DAIOE: how exposed is each job to AI?
DAIOE measures how exposed each occupation is to artificial intelligence, from data rather than expert guesswork. It tracks nine AI subdomains annually since 2010, capturing the potential applicability of AI capabilities to occupational content, not job-loss forecasts or adoption probabilities. It is openly published, versioned and mapped across the US (SOC), international (ISCO) and Swedish (SSYK) classifications, so others can use it directly.
How exposed is your job?
Find your occupation.
Type an occupation to see its DAIOE exposure and where it sits among roughly 420 occupations. Generative AI by default; switch the sub-domain to compare. As featured in Die Zeit ↗
The whole landscape · generative AI, 2023
Every occupation, placed by its exposure.
Roughly 420 occupations, each a dot, placed left to right by how exposed they are to generative AI. Hover any dot to name it.
The exposed end is desk work. Writers, programmers, analysts, marketers and, yes, economists cluster on the right.
The other end is hands and bodies. Care, craft, construction, cleaning and farming sit on the left, where generative AI reaches least.
It cuts against intuition. The more schooling a job needs, the more exposed it tends to be. Exposure is not replacement, but the pattern is stark.
Source: DAIOE v2023 · ISCO-08. Look up your own job in the search above.
The named extremes · generative AI, 2023
Where generative AI reaches, and where it doesn't.
DAIOE's generative-AI exposure across roughly 420 occupations. Writers, marketers, programmers and, yes, economists sit at the very top; hands-on manual, craft and outdoor work sits at the bottom.
Source: DAIOE v2023 · ISCO-08 · higher score = more exposed. Explore every occupation in the Occupations Explorer.
The measure
Data-driven, not expert-guessed.
DAIOE scores each occupation's exposure to AI from data, and publishes those scores openly and with versions, mapped across the US (SOC), international (ISCO) and Swedish (SSYK) classifications, so others can join it straight onto their own data.
Use it in your own work
Open data & crosswalks.
- DAIOE datasets (all versions) · SOC / ISCO / SSYK, versioned
- Technical documentation (construction, vintages, reading the scores) · the release's public reference
- SSYK 2012 & SSYK 96 translation utility · crosswalk helper
Introduced and validated in the working paper “AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries”. See Research.
FAQ
What DAIOE is, and isn't.
Does “exposure” mean automation or job loss?
No. DAIOE captures the potential applicability of AI capabilities to occupational content. Whether this changes jobs depends on adoption, complements and organisational choices.
Is it a forecast of AI adoption?
No. DAIOE captures potential applicability. If, when and how AI is applied in an occupation depends on the cost of acquiring, adapting and using the technology, the institutional setting (laws and regulations), and more.
What is different about DAIOE versus prior indices?
DAIOE is dynamic and grounded in measured AI benchmark progress. It tracks nine subdomains over time and explicitly adjusts for social interaction.
How do I read the raw score values?
The index has no natural units: a value is meaningful only relative to other occupation-years. For cross-sectional standing, use the shipped percentile ranks (more exposed than X per cent of occupations that year). Percentiles are ordinal, so they do not show how much exposure has grown; for level and growth, rescale as in the next question.
Can I express scores relative to the most exposed occupation?
Yes: divide by the highest value in the frozen 2010 to 2023 panel you use and multiply by 100 (score_rel_max = 100 x value / max). A score then reads as per cent of the most exposed occupation-year, and growth survives: an occupation at 31 in 2015 and 62 in 2023 doubled its exposure. Keep the 2010 to 2023 maximum as the denominator for later vintages too; values above 100 then mean exposure beyond the frozen-window peak. We document this transformation rather than shipping it, so the raw values remain the single citable version; details are in the technical documentation below.
Which occupations tend to score highest?
Cognitive, non-physical roles with lower social-interaction intensity, often white-collar, tend to rank higher.
Do the data say anything about employment outcomes?
Higher DAIOE relates to up-skilling: higher high- to low-skill ratios, and a shift from low-skill clerical toward high-skill work.
How do you handle interpersonal-heavy work?
We compute a social-skills index from O*NET (importance × level across six social skills) and discount exposure accordingly.
What are the nine AI subdomains?
Capabilities across games, vision and language, split into nine subdomains, aggregated into an overall index and a generative-AI sub-index.
Can I compare across countries or industries?
Yes. Combine occupation-level DAIOE with local workforce structures (firms, industries, regions) to assess exposure distributions.
Is there a generative-AI version?
Yes. We provide a generative-AI sub-index that combines language modelling and image generation.
See it live
DAIOE in the Monitor.
The Occupations Explorer, part of the AIEL Monitor, sets Swedish employment by occupation against DAIOE exposure levels, in yearly and monthly views.