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Time Machine

Laborers and Freight, Stock, and Material Movers, Hand

Scrub through 216years 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.

2026drag to travel through time
18251850187519001925195019752000now
2026
Known today as Laborers and Freight, Stock, and Material Movers, Hand (BLS SOC 53-7062)
US Employment
2.95M
OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.
Median Annual Wage
$40,240
≈ $39,208 in 2024 dollars
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

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

    For most of the nineteenth century, moving freight meant moving it with your body. The tools were elemental: a cotton hook (a wooden handle with a steel curve) for maneuvering cotton bales; a hand truck (two wheels, a ledge, and a handle) for tipping and rolling barrels; a hand dolly for crates; rope and muscle for everything else. In a major port like New York, a stevedoring gang of eight men could move roughly 2-3 tons of general cargo per hour — a figure constrained entirely by human physiology. The gang foreman's job was to assemble men with complementary skills: a hooker who could manage cotton bales, a barrelman who knew how to leverage a cask without rupturing its seams, a header who could read the weight distribution on a stacked pallet of goods. The work was skilled in ways that were not immediately visible to outside observers — a poorly stowed ship's hold could shift cargo in bad weather and capsize the vessel, and a good stevedore gang knew the physics of load distribution as well as any engineer.

    Effect on the work

    Total employment in hand freight labor grew continuously with the expansion of American commerce from 1820 onward. No technology during this era reduced the number of workers needed; growth in trade volume translated directly to growth in the workforce.

    Work toolChanging equipment
  • Clark Tructractor (1917) + hydraulic pallet jack (1939) + conveyor systems

    The Clark Company of Battle Creek, Michigan, built the first gasoline-powered lift truck in 1917 to move materials inside its own factory — a squat, front-loaded vehicle called the Tructractor that could carry loads a man could not. By the 1920s, lift trucks were entering warehouses and freight terminals. The electric-powered counterbalanced forklift followed in the 1930s; by 1939 the hydraulic pallet jack — a simple, low-profile platform on wheels that could slide under a loaded pallet and raise it an inch off the floor for rolling — gave every warehouse worker the ability to move loads of several hundred pounds with modest effort. The pallet itself, standardized in the 1930s and made universal by the logistics requirements of World War II, was the equally important innovation: a wooden platform that could be loaded uniformly, forklift-lifted, and rail-or-truck-shipped as a unit. By the end of World War II, the pallet + forklift combination had fundamentally changed warehouse labor — the pure muscle requirement for moving heavy goods indoors was substantially reduced, and the job began its shift toward machine-assisted material handling.

    Effect on the work

    Forklift adoption did not reduce the total warehouse workforce because it enabled far larger warehouses and far higher throughput. The Sears Roebuck Chicago warehouse complex, for example, expanded dramatically in the 1920s-1940s as mechanized handling made larger buildings viable. What it changed was the skill mix: a certified forklift operator commanded a pay premium over a pure hand laborer, creating the career-ladder structure that still defines the occupation today.

    Work toolChanging equipment
  • Containerization (Malcom McLean, April 26 1956) — reshaping port labor

    On April 26, 1956, a converted tanker called the Ideal-X left Port Newark, New Jersey, with 58 aluminum truck bodies bolted to its deck — the first container ship voyage. Malcom McLean, a trucker-turned-entrepreneur, had calculated that the per-ton cost of moving freight through traditional break-bulk stevedoring was 30 times the cost of moving it in a standardized container handled by crane. He was right. Within 15 years, containerization had transformed every major deep-water port in the world. A break-bulk ship that required a gang of 20 longshoremen working for three weeks to load and unload could now be processed by a small crane crew in 24 hours. The dock-worker workforce in the Port of New York fell from approximately 35,000 in 1955 to under 10,000 by 1975. The pattern repeated in every containerized port. Containerization is the original warehouse-robotics story: a technology that was genuinely, verifiably catastrophic for the specific workers it displaced (port longshoremen) while reducing the per-unit cost of goods distribution so dramatically that it enabled the growth of global trade, and with it entirely new categories of inland warehouse and distribution labor.

