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Sawing Machine Setters, Operators, and Tenders, Wood

Occupation · SOC 51-7041.00

Set up, operate, or tend wood sawing machines. May operate computer numerically controlled (CNC) equipment. Includes lead sawyers.

Also called: Resaw Operator · Rip Saw Operator · Saw Operator · Sawyer · Bandmill Operator · Cut Off Saw Operator · Edgerman · Knot Saw Operator · Panel Saw Operator · Planer · Automatic Bandsaw Tender · Automatic Edger

Job family: Production Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-51-7041-00/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.

17th-percentile task overlap — yet about 4,800 openings a year (-0.6% 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 12th -1.2
LLM task exposure, γ (OpenAI / Eloundou) Low 16th 0.1
AI assistant applicability (Microsoft) Low 33rd 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

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 · -0.6% by 2034
Projected annual openings 4,800
Employment 2024 → 2034 45,000 → 44,700

“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.

14% mean task exposure (2025)
16th percentile of 427 placed occupations
−4 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Wood Processing Plant Operators · 8172 14% 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 24 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

Control Precision 3.6
Arm-Hand Steadiness 3.4
Manual Dexterity 3.4
Near Vision 3.4
Problem Sensitivity 3.3
Finger Dexterity 3.3
Rate Control 3.3
Reaction Time 3.3
Selective Attention 3.1
Multilimb Coordination 3.1
Trunk Strength 3.1
Stamina 3.1
Deductive Reasoning 3.0
Inductive Reasoning 3.0
Static Strength 3.0
Far Vision 3.0
Depth Perception 3.0
Oral Comprehension 2.9
Oral Expression 2.9
Category Flexibility 2.9
Perceptual Speed 2.9
Visualization 2.9
Auditory Attention 2.9
Speech Clarity 2.9
Information Ordering 2.8
Flexibility of Closure 2.8
Visual Color Discrimination 2.8

Knowledge

Production and Processing 3.2
Mechanical 3.2
Public Safety and Security 2.6

Transferable skills

Operations Monitoring 3.1
Operation and Control 3.1
Troubleshooting 3.0
Quality Control Analysis 3.0
Equipment Maintenance 2.8
Judgment and Decision Making 2.8
Time Management 2.8

Essential skills

Critical Thinking 3.0
Monitoring 3.0
Active Listening 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
Adobe Acrobat Document management software Hot technology
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Word Word processing software Hot technology
Automated inventory software Inventory management software
Computerized numerical control CNC software Industrial control software

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 5.0
Exposed to Hazardous Equipment 4.9
Exposed to Contaminants 4.8
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.7
Importance of Being Exact or Accurate 4.6
Indoors, Not Environmentally Controlled 4.5
Spend Time Standing 4.5
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.4
Face-to-Face Discussions with Individuals and Within Teams 4.3
Freedom to Make Decisions 4.2
Pace Determined by Speed of Equipment 4.2
Time Pressure 4.2
Determine Tasks, Priorities and Goals 4.0
Contact With Others 3.9
Spend Time Making Repetitive Motions 3.9
Spend Time Bending or Twisting Your Body 3.8
Work With or Contribute to a Work Group or Team 3.7
Exposed to Very Hot or Cold Temperatures 3.7
Health and Safety of Other Workers 3.6
Work Outcomes and Results of Other Workers 3.5
Impact of Decisions on Co-workers or Company Results 3.4
Physical Proximity 3.3
Frequency of Decision Making 3.2
Coordinate or Lead Others in Accomplishing Work Activities 3.2
Spend Time Walking or Running 3.1
Exposed to Minor Burns, Cuts, Bites, or Stings 2.9
Importance of Repeating Same Tasks 2.9
Level of Competition 2.8
Consequence of Error 2.6
Exposed to Cramped Work Space, Awkward Positions 2.5
Degree of Automation 2.5
Spend Time Kneeling, Crouching, Stooping, or Crawling 2.3
Exposed to Extremely Bright or Inadequate Lighting Conditions 2.2
Dealing With Unpleasant, Angry, or Discourteous People 2.2
Telephone Conversations 2.2
Outdoors, Exposed to All Weather Conditions 2.0
Conflict Situations 2.0
Exposed to Hazardous Conditions 2.0
Indoors, Environmentally Controlled 2.0
Exposed to High Places 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.

