# Automotive Glass Installers and Repairers

> Replace or repair broken windshields and window glass in motor vehicles.

- **SOC code:** 49-3022.00
- **Canonical URL:** https://singulariki.com/roles/role-49-3022-00
- **Also known as:** Automotive Glass Installer (Auto Glass Installer), Automotive Glass Technician (Auto Glass Technician), Glass Installer Technician, Glass Technician, Automotive Glazier (Auto Glazier), Glass Installer, Windshield Installer, Windshield Repair Technician
- **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):
- Prime all scratches on pinchwelds with primer and allow to dry.
- Remove all dirt, foreign matter, and loose glass from damaged areas, apply primer along windshield or window edges, and allow primer to dry.
- Allow all glass parts installed with urethane ample time to cure, taking temperature and humidity into account.
- Apply a bead of urethane around the perimeter of each pinchweld and dress the remaining urethane on the pinchwelds so that it is of uniform level and thickness.
- Select appropriate tools, safety equipment, and parts, according to job requirements.
- Install replacement glass in vehicles.
- Obtain windshields or windows for specific automobile makes and models from stock and examine them for defects prior to installation.
- Check for and remove moisture or contamination in damaged areas and keep areas dry until repairs are complete.
- Replace all moldings, clips, windshield wipers, or other parts that were removed prior to glass replacement or repair.
- Remove broken or damaged glass windshields or window glass from motor vehicles, using hand tools to remove screws from frames holding glass.
- Remove moldings, clips, windshield wipers, screws, bolts, and inside A-pillar moldings and lower headliners in preparation for installation or repair work.
- Install, repair, or replace safety glass and related materials, such as back glass heating elements, on vehicles or equipment.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- Mechanical _(knowledge)_
- Near Vision _(ability)_
- English Language _(knowledge)_
- Installation _(transferable_skill)_
- Manual Dexterity _(ability)_
- Multilimb Coordination _(ability)_
- Static Strength _(ability)_
- Speaking _(essential_skill)_
- Equipment Selection _(transferable_skill)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Installation _(Specialized Skill)_
- Visualization _(Specialized Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft Windows _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Equipment Selection _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Time Management _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Inductive Reasoning _(Common Skill)_

**Tools & technology:**
- Microsoft Windows _(hot technology)_
- Workday software _(hot technology)_
- Estimating software
- Recordkeeping software

## AI exposure & outlook

- **AI task-overlap index:** 3rd percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 10th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 3rd percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 4th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 49th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.6% growth (About average); 1.4k annual openings; 20.4k → 21.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $47,260; 18,940 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/
- **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-49-3022-00_