

Regular tech debt at least has a history: you can dig up the old PR, find the author, remember the trade-off. With AI debt, it's worse — authorship is blurred. A person seems to own the code, but in reality only accepted a generated version. Because of this, teams start to avoid touching such a module. Any change seems risky because no one is sure what side effects will surface after the edit. The longer such a section lies unattended, the higher the chance that new dependencies will grow around it.

The danger isn't just readability. AI easily creates unnecessary levels of abstraction, duplicates business logic across neighboring services, and leaves non-obvious connections between data, validation, and error handling. In the moment it accelerates the release, but then slows down everything else: onboarding new developers, incident investigation, refactoring, and even task estimation. The team begins to pay for the speed of past months with the current development pace.

How to reduce risk

A working approach is to treat AI-generated code not as a finished asset, but as a draft that must go through human assembly. If the module is important for the business, it should have a specific owner, a clear description of the solution, and a minimal set of tests that capture the current behavior. Before making changes, it's useful to first simplify the structure and remove unnecessary entities, rather than immediately adding new conditions on top of them. Otherwise, the debt just gets reinforced.

• Assign an owner to each AI-generated module
• Document in the PR or ADR what problem the prompt solved and what constraints mattered
• Add characterization tests before the first serious change
• Eliminate unnecessary abstractions and duplicates to simpler code right after merge
• Check that the solution style matches the team's architectural rules

If you don't do this, a familiar scenario emerges: the module seems to work, but any touch turns into a mini-investigation. In practice, it's cheaper to spend an extra hour on review and simplification on release day than to lose a week three months later on excavation. AI is good at accelerating the first pass, but maintenance remains a human task, and cutting corners on it is the most dangerous of all.

What it means

AI is already changing development speed, but along with it, it's shifting some of the complexity into the future. Teams that introduce rules for ownership, context fixation, and mandatory simplification of generated code will preserve the speed gain. Those who settle for green CI and quick merges will almost inevitably get a new layer of debt that's scary to touch.