# Cognitive offloading and the speedup illusion in human-AI interaction

arXiv: https://arxiv.org/abs/2605.23177 · HTML: https://mlsanity.com/paper/2605.23177/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2026.

## Built on by (most cited first, 1 of 1)

- [Ownership in AI-Assisted Everyday Tasks](https://mlsanity.com/paper/2609.20658/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have the potential to boost human productivity by speeding up task completion -- provided users know when to offload cognitive work to them. But we do not know if users are well-calibrated in estimating these potential time savings. We conducted a preregistered large-scale behavioral study (N = 1237) to characterize mismatches between expectations and reality, with a focus on simple cognitive tasks. While actual completion times between independent completion and AI-assisted completion did not differ, participants predicted AI to be significantly faster. The same bias was not observed when imagining help from another human participant. We identify a speedup illusion where people have accurate forecasts of independent completion times but significantly underestimate AI-assisted times. Additionally, time and effort dissociate: participants reported lower subjective effort with AI despite equivalent completion times. This suggests that completion time itself is not sufficient to characterize efficiency gains.