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 Knitting and Weaving Machine Setters, Operators, and Tenders
Occupation · SOC 51-6063.00
Set up, operate, or tend machines that knit, loop, weave, or draw in textiles.
Also called: Knitter · Machine Operator · Tufting Operator · Weaver · Knitting Machine Operator · Loom Fixer · Operator · Tufting Machine Operator · Warp Knit Operator · Winder Operator · Automated Weaver · Automatic Full-Fashioned Hosiery Knitting 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-51-6063-00/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 (-11.2% by 2034)
- 1,700 openings/yr
- Low AI exposure
- Observed AI use leans automation
- Median pay $38,260/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (60th); today's AI task-overlap is comparatively low (17th)
17th-percentile task overlap — yet about 1,700 openings a year (-11.2% 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 | 9th | -1.2 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 24th | 0.2 | |
| AI assistant applicability (Microsoft) Low | 27th | 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.
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 0.7 · 60th percentile among occupations · Moderate
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 · -11.2% by 2034 |
| Projected annual openings | 1,700 |
| Employment 2024 → 2034 | 15,300 → 13,600 |
“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 Knitting and Weaving Machine Setters, Operators, and Tenders sits at the 20th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose 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)
20th percentile of 427 placed occupations
+2 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Weaving and Knitting Machine Operators · 8152 | 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 20 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Observe woven cloth to detect weaving defects. Core · importance 4.7
- Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing. Core · importance 4.6
- Remove defects in cloth by cutting and pulling out filling. Core · importance 4.5
- Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects. Core · importance 4.5
- Inspect products to ensure that specifications are met and to determine if machines need adjustment. Core · importance 4.5
- Notify supervisors or repair staff of mechanical malfunctions. Core · importance 4.5
- Program electronic equipment. Supplemental · importance 4.4
- 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. Core · importance 4.4
- Start machines, monitor operations, and make adjustments as needed. Core · importance 4.3
- Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation. Supplemental · importance 4.3
- Record information about work completed and machine settings. Core · importance 4.3
- Stop machines when specified amounts of product have been produced. Core · importance 4.3
- Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements. Supplemental · importance 4.2
- Inspect machinery to determine whether repairs are needed. Core · importance 4.2
- Repair or replace worn or defective needles and other components, using hand tools. Supplemental · importance 4.1
- Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns. Supplemental · importance 4.0
- Confer with co-workers to obtain information about orders, processes, or problems. Core · importance 4.0
- Operate machines for test runs to verify adjustments and to obtain product samples. Supplemental · importance 3.8
- Adjust machine heating mechanisms, tensions, and speeds to produce specified products. Supplemental · importance 3.8
- Wash and blend wool, yarn, or cloth. Supplemental
Work activities
- Cut fabrics.
- Inspect textile products.
- Feed materials or products into or through equipment.
- Inspect production equipment.
- Notify others of equipment repair or maintenance needs.
- Operate textile cutting or production equipment.
- Record operational or production data.
- Exchange information with colleagues.
- Clean production equipment.
- Lubricate production equipment.
- Conduct test runs of production equipment.
- Program equipment to perform production tasks.
- Study blueprints or other instructions to determine equipment setup requirements.
- Replace worn equipment components.
- Repair production equipment or tools.
- Mount attachments or tools onto production equipment.
- Set equipment controls to meet cutting specifications.
