AI exposure by occupation, O*NET 51-6063.00
Will AI replace Textile Knitting and Weaving Machine Setters, Operators, and Tenders?
Textile Knitting and Weaving Machine Setters, Operators, and Tenders: AI can already handle 1 of 19 tasks; most of the work is hands-on, and the part AI can address mostly needs an efficient-tier model.
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Every task, scored
The 19 tasks O*NET 30.3 lists for Textile Knitting and Weaving Machine Setters, Operators, and Tenders, from most to least exposed. 1 reach 60 or more, the level at which AI already handles a task.
| Task | AI exposure | Evidence | Model tier |
|---|---|---|---|
| Operate machines for test runs to verify adjustments and to obtain product samples. | Estimated | Standard | |
| Inspect products to ensure that specifications are met and to determine if machines need adjustment. | Estimated | Efficient | |
| Notify supervisors or repair staff of mechanical malfunctions. | Estimated | Efficient | |
| Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation. | Estimated | Efficient | |
| Inspect machinery to determine whether repairs are needed. | Estimated | Efficient | |
| Repair or replace worn or defective needles and other components, using hand tools. | Estimated | Efficient | |
| Observe woven cloth to detect weaving defects. | Estimated | No tier | |
| Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing. | Estimated | No tier | |
| Remove defects in cloth by cutting and pulling out filling. | Estimated | No tier | |
| Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects. | Estimated | No tier | |
| Program electronic equipment. | Estimated | No tier | |
| 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. | Estimated | No tier | |
| Start machines, monitor operations, and make adjustments as needed. | Estimated | No tier | |
| Record information about work completed and machine settings. | Estimated | No tier | |
| Stop machines when specified amounts of product have been produced. | Estimated | No tier | |
| Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements. | Estimated | No tier | |
| Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns. | Estimated | No tier | |
| Confer with co-workers to obtain information about orders, processes, or problems. | Estimated | No tier | |
| Adjust machine heating mechanisms, tensions, and speeds to produce specified products. | Estimated | No tier |
Observed: scored from AI use recorded in the Anthropic Economic Index (June 2026 release), 0 of 19 tasks. Estimated: scored from the task's O*NET 30.3 work activity, 19 of 19. Importance is O*NET's 1 to 5 rating of how central the task is. Model tier is the lowest tier that can do the task under the published rubric; "No tier" marks hands-on work.
The model tier this role needs
An efficient-tier model or lower covers 27% of the role's importance-weighted task work. 31% of the work is something AI can address at some tier, and 69% is hands-on.
Efficient, standard and frontier tiers come from the JobRoute model tier reference. The recommended tier is the lowest one that covers 80% of the work AI can address. How the method works.
Wages, employment and outlook
- US employment
- 13,030
- Median annual wage
- $39,530
- Middle half of earners
- $36,390 to $45,220
- Projected change, 2024 to 2034
- -11.2%
Employment and wages: BLS Occupational Employment and Wage Statistics, May 2025, national. Projection: BLS Employment Projections 2024 to 2034.
Adjacent roles
The occupations that reuse the most of this role's skills, with the usual time to move. These are the first places to look when you plan to redeploy or retrain people in this role.
- Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders 85% skill overlap, about 6 months to move Exposure21
- Textile Cutting Machine Setters, Operators, and Tenders 81% skill overlap, about 7 months to move Exposure20
- Textile Bleaching and Dyeing Machine Operators and Tenders 74% skill overlap, about 9 months to move Exposure19
- Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers 72% skill overlap, about 10 months to move Exposure15
- Sewing Machine Operators 71% skill overlap, about 10 months to move Exposure22
Skill overlap and transition time from the JobRoute role adjacency graph on O*NET 30.3 skills.
Questions about Textile Knitting and Weaving Machine Setters, Operators, and Tenders and AI
Will AI replace Textile Knitting and Weaving Machine Setters, Operators, and Tenders?
AI already handles 1 of the 19 tasks O*NET lists for this occupation, at a task exposure of 60 or more, and the role's importance-weighted exposure is 19 out of 100. JobRoute scores tasks, not whole jobs, so the score shows how much of the work changes rather than whether the job disappears. BLS projects employment to change by -11.2% from 2024 to 2034.
What is the AI exposure of Textile Knitting and Weaving Machine Setters, Operators, and Tenders?
19 out of 100 on JobRoute's task exposure measure, the importance-weighted average of the role's O*NET task scores. Higher means more of the work is AI-doable today. 0 of the 19 task scores are observed in the Anthropic Economic Index (June 2026 release), and the other 19 are estimated from each task's O*NET work activity.
Which Textile Knitting and Weaving Machine Setters, Operators, and Tenders tasks can AI already do?
The tasks with the highest exposure are: Operate machines for test runs to verify adjustments and to obtain product samples. It is the only task that reach 60 or more.
What AI model tier does this role need?
An efficient-tier model or lower covers 27% of the role's importance-weighted task work, and 69% is hands-on work that no model tier covers. JobRoute assigns each task the lowest tier that can do it under a published rubric and recommends the lowest tier that covers 80% of the work AI can address.
How many people work as Textile Knitting and Weaving Machine Setters, Operators, and Tenders, and what do they earn?
13,030 people in the United States, with a median annual wage of $39,530 (BLS Occupational Employment and Wage Statistics, May 2025). BLS Employment Projections expect a change of -11.2% from 2024 to 2034.
How to cite
Data version 2026-11-02, JobRoute method v1.0.0. Download all occupations or this occupation as JSON. Methodology.
JobRoute (2026). AI exposure: Textile Knitting and Weaving Machine Setters, Operators, and Tenders. Data version 2026-11-02, method v1.0.0. Built on O*NET 30.3, BLS OEWS May 2025 and the Anthropic Economic Index (June 2026). CC BY 4.0. https://www.jobroute.ai/jobs/textile-knitting-and-weaving-machine-setters-operators-and-tenders
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