Often handed to AI
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
- Program electronic equipment. · 4.7%
Textile Cutting Machine Setters, Operators, and Tenders
Occupation · SOC 51-6062.00
Set up, operate, or tend machines that cut textiles.
Also called: Cutter · Cutter Operator · Fabric Cutter · Spreader · Automated Cutting Machine Operator · CNC Cutting Operator (Computer Numerical Control Cutting Operator) · Die Cut Operator · Laser Operator · Spread Cutter · Textile Slitting Machine Operator · Automatic Die Cutting Machine Operator · Automatic Folding Machine Operator
Job family: Production Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-6062-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Keep a human in the loop
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
- Program electronic equipment. · 70.6% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- Declining employment outlook (-11.7% by 2034)
- 1,000 openings/yr
- Moderate AI exposure
- Observed AI use leans automation
- Median pay $37,940/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (89th); today's AI task-overlap is comparatively low (24th)
24th-percentile task overlap — yet about 1,000 openings a year (-11.7% projected, BLS), and observed AI use leans 2724% copilot, not hand-off (AEI) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Low | 17th | -1.0 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 26th | 0.2 | |
| AI assistant applicability (Microsoft) Moderate | 34th | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.2), and including AI-powered software (γ 0.2). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.9 · 89th percentile among occupations · High
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Program electronic equipment. | 13.7% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -11.7% by 2034 |
| Projected annual openings | 1,000 |
| Employment 2024 → 2034 | 9,300 → 8,200 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Textile Cutting Machine Setters, Operators, and Tenders sits at the 21st percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure eased from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
17% mean task exposure (2025)
21st percentile of 427 placed occupations
−11 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Garment and Related Patternmakers and Cutters · 7532 | 17% | Not exposed |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Working with AI in this job
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 27.2% working with AI · 67.9% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 50.2% |
What people delegate to AI
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Program electronic equipment. | Directive | 4.7% |
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Program electronic equipment. | 70.6% |
What people most often hand AI here
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
-
Help me program electronic equipment.From: Program electronic equipment. · 4.7% of measured AI use · directive
Tasks
All 18 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Thread yarn, thread, or fabric through guides, needles, and rollers of machines. Supplemental · importance 4.5
- Operate machines to cut multiple layers of fabric into parts for articles such as canvas goods, house furnishings, garments, hats, or stuffed toys. Supplemental · importance 4.4
- Adjust cutting techniques to types of fabrics and styles of garments. Supplemental · importance 4.4
- Inspect products to ensure that the quality standards and specifications are met. Core · importance 4.4
- 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 · importance 4.3
- Program electronic equipment. Supplemental · importance 4.2
- Study guides, samples, charts, and specification sheets or confer with supervisors or engineering staff to determine set-up requirements. Supplemental · importance 4.2
- Start machines, monitor operations, and make adjustments as needed. Core · importance 4.2
- Stop machines when specified amounts of product have been produced. Supplemental · importance 4.1
- Adjust machine controls, such as heating mechanisms, tensions, or speeds, to produce specified products. Core · importance 4.1
- Record information about work completed and machine settings. Core · importance 4.1
- Notify supervisors of mechanical malfunctions. Core · importance 4.1
- Inspect machinery to determine whether repairs are needed. Core · importance 4.0
- Operate machines for test runs to verify adjustments and to obtain product samples. Supplemental · importance 4.0
- Confer with coworkers to obtain information about orders, processes, or problems. Core · importance 4.0
- Install, level, and align components, such as gears, chains, guides, dies, cutters, or needles, to set up machinery for operation. Supplemental · importance 3.9
- Repair or replace worn or defective parts or components, using hand tools. Core · importance 3.8
- Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oilcans, and grease guns. Core · importance 3.5
Work activities
- Cut fabrics.
- Position patterns on equipment, materials, or workpieces.
- Notify others of equipment repair or maintenance needs.
- Replace worn equipment components.
- Repair production equipment or tools.
- Set equipment controls to meet cutting specifications.
- Inspect production equipment.
- Operate textile cutting or production equipment.
- Exchange information with colleagues.
- Clean production equipment.
- Lubricate production equipment.
- Record operational or production data.
- Study blueprints or other instructions to determine equipment setup requirements.
- Conduct test runs of production equipment.
- Mount attachments or tools onto production equipment.
- Install mechanical components in production equipment.
- Program equipment to perform production tasks.
- Feed materials or products into or through equipment.
- Inspect products or operations to ensure that standards are met.
- Inspect work to ensure standards are met.
