Scott Robinson highlighted the risks of relying on large language models (LLMs) to generate repetitive code without proper refactoring in a July 10 post on unstack.io. He described how AI-generated code often includes duplicated conditionals across multiple files, which can lead to maintenance challenges despite passing tests and functioning correctly.
Robinson shared his experience of using AI to write similar access checks in different parts of a project, such as route handlers and API endpoints. Each generated snippet contained nearly identical conditional logic with minor variations, which he merged without consolidating into a shared helper function. He noted that while the code worked, it lacked best practices that a human developer would typically apply.
The issue underscores a common pitfall when using AI for coding: developers may neglect clean code principles, assuming the AI will handle future changes. Robinson pointed out that this approach can create technical debt, as duplicated logic scattered across files becomes harder to update and maintain. This contrasts with traditional software engineering where refactoring and code reuse are prioritized.
Robinson’s post serves as a reminder that while LLMs can automate code writing, developers must still enforce maintainability standards. He emphasized that ignoring best practices shifts the burden of updates onto the AI, which may not be reliable in the long term. The discussion appeared on unstack.io on July 10, 2026.