# Potters, Manufacturing

> Operate production machines such as pug mill, jigger machine, or potter's wheel to process clay in manufacture of ceramic, pottery and stoneware products.

- **SOC code:** 51-9195.05
- **Canonical URL:** https://singulariki.com/roles/role-51-9195-05
- **Also known as:** Glazer, Jiggerman, Potter, Production Potter, Clay Mixer, Jigger Artisan, Jigger Machine Operator, Kiln Worker
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Operate gas or electric kilns to fire pottery pieces.
- Start machine units and conveyors and observe lights and gauges on panel board to verify operational efficiency.
- Raise and shape clay into wares, such as vases and pitchers, on revolving wheels, using hands, fingers, and thumbs.
- Mix and apply glazes to pottery pieces, using tools, such as spray guns.
- Adjust wheel speeds according to the feel of the clay as pieces enlarge and walls become thinner.
- Position balls of clay in centers of potters' wheels, and start motors or pump treadles with feet to revolve wheels.
- Move pieces from wheels so that they can dry.
- Prepare work for sale or exhibition, and maintain relationships with retail, pottery, art, and resource networks that can facilitate sale or exhibition of work.
- Attach handles to pottery pieces.
- Press thumbs into centers of revolving clay to form hollows, and press on the inside and outside of emerging clay cylinders with hands and fingers, gradually raising and shaping clay to desired forms and sizes.
- Pack and ship pottery to stores or galleries for retail sale.
- Smooth surfaces of finished pieces, using rubber scrapers and wet sponges.

**Emerging tasks** (O*NET):
- Apply glazes to pottery pieces, using tools such as spray guns.
- Decorate pottery using tools such as brushes.
- Load and unload pottery from kilns.
- Mix chemicals according to recipes to create glazes.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Fine Arts _(knowledge)_
- Arm-Hand Steadiness _(ability)_
- Production and Processing _(knowledge)_
- Manual Dexterity _(ability)_
- Finger Dexterity _(ability)_
- Visualization _(ability)_
- Control Precision _(ability)_
- Near Vision _(ability)_
- Design _(knowledge)_
- Multilimb Coordination _(ability)_
- Customer and Personal Service _(knowledge)_
- Operations Monitoring _(transferable_skill)_

**Skills in demand:**
- Finger Dexterity _(Common Skill)_
- Visualization _(Specialized Skill)_
- Chemistry _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Mathematics _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Inventory control software

## AI exposure & outlook

- **AI task-overlap index:** 17th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 11th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 21st percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 28th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 78th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 6.2% growth (About average); 5.5k annual openings; 41.7k → 44.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $45,690; 34,750 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-51-9195-05_