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I was thinking about this last night. It’s a new version of Gell-Mann amnesia. I call it LLm-man amnesia.

When I ask a programming question, chat GPT hallucinates something about 20% of the time and I can only tell because I’m skilled enough to see it. For all the other domains I ask it questions if I should assume at least as much hallucination and incorrect information.

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I see this as for drill-down thinking from a broad -> specific concept AI seems to be helpful when supplementing specialist work. However like you both mentioned: when needing more focused and integrated answers AI tends hinders performance.

However as the paper noted, when working within AIs areas of strength it improved not only efficiency but the quality of the work as well (accounting for the hallucinations). As you mentioned:

> When I ask a programming question, chat GPT hallucinates something about 20% of the time and I can only tell because I’m skilled enough to see it

This matches their Centaur approach, delineating between AI and one’s own skills for a task which—with generalized work—seems to fair better than not using AI at all.