Efficiency is a distributional question.
A faster task creates a claim on capacity. An employer may allocate it to more output; a worker may seek higher income, learning or leisure. Before measuring a gain, decide what counts as completion and whose time enters the calculation. Prompting, checking, correction and downstream rework belong in the account. Concurrent agents can shorten elapsed time while leaving human attention fully occupied.
A 2026 preprint surveying 5,512 Korean workers reports time savings and increased on-the-job leisure among recent generative-AI users. Berkeley’s eight-month ethnography at one technology company describes an opposing pattern: broader responsibilities and work expanding into pauses. These studies use different methods and cannot establish a universal agentic-work effect. Together they make allocation, rather than speed alone, the central research question. Suh & Oh · preprint, 2026 Ye & Ranganathan · Berkeley Haas, 2026
The proposed response is a negotiated review cycle: establish a quality-adjusted baseline, record the full cost of delivery, then agree how verified gains become pay, reduced hours or protected intervals. Revisit the baseline when the tool or task changes. Treat a productive week as evidence for a discussion, rather than an automatic increase in next week’s quota.
equilibri asks: who can claim the released capacity, and what prevents a temporary efficiency gain from becoming a permanent expansion of workload?
Evidence & limits / time
Observed time savings / what they establish
A randomized field experiment across 66 firms and 7,137 knowledge workers provided access to AI within familiar workplace applications. In the experiment’s second half, treated workers who used the tool spent about two fewer hours per week on email and worked less outside regular hours. The researchers did not detect changes in task quantity or composition. This supports task-specific time savings; it does not demonstrate a general transition to leisure or test a coworking model. Dillon et al. · American Economic Review: Insights, forthcoming
Intraday leisure / an emerging signal
In Suh and Oh’s survey, recent users reported a 3.8% reduction in active working time on average; the estimated workforce-wide reduction, including non-users, was 1.4%. The reported share of on-the-job leisure rose by 1.3 percentage points among users. These are survey estimates from Korea, not observed time logs or randomized treatment effects. The paper also finds uneven experiences of task fulfillment. Less active work cannot by itself establish a better job. Suh & Oh · methods and results, 2026
Work intensification / the countervailing mechanism
Berkeley’s in-progress ethnography describes task expansion, erosion of stopping points and overlapping AI workflows. Separately, an NBER working paper associates occupational AI exposure with longer workdays and less leisure in U.S. time-diary data. Exposure measures are not direct observations of a person using an agent; these findings should not be read as a universal causal estimate. Together they motivate testing workload and bargaining arrangements alongside tool capability. Jiang et al. · NBER working paper, 2025
Measurement / concurrent work changes the denominator
METR’s February 2026 update judged its follow-up developer experiment an unreliable estimate of current speedup because of selection effects and difficulties measuring time across concurrent agents. The implication here is methodological: compare complete workflows, distinguish human effort from elapsed runtime and report task selection. A benchmark or a self-reported speedup cannot alone establish the amount of time available for leisure. METR · methods update, 2026
Agent execution / time that may still require supervision
Anthropic’s February 2026 analysis reports a median Claude Code turn of roughly 45 seconds, while the extreme tail had exceeded 45 minutes. Long unattended intervals exist in some workflows; they are not typical of every task. Tool runtime is neither verified labor saved nor guaranteed leisure. Anthropic · observational product research, 2026
Institutional change / a different kind of intervention
A six-month four-day-week study followed 2,896 employees in 141 organizations, with 12 comparison companies. Reduced working time with maintained pay was associated with improved reported well-being. Organizations participated voluntarily; this was not a randomized trial. It examines deliberate work reorganization, not AI adoption. Fan et al. · Nature Human Behaviour, 2025
Human–AI performance / the quality constraint
A meta-analysis of 106 experiments found that human–AI combinations improved on humans alone on average but underperformed the better of the human or AI alone. Results varied by task. Any time dividend must account for review, correction and coordination, measured at an agreed quality threshold. Vaccaro et al. · Nature Human Behaviour, 2024