Textile Cutting Machine Setters, Operators, and Tenders
In short: today's best AI models could do about 14% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about the same by the end of 2028 if progress keeps its long-run pace. The people and hands-on work, about 86% of the time, stays with people.
Set up, operate, or tend machines that cut textiles.
9,000 people do this job in the US. $38,760 a year.
A typical 40-hour week
Not yet reviewed task by task- AI could take on6 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people34 h
Textile Cutting Machine Setters, Operators, and Tenders, now: of a 40-hour week, AI could take on 6 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 34 hours stay with people.
The largest pieces: Program electronic equipment (about 4 h); Record information about work completed and machine settings (about 1 h); Study guides, samples, charts, and specification sheets or confer with supervisors or engineering staff to determine set-up requirements (under an hour).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 14%
- 3%
- 83%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 2%. With Claude Mythos Preview (early), measured in April 2026, 12%. Today's estimate: 14%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, no reportable share went to this job's tasks in May 2026; 0 of its 18 tasks showed up. Within each of Anthropic's methods, never across a change: 0.039% to 0.07% (released February and March 2025); 0.027% to 0.018% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.039% | 312 of 708 | 1 of 18 |
| Released March 2025 | 0.07% | 214 of 703 | 1 of 18 |
| The week of Aug 4, 2025 (released September 2025) | 0.027% | 334 of 655 | 1 of 18 |
| The week of Nov 13, 2025 (released January 2026) | 0.025% | 358 of 672 | 1 of 18 |
| The week of Feb 5, 2026 (released March 2026) | 0.018% | 415 of 677 | 1 of 18 |
| April 2026 (released June 2026) | none | n/a | 0 of 18 |
| May 2026 (released June 2026) | none | n/a | 0 of 18 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 84% of this job's working time as physical work. For 84% of the working time, a robot has been shown doing most of the task somewhere: 60% in a purpose-built cell, 25% in a structured facility. 1.09 of 3, the 80th highest of the 923 jobs.
- Not physical 16%
- In a purpose-built cell 60%
- In a structured facility 25%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
The tasks
Sorted by . 18 tasks from .
| Task | Kind | |||
|---|---|---|---|---|
| Place patterns on top of layers of fabric and cut fabric following patterns, using electric or manual knives, cutters, or computer numerically controlled cutting devices.Done daily by most who do it. Robots: in a purpose-built cell, Lectra Vector (automotive/fashion multi-ply CNC cutter) (sold). Anthropic's rating. | 14% | 38 min13 min to 1.7 h | Stays with people | |
| Start machines, monitor operations, and make adjustments as needed.Done daily by most who do it. Robots: in a structured facility, Gerber Paragon automated fabric cutter (Lectra) (sold). Anthropic's rating. | 13% | 25 min8 min to 1.3 h | Stays with people | |
| Operate machines to cut multiple layers of fabric into parts for articles such as canvas goods, house furnishings, garments, hats, or stuffed toys.Done hourly or more by most who do it.Robots: in a purpose-built cell, Lectra Vector (automatic fabric cutter) (deployed). Anthropic's rating. | 11% | 50 min17 min to 1.7 h | Stays with people | |
| Install, level, and align components, such as gears, chains, guides, dies, cutters, or needles, to set up machinery for operation.Done daily by most who do it.Robots: in a purpose-built cell, AXIOME robotic blade cutting cells (Stäubli/ABB robots) with automatic blade change (sold). Anthropic's rating. | 10% | 1.3 h25 min to 3.3 h | Stays with people | |
| Program electronic equipment.Done daily by most who do it. AI observed on this task. | 9% | 38 min13 min to 2.5 h | 100%Half by now | |
| Inspect products to ensure that the quality standards and specifications are met.Done daily by most who do it. Robots: in a purpose-built cell, Automated Ply Placement (APP) (Airborne) (sold). Anthropic's rating. | 7% | 13 min4 min to 38 min | Stays with people | |
| Adjust cutting techniques to types of fabrics and styles of garments.Done daily by most who do it.Robots: in a structured facility, Gerber Paragon cutter (sold). Anthropic's rating. | 6% | 21 min8 min to 42 min | Stays with people | |
