The colour of every tile on this map — the AI exposure score, 0–10 — is derived from published exposure research, occupational crosswalks and, where official mappings are unavailable, clearly identified model-assisted mappings. It combines two open, generative-AI-era research datasets, mapped onto each country’s official occupation classification.
The exposure index draws on two published research datasets, both openly licensed for reuse:
Published by the International Labour Organization (a UN agency). Its Annex Table A1 gives a generative-AI exposure mean (0–1) for the 112 ISCO-08 occupations with meaningful exposure. We use these as the authoritative anchor for the high-exposure band.
From OpenAI. Provides a task-based LLM-exposure score (“beta”, 0–1) for ~800 O*NET-SOC occupations, continuous across the whole range. We use it to resolve the low-to-mid exposure band continuously.
The two 0–1 scales agree closely where they overlap (Data Entry Clerks: ILO 0.70 / Eloundou 0.696; Accountants 0.51 / 0.54), so their raw scores can be combined on the same scale without rescaling.
exposure = round(percentile / 10), a 0–10 scale (green = low,
red = high).Each country’s occupations are mapped to ISCO-08 (or, for the US, to SOC):
| Method | Countries | How |
|---|---|---|
| Direct | US · IE · IT · NL | Occupation code is already SOC (US) or ISCO-08 (IE/IT/NL) — joined directly. |
| Crosswalk | AU · NZ · DE · UK · CA · ES | Official classification correspondence (ANZSCO, KldB, SOC, NOC, and Spain’s INE CNO-11↔ISCO-08) → ISCO-08. |
| AI-mapped | FR · JP · KR | No clean official ROME / JSCO / KECO → ISCO table was obtainable, so each occupation is placed
onto the official ISCO-08 structure by an LLM (a clearly identified model-assisted mapping), then scored as
above. Tagged separately (_llmmap); will be upgraded when the official table is wired in. |
Tile area is the size of the workforce in each occupation, from each country’s official statistics (see the “Data sources” note in the sidebar of every country page — e.g. Jobs and Skills Australia & ABS, US BLS & O*NET, Statistics Canada, ONS, Destatis, and so on). Salary and workforce figures blend official data with estimates; treat everything as indicative, not advice.
The full scored dataset — every occupation in all 17 countries, with its AI-exposure score (0–10), global percentile, workforce size and average pay — is available as a single CSV:
Per-country high-resolution PNG maps are linked from each country page and from the home page.
Exposure is recomputed from the sources above. Contains public sector information licensed under CC BY 4.0 (ILO) and MIT (OpenAI); adapted by percentile normalisation and crosswalking. This site is independent and not affiliated with, or endorsed by, the ILO or OpenAI.