Props Masters

Arts, Design, Entertainment & Media

AI exposure

  • Data source: BLSPublished: 2026-08

    High· 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

    0.37

    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, 53% 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 7 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
Examine objects to be included in exhibits to plan where and how to display them.

O*NET Task ID 9284

0.5
Acquire, or arrange for acquisition of, specimens or graphics required to complete exhibits.

O*NET Task ID 9285

0.5
Prepare rough drafts and scale working drawings of sets, including floor plans, scenery, and properties to be constructed.

O*NET Task ID 9286

0.5
Estimate set- or exhibit-related costs, including materials, construction, and rental of props or locations.

O*NET Task ID 9288

0.5
Develop set designs, based on evaluation of scripts, budgets, research information, and available locations.

O*NET Task ID 9289

0.5
Direct and coordinate construction, erection, or decoration activities to ensure that sets or exhibits meet design, budget, and schedule requirements.

O*NET Task ID 9290

0.5
Plan for location-specific issues, such as space limitations, traffic flow patterns, and safety concerns.

O*NET Task ID 9292

0.5
Submit plans for approval, and adapt plans to serve intended purposes, or to conform to budget or fabrication restrictions.

O*NET Task ID 9293

0.5
Prepare preliminary renderings of proposed exhibits, including detailed construction, layout, and material specifications, and diagrams relating to aspects such as special effects or lighting.

O*NET Task ID 9294

0.5
Collaborate with those in charge of lighting and sound so that those production aspects can be coordinated with set designs or exhibit layouts.

O*NET Task ID 9296

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