SimpleOne: how unchecked AI-generated code turns seniors into janitors of other people's code

SimpleOne described an effect that many teams are already recognizing: code generation through AI can accelerate junior developers, but slow down the entire delivery pipeline. If generated code is inserted without understanding the architecture and business logic, the burden simply shifts to seniors, QA, and support.

Speed vs Quality

The article authors begin with a client case where a middle-level developer actively used AI but had poor understanding of the project context. Formally, tasks moved quickly, yet in practice every pull request turned into a long cycle of feedback. A senior spent more time on review and fixes than it would have taken to implement independently. This scenario, according to SimpleOne, creates an illusion of productivity: code appears faster, but the team pays for it later — through hours of review, repeated fixes, and accumulating technical debt.

The article includes a telling hypothetical example with a fintech team of 12 people who spent six months actively generating code through Claude and ChatGPT. Initially, velocity increased by 40%, but then costs began to grow: average AI code review took 2 hours 15 minutes versus 45 minutes for regular code, the number of iterations before merge grew to 4–5 versus 1–2, and defect density reached 12 per thousand lines instead of 4. Simultaneously, testing time increased by 60%, and seniors began to burn out from constantly cleaning up others' PRs.

"AI is not the problem, but a tool."

How to Measure the Problem

The main point of the article is that AI debt is rarely visible immediately. Tasks on the board close faster, the sprint looks good, management sees velocity growth. But the real cost surfaces in other systems and at other stages. Therefore, the authors suggest looking not at a single metric, but at a combination of engineering and operational signals.

• compare code review time for AI code and regular code
• count the number of iterations per pull request before merge
• link modules with AI code to incidents, MTTR, and urgent releases
• track defect density and recurring problems in KEDB
• gather feedback from seniors about modules that no one wants to maintain

SimpleOne separately offers a review checklist: look for duplication of existing utilities, naming inconsistencies, ignoring architectural patterns, lack of edge case checks, formal tests and hardcoding where project configuration is needed. If several such signs emerge in a single review, the problem is usually not in a specific bug, but in the fact that the developer transfers the AI response to the codebase with almost no adaptation.

How to Rebuild the Process

Instead of banning AI, the authors propose three practices for managing AI debt.

The first is a unified backlog where features, defects, AI code refactoring, and technical debt compete by common business criteria. The second is ITSM or Service Desk integration with SDLC, so that incidents are automatically linked to specific modules and turned into refactoring tasks. The third is a change in roles: juniors and middles should understand the architecture more deeply, and seniors should spend time on architectural decisions, not endless cleanup of style and convention violations.

In the hypothetical scenario from the article, this scheme produced tangible results. After the team linked incidents to AI modules, it turned out that the credit rating calculation module caused 8 out of 10 incidents in a month and consumed 64 hours of support. A senior rewrote the critical module in three days, after which the number of incidents dropped to one, and support load decreased to 8 hours per month. After two months, velocity returned to its previous level, but now without the previous growth in defects and with a drop in incidents of approximately 70%.

However, the article does not call for abandoning AI altogether. The authors directly list tasks where it is useful: boilerplate, template CRUD operations, unit tests, documentation, and quick prototypes for discussion. The key difference is in who manages the process. When a developer understands the system's limitations and uses the model as an assistant, AI saves time. When the model actually makes engineering decisions instead of the human, seniors begin to pay for that savings with their own time and attention.

What It Means

The SimpleOne article well captures the shift that teams are currently experiencing: the problem is no longer whether to use AI for code, but how to account for its hidden costs. The winning processes will be those that measure not only the speed of generation, but also the cost of review, support, and team training.