JobBreak

Production · United States (national)

Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic

How exposed this work is to automation, shown as ranges with the reasoning and sources.

The short version: this role is moderately exposed to automation, and meaningful change looks likely over the next 5 to 10 years. It is a sourced range, not a verdict, and the detail is below.

Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown. Data snapshot SNAPSHOT-2026-11-06. How it works
14.1 of 100
AI / softwarerange 2.1 to 26.1
56.0 of 100
Physical / roboticsrange 44.0 to 68.0

When the change is likely

partial change 0y 5y 10y 15y 20+y

Partial, 5 to 10 years local demand stable

What to do about it

Use AI to outpace AI.

Strategies to protect your livelihood, most important first.

1
Use your 5 to 10 year runway
Change here is gradual. Build resilient skills steadily and watch the signals rather than waiting for a shock.
2
Move up from the task to the system
Robotics targets the physical work. Shift toward supervising, maintaining, or programming the automation instead of competing with it.
3
Pivot toward Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic
It is skill-adjacent and less exposed (50.4 vs 56). A lateral move now beats a forced one later.
4
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
5
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.

Which of my tasks are exposed

Inspect workpieces for defects, and measure workpieces to determine accuracy of machine operation, using rules, templates, or other measuring instruments.
56.0
Position, adjust, and secure stock material or workpieces against stops, on arbors, or in chucks, fixtures, or automatic feeding mechanisms, manually or using hoists.
56.0
Read blueprints or job orders to determine product specifications and tooling instructions and to plan operational sequences.
56.0
Select, install, and adjust alignment of drills, cutters, dies, guides, and holding devices, using templates, measuring instruments, and hand tools.
56.0
Observe machine operation to detect workpiece defects or machine malfunctions, adjusting machines as necessary.
56.0
Set up and operate machines, such as lathes, cutters, shears, borers, millers, grinders, presses, drills, or auxiliary machines, to make metallic and plastic workpieces.
56.0
Change worn machine accessories, such as cutting tools or brushes, using hand tools.
56.0
Set machine stops or guides to specified lengths as indicated by scales, rules, or templates.
56.0

Longer, orange bars are more automatable; short green bars are resistant.

What is driving my result

lowers risk
raises risk
Task: Inspect workpieces for defects, and measure workpieces to determine accuracy of machine operation, using rules, templates, or other measuring instruments.
Task: Position, adjust, and secure stock material or workpieces against stops, on arbors, or in chucks, fixtures, or automatic feeding mechanisms, manually or using hoists.

Lower-risk adjacent roles

Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic
50.4

A full, ranked mitigation plan is part of the paid tiers.

How this score is calculated

  1. Map Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic to its standard O*NET task list (8 core tasks).
  2. Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
  3. The two axis scores are the task averages: AI 14.1, robotics 56.0.
  4. The timeline comes from the higher axis (56.0), shifted for local demand (stable), and seniority. It is read against your remaining working years.
  5. Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.

Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS. Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.

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