Textile Knitting and Weaving Machine Setters, Operators, and Tenders
In short: today's best AI models could do about 10% 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 90% of the time, stays with people.
Set up, operate, or tend machines that knit, loop, weave, or draw in textiles.
13,030 people do this job in the US. $39,530 a year.
A typical 40-hour week
Not yet reviewed task by task- AI could take on4 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people36 h
Textile Knitting and Weaving Machine Setters, Operators, and Tenders, now: of a 40-hour week, AI could take on 4 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 36 hours stay with people.
The largest pieces: Program electronic equipment (about 2 h); Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements (about 1 h); Notify supervisors or repair staff of mechanical malfunctions (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.
- 10%
- 1%
- 89%
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, 9%. Today's estimate: 10%.
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 19 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% | 313 of 708 | 1 of 19 |
| Released March 2025 | 0.07% | 215 of 703 | 1 of 19 |
| The week of Aug 4, 2025 (released September 2025) | 0.027% | 335 of 655 | 1 of 19 |
| The week of Nov 13, 2025 (released January 2026) | 0.025% | 359 of 672 | 1 of 19 |
| The week of Feb 5, 2026 (released March 2026) | 0.018% | 416 of 677 | 1 of 19 |
| April 2026 (released June 2026) | none | n/a | 0 of 19 |
| May 2026 (released June 2026) | none | n/a | 0 of 19 |
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 88% of this job's working time as physical work. For 81% of the working time, a robot has been shown doing most of the task somewhere: 45% in a purpose-built cell, 36% in a structured facility. 1.18 of 3, the 61st highest of the 923 jobs.
- Not physical 12%
- No robot shown 7%
- In a purpose-built cell 45%
- In a structured facility 36%
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 . 19 tasks from .
| Task | Kind | |||
|---|---|---|---|---|
| Set up, or set up and operate textile machines that perform textile processing and manufacturing operations such as winding, twisting, knitting, weaving, bonding, or stretching.Done daily by most who do it.Robots: no robot shown. Anthropic's rating. | 19% | 1.7 h38 min to 4.2 h | Stays with people | |
| Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing.Done hourly or more by most who do it. Robots: in a purpose-built cell, SAFIR PRO S67 automatic drawing-in machine (Stäubli) (sold). Anthropic's rating. | 11% | 21 min8 min to 50 min | Stays with people | |
| Start machines, monitor operations, and make adjustments as needed.Done hourly or more by most who do it. Robots: in a structured facility, Picanol PRA II Plus (Pick Repair Automation) on OmniPlus air-jet weaving machines (sold). Anthropic's rating. | 8% | 25 min8 min to 1.3 h | Stays with people | |
| Remove defects in cloth by cutting and pulling out filling.Done hourly or more by most who do it. Robots: in a purpose-built cell, Picanol PRA II Plus (Pick Repair Automation) on OmniPlus-i Connect airjet weaving machine (sold). Anthropic's rating. | 7% | 25 min8 min to 50 min | Stays with people | |
| Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation.Done several times a day by most who do it.Robots: in a purpose-built cell, Kawasaki / Fanuc force-controlled assembly robots (Ford AMTD programme; 4R70W clutch hub and AX4N triple clutch) (demonstrated). Anthropic's rating. | 7% | 1.3 h25 min to 3.3 h | Stays with people | |
| Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects.Done hourly or more by most who do it. Robots: in a purpose-built cell, Electronic warp stop motion (drop-wire warp stop motions fitted to shuttleless weaving machines such as Picanol, Itema and Toyota looms) (deployed). Anthropic's rating. | 7% | 21 min8 min to 50 min | Stays with people | |
| Inspect products to ensure that specifications are met and to determine if machines need adjustment.Done hourly or more by most who do it. Robots: in a purpose-built cell, Cyclops 2 on-loom fabric inspection (BMSvision) (sold). Anthropic's rating. | 6% | 17 min8 min to 33 min | Stays with people | |
| Observe woven cloth to detect weaving defects.Done hourly or more by most who do it. Robots: in a structured facility, Uster Q-Bar 2 / Uster EVS Fabriq Vision (deployed). Anthropic's rating. | 5% | 13 min4 min to 25 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, ANYmal (ANYbotics) (deployed). Anthropic's rating. | 5% | 17 min8 min to 38 min | Stays with people | |
| Program electronic equipment.Done several times a day by most who do it. AI observed on this task. | 4% | 38 min13 min to 2.5 h | 100%Half by now | |
| Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns.Done daily by most who do it.Robots: in a structured facility, Luwa Loom Travel Cleaner (LTC / LoomTravClean) (sold). Anthropic's rating. | 3% | 38 min13 min to 1.7 h | Stays with people | |
| Repair or replace worn or defective needles and other components, using hand tools.Done daily by most who do it.Robots: in a purpose-built cell, Automatic Needle Exchanger (AUTEFA Solutions) (sold). Anthropic's rating. | 3% | 25 min8 min to 1.3 h | Stays with people |
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.
- Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders7% now, 7% by 2028
- Sewing Machine Operators2% now, 2% by 2028
- Textile Cutting Machine Setters, Operators, and Tenders14% now, 14% by 2028
- Paper Goods Machine Setters, Operators, and Tenders0% now, 0% by 2028
- Textile Bleaching and Dyeing Machine Operators and Tenders11% now, 11% by 2028
- Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers5% 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 Knitting and Weaving Machine Setters, Operators, and Tenders is within reach of AI now?
- An estimated 10% of the working time is within reach of AI models now and 10% 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 Knitting and Weaving 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); Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements (100% of the task's time now); and Notify supervisors or repair staff of mechanical malfunctions (100% of the task's time now).
- Which tasks of Textile Knitting and Weaving 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: Set up, or set up and operate textile machines that perform textile processing and manufacturing operations such as winding, twisting, knitting, weaving, bonding, or stretching; Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing; and Start machines, monitor operations, and make adjustments as needed. Together such tasks are about 90% of the working time.
- Do people already use AI for the work of Textile Knitting and Weaving 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 3%. 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 Knitting and Weaving Machine Setters, Operators, and Tenders?
- Anthropic's robot exposure rating (September 2026) finds 88% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 81% of the working time, 36% in a structured facility. Its index is 1.18 of 3. One example: Picanol PRA II Plus (Pick Repair Automation) on OmniPlus air-jet weaving machines 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 19 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.
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