A recent CIO analysis highlights a growing concern among software engineers and researchers: while artificial intelligence tools can help developers generate code more quickly, their heavy use may undermine the very skills programmers need to validate, debug, and deeply understand that code. In controlled tests, developers who relied on AI assistants to write unfamiliar code scored significantly lower on follow-up quizzes about key concepts and debugging than those who wrote code manually, suggesting a potential decline in learning and mastery when cognitive effort is offloaded to machines.
In one experiment involving a group of developers tackling a new Python library, participants using AI tools scored roughly 17 percentage points lower on assessments of comprehension and debugging skills than their peers who did not use AI. The biggest gaps were not just in writing correct code, but in understanding why code worked and how to fix it when it didn’t — a crucial aspect of professional software development that AI alone can’t teach.
The research also revealed that the way AI is used matters. Developers who engaged actively with AI — asking not just for code but for explanations and conceptual clarification — tended to retain more understanding than those who passively accepted AI outputs. In contrast, users who relied primarily on AI to generate entire solutions without deeper engagement showed the greatest drop in mastery and independent problem-solving ability.
Experts say AI isn’t inherently harmful to developers’ skills, but it can become a crutch if used without discipline. To avoid skill degradation, programmers are advised to treat AI as an assistant rather than a replacement for thought: focus on asking for reasoning, debug and verify AI-generated code thoroughly, and continue tackling unfamiliar problems without AI support to build and retain core competencies.
