US dataswitch to UK

Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders

observing bobbins as they are winding and cutting threads to remove loaded bobbins, starting machines, monitor operation and making adjustments, inspecting products to verify that they meet specifications and to determine whether machine adjustment and tending machines that twist together two or more strands of yarn or insert additional twists into single strands of yarn to increase strength. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: starting machines, monitor operation and making adjustments 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: 3%
staying human: 92%

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

Whole-job exposure score 9 out of 100 (7–13 allowing for uncertainty): minimal exposure, across 23 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 textile winding, twisting, and drawing out machine setters, operators, and tenders 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 2026-08-05. Read the full method.

What shifts is recording production data, numbers and types of bobbins wound: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Set up, operate, or tend machines that wind or twist textiles; or draw out and combine sliver, such as wool, hemp, or synthetic fibers. Includes slubber machine and drawing frame operators. The job title says “textile winding”, “twisting”, “drawing out machine setters”, “operators” or “tenders”: officially one job, several names. The real job is the part underneath: starting machines, monitor operation and making adjustments. 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 textile winding, twisting, and drawing out machine setters, operators, and tenders is not one task. It is 23 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is starting machines, monitor operation and making adjustments, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 3%
staying human: 92%

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 2026-08-05. 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.

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
9 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.

Recording production data, numbers and types of bobbins wound
This is reading one thing and writing another: production data, numbers and types of bobbins wound in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record production data such as numbers and types of bobbins wound.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high 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: Logging how many bobbins were wound is straightforward record keeping that systems already capture.
The five ratings: output a model can produce 4/4 · 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.

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.

Studying guides, samples, charts and specification sheets or conferring with supervisors or engineering staff to determine setup requirements
The software now makes the first pass at guides, samples, charts and specification sheets, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Study guides, samples, charts, and specification sheets, or confer with supervisors or engineering staff to determine setup requirements.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 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: Reading specification sheets and charts to work out a setup is document work that drafts 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 1/4 · how much data exists 3/4.

Staying human

21 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.

Notifying supervisors or mechanics of equipment malfunctions
This work happens in the physical world: supervisors, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Notify supervisors or mechanics of equipment malfunctions.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low 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 message itself is easy, but noticing the fault means being on the floor beside the machine.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Starting machines, monitor operation and making adjustments
This work happens in the physical world: machines, monitor operation and making adjustments, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Start machines, monitor operation, and make adjustments as needed.” (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 adjusting them as they run is hands-on operation.
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 2/4.
Stopping machines when specified amount of products
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Stop machines when specified amount of products has been produced.” (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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Stopping the machine at the right count is a physical action 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 3/4.
Tending machines that twist together two or more strands of yarn or insert additional twists into single strands of yarn to increase strength
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Tend machines that twist together two or more strands of yarn or insert additional twists into single strands of yarn to increase strength, smoothness, or uniformity of yarn.” (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: Tending twisting machines is hands-on machine operation.
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 2/4.
Inspecting machinery to determine whether repairs
This work happens in the physical world: machinery, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect machinery to determine whether repairs are needed.” (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: Deciding whether a machine needs repair means looking at and listening to it in person.
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.
Threading yarn, thread or fabric through guides, needles and rollers of machines
This work happens in the physical world: yarn, thread or fabric, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Thread yarn, thread, or fabric through guides, needles, and rollers of machines.” (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: Threading yarn through guides and needles is delicate handwork 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 2/4.
Inspecting products to verify that they meet specifications and to determine whether machine adjustment
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect products to verify that they meet specifications and to determine whether machine adjustment is needed.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking product against specification means handling the yarn and looking at it.
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.
Operating machines for test runs to verify adjustments and to obtain product samples
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Operate machines for test runs to verify adjustments and to obtain product samples.” (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: Running test batches and pulling samples is physical machine 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 2/4.

