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AI exposure Production

Will AI replace textile cutting machine setters?

Textile Cutting Machine Setters, Operators, and Tenders. US occupation 51-6062.

1.5%

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

Ranked 332 of 756 US occupations. More exposed than 56% of them.Further along than 56% 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 cutting machine settersWhat textile cutting machine setters no longer do alone

O*NET lists 18 tasks for textile cutting 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

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

  • Adjust machine controls, such as heating mechanisms, tensions, or speeds, to produce specified products.Core task
  • Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oilcans, and grease guns.Core task
  • Confer with coworkers to obtain information about orders, processes, or problems.Core task
  • Inspect machinery to determine whether repairs are needed.Core task
  • Inspect products to ensure that the quality standards and specifications are met.Core task
  • Notify supervisors of mechanical malfunctions.Core task
Show the other 11
  • 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.Core task
  • Record information about work completed and machine settings.Core task
  • Repair or replace worn or defective parts or components, using hand tools.Core task
  • Start machines, monitor operations, and make adjustments as needed.Core task
  • Adjust cutting techniques to types of fabrics and styles of garments.
  • Install, level, and align components, such as gears, chains, guides, dies, cutters, or needles, to set up machinery for operation.
  • Operate machines for test runs to verify adjustments and to obtain product samples.
  • Operate machines to cut multiple layers of fabric into parts for articles such as canvas goods, house furnishings, garments, hats, or stuffed toys.
  • Stop machines when specified amounts of product have been produced.
  • Study guides, samples, charts, and specification sheets or confer with supervisors or engineering staff to determine set-up requirements.
  • Thread yarn, thread, or fabric through guides, needles, and rollers of machines.

How that compares

Among the 98 production occupations, textile cutting machine setters rank 17. 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

Just above in the ranking

  1. 328Cargo and Freight Agents 1.7%
  2. 328Physical Therapists 1.7%
  3. 331Roofers 1.6%

Just below

  1. 333Industrial Production Managers 1.3%
  2. 334Childcare Workers 1.2%
  3. 334Cooks, Restaurant 1.2%

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.