Time Machine

Laborers and Freight, Stock, and Material Movers, Hand

Scrub through 216 years of this role's history — from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.

Hand tools + cotton hooks + hand truck (pure muscle era)

Clark Tructractor (1917) + hydraulic pallet jack (1939) + conveyor systems

Containerization (Malcom McLean, April 26 1956) — reshaping port labor

Barcode + RF scanner + Warehouse Management System (WMS) — the data layer

18251850187519001925195019752000now

Drag the dot, click anywhere on the track, or use ← → arrow keys (Shift for 10-year jumps, PgUp/PgDn for 25).

2026

Known today as Laborers and Freight, Stock, and Material Movers, Hand (BLS SOC 53-7062)

US Employment

2.99M

BLS National Employment Matrix baseline employment for SOC 53-7062, 2024. Used as the authoritative baseline for the 2024-2034 projection cycle. The BLS projections matrix accessed May 2026 gives 2,988.9 thousand (rounded to 2,989,000 here). This represents the occupation at its largest in recorded history — nearly 3 million workers despite 750,000+ Amazon warehouse robots deployed since 2012. The Bessen (2015) "bank-teller and ATM" dynamic in physical form: demand for e-commerce fulfillment grew faster than per-worker productivity from automation.

Median Annual Wage

$37,060

Source: BLS-OEWS

Tool of the era · Next-generation AI picking systems — Sparrow, Sequoia, Symbotic, vision robotics

By 2023, the warehouse robotics industry had passed through its first phase (AMRs automating transport and walking) and was entering its second: AI-vision systems attempting to automate the actual picking of individual items from unstructured environments. Amazon's Sparrow robot (deployed 2022) uses computer vision and adaptive grasping to handle individual packaged goods, reportedly covering approximately 65% of Amazon's packaged-product SKUs at deployed sites. Sequoia (2023) automates tote induction and stow for uniform-sized items. Symbotic, which went public at a $5.5B valuation in 2022, deploys autonomous case-handling robots in Walmart distribution centers. Berkshire Grey, AutoStore, and Mujin are attacking piece-picking and depalletizing with increasing AI-vision sophistication. The consensus as of 2026: robots are getting closer, but the final 15-20% of SKUs — irregularly shaped, soft-pack, extremely light, or fragile — remain stubbornly hard to grip at acceptable speed and error rates. The job has changed radically in its physical geography (less walking, faster pace, more machine-coordinated) without being eliminated.

BLS projects +1.5% employment growth for SOC 53-7062 over 2024-2034 (44,000 net new jobs) — flat to modest growth as automation pressure and e-commerce demand increases roughly offset each other. The occupation remains near 3 million workers, the largest single hand-labor occupation in the US economy.

Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread you see below is the range of futures credible sources project for this role.

E-commerce continued-growth optimistic scenario

→ 2034

+8%

Optimistic tail of the uncertainty cone. If US e-commerce continues to grow at 8-10% annually from a 2024 base of approximately $1.1 trillion, total e-commerce volume could reach $2.2-2.4 trillion by 2034. At current fulfillment-center labor intensity (even accounting for further robotics productivity improvements), this volume trajectory would generate net employment growth in warehouse labor of 8-12% above 2024 levels — consistent with BLS historical employment growth from 2012-2024 despite simultaneous robot deployment. This scenario requires that (a) e-commerce continues to take share from brick-and-mortar retail, (b) robot dexterity improvements do not accelerate sharply past current trajectory, and (c) nearshoring and onshoring of US manufacturing adds fulfillment-center demand.

BLS National Employment Matrix 2024-34

→ 2034

+1.5%

BLS Employment Projections 2024-34 cycle (most current). Baseline 2,988.9 thousand (2024); projected 3,033.1 thousand (2034); net change +44,300 jobs (+1.5%). Described as "about as fast as average" in BLS framing (the all-occupation average for this cycle is approximately 4%). BLS projects modest growth as e-commerce volume increases continue to create warehouse demand, partially offset by continued automation investment. Annual job openings (new jobs + replacement need) are substantially higher than net change — estimated at 600,000+ per year — because the occupation has high turnover from physically demanding conditions.

Eloundou et al. — "GPTs are GPTs" (2023)

→ 2028

-2%

GPT-4 task-by-task LLM exposure labeling on O*NET tasks. SOC 53-7062 scores very low on LLM exposure because the core tasks — moving boxes, operating forklifts, scanning freight, loading trailers — are physical tasks that an LLM cannot perform. This is not the relevant threat for this occupation. Eloundou's methodology correctly captures that this occupation is not threatened by language models; the threat is physical robotics and AMRs. The -2% estimate here is a floor: it represents displacement from AI-assisted planning tools (WMS AI-directed pick optimization, automated scheduling) rather than from physical robot substitution. The actual robotics-driven displacement risk is captured separately by the F&O and BLS projections.

McKinsey Global Institute — automation scenario (2017, updated 2023)

→ 2030

-20%

McKinsey's 2017 "A Future That Works" study estimated that physical activities in predictable environments (a category that covers most warehouse picking) had approximately 78% technical automation potential — among the highest of any task category. Under their "rapid automation" scenario, physical labor occupations in predictable environments (including warehouse work) could see 20-30% displacement by 2030. The -20% figure represents the mid-range of McKinsey's scenario analysis applied to this occupation's 2024 baseline. McKinsey's analysis is more pessimistic than current BLS projections because it models technical potential rather than observed deployment rates; the actual pace of Sparrow and comparable robot adoption is slower than technical potential suggests, constrained by capital cost, implementation complexity, and the residual unstructured-SKU problem.

