Lathe and Turning 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 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 15.2
Physical / roboticsrange 38.4 to 62.4
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
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.
4
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
Keep the monitor on
Free shows where you stand today. JobBreak Monitor writes your full AI-researched plan and keeps watching, so you know the moment your outlook shifts.
Free
$0
See exactly where you stand.
Full job-risk score & timeline
Task-by-task exposure breakdown
One starter strategy
AI-researched personal plan
Ongoing monitoring & alerts
Current plan
Keep ahead
JobBreak Monitor
$3.99/mo
$29/yr
or $29/yr save 39%
that's about $2.42/mo, billed yearly
Your full researched plan, plus a watch on your job.
Everything in Free
Full AI-researched, cited action plan
Resume-aware personalisation
Re-run your plan any time
Alerts when your outlook shifts
Start monitoring
Secure checkout by Stripe. Cancel anytime.
Which of my tasks are exposed
Adjust machine controls and change tool settings to keep dimensions within specified tolerances.
50.4
Move controls to set cutting speeds and depths and feed rates, and to position tools in relation to workpieces.
50.4
Study blueprints, layouts or charts, and job orders for information on specifications and tooling instructions, and to determine material requirements and operational sequences.
50.4
Inspect sample workpieces to verify conformance with specifications, using instruments such as gauges, micrometers, and dial indicators.
50.4
Replace worn tools, and sharpen dull cutting tools and dies, using bench grinders or cutter-grinding machines.
50.4
Move toolholders manually or by turning handwheels, or engage automatic feeding mechanisms to feed tools to and along workpieces.
50.4
Compute unspecified dimensions and machine settings, using knowledge of metal properties and shop mathematics.
50.4
Crank machines through cycles, stopping to adjust tool positions and machine controls to ensure specified timing, clearances, and tolerances.
50.4
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: Adjust machine controls and change tool settings to keep dimensions within specified tolerances.
Task: Move controls to set cutting speeds and depths and feed rates, and to position tools in relation to workpieces.
How this score is calculated
Map Lathe and Turning Machine Tool 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 3.2, robotics 50.4.
The timeline comes from the higher axis (50.4), 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.