# Laborers and Freight, Stock, and Material Movers, Hand

> Manually move freight, stock, luggage, or other materials, or perform other general labor. Includes all manual laborers not elsewhere classified.

- **SOC code:** 53-7062.00
- **Canonical URL:** https://singulariki.com/roles/role-53-7062-00
- **Also known as:** Laborer, Loader, Material Handler, Warehouse Worker, Merchandise Pick Up Associate, Merchandise Receiving Associate, Receiver, Receiving Associate
- **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):
- Install protective devices, such as bracing, padding, or strapping, to prevent shifting or damage to items being transported.
- Maintain equipment storage areas to ensure that inventory is protected.
- Sort cargo before loading and unloading.
- Attach identifying tags to containers or mark them with identifying information.
- Read work orders or receive oral instructions to determine work assignments or material or equipment needs.
- Move freight, stock, or other materials to and from storage or production areas, loading docks, delivery vehicles, ships, or containers, by hand or using trucks, tractors, or other equipment.
- Record numbers of units handled or moved, using daily production sheets or work tickets.
- Attach slings, hooks, or other devices to lift cargo and guide loads.
- Carry needed tools or supplies from storage or trucks and return them after use.
- Pack containers and re-pack damaged containers.
- Assemble product containers or crates, using hand tools and precut lumber.
- Adjust controls to guide, position, or move equipment, such as cranes, booms, or cameras.

**Emerging tasks** (O*NET):
- Inspect products for damage or leaks.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Static Strength _(ability)_
- Trunk Strength _(ability)_
- Administration and Management _(knowledge)_
- Customer and Personal Service _(knowledge)_
- Multilimb Coordination _(ability)_
- Public Safety and Security _(knowledge)_
- Transportation _(knowledge)_
- Oral Comprehension _(ability)_
- Manual Dexterity _(ability)_
- Stamina _(ability)_
- Extent Flexibility _(ability)_
- Near Vision _(ability)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Microsoft Edge _(Specialized Skill)_
- Google Docs _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- English Language _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Active Listening _(Common Skill)_

**Tools & technology:**
- Apple Safari _(hot technology)_
- Autodesk AutoCAD _(hot technology)_
- Google Docs _(hot technology)_
- Microsoft Edge _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft Word _(hot technology)_
- Mozilla Firefox _(hot technology)_
- Oracle Database _(hot technology)_
- SAP software _(hot technology)_
- Warehouse management system WMS _(in demand)_

## AI exposure & outlook

- **AI task-overlap index:** 4th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 3rd percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 6th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 12th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 71st percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 1.5% growth (About average); 384.3k annual openings; 2,988.9k → 3,033.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $38,940; 2,982,530 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-53-7062-00_