MIT, Stanford, New York Univ, Princeton paper says AI can make people feel more efficient even when they are not actually becoming much more efficient.
that people often use AI for simple tasks because it feels like it saves time and effort, but the measured benefit is often tiny, missing, or even negative.
The biggest point is the feedback loop: once people use AI, they become more likely to use it again, even for easy tasks where doing it themselves would often be just as fast or faster.
i.e. AI dependence can grow from a mistaken feeling of convenience, not just from real productivity gains.
Across three preregistered studies with 2,691 participants, people used AI for basic arithmetic, spelling, recall, and short rewriting at higher rates than they predicted, especially on easy tasks.
They also expected AI to save 55.7 seconds on average, when the measured saving was only 7.5 seconds.
For simple work, the hidden cost is not intelligence but interface friction: writing the prompt, waiting, reading, checking, and deciding whether the answer is acceptable.
Once that loop begins, it can feel like effort has been outsourced, even when effort has only been rearranged.
Here’s the key part: the study suggests that AI use can train its own justification.
After using AI on just two tasks, participants became more likely to use it again, even when independent completion was faster.
The danger is not dramatic dependence, but quiet recalibration.
A person who asks AI for a trivial answer today may not become less capable tomorrow, but they may become less accurate at judging when their own mind is already the faster tool.
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Paper Link – arxiv. org/abs/2605.22687
Paper Title: "The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks"
