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Molders, Shapers, and Casters, Except Metal and Plastic

Occupation · SOC 51-9195.00

Mold, shape, form, cast, or carve products such as food products, figurines, tile, pipes, and candles consisting of clay, glass, plaster, concrete, stone, or combinations of materials.

Also called: Caster · Mold Mechanic · Molder · Molding Line Operator · Bed Laborer · Injection Molding Machine Operator · Machine Operator · Press Operator · Adobe Block Maker · Adobe Maker · Almond Paste Molder · Artificial Candy Maker

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-51-9195-00/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.

10th-percentile task overlap — yet about 5,500 openings a year (+6.2% 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 11th -1.2
LLM task exposure, γ (OpenAI / Eloundou) Low 3rd 0.0
AI assistant applicability (Microsoft) Low 28th 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.

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 · 78th 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.

Verify dimensions of products, using measuring instruments, such as calipers, vernier gauges, or protractors. 1.7%

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook About average · +6.2% by 2034
Projected annual openings 5,500
Employment 2024 → 2034 41,700 → 44,300

“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 6 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.

18% mean task exposure (2025)
29th percentile of 427 placed occupations
−0 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Chemical Products Plant and Machine Operators · 8131 24% Not exposed
Glass Makers, Cutters, Grinders and Finishers · 7315 19% Not exposed
Rubber Products Machine Operators · 8141 18% Not exposed
Potters and Related Workers · 7314 18% Not exposed
Glass and Ceramics Plant Operators · 8181 17% Not exposed
Tobacco Preparers and Tobacco Products Makers · 7516 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 27 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 3.5
Manual Dexterity 3.4
Finger Dexterity 3.4
Control Precision 3.3
Near Vision 3.3
Oral Comprehension 3.1
Selective Attention 3.1
Multilimb Coordination 3.1
Deductive Reasoning 3.0
Inductive Reasoning 3.0
Information Ordering 3.0
Extent Flexibility 3.0
Written Comprehension 2.9
Oral Expression 2.9
Problem Sensitivity 2.9
Category Flexibility 2.9
Visualization 2.9
Rate Control 2.9
Reaction Time 2.9
Trunk Strength 2.9
Far Vision 2.9
Speech Recognition 2.9
Speech Clarity 2.9
Perceptual Speed 2.8
Static Strength 2.8
Visual Color Discrimination 2.8

Knowledge

Production and Processing 3.3
Mechanical 3.0
Administration and Management 2.9
English Language 2.6

Transferable skills

Operation and Control 3.0
Operations Monitoring 2.9
Quality Control Analysis 2.8
Social Perceptiveness 2.6

Essential skills

Reading Comprehension 2.9
Active Listening 2.9
Speaking 2.8
Monitoring 2.8
Writing 2.6
Critical Thinking 2.6

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 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
Inventory control software Inventory management software
Mastercam computer-aided design and manufacturing software Computer aided manufacturing CAM software
Timekeeping software Time accounting 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 Standing 4.8
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.6
Spend Time Making Repetitive Motions 4.3
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.2
Importance of Being Exact or Accurate 4.1
Time Pressure 4.1
Work With or Contribute to a Work Group or Team 4.1
Indoors, Not Environmentally Controlled 4.0
Contact With Others 4.0
Spend Time Walking or Running 3.9
Face-to-Face Discussions with Individuals and Within Teams 3.8
Pace Determined by Speed of Equipment 3.8
Exposed to Contaminants 3.7
Coordinate or Lead Others in Accomplishing Work Activities 3.6
Spend Time Bending or Twisting Your Body 3.5
Exposed to Minor Burns, Cuts, Bites, or Stings 3.5
Importance of Repeating Same Tasks 3.5
Work Outcomes and Results of Other Workers 3.4
Health and Safety of Other Workers 3.4
Physical Proximity 3.4
Consequence of Error 3.4
Exposed to Hazardous Equipment 3.3
Freedom to Make Decisions 3.3
Exposed to Very Hot or Cold Temperatures 3.3
Level of Competition 3.1
Determine Tasks, Priorities and Goals 3.0
Exposed to Extremely Bright or Inadequate Lighting Conditions 2.7
Degree of Automation 2.7
Exposed to Hazardous Conditions 2.5
Impact of Decisions on Co-workers or Company Results 2.5
Deal With External Customers or the Public in General 2.3
Conflict Situations 2.2
Exposed to Cramped Work Space, Awkward Positions 2.2
Dealing With Unpleasant, Angry, or Discourteous People 2.2
Indoors, Environmentally Controlled 2.1
Frequency of Decision Making 2.0
Spend Time Kneeling, Crouching, Stooping, or Crawling 2.0
In an Open Vehicle or Operating Equipment 2.0
Written Letters and Memos 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.

