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    AI Resilient

    Will AI Replace Farmworkers and Laborers, Crop, Nursery, and Greenhouse?

    Farmworkers and Laborers, Crop, Nursery, and Greenhouse are more likely to see AI enhance their work than replace it, with a 1% augmentation probability vs. only 0% displacement. Core capabilities like reaction Time, explosive Strength, and rate Control remain firmly human-led, while oral Expression increasingly see AI assistance. Physical presence requirements and high social interaction provide partial protection.

    O*NET Code: 45-2092.00 · Data from O*NET & BLS · Updated March 2026
    AI Exposure Score
    4.5
    out of 100
    Displacement Prob.
    0%
    low displacement
    Augmentation
    1%
    AI assists, not replaces
    Confidence
    18%
    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 43.5 points. The role's strength lies in Reaction Time and Explosive Strength, which are capabilities that AI consistently struggles to replicate. 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.

    Near Vision
    The ability to see details at close range (within a few feet of the observer).
    44
    Augmentation
    Benchmark: AA Intelligence (visual proxy)
    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.
    11
    Physical barrier
    Benchmark: Estimated
    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.
    10.6
    Physical barrier
    Benchmark: Estimated
    Control Precision
    The ability to quickly and repeatedly adjust the controls of a machine or a vehicle to exact positions.
    10.6
    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.
    10.1
    Physical barrier
    Benchmark: Estimated
    Multilimb Coordination
    The ability to coordinate two or more limbs (for example, two arms, two legs, or one leg and one arm) while sitting, standing, or lying down. It does not involve performing the activities while the whole body is in motion.
    10.1
    Physical barrier
    Benchmark: Estimated
    Extent Flexibility
    The ability to bend, stretch, twist, or reach with your body, arms, and/or legs.
    10.1
    Physical barrier
    Benchmark: Estimated
    Finger Dexterity
    The ability to make precisely coordinated movements of the fingers of one or both hands to grasp, manipulate, or assemble very small objects.
    9.6
    Physical barrier
    Benchmark: Estimated
    Static Strength
    The ability to exert maximum muscle force to lift, push, pull, or carry objects.
    9.6
    Physical barrier
    Benchmark: Estimated
    Rate Control
    The ability to time your movements or the movement of a piece of equipment in anticipation of changes in the speed and/or direction of a moving object or scene.
    9.2
    Physical barrier
    Benchmark: Estimated
    Dynamic Strength
    The ability to exert muscle force repeatedly or continuously over time. This involves muscular endurance and resistance to muscle fatigue.
    9.2
    Physical barrier
    Benchmark: Estimated
    Stamina
    The ability to exert yourself physically over long periods of time without getting winded or out of breath.
    9.2
    Physical barrier
    Benchmark: Estimated
    Explosive Strength
    The ability to use short bursts of muscle force to propel oneself (as in jumping or sprinting), or to throw an object.
    8.8
    Physical barrier
    Benchmark: Estimated
    What This Means

    The bottom line for Farmworkers and Laborers, Crop, Nursery, and Greenhouse

    What's most at risk

    The role's most exposed skills, specifically Oral Expression, reach up to 55.8/100 on AI exposure. AI systems already match or exceed human performance on LCR, directly targeting these core competencies.

    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

    Reaction Time (8.3/100), Explosive Strength (8.8/100), Rate Control (9.2/100) are protected by physical or social barriers AI cannot replicate. Near Vision 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 4.5/100, Farmworkers and Laborers, Crop, Nursery, and Greenhouse 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 Farmworkers and Laborers, Crop, Nursery, and Greenhouse and AI

    Will AI completely replace this occupation?

    Outright replacement is unlikely. With a 1% augmentation probability and only 0% 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 Reaction Time and Explosive Strength — 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 Reaction Time and Explosive Strength, 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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