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AI exposure Production

Will AI replace textile knitting and weaving machine setters?

Textile Knitting and Weaving Machine Setters, Operators, and Tenders. US occupation 51-6063.

3.2%

of the time textile knitting and weaving machine setters spend on their work goes on tasks where AI is already being used to do that work.of textile knitting and weaving machine setters' working hours go on tasks AI has already started taking off their plate.

Ranked 293 of 756 US occupations. More exposed than 60% of them.Further along than 60% of them.

Average US job 7.7%

Around the middle of the pack.Keeping pace with the average job.

What AI already does for textile knitting and weaving machine settersWhat textile knitting and weaving machine setters no longer do alone

O*NET lists 19 tasks for textile knitting and weaving machine setters. AI use at work shows up in 1 of them. Each score runs from 0.5, where every observed use helps a person with the task, to 1, where every use does the task outright.

  1. Program electronic equipment. mostly automated 0.92

What it does not touch, yetWhat still needs a human

18 of the 19 tasks show no AI use at work in the data.

  • Confer with co-workers to obtain information about orders, processes, or problems.Core task
  • Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects.Core task
  • Inspect machinery to determine whether repairs are needed.Core task
  • Inspect products to ensure that specifications are met and to determine if machines need adjustment.Core task
  • Notify supervisors or repair staff of mechanical malfunctions.Core task
  • Observe woven cloth to detect weaving defects.Core task
Show the other 12
  • Operate machines for test runs to verify adjustments and to obtain product samples.Core task
  • Remove defects in cloth by cutting and pulling out filling.Core task
  • Start machines, monitor operations, and make adjustments as needed.Core task
  • Stop machines when specified amounts of product have been produced.Core task
  • Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing.Core task
  • Adjust machine heating mechanisms, tensions, and speeds to produce specified products.
  • Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns.
  • Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation.
  • Record information about work completed and machine settings.
  • Repair or replace worn or defective needles and other components, using hand tools.
  • 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.
  • Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements.

How that compares

Among the 98 production occupations, textile knitting and weaving machine setters rank 12. The group averages 0.8%, and its most exposed job is Computer Numerically Controlled Tool Programmers at 12.1%. The average US occupation sits at 7.7%.

All 756 occupations, ranked

Level with 3.2%

  1. 293Cutting and Slicing Machine Setters, Operators, and Tenders 3.2%
  2. 293First-Line Supervisors of Correctional Officers 3.2%
  3. 293Inspectors, Testers, Sorters, Samplers, and Weighers 3.2%

Just below

  1. 297Architectural and Engineering Managers 3.1%
  2. 297Dentists, General 3.1%
  3. 299First-Line Supervisors of Construction Trades and Extraction Workers 3.0%

What this does and does not mean

Exposure is observed use, not a forecast. Anthropic maps work conversations with Claude onto the tasks O*NET lists for each job and asks, of the tasks AI could speed up, which are actually being used for at work. A use that does the task outright counts in full and a use that assists a person counts half. The task scores are averaged into the occupation figure by the share of time spent on each task.

A high figure means AI is already part of how this work gets done. It does not mean anyone has been replaced. Anthropic's own reading of the labour market finds no systematic rise in unemployment among exposed workers so far, and about 14% fewer 22 to 25 year olds starting exposed jobs.No measurable rise in unemployment among exposed workers so far, and about 14% fewer 22 to 25 year olds starting these jobs. Gap years for everyone. Labor market impacts, 5 March 2026.

Task list and titles from O*NET 31.0 by the US Department of Labor, Employment and Training Administration, used under the CC BY 4.0 licence. Scores from the Anthropic Economic Index (CC BY). Checked 1 November 2026. How the ranking works.