Writers & Authors

Arts, Design, Entertainment & Media

AI exposure

  • Data source: BLSPublished: '26.08

    Very 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: '26.03

    0.246

    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: '25

    0.55

    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, 12% 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

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
Discuss with the client the product, advertising themes and methods, and any changes that should be made in advertising copy.

O*NET Task ID 22653

1.0
Present drafts and ideas to clients.

O*NET Task ID 22654

1.0
Vary language and tone of messages based on product and medium.

O*NET Task ID 22655

1.0
Write articles, bulletins, sales letters, speeches, and other related informative, marketing and promotional material.

O*NET Task ID 22657

1.0
Invent names for products and write the slogans that appear on packaging, brochures and other promotional material.

O*NET Task ID 22659

1.0
Collaborate with other writers on specific projects.

O*NET Task ID 22661

1.0
Edit or rewrite existing written material as necessary, and submit written material for approval by supervisor, editor, or publisher.

O*NET Task ID 22664

1.0
Follow appropriate procedures to get copyrights for completed work.

O*NET Task ID 22665

1.0
Plan project arrangements or outlines, and organize material accordingly.

O*NET Task ID 22666

1.0
Prepare works in appropriate format for publication, and send them to publishers or producers.

O*NET Task ID 22667

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

Recent Changes Affecting This Occupation

Mar 2026: Research shows a 1:33 AI-to-labor substitution ratio in corporate spending, with content-heavy outsourcing most affected.

[Source: Stevens (2026) arXiv]

Mar 2026: Brookings study reveals AI-exposed freelance writers face earnings decline, but long-form journalism, specialist writing, and narrative nonfiction remain harder for AI to replicate convincingly.

[Source: Brookings Institution (Hui & Reshef, 2025)]

Mar 2026: Published evergreen blog post analyzing AI impact on writers and authors (68% exposure, 60% risk, mixed mode)

[Source: AI Changing Work Blog]