Low AI Risk

    Will AI Replace Lighting Technicians?

    Lighting Technicians are more likely to see AI enhance their work than replace it, with a 6% augmentation probability vs. only 1% displacement. Core capabilities like equipment Selection, equipment Maintenance, and control Precision remain firmly human-led, while computers and Electronics and communications and Media increasingly see AI assistance.

    O*NET Code: 27-4015.00 · Data from O*NET & BLS · Updated March 2026
    AI Exposure Score
    23.0
    out of 100
    Displacement Prob.
    1%
    low displacement
    Augmentation
    6%
    AI assists, not replaces
    Confidence
    36%
    analysis confidence
    AI Exposure ScoreA 0–100 scale measuring the overall vulnerability of this role's required skills, knowledge, and abilities.
    Displacement Prob.The estimated likelihood that AI could fully automate and replace the core functions of this occupation.
    AugmentationThe probability that AI will serve as a supportive tool to enhance the worker's productivity rather than replace them.
    ConfidenceThe statistical reliability of these predictions, based on how closely the role's skills map to direct AI benchmarks.
    0 — Safe25 — Low50 — Moderate75 — High100 — Critical

    This occupation scores below the national average of 48/100 by 25 points. The role's strength lies in Equipment Selection and Equipment Maintenance, which are capabilities that AI consistently struggles to replicate. The absence of physical presence or social interaction requirements increases overall exposure.

    Skill-Level Analysis

    Which skills are most at risk?

    Each skill in this occupation analyzed against current AI benchmarks. Higher scores = higher AI exposure.

    Visual Color Discrimination
    The ability to match or detect differences between colors, including shades of color and brightness.
    61.6
    Augmentation
    Benchmark: AA Intelligence (visual proxy)
    Visualization
    The ability to imagine how something will look after it is moved around or when its parts are moved or rearranged.
    44
    Augmentation
    Benchmark: AA Intelligence (visual proxy)
    Installation
    Installing equipment, machines, wiring, or programs to meet specifications.
    11.8
    Physical barrier
    Benchmark: Estimated
    Operation and Control
    Controlling operations of equipment or systems.
    11
    Physical barrier
    Benchmark: Estimated
    Manual Dexterity
    The ability to quickly move your hand, your hand together with your arm, or your two hands to grasp, manipulate, or assemble objects.
    10.3
    Physical barrier
    Benchmark: Estimated
    Static Strength
    The ability to exert maximum muscle force to lift, push, pull, or carry objects.
    10.3
    Physical barrier
    Benchmark: Estimated
    Gross Body Equilibrium
    The ability to keep or regain your body balance or stay upright when in an unstable position.
    10.3
    Physical barrier
    Benchmark: Estimated
    Equipment Selection
    Determining the kind of tools and equipment needed to do a job.
    9.2
    Physical barrier
    Benchmark: Estimated
    Equipment Maintenance
    Performing routine maintenance on equipment and determining when and what kind of maintenance is needed.
    9.2
    Physical barrier
    Benchmark: Estimated
    Control Precision
    The ability to quickly and repeatedly adjust the controls of a machine or a vehicle to exact positions.
    9.2
    Physical barrier
    Benchmark: Estimated
    What This Means

    The bottom line for Lighting Technicians

    What's most at risk

    The role's most exposed skills, specifically Computers and Electronics, reach up to 70/100 on AI exposure. AI systems already match or exceed human performance on LiveCodeBench, directly targeting these core competencies.

    Limited natural protection

    This role has no strong physical presence or social interaction requirements, which are the two most reliable barriers to automation. It is predominantly knowledge-based and remote-compatible, which increases overall AI exposure. Workers should proactively build leadership, ethical judgment, and relationship-management capabilities as an active defence against displacement.

    Skills that remain safe

    Equipment Selection (9.2/100), Equipment Maintenance (9.2/100), Control Precision (9.2/100) are protected by physical or social barriers AI cannot replicate. Visualization and Visual Color Discrimination also sit in the augmentation zone. Workers who lean into these human-centric capabilities will be well positioned as higher-exposure tasks shift to AI.

    How this compares

    At 23/100, Lighting Technicians rank below the national average of 48/100. The role sits among the bottom 30% least AI-exposed occupations.

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    FAQ

    Common questions about Lighting Technicians and AI

    Will AI completely replace this occupation?

    Outright replacement is unlikely. With a 6% augmentation probability and only 1% displacement probability, AI is far more likely to enhance this role than eliminate it. Workers who actively adopt AI tools will find their productivity and professional value increase, rather than decrease, as the technology matures.

    When will AI start affecting this job?

    Not imminently. The skills central to this role — especially Equipment Selection and Equipment Maintenance — remain genuinely difficult for AI to automate. The more relevant near-term shift is AI becoming a standard productivity tool that workers in this field are expected to use fluently.

    What skills should I develop to stay relevant?

    Your strongest assets are Equipment Selection and Equipment Maintenance, representing the lowest-exposure capabilities in this profile. Double down on them. Beyond that, invest in AI tool fluency: workers who know how to direct, verify, and extend AI outputs will capture the productivity upside rather than compete against it.

    What careers can I switch to with my current skills?

    Use PathScorer to map your specific skills against 923 occupations and identify roles with better AI risk profiles. It takes 2 minutes and is free. Start here.

    How is this AI risk score calculated?

    We analyse each occupation's O*NET skill profile, covering 35+ dimensions across knowledge areas, skills, and abilities, and benchmark each against current AI capabilities (MMLU-Pro for language comprehension, τ-bench v2 for task completion, MATH-500 for mathematical reasoning, LiveCodeBench for coding, and others). Each dimension is weighted by its O*NET importance score for the occupation. Physical presence requirements and social interaction levels from O*NET work context data are also factored in. Scores are updated weekly as new AI benchmarks are published. See the full methodology →

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    Methodology: AI exposure scores are calculated by analyzing O*NET occupational skill profiles against current AI capability benchmarks. Skill importance and level data from O*NET 28.1. Employment and salary data from the Bureau of Labor Statistics Occupational Employment and Wage Statistics (OEWS). AI benchmarks include MMLU-Pro (language comprehension), τ-bench v2 (task completion), SWE-bench (code generation), and others. Physical presence and social interaction factors are derived from O*NET work context data. Scores are updated quarterly as new AI benchmarks are published. See full methodology →
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