The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks

2026-05-21

AI Summary

This study addresses a systematic misjudgment in human-AI interaction during simple cognitive tasks: users frequently employ AI yet consistently underestimate their own usage frequency while overestimating the time savings it provides. Through three preregistered large-scale user experiments (N = 2691), integrating behavioral logs and measures of cognitive bias, the research uncovers a dual calibration bias alongside a conversation-level usage reinforcement effect. Findings reveal that individuals often opt for AI even in the absence of tangible benefits, and prior usage intensifies subsequent reliance while amplifying the illusion of efficiency. This dynamic fosters a positive feedback loop of overreliance, highlighting significant cognitive risks inherent in human-AI collaboration.

Abstract

People are increasingly turning to AI assistance for simple tasks, e.g., arithmetic, spell-check, and answering simple questions. But does AI assistance actually save users time and effort? We investigate people's propensity to use AI for cognitively simple tasks and assess whether their reliance is well-calibrated. Across three pre-registered user studies (N = 2691), we find that people frequently choose to use AI even when doing so is inefficient (i.e. provides no meaningful time or effort savings). We identify systematic miscalibration at two levels: (1) a self-estimate miscalibration where people on average believe that they are using AI less than they actually are, and (2) efficiency-gain illusions where people overestimate how much time and effort savings AI use affords. We also identify a session-level carryover effect where a participant's prior AI use leads to further AI adoption and entrenches their miscalibration about time savings. Our results shed light on the mechanisms and biases underlying people's choice of whether to use AI as well as the risk of an overreliance feedback loop.

Problem

• AI reliance
• efficiency illusion
• human-AI interaction
• cognitive bias
• overreliance

Innovation

• efficiency-gain illusion
• AI overreliance
• miscalibration
• human-AI interaction
• cognitive bias