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    Will AI Replace Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons?

    Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons face a relatively low 2.6% AI exposure score with a 0% displacement probability. Most tasks, including arm-Hand Steadiness, manual Dexterity, and static Strength, remain beyond current AI capabilities. Physical presence requirements and high social interaction provide partial protection.

    O*NET Code: 47-3014.00 · Data from O*NET & BLS · Updated March 2026
    AI Exposure Score
    2.6
    out of 100
    Displacement Prob.
    0%
    low displacement
    Augmentation
    0%
    AI assists, not replaces
    Confidence
    10%
    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 45.4 points. No individual skill shows meaningful AI exposure; the overall risk is driven by occupational-level factors. However, physical presence and high social interaction requirements provide meaningful protection.

    Skill-Level Analysis

    Which skills are most at risk?

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

    Arm-Hand Steadiness
    The ability to keep your hand and arm steady while moving your arm or while holding your arm and hand in one position.
    8.8
    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.
    8.8
    Physical barrier
    Benchmark: Estimated
    Static Strength
    The ability to exert maximum muscle force to lift, push, pull, or carry objects.
    8.8
    Physical barrier
    Benchmark: Estimated
    Trunk Strength
    The ability to use your abdominal and lower back muscles to support part of the body repeatedly or continuously over time without "giving out" or fatiguing.
    8.8
    Physical barrier
    Benchmark: Estimated
    Extent Flexibility
    The ability to bend, stretch, twist, or reach with your body, arms, and/or legs.
    8.8
    Physical barrier
    Benchmark: Estimated
    Gross Body Equilibrium
    The ability to keep or regain your body balance or stay upright when in an unstable position.
    8.8
    Physical barrier
    Benchmark: Estimated
    What This Means

    The bottom line for Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons

    Exposure is minimal

    This role shows no critically or moderately exposed skills at the individual level. All assessed dimensions fall in the low-risk or augmentation range, meaning AI is more likely to assist workers than replace them.

    What provides partial protection

    This role requires physical presence and involves high social interaction, such as coordinating with teams, building client trust, and navigating interpersonal dynamics in real time. These human-centric demands are significantly harder to automate and will persist even as the technical components of the role shift to AI.

    Skills that remain safe

    Arm-Hand Steadiness (8.8/100), Manual Dexterity (8.8/100), Static Strength (8.8/100) are protected by physical or social barriers AI cannot replicate. Workers who lean into these human-centric capabilities will be well positioned as higher-exposure tasks shift to AI.

    How this compares

    At 2.6/100, Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons 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 Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons and AI

    Will AI completely replace this occupation?

    Very unlikely. The 0% displacement probability is well below the national average. This role is relatively insulated, as AI is more useful as a productivity multiplier here than as a replacement for the core human work.

    When will AI start affecting this job?

    Not imminently. The skills central to this role — especially Arm-Hand Steadiness and Manual Dexterity — 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 Arm-Hand Steadiness and Manual Dexterity, 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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