What to study: Precision Production . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 59.3%
Less than a High School Diploma 21.8%
Post-Secondary Certificate 9.5%
Some College Courses 9.5%

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 3.9
Investigative 2.4
Social 1.2

Interest areas

Construction/Woodwork 6.2
Physical/Manual Labor 4.8
Mechanics/Electronics 3.4
Transportation/Machine Operation 2.0
Engineering 1.9
Information Technology 1.3
Accounting 1.2
Mathematics/Statistics 1.1

Work styles

Attention to Detail 2.3
Dependability 2.1
Cautiousness 2.0
Stress Tolerance 1.2

Wages & employment

U.S. · annual wages (BLS OEWS)

$30k10th$36k25th$40kMedian$48k75th$57k90th
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.
45k202445k2034 (proj.)-0.6% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $29,670
25th percentile $35,550
Median (50th) $39,950
75th percentile $47,770
90th percentile $56,560
People employed 43,140

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 36,850 $39,780
Wholesale Trade · Sector 3,370 $42,340
Retail Trade · Sector 1,090 $42,380
Administrative and Support and Waste Management and Remediation Services · Sector 810 $34,610
Temporary Help Services · National industry 620 $36,090
Agriculture, Forestry, Fishing and Hunting · Sector 460 $46,500
Construction · Sector 200 $53,330
Transportation and Warehousing · Sector 150 $40,040
Other Services (except Public Administration) · Sector 70 $44,210
Professional, Scientific, and Technical Services · Sector 30 $48,620

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 10.32× 36,850
Agriculture, Forestry, Fishing and Hunting · Sector 3.88× 460
Wholesale Trade · Sector 3,370
Temporary Help Services · National industry 0.84× 620
Administrative and Support and Waste Management and Remediation Services · Sector 0.32× 810
Retail Trade · Sector 0.25× 1,090
Construction · Sector 0.09× 200
Transportation and Warehousing · Sector 0.07× 150

Part of the Advanced Manufacturing career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Sawing Machine Setters, Operators, and Tenders, Wood sits at the 17th percentile of AI task-overlap and the 13th 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 Sawing Machine Setters, Operators, and Tenders, Wood Grinding and Polishing Workers, Hand Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic Tool Grinders, Filers, and Sharpeners Textile Cutting Machine Setters, Operators, and Tenders Tool and Die Makers 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 Sawing Machine Setters, Operators, and Tenders, Wood — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Write a report on thisheadline · factoids · citation

Sawing Machine Setters, Operators, and Tenders, Wood show 17th-percentile AI task overlap — and about 4,800 annual U.S. openings

  • Sawing Machine Setters, Operators, and Tenders, Wood 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 4,800 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 (-0.6%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $39,950, across about 43,140 U.S. workers.BLS OEWS (May 2024)
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Sawing Machine Setters, Operators, and Tenders, Wood show 17th-percentile AI task overlap — and about 4,800 annual U.S. openings

• Sawing Machine Setters, Operators, and Tenders, Wood 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 4,800 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 (-0.6%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $39,950, across about 43,140 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Sawing Machine Setters, Operators, and Tenders, Wood". https://singulariki.com/roles/role-51-7041-00
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. "Sawing Machine Setters, Operators, and Tenders, Wood." 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; 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-51-7041-00

APA

Singulariki. (2026). Sawing Machine Setters, Operators, and Tenders, Wood. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-7041-00

BibTeX
@misc{singulariki-role-51-7041-00,
  title  = {Sawing Machine Setters, Operators, and Tenders, Wood},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; 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-51-7041-00}
}

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

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