Occupation · SOC 51-4031.00
Set up, operate, or tend machines to saw, cut, shear, slit, punch, crimp, notch, bend, or straighten metal or plastic material.
Also called: Machine Operator · Press Operator · Saw Operator · Setup Operator · Die Setter · Fabrication Operator · Machine Setter · Press Brake Operator · Punch Press Operator · Slitter Operator · Adjuster · Angle Shear Operator
Job family: Production Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-4031-00/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.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
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.
12th-percentile task overlap — yet about 14,400 openings a year (-12.1% 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 | 16th | -1.0 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 5th | 0.0 | |
| AI assistant applicability (Microsoft) Low | 23rd | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.0). 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.
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 0.8 · 63rd percentile among occupations · Moderate
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -12.1% by 2034 |
| Projected annual openings | 14,400 |
| Employment 2024 → 2034 | 174,700 → 153,600 |
“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 2 occupations 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 |
|---|---|---|
| Metal Working Machine Tool Setters and Operators · 7223 | 18% | Not exposed |
| Plastic Products Machine Operators · 8142 | 18% | 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.
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.
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
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 |
|---|---|---|
| Measure completed workpieces to verify conformance to specifications, using micrometers, gauges, calipers, templates, or rulers. | Directive | 0.3% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Measure completed workpieces to verify conformance to specifications, using micrometers, gauges, calipers, templates, or rulers. | 96.8% |
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 measure completed workpieces to verify conformance to specifications, using micrometers, gauges, calipers, templates, or rulers. From: Measure completed workpieces to verify conformance to specifications, using micrometers, gauges, calipers, templates, or rulers. · 0.3% of measured AI use · directive
All 32 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).
| Arm-Hand Steadiness | 3.8 | |
| Control Precision | 3.8 | |
| Near Vision | 3.8 | |
| Information Ordering | 3.3 | |
| Manual Dexterity | 3.3 | |
| Reaction Time | 3.3 | |
| Selective Attention | 3.1 | |
| Oral Comprehension | 3.0 | |
| Written Comprehension | 3.0 | |
| Oral Expression | 3.0 | |
| Problem Sensitivity | 3.0 | |
| Deductive Reasoning | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Flexibility of Closure | 3.0 | |
| Perceptual Speed | 3.0 | |
| Visualization | 3.0 | |
| Finger Dexterity | 3.0 | |
| Multilimb Coordination | 3.0 | |
| Rate Control | 3.0 | |
| Static Strength | 3.0 | |
| Trunk Strength | 3.0 | |
| Far Vision | 3.0 | |
| Depth Perception | 3.0 | |
| Speech Recognition | 3.0 | |
| Speech Clarity | 3.0 |
| Operations Monitoring | 3.5 | |
| Operation and Control | 3.4 | |
| Troubleshooting | 3.0 | |
| Quality Control Analysis | 3.0 | |
| Judgment and Decision Making | 2.9 | |
| Coordination | 2.8 |
| Production and Processing | 3.3 | |
| Mechanical | 3.0 | |
| English Language | 3.0 |
| Active Listening | 3.0 | |
| Speaking | 3.0 | |
| Critical Thinking | 3.0 | |
| Monitoring | 3.0 | |
| Reading Comprehension | 2.8 | |
| Active Learning | 2.8 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
| 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 Word | Word processing software | Hot technology |
| SAP software | Enterprise resource planning ERP software | Hot technology |
| Automated inventory software | Inventory management software | |
| Computerized numerical control CNC software | Industrial control software | |
| Operational databases | Data base user interface and query software | |
| Striker Systems SS-Punch | Computer aided manufacturing CAM software |
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.
What to study: Precision Production . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| High School Diploma | 63.4% | |
| Post-Secondary Certificate | 34.9% | |
| Less than a High School Diploma | 0.9% | |
| Some College Courses | 0.9% |
The interests and personal qualities O*NET associates with people who do this work.
| Realistic | 6.9 | |
| Conventional | 4.3 | |
| Investigative | 2.2 |
| Attention to Detail | 2.4 | |
| Dependability | 2.3 | |
| Cautiousness | 2.0 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $35,000 |
| 25th percentile | $38,400 |
| Median (50th) | $45,590 |
| 75th percentile | $52,150 |
| 90th percentile | $62,650 |
| People employed | 174,430 |
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 | 154,400 | $45,810 |
| Wholesale Trade · Sector | 11,340 | $46,420 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 6,590 | $36,060 |
| Temporary Help Services · National industry | 5,730 | $36,240 |
| Machine Shops · National industry | 3,190 | $46,270 |
| Construction · Sector | 800 | $47,520 |
| Jewelry and Silverware Manufacturing · National industry | 380 | $39,780 |
| Professional, Scientific, and Technical Services · Sector | 330 | $46,410 |
| Management of Companies and Enterprises · Sector | 320 | $39,200 |
| Plumbing, Heating, and Air-Conditioning Contractors · National industry | 190 | $82,530 |
| Transportation and Warehousing · Sector | 190 | $42,730 |
| Information · Sector | 190 | $41,170 |
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 |
|---|---|---|
| Jewelry and Silverware Manufacturing · National industry | 16.86× | 380 |
| Machine Shops · National industry | 10.86× | 3,190 |
| Manufacturing · Sector | 10.69× | 154,400 |
| Temporary Help Services · National industry | 1.91× | 5,730 |
| Newspaper Publishers · National industry | 1.85× | 190 |
| Wholesale Trade · Sector | 1.66× | 11,340 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.64× | 6,590 |
| Plumbing, Heating, and Air-Conditioning Contractors · National industry | 0.13× | 190 |
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, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic — 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 26th percentile of 427 international occupations.
Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic show 12th-percentile AI task overlap — and about 14,400 annual U.S. openings
Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic show 12th-percentile AI task overlap — and about 14,400 annual U.S. openings • Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic rank in the 12th 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 14,400 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 (-12.1%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $45,590, across about 174,430 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic". https://singulariki.com/roles/role-51-4031-00 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, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic." 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-51-4031-00
Singulariki. (2026). Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-4031-00
@misc{singulariki-role-51-4031-00,
title = {Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic},
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-51-4031-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.