There’s a habit most regular AI users develop within the first few weeks. It feels like efficiency. It probably is more efficient, in the narrow sense. But it has a side effect that compounds quietly over months: you stop forming an initial position before asking the question.
Here’s what it looks like in practice. You’re weighing a decision — a tool to pick, an approach to a problem, how to structure something you’re writing. Old habit: think it through a bit, maybe jot some notes, arrive at a rough answer, then verify or refine it. New habit: open the chat, describe the situation, let the model lay out the considerations, then adopt one of the options it surfaces. Faster. Less friction. And it quietly trains you to skip the part where you do the thinking.
Why This Matters More Than It Seems
The problem isn’t that the AI’s answer is wrong. Often it’s fine. The problem is what you’re not building.
Forming a position — even a rough, probably-wrong one — before you get more information is how you notice when you’re being led astray. It’s how you develop a felt sense of whether an answer fits your actual situation, or just sounds reasonable in the abstract. Skip that step consistently, and you lose the reference point. You can still evaluate arguments when they’re presented side by side, but you get worse at generating them independently, and worse at noticing when a plausible-sounding response is missing something your own experience would have flagged.
I’ve caught myself doing this — reaching for a chat window before I’ve even decided what I think about a problem, then treating whatever the model says as the starting point for my thinking rather than a check on it. The output is usually fine. But I’d skipped the part of the process that would have made me sharper on that type of problem the next time it came up.
The Mechanism Is Straightforward
Skills you don’t use degrade. Not dramatically, not immediately, but they do. Independent reasoning is a practice, and like any practice, the frequency and quality of your reps matter. If most of your reasoning reps are now “read the AI’s framing, react to it” rather than “generate your own framing, then test it,” you’re getting fewer of the reps that actually build the underlying capability.
There’s a parallel in how GPS changed spatial navigation. Most people navigate better with it. But research on hippocampal function — including work by neuroscientist Eleanor Maguire and colleagues studying London taxi drivers — suggests that building a mental map as you navigate develops spatial reasoning in ways that following a prompted route doesn’t. You get to the destination either way. The difference is what’s being built internally, or not.
The AI version of this is similar. You get the answer either way. But whether you’re getting sharper or flatter as a thinker over time depends on which part of the workflow is doing the generative work.
This Isn’t an Argument for Using AI Less
Using AI heavily and thinking independently aren’t mutually exclusive. The distinction is about sequencing and what you ask for.
Asking AI to stress-test a position you’ve already formed is a completely different cognitive exercise than asking it to generate the position in the first place. In the first case, you’re using the model as a sparring partner — defending, refining, noticing where your reasoning has gaps. In the second, you’re outsourcing the generative step entirely.
Same tool. Very different effect on your thinking.
One reframe that actually helps: before opening a chat, write one sentence — just one — saying what you currently think. Not what you’re unsure about. What you think. Even if it’s half-formed and probably wrong. Then go to the AI. You’ll engage with the response differently. You’ll notice when it contradicts your take rather than just absorbing it. Your follow-up questions will be sharper, because you have something to defend or revise.
Where the Risk Is Highest
Not all tasks carry equal risk here. Asking AI to reformat a table, generate boilerplate code, or summarize a long document — those aren’t reasoning tasks you’re outsourcing. You weren’t going to build a mental muscle by summarizing a 40-page report manually.
The tasks that matter are the ones where the thinking itself is the point: decisions with real trade-offs, arguments you need to be able to make from memory, analysis of your own situation where context only you have should be doing real work. Those are the cases where jumping straight to the model is the costly shortcut.
There’s also a structural limitation worth naming: AI responses are calibrated toward comprehensive, balanced coverage of a topic, not toward the specific constraints of your situation. A model explaining trade-offs between two approaches is working from general patterns. You have context it doesn’t — history, constraints, what’s actually failed before. If you haven’t formed even a provisional view before you ask, you’re unlikely to bring that context to bear when you read the response. You’ll nod along, because it sounds reasonable. Which it probably is — in general.
One Concrete Adjustment
Before any non-trivial query, spend two minutes writing down your current take in your own words. It doesn’t need to be polished or right. The goal is to arrive at the conversation with a position, not a blank slate.
It sounds almost too simple. But it changes the interaction significantly, and it keeps the generative part of the reasoning in your own head where it belongs.
For the last three important decisions you made with AI’s help: could you reconstruct the reasoning behind your conclusion without referring back to the conversation? If not, that’s not AI helping you think. That’s AI thinking instead of you.
