Occupation score change
IMPACT 10writer author exposure increased 1.0 points
AI Exposure Score moved from a previous calculation to 72/100 under methodology v1.0. 4 evidence item(s) contributed.
Poets, Lyricists and Creative Writers
Category: Arts, design & media · SOC 27-3043.05 · Industry: Media & creative
72/100
increased by +1.0 vs last
High exposure
3 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
4 sources
26 core tasks shown · 10 secondary hidden
Poets, Lyricists and Creative Writers Very High exposure: 11 of 26 core tasks show high task automation potential : exposure 72/100 (high exposure).
No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
AI Exposure (composite)72/100
Methodology 2026-09-a · score v1.0 · input hash 40c327a06193cc38
AI capability exposure80/100
Strength of current models on the capabilities this job's tasks require.
Task automation potential75/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption68/100
Evidence that AI is actually being used for this work.
Labour-market signal62/100
Observed changes in postings, employment or earnings.
Evidence strength62/100
Quantity and source quality of the evidence behind the score.
4 evidence item(s) contribute. Calculated 19 hours ago. 3 Oct 2026 UTC.
Overview
Create original written works, such as scripts, essays, prose, poetry or song lyrics, for publication or performance.
Employment
≈45,000
approx. BLS OES
Median wage
≈$78,000
approx. BLS OES
Tasks tracked
36
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Task-level automation exposure
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.5/5
core task · importance 4.5/5
core task · importance 4.5/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.2/5
core task · importance 4.1/5
core task · importance 4.1/5
core task · importance 4/5
core task · importance 3.9/5
core task · importance 3.6/5
core task · importance 3.5/5
core task · importance 3.5/5
core task · importance 3.2/5
core task · importance 3/5
Evidence
Evidence trail
4 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
- task automationincreasesconfidence 92/100 · magnitude 8213 Jul 2023
In a randomised experiment on professional writing tasks, ChatGPT reduced time taken by about 40% and raised graded output quality by about 18% relative to the control group.
Direct experimental evidence for text-production tasks specifically. It supports a high automation component for routine writing, and says nothing about jobs that also involve judgement, sourcing or accountability.
Source: Noy & Zhang — Experimental evidence on the productivity effects of generative AI (Science) · Updated 13 Jul 2023
- researchincreasesconfidence 82/100 · magnitude 7424 Aug 2023
Using O*NET task descriptions scored by GPT-4, the authors estimate that about 80% of the US workforce could have at least 10% of their tasks affected by large language models, and about 19% could see at least 50% of their tasks affected.
This is a *capability and exposure* estimate produced by having a model rate task descriptions — not a measurement of employment change. It is the most-cited exposure figure in the field and is included here as an exposure input, weighted accordingly.
Source: Eloundou, Manning, Mishkin & Rock — GPTs are GPTs (arXiv:2303.10130) · Updated 24 Aug 2023
- labor marketincreasesconfidence 74/100 · magnitude 627 Mar 2024
Using platform data from a large online labour market, the authors find that after ChatGPT's release, jobs more exposed to AI automation experienced a relative decline in the number of projects and in freelancer earnings compared with less-exposed jobs.
One of the few *observational labour-market outcome* studies. It is platform-specific, covers remote freelance work only, and cannot separate AI substitution from other demand shifts. Treated here as a genuine labour-market signal with medium confidence.
Source: Hui, Reshef & Zhou — The Short-Term Effects of Generative AI on Online Labour Markets · Updated 7 Mar 2024
- adoptionincreasesconfidence 70/100 · magnitude 6827 Feb 2025
Aggregated and anonymised Claude conversation data mapped to O*NET tasks shows that usage is concentrated in a small number of occupations and tasks — notably software development, writing and technical analysis — and that a substantial share of usage is augmentation rather than full automation of a task.
This is the primary *observed adoption* input, and it is deliberately kept separate from capability. A model being able to do a task does not mean firms are using it. Usage data is self-selected (Claude users) and therefore over-represents digital and technical occupations.
Source: Anthropic Economic Index · Updated 27 Feb 2025
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Reconstruction, not measurement. 5 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
- AI adoption signalIMPACT 68
Measured AI usage concentrated in software development, writing and analysis
Aggregated and anonymised Claude conversation data mapped to O*NET tasks shows that usage is concentrated in a small number of occupations and tasks — notably software development, writing and technical analysis — and that a substantial share of usage is augmentation rather than full automation of a task.
- Labour market updateIMPACT 62
Automation-prone freelance work saw fewer projects and lower earnings after ChatGPT
Using platform data from a large online labour market, the authors find that after ChatGPT's release, jobs more exposed to AI automation experienced a relative decline in the number of projects and in freelancer earnings compared with less-exposed jobs.
- Research publishedIMPACT 74
LLMs affect at least 10% of tasks for about 80% of US workers
Using O*NET task descriptions scored by GPT-4, the authors estimate that about 80% of the US workforce could have at least 10% of their tasks affected by large language models, and about 19% could see at least 50% of their tasks affected.
- Research publishedIMPACT 82
Writing tasks completed 40% faster with 18% higher quality ratings
In a randomised experiment on professional writing tasks, ChatGPT reduced time taken by about 40% and raised graded output quality by about 18% relative to the control group.
Related
Related Occupations
Occupations sharing major SOC group (27) with Poets, Lyricists and Creative Writers.
faq
Frequently Asked Questions
Common questions regarding Poets, Lyricists and Creative Writers and AI exposure.
Will AI replace Poets, Lyricists and Creative Writers?
Current AI models show an exposure score of 72/100 for Poets, Lyricists and Creative Writers. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. High exposure indicates strong overlap between current LLM/multimodal capabilities and routine tasks in this occupation.
Which tasks in Poets, Lyricists and Creative Writers have the highest AI exposure?
The most exposed tasks for Poets, Lyricists and Creative Writers include "Write articles, bulletins, sales letters, speeches, and other related informative, marketing and promotional material." (85% exposure) and "Write advertising material for use by publication, broadcast, or internet media to promote the sale of goods and servic…" (85% exposure).
What aspects of Poets, Lyricists and Creative Writers can AI not do yet?
Tasks with the lowest AI exposure for Poets, Lyricists and Creative Writers include "Attend book launches and publicity events, or conduct public readings." (5% exposure) and "Follow appropriate procedures to get copyrights for completed work." (5% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
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Notice: All ratings shown are AI-estimated technical exposure scores measuring technical capabilities across O*NET task statements, not predicted job loss. Contains information from O*NET 31.0 by the U.S. Department of Labor, ETA, used under CC BY 4.0.