Frey & Osborne (2013)

→ 2033

-45%

Gaussian-process classifier on O*NET task features. Frey & Osborne assigned SOC 53-7062 a probability of computerization of approximately 0.85, placing it in the highest-risk quartile of the 702-occupation dataset. The bottleneck factors they identified as defending the occupation were minimal — low "finger dexterity" requirements and "cramped work positions" were not classified as significant obstacles. At 0.85 probability over 10-20 years, a -45% employment scenario (as implied by full realization of the probability within the decade) would have brought employment to approximately 1.3M by 2033. In practice, employment grew to ~3M, more than doubling. The F&O prediction was the most dramatically falsified major-occupation forecast in the dataset — not because their methodology was wrong, but because it modeled technical feasibility without modeling demand elasticity: the e-commerce volume growth that would absorb productivity gains from automation.

Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

Three parts of your work where AI is already doing real lifting — and what stays yours.

AI is taking this on

Move freight, packages, and stock between receiving docks, storage racks, and outbound staging areas using hand trucks, pallet jacks, and counterbalanced forklifts — navigating aisles alongside autonomous mobile robots (AMRs) in hybrid facilities where AMR fleets handle pod transport and human associates handle the final placement and any oversized or irregular items the robots cannot grip.^[11],[5]

Tools picking this up

Amazon Robotics (Kiva / Proteus / Sparrow / Sequoia) ↗Locus Robotics ↗GreyOrange Ranger AMR + GreyMatter Platform ↗

Where your edge is

In robotics-deployed facilities, your job is no longer about covering distance — it's about handling the items and situations the robots cannot. Develop speed and accuracy on robot-assisted picking stations (units/hour rate metrics are tracked per associate): workers who consistently exceed rate are first in line for lead roles and forklift cross-training. Learn the robot error codes for your facility's AMR fleet so you can clear minor jams yourself rather than waiting for a tech — that's a visible and valued skill.

AI is sitting alongside you here

Pick individual customer orders from rack locations using WMS-directed workflows — scanning RF handheld or wrist-mounted scanner at each location to confirm pick, following voice-directed (Vocollect) or put-to-light system prompts, meeting unit-per-hour rate targets set by the facility's engineered labor standard, and handling AMR-delivered totes at goods-to-person stations in robotic zones.^[12],[2]

Tools picking this up

Locus Robotics ↗Manhattan Active WMS ↗Blue Yonder Warehouse Management System ↗

Where your edge is

Rate metrics are real and tracked to the individual — in most major DCs, your hourly UPH (units per hour) is visible to your supervisor in real time. Understand the engineered labor standard for your facility and what drives variance: distance to pick face, item weight, and scan-confirm time. Pair assignments (walking a robotic zone vs. a far aisle) affect your rate; being vocal with leads about zone assignment is your lever for consistent performance.

AI is sitting alongside you here

Induct packages onto automated conveyor sort lines — placing individual packages onto induction belts at correct speed and orientation for scanner read, manually scanning label-read failures with handheld scanner for manual divert keying, clearing package jams at scan tunnels within assigned zone, and diverting oversized, damaged, or mis-sorted packages to the appropriate exception lane.^[2],[13]

Tools picking this up

Berkshire Grey AI-Enabled Robotic Systems ↗Symbotic Autonomous Warehouse Platform ↗

Where your edge is

Sort induction is paced by the belt — you cannot slow it down, and falling behind creates jams that back up to the unload dock. The workers who stay calm under belt-pace pressure, maintain scan accuracy on label reads, and know their exception protocols are the ones supervisors pull to train new associates. Label-read failure rates are tracked by induction station; low miss-rate is a recognized performance metric.

Where this role is heading

Natural next steps for someone with your foundation — not exits, evolutions.

A direction you could grow

Logisticians

Logisticians (13-1081.00) plan and manage supply chain flows — carrier procurement, inventory positioning, S&OP input, network design — rather than executing physical material movement. The transition from material mover to logistician requires a credential step (associate's or bachelor's degree in supply chain, logistics, or business, or the APICS CLTD certification) and a functional bridge role (shipping/receiving clerk or inventory control analyst). But it is a realistic multi-year path from the warehouse floor: workers who develop WMS proficiency, inventory accuracy discipline, and metric fluency on the floor are genuinely differentiated candidates for coordinator roles that bridge physical execution and planning. BLS projects 18% growth for Logisticians through 2032. Median annual wage: $77,520 (May 2023) — roughly 2× the material mover median.

What you'd add

· APICS CLTD (Certified in Logistics, Transportation and Distribution): the primary credential for the logistics field, does not require a degree
· Supply chain fundamentals: inventory management, transportation modes, demand planning concepts (APICS coursework)
· WMS and TMS systems proficiency: beyond floor scan use to reporting, KPI dashboards, and inventory control analysis
· Quantitative analysis: Excel (PivotTable, VLOOKUP, basic statistics) for inventory and freight cost analysis
· Supply chain associate's degree or SCM coursework from an accredited community college — typical 2-year path while working

What it takesA real upskill — but a natural one

Present-day sources

Sources

Every claim on this page traces back to one of the following. Updated 2026-05-28.

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