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 →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
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-9032-00/context.md directly.
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.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
15th-percentile task overlap — yet about 5,300 openings a year (-2.3% projected, BLS) . What exposure means →
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.
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.
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
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% |
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.
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.
| 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.
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.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| 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 |
| 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 |
| Production and Processing | 3.5 | |
| Mathematics | 3.2 | |
| Mechanical | 3.1 |
| Monitoring | 3.3 | |
| Reading Comprehension | 3.1 | |
| Critical Thinking | 3.0 | |
| Active Listening | 2.9 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
| 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 |
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.
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% |
The interests and personal qualities O*NET associates with people who do this work.
| Realistic | 6.7 | |
| Conventional | 4.5 | |
| Investigative | 1.6 | |
| Enterprising | 1.2 |
| 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 |
| Dependability | 3.0 | |
| Attention to Detail | 2.3 | |
| Cautiousness | 2.1 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $34,890 |
| 25th percentile | $38,540 |
| Median (50th) | $45,700 |
| 75th percentile | $52,000 |
| 90th percentile | $60,430 |
| People employed | 47,540 |
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 |
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.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
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.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 41st percentile of 427 international occupations.
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 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.
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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.
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.
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
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
@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.