Interests & work styles

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

Career interests (Holland / RIASEC)

Realistic 6.6
Conventional 4.3
Investigative 2.3
Artistic 2.1

Interest areas

Physical/Manual Labor 5.3
Construction/Woodwork 2.3
Engineering 2.1
Mechanics/Electronics 2.1
Applied Arts and Design 1.8
Transportation/Machine Operation 1.7
Visual Arts 1.7
Culinary Art 1.3
Physical Science 1.2
Mathematics/Statistics 1.2

Work styles

Attention to Detail 1.9
Dependability 1.8

Wages & employment

U.S. · annual wages (BLS OEWS)

$35k10th$39k25th$46kMedian$51k75th$61k90th
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.
42k202444k2034 (proj.)+6.2% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $34,950
25th percentile $38,500
Median (50th) $45,690
75th percentile $51,340
90th percentile $61,050
People employed 34,750

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 28,900 $45,490
Construction · Sector 3,060 $48,720
Retail Trade · Sector 840 $49,730
Administrative and Support and Waste Management and Remediation Services · Sector 840 $36,100
Temporary Help Services · National industry 790 $36,100
Wholesale Trade · Sector 520 $43,310
Masonry Contractors · National industry 320 $46,630
Mining, Quarrying, and Oil and Gas Extraction · Sector 140 $48,040
Other Services (except Public Administration) · Sector 40 $41,700
Arts, Entertainment, and Recreation · Sector — $30,670

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 10.05× 28,900
Masonry Contractors · National industry 9.89× 320
Construction · Sector 1.67× 3,060
Temporary Help Services · National industry 1.32× 790
Mining, Quarrying, and Oil and Gas Extraction · Sector 1.08× 140
Administrative and Support and Waste Management and Remediation Services · Sector 0.41× 840
Wholesale Trade · Sector 0.38× 520
Retail Trade · Sector 0.24× 840

Part of the Advanced Manufacturing career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Molders, Shapers, and Casters, Except Metal and Plastic sits at the 10th percentile of AI task-overlap and the 21st 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 Molders, Shapers, and Casters, Except Metal and Plastic Machine Feeders and Offbearers Refractory Materials Repairers, Except Brickmasons Grinding and Polishing Workers, Hand 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 Molders, Shapers, and Casters, Except Metal and Plastic — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 29th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Molders, Shapers, and Casters, Except Metal and Plastic show 10th-percentile AI task overlap — and about 5,500 annual U.S. openings

  • Molders, Shapers, and Casters, Except Metal and Plastic rank in the 10th 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 5,500 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 about average (+6.2%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $45,690, across about 34,750 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Molders, Shapers, and Casters, Except Metal and Plastic show 10th-percentile AI task overlap — and about 5,500 annual U.S. openings

• Molders, Shapers, and Casters, Except Metal and Plastic rank in the 10th 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 5,500 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 about average (+6.2%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $45,690, across about 34,750 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Molders, Shapers, and Casters, Except Metal and Plastic". https://singulariki.com/roles/role-51-9195-00
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. "Molders, Shapers, and Casters, Except 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-51-9195-00

APA

Singulariki. (2026). Molders, Shapers, and Casters, Except Metal and Plastic. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-9195-00

BibTeX 
@misc{singulariki-role-51-9195-00,
  title  = {Molders, Shapers, and Casters, Except 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-51-9195-00}
}

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

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