- Inspect textile products.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Transferable skills
| Operations Monitoring | 3.8 | |
| Operation and Control | 3.4 | |
| Quality Control Analysis | 3.1 | |
| Coordination | 3.0 | |
| Equipment Maintenance | 3.0 | |
| Troubleshooting | 3.0 | |
| Repairing | 3.0 | |
| Judgment and Decision Making | 2.9 | |
| Time Management | 2.9 |
Abilities
| Arm-Hand Steadiness | 3.6 | |
| Information Ordering | 3.3 | |
| Manual Dexterity | 3.3 | |
| Problem Sensitivity | 3.1 | |
| Selective Attention | 3.1 | |
| Finger Dexterity | 3.1 | |
| Control Precision | 3.1 | |
| Multilimb Coordination | 3.1 | |
| Reaction Time | 3.1 | |
| Static Strength | 3.1 | |
| Trunk Strength | 3.1 | |
| Near Vision | 3.1 | |
| Auditory Attention | 3.1 | |
| Oral Comprehension | 3.0 | |
| Oral Expression | 3.0 | |
| Deductive Reasoning | 3.0 | |
| Category Flexibility | 3.0 | |
| Perceptual Speed | 3.0 | |
| Visualization | 3.0 | |
| Response Orientation | 3.0 | |
| Rate Control | 3.0 | |
| Depth Perception | 3.0 | |
| Speech Clarity | 3.0 | |
| Inductive Reasoning | 2.9 | |
| Flexibility of Closure | 2.9 | |
| Extent Flexibility | 2.9 |
Knowledge
| Production and Processing | 3.3 |
Essential skills
| Monitoring | 3.1 | |
| Active Listening | 3.0 | |
| Speaking | 3.0 | |
| Critical Thinking | 2.9 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Autodesk AutoCAD | Computer aided design CAD software | Hot technology |
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft Windows | Operating system software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| SAP software | Enterprise resource planning ERP software | Hot technology |
| HAISEN SoftWare System | Industrial control software |
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- High school diploma or equivalent · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 67.7% | |
| Less than a High School Diploma | 32.3% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 6.4 | |
| Conventional | 4.5 | |
| Enterprising | 1.6 | |
| Investigative | 1.6 | |
| Artistic | 1.5 |
Interest areas
Work styles
| Cautiousness | 2.2 | |
| Dependability | 2.1 | |
| Attention to Detail | 1.7 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $27,230 |
| 25th percentile | $32,380 |
| Median (50th) | $37,940 |
| 75th percentile | $43,710 |
| 90th percentile | $49,080 |
| People employed | 8,960 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Manufacturing · Sector | 7,420 | $38,030 |
| Wholesale Trade · Sector | 690 | $38,910 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 220 | $37,940 |
| Temporary Help Services · National industry | 190 | $37,940 |
| Retail Trade · Sector | 170 | $37,290 |
| Other Services (except Public Administration) · Sector | 160 | $28,200 |
| Transportation and Warehousing · Sector | 140 | $37,500 |
| Professional, Scientific, and Technical Services · Sector | 70 | $37,500 |
| Health Care and Social Assistance · Sector | — | $31,200 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Manufacturing · Sector | 10× | 7,420 |
| Wholesale Trade · Sector | 1.97× | 690 |
| Temporary Help Services · National industry | 1.23× | 190 |
| Other Services (except Public Administration) · Sector | 0.62× | 160 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.42× | 220 |
| Transportation and Warehousing · Sector | 0.33× | 140 |
| Retail Trade · Sector | 0.19× | 170 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Cutting and Slicing Machine Setters, Operators, and Tenders · 51-9032.00 Compare →
- Paper Goods Machine Setters, Operators, and Tenders · 51-9196.00 Compare →
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing · 51-7042.00 Compare →
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4033.00 Compare →
- Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4031.00 Compare →
- Cutters and Trimmers, Hand · 51-9031.00 Compare →
- Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic · 51-4035.00 Compare →
- Adhesive Bonding Machine Operators and Tenders · 51-9191.00 Compare →
- Machine Feeders and Offbearers · 53-7063.00 Compare →
- Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic · 51-4081.00 Compare →
- Tool Grinders, Filers, and Sharpeners · 51-4194.00 Compare →
- Sawing Machine Setters, Operators, and Tenders, Wood · 51-7041.00 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Textile Cutting Machine Setters, Operators, and Tenders — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
- Cutting and Slicing Machine Setters, Operators, and Tenders Compare →
- Paper Goods Machine Setters, Operators, and Tenders Compare →
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing Compare →
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic Compare →
- Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic Compare →
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 21st percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Textile Cutting Machine Setters, Operators, and Tenders show 24th-percentile AI task overlap — and about 1,000 annual U.S. openings
- Textile Cutting Machine Setters, Operators, and Tenders rank in the 24th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 1,000 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be declining (-11.7%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $37,940, across about 8,960 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 27% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
Textile Cutting Machine Setters, Operators, and Tenders show 24th-percentile AI task overlap — and about 1,000 annual U.S. openings • Textile Cutting Machine Setters, Operators, and Tenders rank in the 24th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 1,000 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be declining (-11.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $37,940, across about 8,960 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 27% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Textile Cutting Machine Setters, Operators, and Tenders". https://singulariki.com/roles/role-51-6062-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Textile Cutting Machine Setters, Operators, and Tenders." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-51-6062-00
APA
Singulariki. (2026). Textile Cutting Machine Setters, Operators, and Tenders. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-6062-00
BibTeX
@misc{singulariki-role-51-6062-00,
title = {Textile Cutting Machine Setters, Operators, and Tenders},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-51-6062-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.