    Effect on the work

    ILA (East Coast) membership fell from approximately 60,000 in the early 1960s to under 40,000 by 1975. ILWU (West Coast) experienced similar proportional declines. The displaced workers received negotiated severance and "registered longshoreman" protections in the landmark 1966 Mechanization and Modernization Agreement, but the jobs did not return. Inland warehouse employment grew substantially during the same period, driven by the retail distribution chains that containerization made economically viable — Walmart's first distribution center opened in 1970.

    Work toolChanging equipment
  • OSHA 29 CFR 1910.178 (1974) + big-box retail distribution centers

    OSHA's Powered Industrial Trucks standard (29 CFR 1910.178), first established June 27, 1974, formalized what had been a chaotic and often deadly corner of American industry. Forklift accidents killed approximately 100 workers per year and injured 95,000 more annually before OSHA standardized certification, inspection, and operating requirements. The 1974 standard required formal operator training, mandatory pre-shift equipment inspections, and specific load-capacity ratings — transforming the forklift operator into a certified skilled-trade rather than anyone who could climb on a seat. Simultaneously, Walmart's distribution center network — pioneered in the 1970s under logistics chief David Glass and enabled by Sam Walton's early adoption of computer-driven inventory systems — was creating the template for the modern large-format distribution center. Where older warehouses were measured in thousands of square feet, the new Walmart DCs ran to hundreds of thousands of square feet, employed hundreds of material handlers, and processed millions of case units per week. The 1980 Motor Carrier Act deregulated trucking, accelerating the buildout of hub-and-spoke distribution networks that required large regional DCs staffed by hand-labor workforces.

    Effect on the work

    OSHA certification requirements raised the wage floor for forklift operators and separated the occupation into certified (better paid, more protected) and non-certified (casual, lower-paid) tiers. Big-box distribution center growth drove sustained employment expansion through the 1980s even as manufacturing-adjacent warehouse work declined with US deindustrialization.

    Work toolChanging equipment
  • Barcode + RF scanner + Warehouse Management System (WMS) — the data layer

    The barcode had existed since 1974 (the UPC standard first scanned at a Marsh supermarket in Troy, Ohio, on June 26, 1974), but it took until the late 1980s and early 1990s for radio-frequency handheld scanners connected to mainframe-era Warehouse Management Systems to reach the warehouse floor broadly. The transformation was profound. Before WMS, a warehouse picker navigated by paper pick-list, memory, and experience — knowing roughly where the "sporting goods fast-movers" lived without being told. With an RF scanner and a WMS, a picker could start the job with minimal product knowledge: the system directed every move, confirmed every pick with a scan, and tracked every worker's productivity in real time. Engineered labor standards — productivity targets set by industrial engineers to the unit-per-hour — became universal in large DCs. The WMS didn't reduce employment; it enabled warehouses to scale to sizes that would have been unmanageable without real-time inventory visibility. A 1-million-square-foot distribution center with 500 pickers is only possible with a WMS managing the pick waves. The WMS era also introduced voice-directed picking (Honeywell Vocollect, broadly adopted from the early 2000s), which increased hands-free pick accuracy by 25-35% over RF scanner workflows.

    Effect on the work

    WMS adoption enabled much larger warehouses and drove substantial employment growth in the distribution sector through the 1990s and 2000s. It also made individual-worker productivity highly visible and measurable for the first time, intensifying productivity pressure on workers in ways that became a significant labor-relations issue by the 2010s.

    Bedside monitoringVitals at a glance
  • Amazon acquires Kiva Systems (2012) — warehouse robotics at scale

    On March 19, 2012, Amazon announced it would acquire Kiva Systems — a Woburn, Massachusetts robotics company — for $775 million. Kiva had built autonomous mobile robots (orange drive units roughly the size of a Roomba on steroids) that could navigate under wheeled inventory shelving pods and carry entire shelving units to stationary human pickers. The "goods-to-person" concept was not new; automated storage and retrieval systems (AS/RS) had existed in large manufacturing environments since the 1960s. What was new was the scale and the flexibility: Kiva robots used QR codes on the warehouse floor to navigate without fixed tracks, could be deployed in existing buildings without major construction, and could be scaled up by simply ordering more robots. Amazon deployed the first Kiva systems at its fulfillment centers in 2014. By 2016, it had deployed 45,000. By 2019, 200,000. By 2024, 750,000+, across 900+ sites. Every major competitor — Walmart, Target, FedEx, UPS — moved to acquire or build comparable AMR systems. Locus Robotics, GreyOrange, 6 River Systems, and Fetch Robotics raised hundreds of millions of dollars collectively to serve the non-Amazon market. It was the largest concentrated automation investment ever directed at a single occupational category in American economic history.

