US dataswitch to UK

Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic

inspecting workpieces for defects, setting up and operating machines, performing minor machine maintenance and removing burrs, sharp edges. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: selecting, installing and adjusting alignment of drills, cutters, dies, guides and holding devices, using templates, measuring instruments and hand tools is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 2%
staying human: 93%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 10 out of 100 (8–15 allowing for uncertainty): minimal exposure, across 21 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for multiple machine tool setters, operators, and tenders, metal and plastic is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

What shifts is computing data, such as gear dimensions or machine settings, applying knowledge of shop mathematics: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Set up, operate, or tend more than one type of cutting or forming machine tool or robot. The job title says “multiple machine tool setters”, “operators”, “tenders”, “metal” or “plastic”: officially one job, several names. The real job is the part underneath: selecting, installing and adjusting alignment of drills, cutters, dies, guides and holding devices, using templates, measuring instruments and hand tools. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But multiple machine tool setters, operators, and tenders, metal and plastic is not one task. It is 21 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is selecting, installing and adjusting alignment of drills, cutters, dies, guides and holding devices, using templates, measuring instruments and hand tools, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 2%
staying human: 93%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

1 task

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Computing data, such as gear dimensions or machine settings, applying knowledge of shop mathematics
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compute data, such as gear dimensions or machine settings, applying knowledge of shop mathematics.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Working out gear sizes and machine settings is arithmetic that software already does reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

1 task

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Writing programs for computer numerical control
The software now makes the first pass at programs, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Write programs for computer numerical control (CNC) machines to cut metal and plastic materials.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing machine code from a part drawing is well-documented programming that software handles well.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

19 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Inspecting workpieces for defects
This work happens in the physical world: workpieces, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Inspect workpieces for defects, and measure workpieces to determine accuracy of machine operation, using rules, templates, or other measuring instruments.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking a part for defects means handling it and reading gauges against the metal itself.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Selecting, installing and adjusting alignment of drills, cutters, dies, guides and holding devices, using templates, measuring instruments and hand tools
This work happens in the physical world: alignment of drills, cutters, dies, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Select, install, and adjust alignment of drills, cutters, dies, guides, and holding devices, using templates, measuring instruments, and hand tools.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Fitting and aligning cutters and dies is done by hand with tools at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Positioning, adjusting and securing stock material or workpieces against stops, on arbors or in chucks, fixtures or automatic feeding mechanisms
This work happens in the physical world: stock material, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Position, adjust, and secure stock material or workpieces against stops, on arbors, or in chucks, fixtures, or automatic feeding mechanisms, manually or using hoists.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: This is placing and clamping physical material into the machine, which software cannot do.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Observing machine operation to detect workpiece defects or machine malfunctions
This work happens in the physical world: machine operation, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Observe machine operation to detect workpiece defects or machine malfunctions, adjusting machines as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Watching a running machine and adjusting it needs a person standing at it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Reading blueprints or job orders to determine product specifications and tooling instructions and to plan operational sequences
The ratings behind this row put blueprints well outside what today's tools can do on their own.
importance 4 · Core
Source: “Read blueprints or job orders to determine product specifications and tooling instructions and to plan operational sequences.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Reading a drawing and planning the steps is desk work, though shop-specific tooling knowledge still matters.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Setting up and operating machines
This work happens in the physical world: and operating machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Set up and operate machines, such as lathes, cutters, shears, borers, millers, grinders, presses, drills, or auxiliary machines, to make metallic and plastic workpieces.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting up and running metal-cutting machines is hands-on work at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Changing worn machine accessories, such as cutting tools or brushes, using hand tools
This work happens in the physical world: worn machine accessories, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Change worn machine accessories, such as cutting tools or brushes, using hand tools.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Swapping worn cutting tools by hand is physical work no software can do.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Performing minor machine maintenance, such as oiling or cleaning machines, dies or workpieces or adding coolant to machine reservoirs
This work happens in the physical world: minor machine maintenance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform minor machine maintenance, such as oiling or cleaning machines, dies, or workpieces, or adding coolant to machine reservoirs.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Oiling, cleaning and refilling machines is physical maintenance at the equipment.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