Show the other 13 tasks

Observing operations to detect defects
staying human
This work happens in the physical world: operations, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Observe operations to detect defects, malfunctions, or supply shortages.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Spotting defects and shortages as they happen depends on being on the floor watching.
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 0/4 · how much data exists 2/4.
Tending machines with multiple winding units that wind thread onto shuttle bobbins for use on sewing machines or other kinds of bobbins for sole-stitching
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Tend machines with multiple winding units that wind thread onto shuttle bobbins for use on sewing machines or other kinds of bobbins for sole-stitching, knitting, or weaving machinery.” (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: Tending multi-unit winding machines is hands-on operation.
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 2/4.
Replacing depleted supply packages with full packages
staying human
This work happens in the physical world: depleted supply packages, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Replace depleted supply packages with full packages.” (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 empty packages for full ones is manual machine tending.
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.
Placing bobbins on spindles and insert spindles into bobbin-winding machines
staying human
This work happens in the physical world: bobbins, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Place bobbins on spindles and insert spindles into bobbin-winding machines.” (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: Loading bobbins onto spindles is physical 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 2/4.
Measuring bobbins periodically, using gauges and turn screws to adjust tension if bobbins are not of specified size
staying human
This work happens in the physical world: bobbins, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Measure bobbins periodically, using gauges, and turn screws to adjust tension if bobbins are not of specified size.” (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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Gauging bobbins and turning tension screws is done by hand 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 3/4.
Installing level and align machine components, gears, chains, guides, dies, cutters or needles to set up machinery
staying human
This work happens in the physical world: level, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation.” (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 gears, guides and needles is done by hand 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.
Tending spinning frames that draw out and twist roving or sliver into yarn
staying human
This work happens in the physical world: frames, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Tend spinning frames that draw out and twist roving or sliver into yarn.” (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: Tending spinning frames is hands-on machine operation.
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 2/4.
Unwinding lengths of yarn, thread or twine from spools and wind onto bobbins
staying human
This work happens in the physical world: lengths of yarn, thread or twine, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Unwind lengths of yarn, thread, or twine from spools and wind onto bobbins.” (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: Unwinding and rewinding yarn is physical handling.
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 2/4.
Observing bobbins as they are winding and cutting threads to remove loaded bobbins
staying human
This work happens in the physical world: bobbins, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Observe bobbins as they are winding and cut threads to remove loaded bobbins, using knives.” (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 bobbins and cutting threads with a knife is manual work at the winder.
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 2/4.
Adjusting machine settings, speed or tension to produce products that meet specifications
staying human
This work happens in the physical world: machine settings, speed or tension, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Adjust machine settings such as speed or tension to produce products that meet specifications.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Changing machine speed and tension settings is done 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 2/4.
Cleaning, oiling and lubricate machines, using air hoses, cleaning solutions, rags, oilcans and grease guns
staying human
This work happens in the physical world: lubricate machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oilcans, and grease guns.” (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: Cleaning, oiling and greasing machines is manual maintenance.
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 2/4.
Removing spindles from machines and bobbins from spindles
staying human
This work happens in the physical world: spindles, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Remove spindles from machines and bobbins from spindles.” (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: Pulling spindles and bobbins off machines is manual 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 2/4.
Repairing or replacing worn or defective parts or components
staying human
This work happens in the physical world: worn, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Repair or replace worn or defective parts or components, 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: Replacing worn parts with hand tools is manual repair 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 2/4.

What this job pays, and how many people do it

Median pay: $38,670a 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: 22,020in 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: production data, numbers and types of bobbins wound in, a record out. The rows above are exactly that shape: recording production data, numbers and types of bobbins wound and studying guides, samples. What it cannot do is be there in the room, and that is still where machines, monitor operation and making adjustments 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: starting machines, monitor operation and making adjustments 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, 3% in rows that change shape rather than disappear, and 92% in rows it is nowhere near. That is the position, measured across 23 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. Recording production data, numbers and types of bobbins wound 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 production data, numbers and types of bobbins wound, 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 machines, monitor operation and making adjustments 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 studying guides, samples, charts and specification sheets or conferring with supervisors or engineering staff to determine setup requirements. 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

You did not come here for a career change and I am not selling you one. But two roads out of here are worth knowing about, so here they are with the bill attached.

Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and TendersTextile Cutting Machine Setters, Operators, and Tenders
a year or morematched on shared tasks
You already start machines, monitor operation, and make adjustments as needed. In that job the same thing shows up as start machines, monitor operations, and make adjustments as needed. Take both published task lists together and about 48% of the work in that job that the software is not taking is work you are doing today.
About 48% of it you could do on Monday. The rest is the price of the ticket.
The work the two jobs share
- You already do
  “Start machines, monitor operation, and make adjustments as needed.”
  They do
  “Start machines, monitor operations, and make adjustments as needed.”
- You already do
  “Thread yarn, thread, or fabric through guides, needles, and rollers of machines.”
  They do
  “Thread yarn, thread, or fabric through guides, needles, and rollers of machines.”
- You already do
  “Stop machines when specified amount of products has been produced.”
  They do
  “Stop machines when specified amounts of product have been produced.”
What you would not already have: Nothing in your task list touches “inspect products to ensure that the quality standards and specifications are met”, “confer with coworkers to obtain information about orders, processes, or problems” or “place patterns on top of layers of fabric and cut fabric following…”. That is the part you would be learning from scratch, and it is roughly the 52% of their durable work you do not already hold.
The honest bill
Pay: $38,760 against your $38,670 (OEWS May 2025 (both)).
- The licence gate: Default-closed. This release carries no licence-register snapshot, so I could not check whether that job is regulated, which means I have to assume it might be. Before you spend a penny, look it up on the US Labor Department’s licensed-occupations finder; the free routes below link straight to it. The route is banded a year or more because of that unknown, not in spite of it.
- The entry ticket: Typical entry-level education is published for only ten occupations in this release, and neither this job nor that one is among them. So I cannot tell you whether a qualification stands in the way. Treat that as an open question to settle before you commit, not as a green light.
- What the pay gap is telling you: $38,760 against your $38,670 (OEWS May 2025 (both)): within a percent of each other, which is to say the same money. Whatever this move is for, it is not for the pay.
- What you live on meanwhile: Nobody is going to pay you to retrain. This is evenings and weekends alongside the job you already have, for a year or more, and if that is not possible right now then this route is not open right now, which is worth knowing before you start. The free American Job Center service listed below will talk training funding through with you before you pay anyone.
- Is the target job itself holding up: Textile Cutting Machine Setters, Operators, and Tenders scores 12/100 on this site’s own exposure measure (minimal), with 6% of its tasks in the top band. Employment projections are not published for this occupation in this release, so this is the exposure leg of the check only. It passed, which is the only reason it is here.
How long: A year or more, part-time, alongside the job you have. That band is set by the unchecked licence question and by the 52% of their work you would be learning, not by any one course.
See what AI is doing to textile cutting machine setters, operators, and tenders
Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and TendersTextile Knitting and Weaving Machine Setters, Operators, and Tenders
a year or morematched on shared tasks
You already start machines, monitor operation, and make adjustments as needed. In that job the same thing shows up as start machines, monitor operations, and make adjustments as needed. Take both published task lists together and about 47% of the work in that job that the software is not taking is work you are doing today.
About 47% of it you could do on Monday. The rest is the price of the ticket.
The work the two jobs share
- You already do
  “Start machines, monitor operation, and make adjustments as needed.”
  They do
  “Start machines, monitor operations, and make adjustments as needed.”
- You already do
  “Stop machines when specified amount of products has been produced.”
  They do
  “Stop machines when specified amounts of product have been produced.”
- You already do
  “Inspect machinery to determine whether repairs are needed.”
  They do
  “Inspect machinery to determine whether repairs are needed.”
What you would not already have: Nothing in your task list touches “observe woven cloth to detect weaving defects”, “remove defects in cloth by cutting and pulling out filling” or “examine looms to determine causes of loom stoppage”. That is the part you would be learning from scratch, and it is roughly the 53% of their durable work you do not already hold.
The honest bill
Pay: $39,530 against your $38,670 (OEWS May 2025 (both)).
- The licence gate: Default-closed. This release carries no licence-register snapshot, so I could not check whether that job is regulated, which means I have to assume it might be. Before you spend a penny, look it up on the US Labor Department’s licensed-occupations finder; the free routes below link straight to it. The route is banded a year or more because of that unknown, not in spite of it.
- The entry ticket: Typical entry-level education is published for only ten occupations in this release, and neither this job nor that one is among them. So I cannot tell you whether a qualification stands in the way. Treat that as an open question to settle before you commit, not as a green light.
- What the pay gap is telling you: $39,530 against your $38,670, 2.2% more (OEWS May 2025 (both)). A real difference, not a life-changing one. Take the move for the work, not the raise.
- What you live on meanwhile: Nobody is going to pay you to retrain. This is evenings and weekends alongside the job you already have, for a year or more, and if that is not possible right now then this route is not open right now, which is worth knowing before you start. The free American Job Center service listed below will talk training funding through with you before you pay anyone.
- Is the target job itself holding up: Textile Knitting and Weaving Machine Setters, Operators, and Tenders scores 12/100 on this site’s own exposure measure (minimal), with 5% of its tasks in the top band. Employment projections are not published for this occupation in this release, so this is the exposure leg of the check only. It passed, which is the only reason it is here.
How long: A year or more, part-time, alongside the job you have. That band is set by the unchecked licence question and by the 53% of their work you would be learning, not by any one course.
See what AI is doing to textile knitting and weaving machine setters, operators, and tenders

