The 2026 Edition · Employment data 2025 · Compiled by Christopher Martin
Colorado AI Exposure Atlas
The one-page summary — set to print on a single sheet.
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About 1 in 4 jobs in Colorado — roughly 784,400 of 2,860,276, or 27.4% — are in occupations where most or a large minority of tasks overlap with what AI systems can already do. Nationally the figure is 25.9%. Measured by payroll rather than headcount Colorado's share rises to 34.8%, because the exposed work is concentrated in higher-paid office occupations along the Front Range. Across all Colorado jobs, 6.3% are in high-overlap occupations, 21.1% substantial, 27.9% some, and 44.7% little.
- High + substantial share of jobs
- 27.4% about 1 in 4 jobs · score ≥ 45
- High + substantial jobs
- 784,367 of 2,860,276 UI-covered jobs
- High + substantial share of payroll
- 34.8% exposure skews toward higher pay
- United States
- 25.9% same method, national employment
The ten counties with the largest share
Ordered by the share of jobs in high- or substantial-overlap occupations — those scoring 45 or above on the 0–100 measure. The band compares a county with the typical (median) Colorado county, not with the statewide figure. All ten fall in the same band.
| County | High + substantial share | High + substantial jobs | UI-covered jobs | Band |
|---|---|---|---|---|
| Broomfield† | 35.3% | 14,367 | 40,750 | Well above typical |
| Denver | 33.1% | 185,864 | 560,749 | Well above typical |
| Boulder | 32.6% | 62,359 | 191,474 | Well above typical |
| Arapahoe | 31.0% | 105,532 | 340,882 | Well above typical |
| Douglas | 30.8% | 46,029 | 149,574 | Well above typical |
| Crowley‡ Heavily imputed — 54.8% of jobs | 28.8% | 322 | 1,121 | Well above typical |
| Jefferson | 27.1% | 67,103 | 247,212 | Well above typical |
| El Paso | 26.4% | 81,447 | 308,056 | Well above typical |
| Bent‡ Heavily imputed — 55.4% of jobs | 25.3% | 296 | 1,172 | Well above typical |
| Larimer | 24.7% | 42,348 | 171,290 | Well above typical |
† 10–50% of county employment imputed. ‡ Over half of county employment imputed — treat as a rough estimate. County industry detail suppressed by QCEW is estimated from state-level mix; see the methodology page. Where more than 30% of a county's employment is imputed the atlas withholds that county's rank outright; the band and the marker above stand in its place. No county is numbered 1–64 on this sheet: the ordinal orders the 0–100 mean score, and all 64 counties sit between 24.7 and 35.2 on it, so adjacent ranks separate on differences the estimates do not support. Ranks and the mean score are published with the dataset.
The method in one paragraph
The atlas combines county employment records (BLS QCEW, 2025 annual averages) with national occupation staffing patterns (BLS OEWS) and published occupation-level AI exposure scores (Eloundou et al., Science, 2024). Occupations are sorted into four tiers on that 0–100 measure — high overlap (≥ 60), substantial (≥ 45), some (≥ 30), and little — and a county's headline figure is the share of its jobs in the top two tiers (score 45 or more). The former binary cut at 50 is published with the dataset as an alternative cut. The five bands cut that share into fifths of about 13 counties each, labeled against the typical (median) Colorado county, which sits at 21.2%. The statewide 27.4% is employment-weighted and therefore runs above 58 of the 64 counties, because most Colorado jobs are in a handful of metro counties. County occupation mixes are estimated from industry mix, not observed, and every county page reports how much of its underlying data was imputed. Exposure is not a job-loss forecast: it measures where AI-driven change — augmentation and automation both — is likely to arrive first. Full methods, sources, validation, and limitations: https://coloradoaiexposureatlas.com/methodology/
Colorado AI Exposure Atlas
All 64 counties, full dataset (CC BY 4.0), and methodology:
https://coloradoaiexposureatlas.com/
Cite as: Colorado AI Exposure Atlas, 2026 edition. Christopher Martin. https://coloradoaiexposureatlas.com/
2026 Edition · Employment data 2025 · Compiled by Christopher Martin