Hand laborers and material movers

AI Overlap Index
35.6 / 100
Selectively Exposed

Physical, social, or oversight-heavy work that AI augments rather than replaces.

SOC · Transportation And Material Moving

Bureau of Labor Statistics
Median pay
$37,680/yr
Hourly
$18/hr
Jobs 2024
6,950,000
Projected 2034
7,208,400
10-yr outlook
+4% · As fast as average
Employment change
258,400
Entry education
No formal educational credential
SOC code

Signal composition

how the 0-100 score is assembled

Task Automation Impact weight 60%
24.7
contribution to AOI: 14.8
Automation Potential weight 10%
20.0
contribution to AOI: 2.0
Market Pressure weight 15%
45.0
contribution to AOI: 6.8
Entry Barrier Erosion weight 15%
80.0
contribution to AOI: 12.0

By seniority

multiplicative adjustment from category curve

Entry
44.5
mult 1.25x
Mid
35.6
mult 1.00x
Senior
28.5
mult 0.80x

Entry-level roles carry the brunt because they concentrate the most automatable subset of tasks. Senior work is insulated by judgment, relationships, and accountability.

Task-level analysis

scored 0-100 for current-generation AI feasibility, weighted by BLS-stated importance

10 tasks · model: claude-sonnet-4-5-20250929
Supporting t8

Track and record materials moved using scanners or documentation systems

Barcode scanning, RFID tracking, and digital documentation are already highly automated; AI can handle data entry, pattern recognition for inventory tracking, and system updates with minimal human oversight, though humans currently verify and handle exceptions.

BLS evidence: Duties include keeping a record of the material they move, and pickers keep track of the package number, sometimes with a hand-held scanner, to ensure proper delivery.

72
automation
Supporting t10

Signal machine operators to coordinate material movement

AI systems can coordinate machine operations and send signals based on sensor data and workflow logic, and some automated coordination exists in modern facilities, but the real-time human judgment about safety, timing, and non-standard situations keeps humans in the loop for most current applications.

BLS evidence: Duties include signaling machine operators to help move material.

55
automation
Supporting t9

Inspect items for defects and sort materials as needed

Computer vision AI can detect many defects in controlled settings and sort standardized items, but the diversity of materials, defect types, and sorting criteria in general material handling still requires substantial human judgment and physical manipulation, making this a strong assist case rather than autonomous.

BLS evidence: Hand packers may inspect items for defects, and stock clerks inspect for damaged goods, and sometimes workers open containers and sort the material.

48
automation
Important t4

Retrieve and fill customer orders from warehouse storage

AI can optimize pick paths and guide workers via screens, and some automated retrieval systems exist in highly structured warehouses, but most order fulfillment still requires human navigation of aisles, recognition of products in varied orientations, and physical retrieval from shelves of different heights.

BLS evidence: Order fillers retrieve customer orders and prepare them to be shipped, and some workers retrieve products from storage and move them to loading areas.

35
automation
Important t7

Feed materials into machines or remove materials from equipment

Some automated feeding systems exist for highly standardized manufacturing, but typical material handling around machines involves varied materials, non-standard orientations, and real-time coordination with machine cycles in ways that require human adaptability and physical presence.

BLS evidence: Machine feeders and offbearers process materials by feeding them into equipment or by removing them from equipment.

25
automation
Core t3

Pack or wrap products by hand for shipment or storage

While some automated packing systems exist for standardized items, hand-packing typically involves irregular products, custom wrapping decisions, and fine motor manipulation in varied contexts that AI-guided robotics struggles with; humans remain substantially more adaptable and cost-effective.

BLS evidence: Duties include packing or wrapping products by hand, and hand packers and packagers package a variety of materials by hand.

22
automation
Important t6

Clean vehicles, equipment, and storage tanks using cleaning products and tools

Cleaning varied equipment and vehicles requires adapting techniques to different surfaces, geometries, and contamination types in physical spaces; while some automated cleaning exists for standardized settings, the general case requires human dexterity and judgment about cleanliness standards.

BLS evidence: Cleaners of vehicles and equipment wash automobiles and other vehicles, as well as storage tanks, pipelines, and related machinery using cleaning products, vacuums, hoses, and brushes.

18
automation
Core t2

Load and unload cargo from trucks, ships, and containers

Involves heavy physical labor in dynamic, unstructured environments (truck beds, ship holds, containers) with varied package sizes, weights, and stacking requirements that exceed current autonomous robotics capabilities for general-purpose loading/unloading.

BLS evidence: Hand laborers and freight, stock, and material movers move materials to and from loading docks, delivery trucks, ships, and containers, and other workers load and unload cargo from a truck.

15
automation
Core t1

Manually move freight, stock, or other materials from one place to another

Requires physical manipulation of varied objects in unpredictable warehouse environments with obstacles, uneven surfaces, and real-time spatial reasoning that current robotics cannot reliably handle across the diversity of materials and settings typical in this work.

BLS evidence: Hand laborers and material movers manually move freight, stock, or other materials, and duties include manually moving material from one place to another.

12
automation
Important t5

Collect garbage and recyclables from homes and businesses for transport

Requires navigating residential streets, identifying correct bins among varied placements, handling containers of different types and fullness levels, and operating collection mechanisms in highly variable outdoor environments—far beyond current autonomous vehicle and manipulation capabilities for this application.

BLS evidence: Refuse and recyclable material collectors gather garbage and recyclables from homes and businesses to transport to a dump, landfill, or recycling center.

8
automation

Task heatmap

automation score by task, sorted by weighted contribution

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External signals and sources

category-level priors and BLS fields that feed the four non-task signals

Automation Potential
20
karpathy 2/10
  • Karpathy/BLS Digital AI Exposure (0-10 scale rescaled to 0-100)
Market Pressure
45
outlook: As fast as average
  • BLS projected outlook: As fast as average (4%)
  • Indeed demand signal (monthly refresh pending)
Entry Barrier Erosion
80
ed: No formal educational credential
  • BLS typical entry-level education: No formal educational credential
  • Credential trend signal (annual refresh)

Related in Transportation And Material Moving

closest AOI neighbors in the same category