The efficiency-gain illusion: People underestimate the rate of AI use

A new study with 2,691 participants found that people frequently use AI for simple tasks even when it provides no meaningful time or effort savings, and they systematically underestimate their own AI usage while overestimating the efficiency gains. The research identifies a carryover effect where prior AI use leads to further adoption and entrenches miscalibrated beliefs about time savings, highlighting a risk of an overreliance feedback loop.

Computer Science Computers and Society Submitted on 21 May 2026 Title:The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks View PDF /pdf/2605.22687 HTML experimental https://arxiv.org/html/2605.22687v1 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. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .