Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders
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
AI / softwarerange 0.0 to 20.6
Physical / roboticsrange 41.3 to 65.3
When the change is likely
Partial, 5 to 10 yearslocal 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
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.
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Which of my tasks are exposed
Dump, pour, or load specified amounts of refined or unrefined materials into equipment or containers for further processing or storage.
53.3
Operate machines to process materials in compliance with applicable safety, energy, or environmental regulations.
53.3
Monitor material flow or instruments, such as temperature or pressure gauges, indicators, or meters, to ensure optimal processing conditions.
53.3
Turn valves or move controls to admit, drain, separate, filter, clarify, mix, or transfer materials.
53.3
Set up or adjust machine controls to regulate conditions such as material flow, temperature, or pressure.
53.3
Examine samples to verify qualities such as clarity, cleanliness, consistency, dryness, or texture.
100.0
Start agitators, shakers, conveyors, pumps, or centrifuge machines.
53.3
Inspect machines or equipment for hazards, operating efficiency, malfunctions, wear, or leaks.
53.3
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Examine samples to verify qualities such as clarity, cleanliness, consistency, dryness, or texture.
Task: Dump, pour, or load specified amounts of refined or unrefined materials into equipment or containers for further processing or storage.
How this score is calculated
Map Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders 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 8.6, robotics 53.3.
The timeline comes from the higher axis (53.3), shifted for local demand (stable), 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.