Given generative AI, 26,811 Chinese secondary students raised their homework scores by 18% and lost as much as 24% on the exams that decide their futures.
That’s the headline from a study that tracked the students for thirty months. The AI users also finished their homework 30% faster. Within six months their monthly exam scores had dropped 20%, and the high-stakes entrance exams fell 18% and 24%, with the full penalty taking about two years to show. Benjamin Riley, who dug into the data, gave it a name in August: the 50 Percent Problem. By his reading, about half the students who started using AI had fully outsourced their homework within five months.
The paper is a working paper and hasn’t been through peer review yet. Even so, the pattern it describes is one I read about constantly: a spike in usage, then a flat line in anything that matters.
The easy conclusion is that AI makes people dumber, so the answer is less of it. Ban it in schools. Ration it at work. Or go the other way and send everyone on a course to learn the tools properly.
A detail further down the abstract complicates both answers. The losses were concentrated in the students whose behaviour looked like outsourcing. Students who kept spending about as much time on their homework as non-users saw only small losses. They had the same tool and the same access. What they kept was the thinking.
The answer that looks finished
The name I’ve settled on for what went wrong is the clockwork mind. It’s the belief that thinking produces one perfect answer, the way a clock produces the time, and that once you have it you’re done.
AI feeds that belief beautifully. Its answers arrive formatted, confident, and complete. Nothing about a polished paragraph tells you it’s the first draft of a thought. It looks like the thing a clock would give you.
The pull is always there for me, gnawing at the edges of the work: a desire to take whatever the model hands back, copy it, paste it, and skip the verification. The easy way is to accept what it presents. The first answer looks good enough, and it’s right there.
For the students, homework was where the thinking happened, and the finished homework was only evidence of it. AI let them produce the evidence without the thinking, and the exam was where the difference showed. Adults at work rarely sit an exam. The equivalent turns up as a strategy that fails, long after everyone committed to it.
Where the pull costs the most
I don’t resist the pull equally everywhere, and I don’t think anyone should.
Tactics are short term. A code change, a research summary, a draft email: if the AI gets it wrong, I find out quickly and change it cheaply. Letting a model do that work fast is a good trade, and pretending otherwise turns every small task into a seminar.
Strategy is different, because tactics are built on top of it. Stephen Covey popularized a line about climbing the ladder of success only to find it leaning against the wrong wall. That’s the failure I worry about: going a long way down the road of an AI-generated strategy and discovering I’ve spent the time and money on something a few better questions would have caught.
Research on BCG consultants shows why the stakes should decide. Given GPT-4 in 2023, consultants completed 12% more tasks, 25% faster, at over 40% higher quality on the tasks the model handled well. On a task it handled badly, the same consultants were 19 percentage points less likely to reach the right answer than colleagues working without it. The model didn’t flag which task was which. It sounded equally sure both times.
So the rule I use is simple. The risk associated with the decision should set the intensity of the thought. A cheap, reversible decision can move at the model’s speed. The expensive ones get my own thinking first, with the AI in the room as a sparring partner.
A direction to walk in
My process isn’t exotic. I provide sources, ask, iterate, and refine, the same as everyone else. The part that matters is what I ask once the first answer is on the screen. The questions I lean on each ask the AI to show me something I haven’t seen yet:
What am I missing?
What would someone who disagrees with me say?
What question haven’t I asked?
Are there alternatives?
Think of the list as a compass. It gives you a direction and leaves the terrain to you, which is the honest position when you’re working somewhere new.
The harder skill is knowing when to stop. The clockwork mind stops at the first finished-looking answer. The rest of us are exploring unknown territory, and there’s no finished answer out there to find. What you’re looking for is somewhere the ground is solid enough to stand on, even if it isn’t perfect or complete, and that you can call yours.
Plenty of people stop for a different reason. Think of two students with the same assignment. One keeps going until they’re tired and doesn’t want to do any more. The other keeps going until they’ve arrived somewhere they couldn’t have reached at the start. From the outside, both handed in the work. Only one of them learned anything.
Is this just theatre?
The best objection to all of this is that it’s cognitive theatre. You ask the model what you’re missing, it tells you, and you nod. You’ve repeated the AI’s words with more steps and called it thinking.
Some of that has to be conceded. The words often do come from the machine. And a survey of 319 knowledge workers by Microsoft Research and Carnegie Mellon found that the more people trusted the AI, the less critical thinking they reported doing. The ones who trusted their own judgement more did more of it. The objection describes something that happens.
Where it goes too far is in assuming human ideas come from somewhere purer. The “stochastic parrot” critique of language models says they stitch together patterns without meaning behind them. Humans are the wetware version of the same thing: our ideas also came from somewhere, from books and arguments and people we worked with. That doesn’t make them gibberish. The measure that matters is whether the answer got better and whether the person asking got sharper.
The test I hold myself to is ownership. I should be in control of the ideas, able to defend them, and confident I could have arrived there on my own. The AI is a reasoning partner that gets me there faster and makes the iterations cheaper, so it becomes clearer where the value is.
I can’t prove I’d have got there alone, and nobody can. Once you’ve taken the fast route, you can’t go back and walk the slow one to compare. Ownership is something you keep checking, and part of the work is noticing when you’ve stopped.
The students who kept their homework hours had exactly the same tool as the ones who fell behind. Nobody could see the difference on the homework itself. It showed up two years later, on the exam that counted, and by then it was a long way to walk back.
That’s the version worth building toward: AI that makes us better, not AI as a substitute for hard work.
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