Moderate AI Risk

    Will AI Replace Disc Jockeys, Except Radio?

    Disc Jockeys, Except Radio face a 38% AI exposure score with a 7% displacement probability. Core tasks in computers and Electronics, customer and Personal Service, and speaking are increasingly automatable, though manual Dexterity and control Precision provide partial protection.

    O*NET Code: 27-2091.00 · Data from O*NET & BLS · Updated March 2026
    AI Exposure Score
    38.0
    out of 100
    Displacement Prob.
    7%
    low displacement
    Augmentation
    18%
    AI assists, not replaces
    Confidence
    63%
    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 10 points. The primary risk comes from AI's strong performance in coding software and customer service, representing core functions of this role. 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.

    Speaking
    Talking to others to convey information effectively.
    70.9
    Medium displacement
    Benchmark: MMLU-Pro
    Oral Expression
    The ability to communicate information and ideas in speaking so others will understand.
    70.9
    Medium displacement
    Benchmark: MMLU-Pro
    Speech Clarity
    The ability to speak clearly so others can understand you.
    70.9
    Medium displacement
    Benchmark: MMLU-Pro
    Active Listening
    Giving full attention to what other people are saying, taking time to understand the points being made, asking questions as appropriate, and not interrupting at inappropriate times.
    59.1
    Medium displacement
    Benchmark: MMLU-Pro
    Operation and Control
    Controlling operations of equipment or systems.
    11.8
    Physical barrier
    Benchmark: Estimated
    Social Perceptiveness
    Being aware of others' reactions and understanding why they react as they do.
    10.9
    Physical barrier
    Benchmark: MMLU-Pro (social proxy)
    Hearing Sensitivity
    The ability to detect or tell the differences between sounds that vary in pitch and loudness.
    10.9
    Physical barrier
    Benchmark: MMLU-Pro (social proxy)
    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.
    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 Disc Jockeys, Except Radio

    What's most at risk

    The role's most exposed skills, specifically Computers and Electronics, Customer and Personal Service, reach up to 62.5/100 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

    Manual Dexterity (9.2/100), Control Precision (9.2/100), Social Perceptiveness (10.9/100) 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 38/100, Disc Jockeys, Except Radio rank below the national average of 48/100. The role sits among the middle third least AI-exposed occupations.

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    FAQ

    Common questions about Disc Jockeys, Except Radio and AI

    Will AI completely replace this occupation?

    Very unlikely. The 7% 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?

    Gradually, over the next 3–7 years. The tools exist but aren't yet uniformly adopted at scale. Early movers who reskill now will have a significant head start over those who wait for disruption to arrive at their specific workplace.

    What skills should I develop to stay relevant?

    Your strongest assets are Manual Dexterity and Control Precision, 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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