The Cognitive Intensity Metric
The first feasible measurement reform Coyle proposes for the AI era is the adaptation of time-use surveys to capture cognitive intensity. Time-use surveys already exist in most OECD countries. They ask respondents to record activities in fine-grained intervals across representative days. The surveys capture what people do with time. They do not currently capture how intensely they do it. Adding intensity measures — self-reported cognitive load, stress, engagement quality, and perceived sustainability — to existing survey instruments would provide, for the first time, a national-level dataset on the cognitive composition of working time.
In the AI Story
The data would not be perfect. Self-reported intensity is subject to recall bias, social desirability effects, and the fundamental difficulty of introspection about cognitive states. But approximate data on a critical variable is infinitely more useful than no data at all, and the marginal cost of adding intensity questions to existing surveys is modest relative to the value of the information.
The metric would operationalize the efficiency-versus-intensity distinction that currently lives in theoretical discussion rather than empirical practice. A productivity figure accompanied by a cognitive intensity figure would tell policymakers not just how much the economy is producing but whether the production rate can be maintained without depleting the human capital it depends on.
The Berkeley study provides proof of concept. Ye and Ranganathan's eight-month ethnography documented task seepage, boundary erosion, and the specific cognitive patterns of AI-augmented work — but did so through intensive qualitative methods that cannot scale to population-level measurement. The cognitive intensity metric would translate these insights into survey instruments deployable at national scale.
For the AI-revolution reader, the metric is the single most important near-term reform. It would not require new institutional infrastructure — time-use surveys already exist. It would not require methodological innovation beyond what survey researchers routinely produce. It would simply require that statistical offices adopt intensity questions as core survey items. The barrier is institutional attention, not technical capacity.
Origin
The proposal synthesizes Coyle's work on time-use measurement (with Leonard Nakamura) with the burnout-research literature (Christina Maslach and Michael Leiter) and the flow-state psychology (Mihaly Csikszentmihalyi). The specific application to AI-augmented work draws on the Berkeley study findings and Coyle's 2024 Stanford Digital Economy Lab white paper.
Key Ideas
Survey extension. Adding intensity questions to existing time-use surveys is methodologically feasible and institutionally modest.
Self-report limitations. The data is imperfect, but approximate intensity data is infinitely more useful than no intensity data.
Efficiency-intensity operationalization. The metric transforms a theoretical distinction into an empirical one.
Feasibility advantage. Unlike quality-adjusted output or wellbeing composites, intensity measurement requires modest institutional adaptation of existing instruments.
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Further reading
- Diane Coyle and Leonard Nakamura, 'Time Use, Productivity, and Household-centric Measurement of Welfare', Bennett Institute Working Paper, 2020
- Christina Maslach and Michael Leiter, The Burnout Challenge (Harvard University Press, 2022)
- OECD, OECD Guidelines on Measuring the Subjective Well-being (2013)