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Cutting and Slicing Machine Setters, Operators, and Tenders

Occupation · SOC 51-9032.00

Set up, operate, or tend machines that cut or slice materials, such as glass, stone, cork, rubber, tobacco, food, paper, or insulating material.

Also called: Cutter · Cutter Operator · Machine Operator · Paper Cutter · Cutting Pressman · Die Cutter Operator · Flat Cutter · Sheeter · Skiver Operator · Slitter · Abrasive Sawyer · Almond Cutting Machine Tender

Job family: Production Occupations

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A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-51-9032-00/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.

15th-percentile task overlap — yet about 5,300 openings a year (-2.3% projected, BLS) . 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 15th -1.1
LLM task exposure, γ (OpenAI / Eloundou) Low 13th 0.1
AI assistant applicability (Microsoft) Low 25th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). 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 · 72nd 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.

Review work orders, blueprints, specifications, or job samples to determine components, settings, and adjustments for cutting and slicing machines. 0.5%

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 · -2.3% by 2034
Projected annual openings 5,300
Employment 2024 → 2034 49,000 → 47,900

“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.

23% mean task exposure (2025)
41st percentile of 427 placed occupations
+1 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Cement, Stone and Other Mineral Products Machine Operators · 8114 23% 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.

Tasks

All 28 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Abilities

Finger Dexterity 3.9
Control Precision 3.9
Near Vision 3.9
Arm-Hand Steadiness 3.8
Problem Sensitivity 3.6
Manual Dexterity 3.6
Oral Comprehension 3.4
Visualization 3.4
Multilimb Coordination 3.4
Reaction Time 3.4
Deductive Reasoning 3.3
Information Ordering 3.3
Selective Attention 3.3
Rate Control 3.3
Written Comprehension 3.1
Category Flexibility 3.1
Perceptual Speed 3.1
Static Strength 3.1
Trunk Strength 3.1
Extent Flexibility 3.1
Depth Perception 3.1
Oral Expression 3.0
Inductive Reasoning 3.0
Response Orientation 3.0
Written Expression 2.9
Flexibility of Closure 2.9

Transferable skills

Operations Monitoring 3.8
Operation and Control 3.6
Quality Control Analysis 3.5
Coordination 3.0
Equipment Maintenance 3.0
Troubleshooting 3.0
Complex Problem Solving 2.9

Knowledge

Production and Processing 3.5
Mathematics 3.2
Mechanical 3.1

Essential skills

Monitoring 3.3
Reading Comprehension 3.1
Critical Thinking 3.0
Active Listening 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
Microsoft Excel Spreadsheet software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft Word Word processing software Hot technology
SAP software Enterprise resource planning ERP 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.

Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 4.8
Spend Time Standing 4.8
Face-to-Face Discussions with Individuals and Within Teams 4.5
Importance of Being Exact or Accurate 4.5
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.4
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.3
Work With or Contribute to a Work Group or Team 4.2
Exposed to Hazardous Equipment 4.1
Spend Time Making Repetitive Motions 4.1
Health and Safety of Other Workers 4.0
Impact of Decisions on Co-workers or Company Results 4.0
Pace Determined by Speed of Equipment 4.0
Time Pressure 3.9
Importance of Repeating Same Tasks 3.8
Spend Time Walking or Running 3.8
Exposed to Contaminants 3.8
Contact With Others 3.8
Frequency of Decision Making 3.7
Coordinate or Lead Others in Accomplishing Work Activities 3.7
Work Outcomes and Results of Other Workers 3.7
Determine Tasks, Priorities and Goals 3.6
Freedom to Make Decisions 3.6
Spend Time Bending or Twisting Your Body 3.5
Indoors, Environmentally Controlled 3.3
Physical Proximity 3.3
Consequence of Error 3.2
Indoors, Not Environmentally Controlled 3.2
Exposed to Very Hot or Cold Temperatures 3.0
Degree of Automation 2.9
Telephone Conversations 2.8
Level of Competition 2.6
E-Mail 2.6
Dealing With Unpleasant, Angry, or Discourteous People 2.6
Conflict Situations 2.6
Exposed to Minor Burns, Cuts, Bites, or Stings 2.5
Exposed to Cramped Work Space, Awkward Positions 2.4
Spend Time Kneeling, Crouching, Stooping, or Crawling 2.3
In an Open Vehicle or Operating Equipment 2.0
Spend Time Keeping or Regaining Balance 2.0
Exposed to Hazardous Conditions 1.9

