Music Directors

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

    0.037

    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

    0.28

    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, 73% 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 3 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 scores to learn the music in detail, and to develop interpretations.

O*NET Task ID 22546

1.0
Apply elements of music theory to create musical and tonal structures, including harmonies and melodies.

O*NET Task ID 22547

1.0
Determine voices, instruments, harmonic structures, rhythms, tempos, and tone balances required to achieve the effects desired in a musical composition.

O*NET Task ID 22549

1.0
Transcribe ideas for musical compositions into musical notation, using instruments, pen and paper, or computers.

O*NET Task ID 22551

1.0
Write musical scores for orchestras, bands, choral groups, or individual instrumentalists or vocalists, using knowledge of music theory and of instrumental and vocal capabilities.

O*NET Task ID 22554

1.0
Fill in details of orchestral sketches, such as adding vocal parts to scores.

O*NET Task ID 22559

1.0
Explore and develop musical ideas based on sources such as imagination or sounds in the environment.

O*NET Task ID 22560

1.0
Write music for commercial mediums, including advertising jingles or film soundtracks.

O*NET Task ID 22561

1.0
Transpose music from one voice or instrument to another to accommodate particular musicians.

O*NET Task ID 22562

1.0
Rewrite original musical scores in different musical styles by changing rhythms, harmonies, or tempos.

O*NET Task ID 22563

1.0

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