Will AI replace Textile Cutting Machine Setters, Operators, and Tenders?
Estimated risk score: 23 out of 100
Very low replacement risk
Estimate from published task research, not reviewed by a person · how we estimate
Within about 19 points for 4 in 5 of the jobs we checked (4 to 42).
For the occupation as a whole, from ratings of its 18 O*NET tasks published in 2023 and calibrated to our hand scores. A specialty inside it can sit higher or lower.
O*NET-SOC 51-6062.00 · Production
Tasks rated in 2023 as ones AI could do or speed up
Tasks a GPT-4-class model could do, or do in half the time or less: 2 of its 18 rated tasks. In O*NET’s words.
- Record information about work completed and machine settings.
- Program electronic equipment.
Tasks rated in 2023 as not sped up by AI
Tasks such a model was rated as not speeding up or taking over: 14 of its 18 rated tasks; the first 6 are shown. In O*NET’s words.
- Adjust machine controls, such as heating mechanisms, tensions, or speeds, to produce specified products.
- Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oilcans, and grease guns.
- 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.
- Repair or replace worn or defective parts or components, using hand tools.
- Start machines, monitor operations, and make adjustments as needed.
- Confer with coworkers to obtain information about orders, processes, or problems.
What the job is
Set up, operate, or tend machines that cut textiles.
Also called: Silker, Cropper, Hat Cutter, Rib Cutter, Tab Cutter, Cuff Cutter, Felt Cutter, Lace Cutter.
Related jobs, estimated
- Textile Bleaching and Dyeing Machine Operators and Tendersestimate ±1923Very low
- Textile Knitting and Weaving Machine Setters, Operators, and Tendersestimate ±1923Very low
- Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tendersestimate ±1915Very low
- Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibersestimate ±1913Very low
Source. This site includes information from the O*NET 30.1 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not approved, endorsed, or tested these modifications. (O*NET, CC BY 4.0.) Task ratings: Eloundou, Manning, Mishkin and Rock (2023), “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”, data used under the MIT licence (licence texts). Our change: we averaged the ratings over this occupation’s tasks and calibrated them to our 0–100 scale. It is not a prediction about any one person and not career advice. Disagree? Write to hello@yourstopwashit.com. How we estimate