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Will AI replace Lighting Technicians?

Set up, maintain, and dismantle light fixtures, lighting control devices, and the associated lighting electrical and rigging equipment used for photography, television, film, video, and live productions. The percentage shown is a task-exposure model, not a prediction of certain job loss.

O*NET 30.3 · 27-4015.00 · modeled exposure, not guaranteed job loss
MODERATE TASK EXPOSURE
40%
Estimated share of representative tasks exposed to current AI capabilities.
Task automation shareⓘ
40%Automatable
Automatable40%
Augmentable30%
Hard to automate30%
Based on analysis of representative responsibilities and current AI capability maturity.
Category#
Creative & Design
Design & Visual
Modeled pay midpoint$
$74,300/yr
Directional estimate — verify locally
Time until impact⌁
2–4 years
Earliest 2027 · widespread 2029–2031
Task-level replaceability ⓘ
Representative tasks ranked from highest to lowest AI exposure.
◇Organize role-specific information and routine work50%
⚙Prepare standard outputs and records35%
✦Original taste and cultural judgment20%
✦Understanding what a specific client actually means16%
Impact timeline ⓘ
How AI assistance may move from early support to wider adoption.
2027Next2029–2031
Early impactAI begins supporting organize role-specific information and routine work.
Role reshapingRepeatable tasks become faster and more automated.
Wider adoptionThe role centers more on original taste and cultural judgment.
2–4 yearsdirectional confidence window
HOW AI CHANGES THIS JOBtyping the logic in plain English
01>Start with the real work done by Lighting Technicians.
02>AI can help with: Organize role-specific information and routine work.
03>If a task is repetitive and easy to check, AI pressure goes up.
04>But a person is still needed for: Original taste and cultural judgment.
05>Likely result: AI is more likely to change this job than remove it.
06>Important: 40% is estimated task exposure—not a 40% chance of losing the job.
What AI can do
Organize role-specific information and routine workMEDIUM
Prepare standard outputs and recordsLOW MATCH
How estimates work →
Your human moat
◆

Original taste and cultural judgment

HIGH MOAT

Combined with understanding what a specific client actually means, this keeps a human in the loop.

Strength8.5
Similar-role comparison
RoleRisk
Lighting Technicians40%
Audio and Video Technicians40%
Floral Designers42%
Logo Designer43%
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Why this score?

✓The model evaluates 2 representative core tasks.
✓The role mixes repeatable work with human judgment.
✓Occupation and task language comes from O*NET 30.3.
✓The strongest human advantage is original taste and cultural judgment.
Research basis

O*NET 30.3 occupation tasks plus the ILO 2025 exposure framework. Scores are directional estimates.

Read ILO research →
Data: O*NET 30.3 · Model framework: ILO 2025About O*NETShare report
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Audio and Video Technicians · 40% exposureFloral Designers · 42% exposureLogo Designer · 43% exposure