Will AI replace Makeup Artists, Theatrical and Performance?
Most of the work in Makeup Artists, Theatrical and Performance still leans on things AI struggles with — research rates its theoretical AI reach at only ~34%, and real-world use lower still.
O*NET-SOC 39-5091
How your 20 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~34%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~34%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 20 tasks, ratedBased on real task-level AI scores — click to collapse
- Study production information, such as character descriptions, period settings, and situations, to determine makeup requirements.
- Apply makeup to enhance or alter the appearance of people appearing in productions such as movies.
- Duplicate work precisely to replicate characters' appearances on a daily basis.
- Alter or maintain makeup during productions as necessary to compensate for lighting changes or to achieve continuity of effect.
- Analyze a script, noting events that affect each character's appearance, so that plans can be made for each scene.
- Establish budgets, and work within budgetary limits.
- Write makeup sheets and take photos to document specific looks and the products used to achieve the looks.
- Evaluate environmental characteristics, such as venue size and lighting plans, to determine makeup requirements.
- Attach prostheses to performers and apply makeup to create special features or effects, such as scars, aging, or illness.
- Examine sketches, photographs, and plaster models to obtain desired character image depiction.
- Advise hairdressers on the hairstyles required for character parts.
- Design rubber or plastic prostheses that can be used to change performers' appearances.
- Create character drawings or models, based upon independent research, to augment period production files.
- Select desired makeup shades from stock, or mix oil, grease, and coloring to achieve specific color effects.
- Cleanse and tone the skin to prepare it for makeup application.
- Assess performers' skin type to ensure that makeup will not cause break-outs or skin irritations.
- Confer with stage or motion picture officials and performers to determine desired effects.
- Provide performers with makeup removal assistance after performances have been completed.
- Requisition or acquire needed materials for special effects, including wigs, beards, and special cosmetics.
- Demonstrate products to clients, and provide instruction in makeup application.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.