Will AI Replace Coin, Vending, and Amusement Machine Servicers and Repairers?
Coin, Vending, and Amusement Machine Servicers and Repairers face a relatively low 27.4% AI exposure score with a 8% displacement probability. Most tasks, including multilimb Coordination, equipment Maintenance, and arm-Hand Steadiness, remain beyond current AI capabilities. Physical presence requirements and high social interaction provide partial protection.
This occupation scores below the national average of 48/100 by 20.6 points. The primary risk comes from AI's strong performance in coding software and complex problem solving, representing core functions of this role. However, physical presence and high social interaction requirements provide meaningful protection.
Which skills are most at risk?
Each skill in this occupation analyzed against current AI benchmarks. Higher scores = higher AI exposure.
The bottom line for Coin, Vending, and Amusement Machine Servicers and Repairers
What's most at risk
The role's most exposed skills, specifically Computers and Electronics, Troubleshooting, Customer and Personal Service, reach up to 74.5/100 on AI exposure. AI systems already match or exceed human performance on TerminalBench, 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
Multilimb Coordination (8.3/100), Equipment Maintenance (8.8/100), Arm-Hand Steadiness (8.8/100) 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 27.4/100, Coin, Vending, and Amusement Machine Servicers and Repairers rank below the national average of 48/100. More exposed than Watch and Clock Repairers (25.2/100). The role sits among the bottom 30% least AI-exposed occupations.
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Based on skill overlap analysis — these occupations share core competencies with Coin, Vending, and Amusement Machine Servicers and Repairers but have significantly lower automation exposure.
Common questions about Coin, Vending, and Amusement Machine Servicers and Repairers and AI
Very unlikely. The 8% 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.
Not imminently. The skills central to this role — especially Multilimb Coordination 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.
Your strongest assets are Multilimb Coordination 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.
Your skills transfer well to roles like Watch and Clock Repairers (25.2/100 AI risk, 100% skill overlap). PathScorer can analyse your full profile and surface even more personalised matches. Try it free here.
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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