Makeup Artists

Arts, Design, Entertainment & Media

AI exposure

  • Data source: BLSPublished: 2026-08

    Moderate· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.000

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.18

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 90% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Show 8 hidden tasks

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Study production information, such as character descriptions, period settings, and situations, to determine makeup requirements.

O*NET Task ID 14814

1.0
Duplicate work precisely to replicate characters' appearances on a daily basis.

O*NET Task ID 14798

0.5
Establish budgets, and work within budgetary limits.

O*NET Task ID 14799

0.5
Apply makeup to enhance or alter the appearance of people appearing in productions such as movies.

O*NET Task ID 14800

0.5
Alter or maintain makeup during productions as necessary to compensate for lighting changes or to achieve continuity of effect.

O*NET Task ID 14801

0.5
Analyze a script, noting events that affect each character's appearance, so that plans can be made for each scene.

O*NET Task ID 14805

0.5
Write makeup sheets and take photos to document specific looks and the products used to achieve the looks.

O*NET Task ID 14807

0.5
Examine sketches, photographs, and plaster models to obtain desired character image depiction.

O*NET Task ID 14808

0.5
Attach prostheses to performers and apply makeup to create special features or effects, such as scars, aging, or illness.

O*NET Task ID 14809

0.5
Evaluate environmental characteristics, such as venue size and lighting plans, to determine makeup requirements.

O*NET Task ID 14810

0.5

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information