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 12.0
Physical / roboticsrange 35.0 to 59.0
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 Sewing Machine Operators
It is skill-adjacent and less exposed (40.1 vs 47). 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.
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Which of my tasks are exposed
Mark or discard items with defects such as spots, stains, scars, snags, chips, scratches, or unacceptable shapes or finishes.
47.0
Trim excess material or cut threads off finished products, such as cutting loose ends of plastic off a manufactured toy for a smoother finish.
47.0
Cut, shape, and trim materials, such as textiles, food, glass, stone, and metal, using knives, scissors, and other hand tools, portable power tools, or bench-mounted tools.
47.0
Position templates or measure materials to locate specified points of cuts or to obtain maximum yields, using rules, scales, or patterns.
47.0
Read work orders to determine dimensions, cutting locations, and quantities to cut.
47.0
Mark cutting lines around patterns or templates, or follow layout points, using squares, rules, and straightedges, and chalk, pencils, or scribes.
47.0
Mark identification numbers, trademarks, grades, marketing data, sizes, or model numbers on products.
47.0
Unroll, lay out, attach, or mount materials or items on cutting tables or machines.
47.0
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: Mark or discard items with defects such as spots, stains, scars, snags, chips, scratches, or unacceptable shapes or finishes.
Task: Trim excess material or cut threads off finished products, such as cutting loose ends of plastic off a manufactured toy for a smoother finish.
Lower-risk adjacent roles
Sewing Machine Operators
40.1
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Cutters and Trimmers, Hand 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 0.0, robotics 47.0.
The timeline comes from the higher axis (47.0), 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.