SOC 49-9091

Coin, Vending, and Amusement Machine Servicers AI displacement risk

Coin, vending, and amusement machine servicers stock, maintain, and repair machines on route. Telemetry now reports inventory and faults before the visit, changing routes from rounds to targeted calls — but stocking, jam clearing, and component replacement happen at the machine.

Exposure34

Share and intensity of work current AI systems can materially affect.

Automation15%

Likely potential for exposed tasks to move to software after workflow integration.

Risk bandLow

Cashless payment telemetry made the role data-driven: machines report themselves sick. The service call itself — the physical fix and restock — is why the job persists.

Distribution

Where Coin, Vending, and Amusement Machine Servicers sits across 620 tracked roles

Coin, Vending, and Amusement Machine Servicers · 26050100

Displacement pressure 26 — higher than 37% of the 620 occupations tracked on displacement.ai.

Score version

This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.

18 O*NET task statements matched to SOC 49-9091. The displayed task profile combines these official task statements with the current public score model.

Median wage context: $47,450 (May 2025, US national). The latest BLS row matched SOC 49-9091.

Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.

2030 economic stress test

How Anthropic's scenarios classify Coin, Vending, and Amusement Machine Servicers

SOC 49-9091 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 26/100 role score and are not an occupation forecast.

Modest change

+1.1% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

Economy-wide: +1.6% GDP and 3.9% unemployment.

Substantial change

+5.9% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

Economy-wide: +8.3% GDP and 4.6% unemployment.

Extreme change

+33.6% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

Economy-wide: +32.4% GDP and 11.9% unemployment.

Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.

Official task evidence

O*NET task matches for Coin, Vending, and Amusement Machine Servicers

The current evidence import matched 18 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.

Dataset31.0 (August 2026)
Matched tasks18
SOC49-9091
  • Core task / ID 13835

    Fill machines with products, ingredients, money, and other supplies.

  • Core task / ID 13839

    Inspect machines and meters to determine causes of malfunctions and fix minor problems such as jammed bills or stuck products.

  • Core task / ID 13840

    Test machines to determine proper functioning.

  • Core task / ID 13846

    Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (ATM) card readers.

  • Core task / ID 13845

    Maintain records of machine maintenance and repair.

  • Core task / ID 13842

    Clean and oil machine parts.

Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.

Task profile

Where AI changes the work

physical

Fill machines with products and supplies

Exposure 24, automation 11%, augmentation 32%.

O*NET evidence: Fill machines with products, ingredients, money, and other supplies. (ID 13835)

technical

Inspect machines to determine causes of malfunctions

Exposure 32, automation 15%, augmentation 48%.

O*NET evidence: Inspect machines and meters to determine causes of malfunctions and fix minor problems ... (ID 13839)

physical

Replace malfunctioning parts such as card readers

Exposure 26, automation 12%, augmentation 44%.

O*NET evidence: Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (... (ID 13846)

information

Maintain records of machine maintenance and repair

Exposure 50, automation 28%, augmentation 54%.

O*NET evidence: Maintain records of machine maintenance and repair. (ID 13845)

TaskExposureAutomationAugmentation
Fill machines with products and supplies2411%32%
Inspect machines to determine causes of malfunctions3215%48%
Replace malfunctioning parts such as card readers2612%44%
Maintain records of machine maintenance and repair5028%54%

Transition pathways

Adjacent moves that preserve existing skills

role redesign

Route Operations Supervisor

Training horizon: 6-12 months. Skill overlap 64. Wage preservation signal 120.

  • Lead service routes
  • Own telemetry response metrics
  • Manage warehouse stocking
Low
role redesign

Field Service Technician

Training horizon: 3-6 months. Skill overlap 62. Wage preservation signal 116.

  • Broaden to ATM and kiosk systems
  • Learn networked device repair
  • Serve contract clients
Low

Comparison guides

Compare the next move before you commit

What the AI risk score means for Coin, Vending, and Amusement Machine Servicers

The displacement pressure score for Coin, Vending, and Amusement Machine Servicers is 26. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.

For this role, the clearest risk pattern is visible at the task level. Maintain records of machine maintenance and repair carries 28% automation pressure, while Maintain records of machine maintenance and repair carries 54% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.

Labor-market context and wage risk

Median wage: $47,450 (May 2025, US national). Employment context: Route service with telemetry calling the shots. Typical education: High school plus on-the-job training.

Wage vulnerability is 44, while transition feasibility is 60. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.

  • Low displacement pressure
  • Telemetry targets the route
  • The fix happens at the machine

Upskilling priorities

Skills that make this role more resilient

The safest upskilling plan starts with skills already close to the work. For Coin, Vending, and Amusement Machine Servicers, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.

Priority 1

Machine diagnostics

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 2

Route stocking

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 3

Component repair

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 4

Service documentation

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

90-day transition plan

The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.

  1. In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
  2. By 60 days, complete one small project connected to Route Operations Supervisor, such as lead service routes.
  3. By 90 days, compare internal openings and external postings for Route Operations Supervisor or Field Service Technician and update your resume around measurable workflow outcomes.

FAQ

Questions about AI and Coin, Vending, and Amusement Machine Servicers

Will AI replace Coin, Vending, and Amusement Machine Servicers?

Coin, vending, and amusement machine servicers stock, maintain, and repair machines on route. Telemetry now reports inventory and faults before the visit, changing routes from rounds to targeted calls — but stocking, jam clearing, and component replacement happen at the machine. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.

Which parts of Coin, Vending, and Amusement Machine Servicers work are most exposed to AI?

Maintain records of machine maintenance and repair and Inspect machines to determine causes of malfunctions show the strongest automation pressure in this model. Maintain records of machine maintenance and repair and Inspect machines to determine causes of malfunctions are better treated as AI-augmented work.

What should Coin, Vending, and Amusement Machine Servicers learn next?

Start with Machine diagnostics, Route stocking, Component repair. The most practical adjacent paths in this model are Route Operations Supervisor and Field Service Technician.

How should this score be used?

Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.

Sources

Evidence trail