AI Coding Risks Eroding Junior Engineers’ Judgment

Generative AI is becoming embedded in software development, helping programmers draft code, troubleshoot errors and ship products faster. Veteran engineer Lars Faye argues that the productivity gains carry a long-term risk for junior developers. Engineering judgment is built through the friction of debugging, confronting system constraints and weighing architectural trade-offs. Outsourcing those experiences to large language models may boost immediate output while weakening the foundations needed for independent technical decisions.

In a recent essay, Faye highlighted research indicating that novices who were most confident using AI and relied on it most heavily delivered the weakest performance. The finding challenges the assumption that faster coding automatically produces better engineers. Faye recommends treating an LLM as a tutor that asks questions, tests assumptions and offers targeted hints, rather than as a worker that completes the assignment. Junior engineers, he said, still need direct practice diagnosing failures and designing systems.