Generative AI Risks: When Novices Impersonate Experts

Generative AI can produce expert‑level output without the user’s expertise, creating a dangerous decoupling between output quality and creator competence. The article cites a colleague who used Claude to generate code and documentation that looked correct but was fundamentally wrong, leading to project delays and stakeholder mistrust. Studies from Stanford, Berkeley, NBER, and Harvard Business School confirm that novices boost productivity by about a third while experts see little benefit, yet they cannot verify correctness. The phenomenon, termed output‑competence decoupling, erodes the traditional signal that quality work indicates skill. As a result, documentation length and complexity increase, while human review is often skipped, amplifying errors. The author argues that AI should be used only where outputs can be quickly verified and human judgment remains the final arbiter. He recommends disciplined use of AI for drafting, summarizing, and brainstorming, while insisting on human verification for all critical artifacts. The piece serves as a cautionary note for teams integrating generative models into production workflows.