Foley 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

    0.35

    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, 61% 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

Exposed tasks only

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
Keep logs of recordings.

O*NET Task ID 7730

1.0
Confer with producers, performers, and others to determine and achieve the desired sound for a production, such as a musical recording or a film.

O*NET Task ID 7720

0.5
Regulate volume level and sound quality during recording sessions, using control consoles.

O*NET Task ID 7722

0.5
Mix and edit voices, music, and taped sound effects for live performances and for prerecorded events, using sound mixing boards.

O*NET Task ID 7725

0.5
Synchronize and equalize prerecorded dialogue, music, and sound effects with visual action of motion pictures or television productions, using control consoles.

O*NET Task ID 7726

0.5
Record speech, music, and other sounds on recording media, using recording equipment.

O*NET Task ID 7727

0.5
Reproduce and duplicate sound recordings from original recording media, using sound editing and duplication equipment.

O*NET Task ID 7728

0.5
Separate instruments, vocals, and other sounds, and combine sounds during the mixing or postproduction stage.

O*NET Task ID 7729

0.5
Create musical instrument digital interface programs for music projects, commercials, or film postproduction.

O*NET Task ID 7731

0.5
Convert video and audio recordings into digital formats for editing or archiving.

O*NET Task ID 18661

0.5
Prepare for recording sessions by performing such activities as selecting and setting up microphones.

O*NET Task ID 7723

0.0
Report equipment problems and ensure that required repairs are made.

O*NET Task ID 7724

0.0
Tear down equipment after event completion.

O*NET Task ID 18660

0.0

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