Share and intensity of work current AI systems can materially affect.
SOC 53-7062
Laborers and Freight, Stock, and Material Movers, Hand AI displacement risk
Manual loading, sorting, and moving work faces pressure from warehouse robotics and mechanized handling, especially in high-volume facilities. Irregular environments, construction sites, docks, and mixed physical tasks resist full automation.
Likely potential for exposed tasks to move to software after workflow integration.
Automation concentrates where work is standardized and volumes justify capital. Manual laborers outside fulfillment centers face slower change, but wage vulnerability makes upskilling urgent.
Distribution
Where Laborers and Freight, Stock, and Material Movers, Hand sits across 620 tracked roles
Displacement pressure 48 — higher than 78% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
27 O*NET task statements matched to SOC 53-7062. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $40,240 (May 2025, US national). The latest BLS row matched SOC 53-7062.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Laborers and Freight, Stock, and Material Movers, Hand
SOC 53-7062 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 48/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Laborers and Freight, Stock, and Material Movers, Hand
The current evidence import matched 27 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks27
SOC53-7062
- Core task / ID 10789
Maintain equipment storage areas to ensure that inventory is protected.
- Core task / ID 10779
Read work orders or receive oral instructions to determine work assignments or material or equipment needs.
- Core task / ID 10781
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.
- Supplemental task / ID 10788
Install protective devices, such as bracing, padding, or strapping, to prevent shifting or damage to items being transported.
- Supplemental task / ID 10782
Sort cargo before loading and unloading.
- Supplemental task / ID 10778
Attach identifying tags to containers or mark them with identifying information.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Move freight by hand
Exposure 44, automation 34%, augmentation 16%.
O*NET evidence: Move freight, stock, or other materials to and from storage or production areas, loadin... (ID 10781)
Sort and stack cargo
Exposure 52, automation 40%, augmentation 22%.
O*NET evidence: Sort cargo before loading and unloading. (ID 10782)
Record units handled
Exposure 70, automation 52%, augmentation 34%.
O*NET evidence: Record numbers of units handled or moved, using daily production sheets or work tickets. (ID 10780)
Read work orders and instructions
Exposure 60, automation 36%, augmentation 42%.
O*NET evidence: Read work orders or receive oral instructions to determine work assignments or material... (ID 10779)
TaskExposureAutomationAugmentation
Move freight by hand4434%16%
Sort and stack cargo5240%22%
Record units handled7052%34%
Read work orders and instructions6036%42%
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
ModerateHeavy Equipment Operator
Training horizon: 3-9 months. Skill overlap 58. Wage preservation signal 138.
- Get equipment certifications
- Log supervised operating hours
- Study site safety rules
role redesign
ModerateCrew Lead
Training horizon: 1-3 months. Skill overlap 78. Wage preservation signal 122.
- Own shift task assignments
- Track daily completion counts
- Coach new crew members
Comparison guides
Compare the next move before you commit
3-9 months
Laborers and Freight, Stock, and Material Movers, Hand to Heavy Equipment Operator
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Laborers and Freight, Stock, and Material Movers, Hand into Heavy Equipment Operator.
1-3 months
Laborers and Freight, Stock, and Material Movers, Hand to Crew Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Laborers and Freight, Stock, and Material Movers, Hand into Crew Lead.
What the AI risk score means for Laborers and Freight, Stock, and Material Movers, Hand
The displacement pressure score for Laborers and Freight, Stock, and Material Movers, Hand is 48. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Record units handled carries 52% automation pressure, while Read work orders and instructions carries 42% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $40,240 (May 2025, US national). Employment context: Very large manual handling workforce across ports, sites, and warehouses. Typical education: No formal educational credential.
Wage vulnerability is 76, while transition feasibility is 64. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- High wage vulnerability
- Equipment skills open better-paid paths
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Laborers and Freight, Stock, and Material Movers, Hand, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Physical reliability
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Safety habits
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Equipment familiarity
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Basic inventory literacy
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Heavy Equipment Operator, such as get equipment certifications.
- By 90 days, compare internal openings and external postings for Heavy Equipment Operator or Crew Lead and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Laborers and Freight, Stock, and Material Movers, Hand
Will AI replace Laborers and Freight, Stock, and Material Movers, Hand?
Manual loading, sorting, and moving work faces pressure from warehouse robotics and mechanized handling, especially in high-volume facilities. Irregular environments, construction sites, docks, and mixed physical tasks resist full automation. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Laborers and Freight, Stock, and Material Movers, Hand work are most exposed to AI?
Record units handled and Sort and stack cargo show the strongest automation pressure in this model. Read work orders and instructions and Record units handled are better treated as AI-augmented work.
What should Laborers and Freight, Stock, and Material Movers, Hand learn next?
Start with Physical reliability, Safety habits, Equipment familiarity. The most practical adjacent paths in this model are Heavy Equipment Operator and Crew Lead.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics