Every occupation figure on this site comes from one of four independent studies, or is our own estimate derived from them — and each is labelled so you can tell which. Three studies measure AI exposure directly: Microsoft Research (from 200,000 Copilot conversations), the Anthropic Economic Index (from observed Claude usage), and the Felten-Raj-Seamans AIOE index (the index Statistics Canada uses for its own Canadian estimates). Goldman Sachs supplies whether AI augments or substitutes the work. Statistics Canada and ESDC supply the Canadian occupation code and labour-market outlook.
Coverage is not universal and we do not pretend otherwise: 336 of 342 occupations carry a measured exposure composite. Where a study has no data for an occupation, that occupation's page says “not available” and does not cite the study.
Coverage counts are computed from the dataset each time this page is built.
AI applicability score, 0–1, per occupation.
200,000 anonymised Copilot conversations classified against the O*NET catalogue of work activities, weighted by how often each activity came up and how well AI completed it. Tomlinson et al., arXiv:2507.07935.
Observed AI usage, 0–1, per occupation.
The share of an occupation's tasks observed being performed with Claude, from Anthropic's openly published per-occupation dataset (CC-BY 4.0). This is usage that happened, not usage that might.
Academic exposure percentile, 0–100.
The AIOE index, which scores occupations by how much their required abilities overlap with what AI systems can do, across 774 occupations. This is the index underlying Statistics Canada's own Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
Augmentation versus substitution.
Whether AI is expected to primarily amplify the person doing the work, replace parts of it, or both. Research mapping 900+ occupations across the US and Europe, April 2026.
Canadian occupation code and TEER level.
Each US SOC 2018 code is mapped through StatCan's published correspondence tables — SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0. TEER is read off the resolved NOC code rather than stored separately, so the two cannot disagree.
Projected Canadian labour-market outlook.
The Canadian Occupational Projection System's assessment for each NOC 2021 unit group: balance, or a moderate or strong risk of shortage or surplus over the projection period.
Comes straight from one of the studies above, for that specific occupation:
Derived by us from the measured values, calibrated against published aggregate findings:
Calibrated against aggregates published by Anthropic and by BCG. Neither published per-occupation figures; these estimates are ours, and are not presented as theirs.
A high exposure score says AI reaches deep into the work. It does not say whether that replaces the person or amplifies them — and those are opposite outcomes. Statistics Canada, Goldman Sachs and the IMF have each converged on pairing an exposure measure with a second axis describing whether AI complements the work or substitutes for it.
That second axis here is Goldman's classification. Combining the two puts every occupation in one of three groups:
The counts above are computed from the current dataset. See how they break down by domain.
The occupation taxonomy and the measured telemetry are reviewed quarterly; the Canadian concordance and COPS outlook annually, tracking the publication cycle of the underlying government tables. Each dataset records when it was last updated and how long it may go before it is considered stale, and a health check reports any that have run past their window.
A dataset whose date cannot be read is reported as stale rather than skipped — the one nobody can date is the one most likely to have been forgotten. Occupation pages carry the date of the figures they show.
An occupation average tells you where the ground is moving, not where you stand on it. Map the skills you actually own against it, and see which pathways stay open.