How AI Tools Generate Technical Debt in IoT Systems — and What to Do About It

AI tools can accelerate IoT development but also introduce significant technical debt by generating code that seems correct locally but fails system-wide. This debt manifests in four main ways: reproducing legacy errors, lacking architectural awareness, duplicating logic, and ignoring specific hardware constraints. Studies show that AI-generated code often increases code duplication and contains quality issues that go unfixed. To mitigate these risks, teams should enforce mandatory human code reviews, restrict AI use in critical system components, and implement regular refactoring and monitoring.