Cutting, Punching, and Press 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 moderately 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
AI / softwarerange 0.0 to 14.0
Physical / roboticsrange 52.5 to 76.5
When the change is likely
Near-term, highlocal 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 64). 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.
Keep the monitor on
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Which of my tasks are exposed
Examine completed workpieces for defects, such as chipped edges or marred surfaces and sort defective pieces according to types of flaws.
64.5
Measure completed workpieces to verify conformance to specifications, using micrometers, gauges, calipers, templates, or rulers.
64.5
Set stops on machine beds, change dies, and adjust components, such as rams or power presses, when making multiple or successive passes.
64.5
Start machines, monitor their operations, and record operational data.
64.5
Set up, operate, or tend machines to saw, cut, shear, slit, punch, crimp, notch, bend, or straighten metal or plastic material.
64.5
Test and adjust machine speeds or actions, according to product specifications, using gauges and hand tools.
64.5
Install, align, and lock specified punches, dies, cutting blades, or other fixtures in rams or beds of machines, using gauges, templates, feelers, shims, and hand tools.
64.5
Read work orders or production schedules to determine specifications, such as materials to be used, locations of cutting lines, or dimensions and tolerances.
64.5
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Local demand is declining
Task: Examine completed workpieces for defects, such as chipped edges or marred surfaces and sort defective pieces according to types of flaws.
Task: Measure completed workpieces to verify conformance to specifications, using micrometers, gauges, calipers, templates, or rulers.
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
Tool and Die Makers
56.2
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
Map Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic to its standard O*NET task list (8 core tasks).
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.
The two axis scores are the task averages: AI 2.0, robotics 64.5.
The timeline comes from the higher axis (64.5), shifted for local demand (declining), and seniority. It is read against your remaining working years.
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.