Occupation score change
IMPACT 30marketing copywriter exposure decreased 3.0 points
AI Exposure Score moved from a previous calculation to 66/100 under methodology v1.0. 5 evidence item(s) contributed.
66/100
0.0
High exposure
12 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
5 sources
3 core tasks shown · 2 secondary hidden
Copy Writers Very High exposure: 2 of 3 core tasks show high task automation potential : exposure 66/100 (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)66/100
Methodology 2026-09-a · score v1.0 · input hash 1c20d69eae11217c
AI capability exposure79/100
Strength of current models on the capabilities this job's tasks require.
Task automation potential60/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption57/100
Evidence that AI is actually being used for this work.
Labour-market signal62/100
Observed changes in postings, employment or earnings.
Evidence strength66/100
Quantity and source quality of the evidence behind the score.
5 evidence item(s) contribute. Calculated 19 hours ago. 12 Oct 2026 UTC.
Overview
Writes advertising and marketing copy. Experimental studies measure large time savings on routine writing; commodity copy demand shows the clearest substitution effects.
Employment
≈60,000
approx. BLS OES
Median wage
≈$60,000
approx. BLS OES
Tasks tracked
5
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 5/5 · best current capability 88/100
core task · importance 4/5 · best current capability 84/100
core task · importance 4/5 · best current capability 82/100
Evidence
Evidence trail
5 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
- 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
- adoptionincreasesconfidence 68/100 · magnitude 5214 Aug 2025
Indeed Hiring Lab scores the skills appearing in job postings against AI capability and reports that the share of skills generative AI could perform has continued to increase, with larger effects for software development and customer-facing roles than for hands-on occupations.
Job postings measure what employers ask for, which is a leading indicator but not employment. Exact series values are not reproduced here; read them from the source. Used to inform the job-posting-change signal type.
Source: Indeed Hiring Lab — AI at Work series · Updated 14 Aug 2025
- adoptionincreasesconfidence 52/100 · magnitude 5013 Jul 2023
A meta-analysis of early controlled studies reported an average productivity increase of about 66% across supported tasks, with wide variance by task type.
Useful directionally but the average is dominated by small short-duration studies on well-specified tasks. Weighted low. The variance, not the mean, is the useful finding.
Source: Nielsen Norman Group — AI Improves Employee Productivity by 66% (meta-analysis) · Updated 13 Jul 2023
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Reconstruction, not measurement. 14 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
- Occupation score changeIMPACT 10
marketing copywriter exposure increased 1.0 points
AI Exposure Score moved from a previous calculation to 69/100 under methodology v1.0. 5 evidence item(s) contributed.
- AI adoption signalIMPACT 52
Share of skills in job postings that generative AI could perform continues to rise
Indeed Hiring Lab scores the skills appearing in job postings against AI capability and reports that the share of skills generative AI could perform has continued to increase, with larger effects for software development and customer-facing roles than for hands-on occupations.
- 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 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.
- AI adoption signalIMPACT 50
Meta-analysis of early AI productivity studies reports large average gains
A meta-analysis of early controlled studies reported an average productivity increase of about 66% across supported tasks, with wide variance by task type.
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