JobBreak

Production · United States (national)

Milling and Planing Machine 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 highly exposed to automation, and the bigger shift looks near-term. 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
6.9 of 100
AI / softwarerange 0.0 to 18.9
66.0 of 100
Physical / roboticsrange 54.0 to 78.0

When the change is likely

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

Near-term, high local demand declining

What to do about it

Use AI to outpace AI.

Strategies to protect your livelihood, most important first.

1
Act within the next 1 to 2 years
Displacement for this role is near-term. Start a concrete reskilling plan now, while you still have income and leverage.
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 66). 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

Remove workpieces from machines, and check to ensure that they conform to specifications, using measuring instruments such as microscopes, gauges, calipers, and micrometers.
100.0
Verify alignment of workpieces on machines, using measuring instruments such as rules, gauges, or calipers.
100.0
Move controls to set cutting specifications, to position cutting tools and workpieces in relation to each other, and to start machines.
66.0
Observe milling or planing machine operation, and adjust controls to ensure conformance with specified tolerances.
66.0
Select and install cutting tools and other accessories according to specifications, using hand tools or power tools.
66.0
Position and secure workpieces on machines, using holding devices, measuring instruments, hand tools, and hoists.
66.0
Replace worn tools, using hand tools, and sharpen dull tools, using bench grinders.
66.0
Study blueprints, layouts, sketches, or work orders to assess workpiece specifications and to determine tooling instructions, tools and materials needed, and sequences of operations.
66.0

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

What is driving my result

lowers risk
raises risk
Task: Remove workpieces from machines, and check to ensure that they conform to specifications, using measuring instruments such as microscopes, gauges, calipers, and micrometers.
Task: Verify alignment of workpieces on machines, using measuring instruments such as rules, gauges, or calipers.
Local demand is declining

Lower-risk adjacent roles

Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic
50.4
Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic
56.0
Computer Numerically Controlled Tool Operators
59.7
Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic
60.5

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

How this score is calculated

  1. Map Milling and Planing Machine 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 6.9, robotics 66.0.
  4. The timeline comes from the higher axis (66.0), shifted for local demand (declining), 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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