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Tool Grinders, Filers, and Sharpeners

Occupation · SOC 51-4194.00

Perform precision smoothing, sharpening, polishing, or grinding of metal objects.

Also called: Grinder · Grinder Operator · Saw Filer · Tool Grinder · Cutter Grinder · Finisher · OD Grinder Operator (Outer Diameter Grinder Operator) · Tool and Cutter Grinder · Card Grinder · Computer Numerical Control Grinding Technician (CNC Grinding Technician) · Crankshaft Grinder · Cutter Grind Tool Technician

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-51-4194-00/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 500 openings a year (-7.8% 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 22nd -0.9
LLM task exposure, γ (OpenAI / Eloundou) Low 9th 0.1
AI assistant applicability (Microsoft) Low 29th 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 0.1). 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 · 75th 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 · -7.8% by 2034
Projected annual openings 500
Employment 2024 → 2034 5,800 → 5,400

“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.

17% mean task exposure (2025)
22nd percentile of 427 placed occupations
+0 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Metal Polishers, Wheel Grinders and Tool Sharpeners · 7224 17% 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 18 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

Arm-Hand Steadiness 4.0
Manual Dexterity 3.9
Finger Dexterity 3.9
Control Precision 3.9
Near Vision 3.8
Problem Sensitivity 3.4
Visualization 3.4
Selective Attention 3.4
Flexibility of Closure 3.3
Perceptual Speed 3.3
Written Comprehension 3.1
Rate Control 3.1
Reaction Time 3.1
Deductive Reasoning 3.0
Information Ordering 3.0
Category Flexibility 3.0
Multilimb Coordination 3.0
Static Strength 3.0
Hearing Sensitivity 3.0
Auditory Attention 3.0
Oral Comprehension 2.9
Oral Expression 2.9
Inductive Reasoning 2.9
Mathematical Reasoning 2.9

Transferable skills

Operations Monitoring 3.9
Operation and Control 3.8
Quality Control Analysis 3.5
Equipment Maintenance 3.4
Repairing 3.4
Equipment Selection 3.0
Troubleshooting 3.0
Complex Problem Solving 2.9
Judgment and Decision Making 2.9

Knowledge

Mechanical 3.6
Mathematics 3.5
English Language 3.1
Production and Processing 2.9

Essential skills

Critical Thinking 3.3
Monitoring 3.0
Reading Comprehension 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
Dassault Systemes SolidWorks Computer aided manufacturing CAM software Hot technology
Microsoft Excel Spreadsheet software Hot technology
ANCA ToolRoom Computer aided manufacturing CAM software
IBM Lotus Notes Electronic mail software
Vero Software Edgecam Computer aided design CAD and computer aided manufacturing CAM system
Zoller Data base user interface and query 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
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.9
Spend Time Standing 4.7
Importance of Being Exact or Accurate 4.6
Face-to-Face Discussions with Individuals and Within Teams 4.5
Pace Determined by Speed of Equipment 4.1
Spend Time Making Repetitive Motions 4.1
Exposed to Contaminants 4.1
Time Pressure 4.0
Health and Safety of Other Workers 4.0
Work With or Contribute to a Work Group or Team 3.9
Exposed to Minor Burns, Cuts, Bites, or Stings 3.9
Frequency of Decision Making 3.8
Freedom to Make Decisions 3.8
Determine Tasks, Priorities and Goals 3.8
Indoors, Not Environmentally Controlled 3.7
Exposed to Hazardous Equipment 3.7
Contact With Others 3.6
Coordinate or Lead Others in Accomplishing Work Activities 3.6
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 3.6
Impact of Decisions on Co-workers or Company Results 3.4
Work Outcomes and Results of Other Workers 3.4
Spend Time Walking or Running 3.3
Level of Competition 3.3
Spend Time Bending or Twisting Your Body 3.2
Physical Proximity 2.9
Importance of Repeating Same Tasks 2.8
Exposed to Very Hot or Cold Temperatures 2.7
Consequence of Error 2.6
Dealing With Unpleasant, Angry, or Discourteous People 2.5
Indoors, Environmentally Controlled 2.5
Exposed to Extremely Bright or Inadequate Lighting Conditions 2.5
Spend Time Kneeling, Crouching, Stooping, or Crawling 2.5
Conflict Situations 2.5
Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection 2.4
Exposed to Hazardous Conditions 2.2
Telephone Conversations 2.2
Exposed to Cramped Work Space, Awkward Positions 2.2
Degree of Automation 2.2
Exposed to Whole Body Vibration 2.1

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 45.0%
Post-Secondary Certificate 35.7%
Less than a High School Diploma 13.3%
Associate's Degree (or other 2-year degree) 6.1%

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 4.2
Investigative 2.0
Artistic 1.3

Interest areas

Mechanics/Electronics 4.7
Physical/Manual Labor 4.6
Engineering 3.6
Construction/Woodwork 2.3
Transportation/Machine Operation 1.6
Mathematics/Statistics 1.6
Accounting 1.2
Physical Science 1.2
Applied Arts and Design 1.1

Work styles

Dependability 3.0
Attention to Detail 2.7
Cautiousness 2.0

Wages & employment

U.S. · annual wages (BLS OEWS)

$34k10th$40k25th$49kMedian$60k75th$74k90th
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.
6k20245k2034 (proj.)-7.8% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $33,770
25th percentile $39,520
Median (50th) $48,970
75th percentile $60,210
90th percentile $74,120
People employed 5,730

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 4,400 $50,380
Wholesale Trade · Sector 510 $40,040
Other Services (except Public Administration) · Sector 470 $42,030
Administrative and Support and Waste Management and Remediation Services · Sector 200 $33,110
Temporary Help Services · National industry 190 $32,790
Machine Shops · National industry 180 $52,210
Retail Trade · Sector 130 $36,170

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
Machine Shops · National industry 18.65× 180
Manufacturing · Sector 9.28× 4,400
Other Services (except Public Administration) · Sector 2.86× 470
Wholesale Trade · Sector 2.27× 510
Temporary Help Services · National industry 1.93× 190
Administrative and Support and Waste Management and Remediation Services · Sector 0.6× 200
Retail Trade · Sector 0.22× 130

Part of the Advanced Manufacturing career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Tool Grinders, Filers, and Sharpeners sits at the 17th percentile of AI task-overlap and the 31st 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 Tool Grinders, Filers, and Sharpeners Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders Grinding and Polishing Workers, Hand Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic 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 Tool Grinders, Filers, and Sharpeners — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Write a report on thisheadline · factoids · citation

Tool Grinders, Filers, and Sharpeners show 17th-percentile AI task overlap — and about 500 annual U.S. openings

  • Tool Grinders, Filers, and Sharpeners 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 500 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 (-7.8%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $48,970, across about 5,730 U.S. workers.BLS OEWS (May 2024)
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Tool Grinders, Filers, and Sharpeners show 17th-percentile AI task overlap — and about 500 annual U.S. openings

• Tool Grinders, Filers, and Sharpeners 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 500 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 (-7.8%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $48,970, across about 5,730 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Tool Grinders, Filers, and Sharpeners". https://singulariki.com/roles/role-51-4194-00
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. "Tool Grinders, Filers, and Sharpeners." 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-51-4194-00

APA

Singulariki. (2026). Tool Grinders, Filers, and Sharpeners. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-4194-00

BibTeX
@misc{singulariki-role-51-4194-00,
  title  = {Tool Grinders, Filers, and Sharpeners},
  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-51-4194-00}
}

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

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