Companion to What AI job loss estimates actually measure

Understand the connections

Each relationship explains what connects two topics, which sources support it and where the evidence stops.

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Lines indicate a relationship discussed in the research. They do not establish causation, strength or probability. Records, our analysis and open questions are labelled separately. Edition 1 · 14 November 2026.

Documented relationship

Task exposureOccupational classification

Task exposure and Occupational classification

Exposure research classifies work through occupational and task frameworks.

Evidence limit: A category is not an individual dismissal forecast.

Sources: [1] [3]

Our analysis

Occupational classificationCensus population

Occupational classification and Census population

A defensible population estimate requires a documented match between study codes and Census categories.

Evidence limit: Ambiguous mappings must remain visible.

Sources: [6] [8]

Documented relationship

Census populationDemographic comparison

Census population and Demographic comparison

PUMS documentation supplies variables and weighting guidance for population analysis.

Evidence limit: This brief has not estimated demographic exposure counts.

Sources: [7] [9]

Documented relationship

AI adoptionProductivity

AI adoption and Productivity

The customer-support study evaluates productivity after an AI assistant was introduced.

Evidence limit: Its outcome is not the number of eliminated jobs.

Sources: [4]

Open question

ProductivityEmployment outcomes

Productivity and Employment outcomes

Do changes in output translate into staffing, pay or hours changes, and for whom?

Evidence limit: This relationship must be investigated; it is not established by a single percentage.

Sources: [4] [5]

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