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 55.4%
Less than a High School Diploma 38.1%
Post-Secondary Certificate 4.9%
Some College Courses 1.3%
Post-Baccalaureate Certificate 0.3%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 6.7
Conventional 4.5
Investigative 1.6
Enterprising 1.2

Interest areas

Physical/Manual Labor 4.5
Mechanics/Electronics 2.6
Construction/Woodwork 2.2
Transportation/Machine Operation 2.0
Engineering 2.0
Culinary Art 1.6
Mathematics/Statistics 1.5
Accounting 1.2
Agriculture 1.2

Work styles

Dependability 3.0
Attention to Detail 2.3
Cautiousness 2.1

Wages & employment

U.S. · annual wages (BLS OEWS)

$35k10th$39k25th$46kMedian$52k75th$60k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
49k202448k2034 (proj.)-2.3% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $34,890
25th percentile $38,540
Median (50th) $45,700
75th percentile $52,000
90th percentile $60,430
People employed 47,540

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 39,910 $46,150
Wholesale Trade · Sector 3,070 $43,730
Administrative and Support and Waste Management and Remediation Services · Sector 1,390 $38,460
Construction · Sector 1,230 $48,880
Retail Trade · Sector 860 $42,100
Temporary Help Services · National industry 660 $37,070
Mining, Quarrying, and Oil and Gas Extraction · Sector 360 $38,650
Transportation and Warehousing · Sector 240 $47,200
Machine Shops · National industry 150 $50,640
Information · Sector 70 $35,670
Health Care and Social Assistance · Sector 70 $33,640
Professional, Scientific, and Technical Services · Sector 50 $45,760

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 10.14× 39,910
Mining, Quarrying, and Oil and Gas Extraction · Sector 2.04× 360
Machine Shops · National industry 1.87× 150
Wholesale Trade · Sector 1.65× 3,070
Temporary Help Services · National industry 0.81× 660
Administrative and Support and Waste Management and Remediation Services · Sector 0.5× 1,390
Construction · Sector 0.49× 1,230
Retail Trade · Sector 0.18× 860

Part of the Advanced Manufacturing career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Cutting and Slicing Machine Setters, Operators, and Tenders sits at the 15th percentile of AI task-overlap and the 22nd percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Cutting and Slicing Machine Setters, Operators, and Tenders Cutters and Trimmers, Hand Grinding and Polishing Workers, Hand Paper Goods Machine Setters, Operators, and Tenders Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

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 Cutting and Slicing Machine Setters, Operators, and Tenders — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Write a report on thisheadline · factoids · citation

Cutting and Slicing Machine Setters, Operators, and Tenders show 15th-percentile AI task overlap — and about 5,300 annual U.S. openings

  • Cutting and Slicing Machine Setters, Operators, and Tenders rank in the 15th 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 5,300 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 (-2.3%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $45,700, across about 47,540 U.S. workers.BLS OEWS (May 2024)
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Cutting and Slicing Machine Setters, Operators, and Tenders show 15th-percentile AI task overlap — and about 5,300 annual U.S. openings

• Cutting and Slicing Machine Setters, Operators, and Tenders rank in the 15th 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 5,300 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 (-2.3%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $45,700, across about 47,540 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Cutting and Slicing Machine Setters, Operators, and Tenders". https://singulariki.com/roles/role-51-9032-00
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.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Cutting and Slicing 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-51-9032-00

APA

Singulariki. (2026). Cutting and Slicing Machine Setters, Operators, and Tenders. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-9032-00

BibTeX
@misc{singulariki-role-51-9032-00,
  title  = {Cutting and Slicing 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-51-9032-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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