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.

Textile Bleaching and Dyeing Machine Operators and Tenders
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “notify supervisors or mechanics of equipment malfunctions”. Across the whole of both lists that adds up to about 22% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 22% 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 5,310 of those jobs against 22,020 of yours (OEWS May 2025), 24% as many seats.
Look at that job’s page anyway →
Woodworking Machine Setters, Operators, and Tenders, Except Sawing
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already start machines, monitor operation, and make adjustments as needed, and their equivalent is to start machines, adjust controls, and make trial cuts to ensure that machinery is…. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Maintenance Workers, Machinery
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already start machines, monitor operation, and make adjustments as needed, and their equivalent is to start machines and observe mechanical operation to determine efficiency and to detect problems. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
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 23 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 starting machines, monitor operation and making adjustments 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 Textile process operatives 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

In UK official statistics this job is counted as Textile process operatives. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

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.

Nearby moves

The jobs above, as pages you can read the same way as this one. Your job shares its core work with these. That is what the match is, and it is all it is.

Noted, and thank you. We’ll email you if a Space for textile winding / twisting / drawing out machine setters / operators / tenders launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for textile winding / twisting / drawing out machine setters / operators / tenders 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.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for textile winding / twisting / drawing out machine setters / operators / tenders existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for textile winding / twisting / drawing out machine setters / operators / tenders launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders?: Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders (United States, SOC 51-6064), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 9 out of 100 (range 7–13, 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 “Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record production data such as numbers and types of bobbins wound” (69/100, high); “Study guides, samples, charts, and specification sheets, or confer with supervisors or engineering staff to determine setup requirements” (48/100, partial); “Notify supervisors or mechanics of equipment malfunctions” (32/100, 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 “Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders” stay human?: About 92% 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: “Repair or replace worn or defective parts or components, using hand tools” (0/100, minimal); “Remove spindles from machines and bobbins from spindles” (0/100, minimal); “Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oilcans, and grease guns” (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 “Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders” do about AI?: Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 92% 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 Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders 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 23 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

About the data on this page

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
9 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 2026-08-05.
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.

How we score a job Download this release Look up another job

Using these figures?

Cite this

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.