Will AI replace Textile Knitting and Weaving Machine Setters, Operators, and Tenders?
AI reaches little of this job’s work.
12/100
Low exposure
More exposed to AI than 23% of US workers.
- AI already used in
- 3% of the work
- As AI tools mature: up to
- 24% of the work
What the job involves
Set up, operate, or tend machines that knit, loop, weave, or draw in textiles.
Where AI reaches the work
The job’s main tasks, heaviest first, by how far AI reaches them.
Already happening(1)
- Program electronic equipment.
Within reach(4)
- Notify supervisors or repair staff of mechanical malfunctions.
- Confer with co-workers to obtain information about orders, processes, or problems.
- Record information about work completed and machine settings.
- Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements.
Out of reach(14)
- Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing.
- Inspect products to ensure that specifications are met and to determine if machines need adjustment.
- Observe woven cloth to detect weaving defects.
- Inspect machinery to determine whether repairs are needed.
- Start machines, monitor operations, and make adjustments as needed.
and 9 more
Also called
Weaver · Knitter Operator · Threader · Knitter · Carpet Weaver · Harness Placer · Jacquard Loom Weaver · Loom Changer
Related jobs
What these numbers mean
The score is the share of this job’s work, weighted by how important and frequent each task is, that AI could do in half the time: tasks an AI model could do alone count in full, tasks that need software built on AI count half.
“AI already used” is the share of the work in tasks where people are recorded using AI today. “As AI tools mature” counts every task AI could halve, with or without extra software.
The percentile compares the score with every job’s, weighted by how many people in the US do each one. Exposure means AI could change how the work is done, not that the job goes away. These are estimates for the typical job, not predictions.
Sources
- O*NET 31.0 Database, U.S. Department of Labor, Employment and Training Administration
- GPTs are GPTs: Labor market impact potential of LLMs (task labels), Eloundou, Manning, Mishkin & Rock (2023), OpenAI and University of Pennsylvania
- Anthropic Economic Index: labor market impacts (task penetration), Anthropic
- Occupational Employment and Wage Statistics, May 2025 (national and metropolitan areas), U.S. Bureau of Labor Statistics