Medium AI Risk Declining

Textile cutting machine setters, operators, and tenders

SOC Code: 51-6062

Textile cutting machine setters, operators, and tenders carries a 39% AI exposure score (Medium automation risk), with a median annual wage of $37,940 and -11.7% projected employment growth from 2024 to 2034 (BLS), affecting approximately 9,300 workers. Full task breakdown, skills, and employer data are below.

AI Exposure Score
39% Medium

Proportion of tasks susceptible to AI automation (O*NET analysis)

Projected Growth
-11.7%
2024–2034 (BLS)
-1,100 jobs
Median Annual Wage
$37,940
BLS May 2024
How wage figures are sourced →
The verdict

At 39% AI exposure, Textile cutting machine setters, operators, and tenders sits 2 points above the 37.2% average across 832 U.S. occupations - more exposed than 67% of them. Most of its core tasks still require human judgment.

39%
AI exposure
Top 33%
most exposed
-11.7%
Job growth 2024–34
$37,940
Median wage

Where Textile cutting machine setters, operators, and tenders sits among all occupations

AI-exposure score distribution across 832 U.S. occupations (O*NET task analysis). This occupation is marked in rose.

AI exposure distribution - Textile cutting machine setters, operators, and tenders marked 1 occupations at 0–10% 1 0 43 occupations at 10–20% 43 10 184 occupations at 20–30% 184 20 332 occupations at 30–40% 332 30 158 occupations at 40–50% 158 40 64 occupations at 50–60% 64 50 29 occupations at 60–70% 29 60 15 occupations at 70–80% 15 70 5 occupations at 80–90% 5 80 1 occupations at 90–100% 1 90 AI exposure (%) →
Top 33% by AI exposure Higher AI exposure than 67% of all 832 occupations.

Textile cutting machine setters, operators, and tenders has ai exposure of 39%. Distribution: 1 occupations at 0-10%; 43 occupations at 10-20%; 184 occupations at 20-30%; 332 occupations at 30-40%; 158 occupations at 40-50%; 64 occupations at 50-60%; 29 occupations at 60-70%; 15 occupations at 70-80%; 5 occupations at 80-90%; 1 occupations at 90-100%.

Source: O*NET task data (U.S. Department of Labor); AI-exposure scores are PlainWorkforce's analysis. As of BLS 2024–2034.

Textile cutting machine setters, operators, and tenders vs. its occupational neighbors

AI exposure (horizontal) vs projected 2024–2034 growth (vertical) for occupations in the same SOC group. Textile cutting machine setters, operators, and tenders is marked in rose; the top-left corner is the most future-proof.

Future-proofGrowing but exposedStable, lower-AIMost at risk28.1%33.0%38%43.0%47.9%-12.6%-7.8%-3.0%1.7%6.5%AI exposure (%) →Projected growth (%) →Textile cutting machMiscellaneous assembFirst-line supervisoInspectorsWeldersPackagingMachinistsProduction workersElectricalBakersLaundryComputer numerically

Source: BLS Employment Projections 2024–2034 (growth) and O*NET (AI-exposure analysis by PlainWorkforce).

Employment Projections

9,300
Employment 2024
8,200
Projected 2034
-11.7%
Change (%)
-1,100
Change (jobs)

Occupation Insight

Textile cutting machine setters, operators, and tenders (SOC 51-6062) carries an AI exposure score of 39%, placing it in the Medium automation-risk tier. This score is computed from O*NET Database 30.0 task-level analysis, where each task an occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score below 40% reflects tasks anchored in physical dexterity, unstructured environments, or high-touch human interaction that current AI cannot reliably replicate.

The economic context matters alongside the risk score. BLS counted approximately 9,300 workers in this occupation in 2024, and projects a -11.7% change through 2034 - a decline that often compounds with high AI exposure to create displacement headwinds. Median annual compensation stands at $37,940, reflecting both skill scarcity and the value employers place on the tasks that remain difficult to automate. Entry typically requires High school diploma or equivalent, plus None of related experience.

