If you'd rather not think hard, asking AI for the whole answer might quietly cost you the very skills you're skipping.

This explores why people who enjoy effortful thinking less tend to ask AI for finished answers instead of hints or partial help. The corpus has no note that measures need for cognition or tests who asks for what, so it can't answer the "why" directly. Several neighboring notes do bear on it, and they suggest the request is a mistake that feeds on itself.

The most direct neighbor is a four-month EEG study of 54 people. Brain connectivity scaled down as AI reliance went up, and the heaviest LLM users showed the weakest neural engagement, the poorest memory retention, and trouble recalling their own recent work (Does AI assistance weaken our brain's ability to think independently?). It measured reliance, not the personality trait, so it doesn't say low-need-for-cognition people are the ones who rely most. If they are, the pattern is a loop: asking for the whole answer skips the thinking, and skipping the thinking leaves less capacity to do it next time. A related framing calls LLMs "scaled System-1 cognition" and warns that users confuse intuition with reasoning (Why do people trust AI outputs they shouldn't?). A fluent, complete answer is the kind of output that invites that confusion. That connection is my inference, not something the note tests.

The library also has evidence that the complete answer isn't what serves these users best. In a lab study of 80 people, an assistant that paired reflection questions with advice beat assistants that only advised, only questioned, or did neither (Do reflection questions help people make better decisions with AI?). Asking for the full answer looks like the cheapest path, but it lost to a slightly more effortful one. The catch is that a helper who asks questions demands the effort these users are avoiding. The "gulf of envisioning" note points to a way through: users often can't articulate what they want, and offering model-generated options turns an open-ended "describe what you need" into a constrained "pick one" (Why can't users articulate what they want from AI?). For someone who dislikes effortful thinking, choosing among options costs much less than writing a specification.

The "complete answer" also tends to be less complete than it looks. All 11 frontier models tested scored at least 9 points lower on implicit needs than on explicit ones (Why do AI models struggle with unspoken user needs?). A person who invests little effort in spelling out their situation leaves more unstated, and unstated needs are where models fail most. Models can be trained to ask before guessing (Can models learn to ask clarifying questions instead of guessing?) and to ask better clarifying questions (Can models learn to ask genuinely useful clarifying questions?). That would spare the user from having to volunteer the missing context.

One more note pulls the other way. Even correct AI suggestions can damage reasoning by breaking cognitive immersion and forcing people to rebuild focus (Does AI assistance always help reasoning or does it carry hidden costs?). Interruptions have a real cost, so a person who finds thinking effortful may prefer one complete answer over piecemeal exchanges. That would be a rational choice of interaction style. The open question the corpus doesn't reach is whether those users would take guided dialogue if it were made cheap enough.