Counting the Bandages: On measuring the time that AI saves

The article examines how to interpret the time savings that artificial‑intelligence (AI) assistance brings to review tasks. The author contrasts two viewpoints: a “clock” perspective that measures the reduced time directly, and a “ledger” perspective that treats the saved time as a counterfactual that must be weighed against the errors that AI allows to slip through. He proposes a framework that defines two model‑free metrics—time saved and excess defects escaped—and introduces a decision threshold called the equilibrium defect cost. Using a simple verification model, he shows that as long as the AI model is not accurate enough to eliminate review entirely, the defect‑escape rate of a calibrated worker does not depend on model quality; however, review time grows logarithmically with the model’s error rate, and overreliance on AI cuts minutes on the clock at the expense of a higher total number of minutes lost to defects. The study replicates METR data, finding a 19‑24 % slowdown depending on the metric and that AI contributed between 3 % and 82 % of cases. Additionally, a controlled experiment revealed that human reviewers reduced effort when told the author made fewer mistakes, resulting in faster reviews but a higher failure rate. This research is important because it shows that the apparent time savings of AI can mask hidden quality costs, a critical consideration when deciding whether to adopt AI in high‑stakes processes.

Read the original article on arXiv cs.AI

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