- Install mechanical components in production equipment.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Abilities
| Arm-Hand Steadiness | 3.9 | |
| Finger Dexterity | 3.6 | |
| Control Precision | 3.6 | |
| Multilimb Coordination | 3.5 | |
| Manual Dexterity | 3.3 | |
| Trunk Strength | 3.3 | |
| Oral Comprehension | 3.1 | |
| Problem Sensitivity | 3.1 | |
| Selective Attention | 3.1 | |
| Near Vision | 3.1 | |
| Oral Expression | 3.0 | |
| Visualization | 3.0 | |
| Reaction Time | 3.0 | |
| Auditory Attention | 3.0 | |
| Deductive Reasoning | 2.9 | |
| Information Ordering | 2.9 | |
| Perceptual Speed | 2.9 | |
| Extent Flexibility | 2.9 | |
| Visual Color Discrimination | 2.9 | |
| Speech Recognition | 2.9 | |
| Speech Clarity | 2.9 | |
| Written Comprehension | 2.8 | |
| Inductive Reasoning | 2.8 | |
| Category Flexibility | 2.8 | |
| Flexibility of Closure | 2.8 | |
| Stamina | 2.8 |
Knowledge
| Production and Processing | 3.4 | |
| English Language | 3.2 | |
| Mechanical | 3.0 | |
| Public Safety and Security | 2.6 | |
| Education and Training | 2.6 |
Transferable skills
| Operations Monitoring | 3.4 | |
| Operation and Control | 3.0 | |
| Quality Control Analysis | 2.9 | |
| Time Management | 2.6 |
Essential skills
| Monitoring | 3.1 | |
| Active Listening | 3.0 | |
| Speaking | 2.9 | |
| Critical Thinking | 2.9 | |
| Reading Comprehension | 2.8 |
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 | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
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 | 94.7% | |
| Less than a High School Diploma | 5.3% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 7.0 | |
| Conventional | 4.3 | |
| Investigative | 1.8 | |
| Artistic | 1.6 | |
| Enterprising | 1.4 |
Interest areas
Work styles
| Attention to Detail | 2.1 | |
| Dependability | 2.1 | |
| Cautiousness | 2.0 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $29,640 |
| 25th percentile | $34,530 |
| Median (50th) | $38,260 |
| 75th percentile | $44,180 |
| 90th percentile | $48,070 |
| People employed | 14,530 |
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 | 13,880 | $38,280 |
| Wholesale Trade · Sector | 340 | $35,260 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 260 | $35,430 |
| Temporary Help Services · National industry | 150 | $36,310 |
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 | 11.54× | 13,880 |
| Wholesale Trade · Sector | 0.6× | 340 |
| Temporary Help Services · National industry | 0.6× | 150 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.31× | 260 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders · 51-6064.00 Compare →
- Sewing Machine Operators · 51-6031.00 Compare →
- Textile Cutting Machine Setters, Operators, and Tenders · 51-6062.00 Compare →
- Paper Goods Machine Setters, Operators, and Tenders · 51-9196.00 Compare →
- Textile Bleaching and Dyeing Machine Operators and Tenders · 51-6061.00 Compare →
- Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers · 51-6091.00 Compare →
- Shoe Machine Operators and Tenders · 51-6042.00 Compare →
- Cutting and Slicing Machine Setters, Operators, and Tenders · 51-9032.00 Compare →
- Adhesive Bonding Machine Operators and Tenders · 51-9191.00 Compare →
- Print Binding and Finishing Workers · 51-5113.00 Compare →
- Sewers, Hand · 51-6051.00 Compare →
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing · 51-7042.00 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 Knitting and Weaving 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.
- Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders Compare →
- Sewing Machine Operators Compare →
- Textile Cutting Machine Setters, Operators, and Tenders Compare →
- Paper Goods Machine Setters, Operators, and Tenders Compare →
- Textile Bleaching and Dyeing Machine Operators and Tenders 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 20th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Textile Knitting and Weaving Machine Setters, Operators, and Tenders show 17th-percentile AI task overlap — and about 1,700 annual U.S. openings
- Textile Knitting and Weaving Machine Setters, Operators, and Tenders rank in the 17th 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 1,700 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 (-11.2%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $38,260, across about 14,530 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 Knitting and Weaving Machine Setters, Operators, and Tenders show 17th-percentile AI task overlap — and about 1,700 annual U.S. openings • Textile Knitting and Weaving Machine Setters, Operators, and Tenders rank in the 17th 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 1,700 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 (-11.2%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $38,260, across about 14,530 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 Knitting and Weaving Machine Setters, Operators, and Tenders". https://singulariki.com/roles/role-51-6063-00 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 Knitting and Weaving 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-51-6063-00
APA
Singulariki. (2026). Textile Knitting and Weaving Machine Setters, Operators, and Tenders. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-6063-00
BibTeX
@misc{singulariki-role-51-6063-00,
title = {Textile Knitting and Weaving 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-51-6063-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.