For career planners, this profile should be read alongside the task, skill, and knowledge breakdowns below and the list of employers whose workforce composition includes Textile cutting machine setters, operators, and tenders. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure. Pair the AI exposure score with the BLS employment projection and wage percentiles above for a complete career assessment.

Education & Entry Requirements

Typical Education
High school diploma or equivalent
Work Experience
None
On-the-Job Training
Moderate-term on-the-job training

Top Tasks (O*NET)

  1. 1. Thread yarn, thread, or fabric through guides, needles, and rollers of machines.
  2. 2. Operate machines to cut multiple layers of fabric into parts for articles such as canvas goods, house furnishings, garments, hats, or stuffed toys.
  3. 3. Inspect products to ensure that the quality standards and specifications are met.
  4. 4. Adjust cutting techniques to types of fabrics and styles of garments.
  5. 5. 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.
  6. 6. Program electronic equipment.
  7. 7. Study guides, samples, charts, and specification sheets or confer with supervisors or engineering staff to determine set-up requirements.
  8. 8. Start machines, monitor operations, and make adjustments as needed.
  9. 9. Stop machines when specified amounts of product have been produced.
  10. 10. Adjust machine controls, such as heating mechanisms, tensions, or speeds, to produce specified products.

Key Skills Required

  • Operations Monitoring
  • Operation and Control
  • Monitoring
  • Quality Control Analysis
  • Active Listening
  • Speaking
  • Coordination
  • Equipment Maintenance
  • Troubleshooting
  • Repairing

Knowledge Areas

  • Production and Processing
  • Mathematics
  • Mechanical
  • English Language
  • Design
  • Education and Training
  • Administration and Management
  • Computers and Electronics
  • Public Safety and Security
  • Engineering and Technology

Frequently Asked Questions

Will AI replace Textile cutting machine setters, operators, and tenders?

Textile cutting machine setters, operators, and tenders has an AI exposure score of 39%, indicating a medium level of automation risk. The majority of tasks in this role require human judgment, creativity, or physical presence that AI cannot easily replicate.

What is the job outlook for Textile cutting machine setters, operators, and tenders?

According to BLS Employment Projections 2024-2034, Textile cutting machine setters, operators, and tenders is projected to decline by 11.7% over the decade. Current employment stands at approximately 9,300 workers.

What skills are needed for Textile cutting machine setters, operators, and tenders?

Key skills for Textile cutting machine setters, operators, and tenders include Operations Monitoring, Operation and Control, Monitoring, and others. Typical entry-level education is High school diploma or equivalent.

How much do Textile cutting machine setters, operators, and tenders earn?

The median annual wage for Textile cutting machine setters, operators, and tenders is $37,940, according to BLS Occupational Employment and Wage Statistics (May 2024). Actual earnings vary by location, experience, industry, and employer. The BLS publishes detailed wage percentiles by region in its Occupational Employment and Wage Statistics program.

What education is required for Textile cutting machine setters, operators, and tenders?

The typical entry-level education for Textile cutting machine setters, operators, and tenders is High school diploma or equivalent. Employers generally expect None of related work experience. On-the-job training typically involves Moderate-term on-the-job training. Requirements can vary by employer and specialization.

Which companies employ Textile cutting machine setters, operators, and tenders?

Textile cutting machine setters, operators, and tenders roles exist across many industries and employers. Workforce composition is estimated from BLS industry-occupation employment distributions matched to SEC-registered public companies.

AI Exposure Rating

1.9
out of 5.0

Medium automation risk based on 10 analyzed tasks. Most tasks require human judgment and are resistant to automation.

Data sources: Bureau of Labor Statistics Employment Projections 2024–2034 and O*NET Database 30.0. Employment figures are rounded. Wage data from BLS Occupational Employment Statistics (OES).

Data sourced from official public datasets. See our methodology for details. Retrieved and formatted by PlainWorkforce Editorial

Every figure on PlainWorkforce is rendered directly from BLS/O*NET source data, no number is typed in by an editor. See our editorial standards & corrections policy, the methodology behind these figures, or report a data error. Data current as of BLS Employment Projections 2024-2034.