Show the other 11 tasks

Recording operational data, such as pressure readings, lengths of strokes, feed rates or speeds
staying human
This work happens in the physical world: operational data, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Record operational data, such as pressure readings, lengths of strokes, feed rates, or speeds.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The readings are easy to log, but someone has to be at the machine to take them.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Selecting the proper coolants and lubricants and starting their flow
staying human
This work happens in the physical world: the proper coolants, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Select the proper coolants and lubricants and start their flow.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Picking the right coolant is a lookup, but starting the flow happens at the machine.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Instructing other workers in machine set-up and operation
staying human
This work happens in the physical world: other workers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Instruct other workers in machine set-up and operation.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Training materials can be drafted, but showing someone how to set up a machine happens at it.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Making minor electrical and mechanical repairs and adjustments to machines and notifying supervisors when major service
staying human
This work happens in the physical world: minor electrical and mechanical repairs and adjustments, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Make minor electrical and mechanical repairs and adjustments to machines and notify supervisors when major service is required.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Repairing and adjusting machinery means working on the machine with tools.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Starting machines and turn handwheels or valves to engage feeding
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Start machines and turn handwheels or valves to engage feeding, cooling, and lubricating mechanisms.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Starting machines and turning handwheels and valves needs a person at the controls.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Removing burrs, sharp edges, rust or scale from workpieces, using files, hand grinders, wire brushes or power tools
staying human
This work happens in the physical world: burrs, sharp edges, rust or scale, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Remove burrs, sharp edges, rust, or scale from workpieces, using files, hand grinders, wire brushes, or power tools.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Filing and grinding rough edges off metal parts is entirely hands-on work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Extracting or lifting jammed pieces from machines
staying human
This work happens in the physical world: jammed pieces, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Extract or lift jammed pieces from machines, using fingers, wire hooks, or lift bars.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Freeing jammed metal from a machine is done with fingers, hooks and bars.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Setting machine stops or guides to specified lengths
staying human
This work happens in the physical world: machine stops, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Set machine stops or guides to specified lengths as indicated by scales, rules, or templates.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting the physical stops and guides on a machine requires hands on the equipment.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Moving controls or mounting gears
staying human
This work happens in the physical world: controls, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Move controls or mount gears, cams, or templates in machines to set feed rates and cutting speeds, depths, and angles.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Moving controls and mounting gears or cams is physical work on the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Aligning layout marks with dies or blades
staying human
This work happens in the physical world: layout marks, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Align layout marks with dies or blades.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Lining up marks with a die or blade is a hands-on judgment at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Measuring and marking reference points and cutting lines on workpieces
staying human
This work happens in the physical world: reference points, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Measure and mark reference points and cutting lines on workpieces, using traced templates, compasses, and rules.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Marking cutting lines on a workpiece is done by hand on the metal itself.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $47,180a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 124,590in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: data in, a record out. The rows above are exactly that shape: computing data and writing programs for computer numerical control. What it cannot do is be there in the room, and that is still where alignment of drills, cutters, dies get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: selecting, installing and adjusting alignment of drills, cutters, dies, guides and holding devices, using templates, measuring instruments and hand tools is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 5% of this job's task weight sits in rows the software is already learning, 2% in rows that change shape rather than disappear, and 93% in rows it is nowhere near. That is the position, measured across 21 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Computing data, such as gear dimensions or machine settings, applying knowledge of shop mathematics is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to data, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether alignment of drills, cutters, dies are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near writing programs for computer numerical control. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to multiple machine tool setters, operators, and tenders, metal and plastic (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was milling and planing machine setters, operators, and tenders, metal and plastic: only about 21% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 10/100 here, with only 5% of the task list in the top band, and “inspect workpieces for defects, and measure workpieces to determine accuracy of machine…” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect workpieces for defects, and measure workpieces to determine accuracy of machine operation…, and their equivalent is to verify alignment of workpieces on machines, using measuring instruments. Across both published task lists that is about 21% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 21% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. And it is a narrow door: about 12,460 of those jobs against 124,590 of yours (OEWS May 2025), 10% as many seats.
Look at that job’s page anyway →
Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect workpieces for defects, and measure workpieces to determine accuracy of machine operation…, and their equivalent is to inspect or measure finished workpieces to determine conformance to specifications, using measuring instruments. Across both published task lists that is about 20% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 20% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Computer Numerically Controlled Tool Operators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect workpieces for defects, and measure workpieces to determine accuracy of machine operation…, and their equivalent is to measure dimensions of finished workpieces to ensure conformance to specifications, using precision measuring…. Across both published task lists that is about 15% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 15% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 5% of its task weight, across 21 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like selecting, installing and adjusting alignment of drills, cutters, dies, guides and holding devices, using templates, measuring instruments and hand tools arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Metal machining setters and setter-operators is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

Metal working machine operatives

In UK official statistics this job is counted as Metal machining setters and setter-operators and Metal working machine operatives. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Noted, and thank you. We’ll email you if a Space for multiple machine tool setters / operators / tenders / metal / plastic launches. Nothing else.

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No Space for multiple machine tool setters / operators / tenders / metal / plastic yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

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Collab365 launches new communities where the need is real. If one for multiple machine tool setters / operators / tenders / metal / plastic existed, with researched problems, courses and people in the same boat, would you want in?

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Questions people ask about this job

Will AI replace Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic?: Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic (United States, SOC 51-4081), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 10 out of 100 (range 8–15, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Compute data, such as gear dimensions or machine settings, applying knowledge of shop mathematics” (93/100, very high); “Write programs for computer numerical control (CNC) machines to cut metal and plastic materials” (56/100, partial); “Read blueprints or job orders to determine product specifications and tooling instructions and to plan operational sequences” (38/100, low). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic” stay human?: About 93% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Measure and mark reference points and cutting lines on workpieces, using traced templates, compasses, and rules” (0/100, minimal); “Align layout marks with dies or blades” (0/100, minimal); “Move controls or mount gears, cams, or templates in machines to set feed rates and cutting speeds, depths, and angles” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic” do about AI?: Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 93% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 21 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.