| Thread yarn, thread, or fabric through guides, needles, and rollers of machines.Done daily by most who do it.Robots: in a purpose-built cell, Voith PrevoThread / TailStabilizer tail threading systems (sold). Anthropic's rating. | 5% | 13 min4 min to 38 min | Stays with people | |
| Adjust machine controls, such as heating mechanisms, tensions, or speeds, to produce specified products.Done daily by most who do it. Robots: in a structured facility, Gerber Paragon cutter (KERI control) (sold). Anthropic's rating. | 4% | 13 min4 min to 33 min | Stays with people | |
| Inspect machinery to determine whether repairs are needed.Done daily by most who do it. Robots: in a purpose-built cell, Spot (Spot CAM+IR / acoustic payload, Orbit) (Boston Dynamics / ST Engineering MRAS) (deployed). Anthropic's rating. | 3% | 17 min8 min to 38 min | Stays with people | |
| Repair or replace worn or defective parts or components, using hand tools.Done more than monthly by most who do it. Robots: in a purpose-built cell, DISCO Auto Blade Changer (ABC) on DFD6362 / DFD6342 dicing saws (sold). Anthropic's rating. | 3% | 38 min17 min to 1.7 h | Stays with people | |
| Record information about work completed and machine settings.Done daily by most who do it. | 3% | 8 min4 min to 17 min | 100%Half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Cutting and Slicing Machine Setters, Operators, and Tenders7% now, 7% by 2028
- Paper Goods Machine Setters, Operators, and Tenders0% now, 0% by 2028
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing9% now, 9% by 2028
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic10% now, 10% by 2028
- Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic6% now, 6% by 2028
- Cutters and Trimmers, Hand5% now, 5% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Textile Cutting Machine Setters, Operators, and Tenders is within reach of AI now?
- An estimated 14% of the working time is within reach of AI models now and 14% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Textile Cutting Machine Setters, Operators, and Tenders come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Program electronic equipment (100% of the task's time now); Record information about work completed and machine settings (100% of the task's time now); and Study guides, samples, charts, and specification sheets or confer with supervisors or engineering staff to determine set-up requirements (100% of the task's time now).
- Which tasks of Textile Cutting Machine Setters, Operators, and Tenders stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Place patterns on top of layers of fabric and cut fabric following patterns, using electric or manual knives, cutters, or computer numerically controlled cutting devices; Start machines, monitor operations, and make adjustments as needed; and Operate machines to cut multiple layers of fabric into parts for articles such as canvas goods, house furnishings, garments, hats, or stuffed toys. Together such tasks are about 86% of the working time.
- Do people already use AI for the work of Textile Cutting Machine Setters, Operators, and Tenders?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 2%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Textile Cutting Machine Setters, Operators, and Tenders?
- Anthropic's robot exposure rating (September 2026) finds 84% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 84% of the working time, 25% in a structured facility. Its index is 1.09 of 3. One example: Gerber Paragon automated fabric cutter (Lectra) for "start machines, monitor operations, and make adjustments as needed". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 18 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, each classed as desk, people or body work, and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
This job in your state or metro area
Embed these figures on your site
A small card with the share of working time within reach now and at the end of 2027, 2028 and 2030, the date and a link back. Free under CC BY 4.0; the card carries the credit.
Your own version of this job
The page above is the occupation. Your job is your title and what you actually do: give both, and they are read against these tasks in your browser, nothing sent anywhere. The headline is free: how much of your working time is within reach now and by the end of 2028, and how many of the tasks your description names.
This occupation has not been reviewed task by task yet, so the full personal report is not offered for it; 402 of 923 occupations are reviewed so far, largest first.