    Effect on the work

    Despite 750,000+ robots deployed by Amazon alone, BLS employment for SOC 53-7062 grew from approximately 2.4M (2014) to nearly 3.0M (2024). The robots automated horizontal pod transport — the walking component of picking — but created new demand for workers at goods-to-person stations who could pick at a faster pace from robot-delivered pods. E-commerce volume grew from ~$300B annually (2014) to ~$1.1T (2024), adding more total picking volume than robot productivity gains could absorb.

    Work toolChanging equipment
  • COVID e-commerce surge (2020-21) — demand shock overwhelms automation

    In March 2020, US retail stores closed for COVID-19. E-commerce, which had been growing at a steady 15-20% annually, exploded: Q2 2020 e-commerce sales grew 44.5% year-over-year, the largest single-quarter increase ever recorded (US Census Bureau). Consumers who had never ordered groceries, cleaning supplies, or furniture online were suddenly doing so simultaneously. The effect on warehouse labor demand was immediate and overwhelming: Amazon hired 175,000 people in a matter of weeks in spring 2020. FedEx, UPS, Walmart, Target, and every 3PL that handled e-commerce fulfillment hired at scale. The warehouse labor market went from chronically oversupplied to briefly undersupplied; base wages at major DCs rose materially. Kiva robots, now rebranded "Amazon Robotics," could not be manufactured fast enough to keep pace with demand. The occupational employment for SOC 53-7062 hit its historical peak during the 2020-2022 period. This was the moment the "automation will eliminate warehouse jobs" thesis was empirically tested against reality — and the test result was: not yet, not at this growth rate.

    Effect on the work

    BLS recorded substantial year-over-year employment gains for SOC 53-7062 through 2020-2022, consistent with reported hiring surges at Amazon (175,000 in spring 2020 alone) and across the e-commerce supply chain. Real wage gains materialized as the labor market tightened.

    Work toolChanging equipment
  • 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.

    Effect on the work

    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.

    Work toolChanging equipment
Projection cone · present → 2034

What credible sources project

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

Employment outlook
Projected change in the number of people doing this work.
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.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Frey & Osborne (2013)
2033
45%
of tasks
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.
McKinsey Global Institute — automation scenario (2017, updated 2023)
2030
20%
of tasks
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.
Eloundou et al. — "GPTs are GPTs" (2023)
2028
2%
of tasks
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.
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 onMove 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.

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.[5]

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 herePick 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.

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.[11],[2]

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 hereInduct 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.

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],[12]

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
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The data behind this timeline

On record since1820
Latest tracked employment2,950,280 (US, 2025)
Latest median pay$40,240 (2025)
Outlook+1.5% by 2034 (BLS National Employment Matrix 2024-34)
View all 31 cited data points
YearUS employmentMedian annual paySource
1870220,000n/aESTIMATE
1900500,000n/aESTIMATE
1935700,000n/aESTIMATE
1956900,000n/aESTIMATE
1970n/a$5,600ESTIMATE
19801,500,000n/aESTIMATE
1990n/a$16,000ESTIMATE
20002,100,000n/aESTIMATE
20032,255,780$19,930BLS-OEWS
20042,390,910$20,120BLS-OEWS
20052,363,960$20,610BLS-OEWS
20062,372,130$21,220BLS-OEWS
20072,363,440$21,900BLS-OEWS
20082,335,510$22,660BLS-OEWS
20092,135,790$23,110BLS-OEWS
20102,200,000$23,460ESTIMATE
20112,063,580$23,750BLS-OEWS
20122,143,940$23,890BLS-OEWS
20132,284,650$23,970BLS-OEWS
20142,400,490$24,430BLS-OEWS
20152,500,000$25,010ESTIMATE, BLS-OEWS
20162,587,900$25,980BLS-OEWS
20172,711,320$27,040BLS-OEWS
20182,893,180$28,260BLS-OEWS
20192,953,170$29,510BLS-OEWS
20202,805,200$31,120BLS-OEWS
20212,729,010$31,230BLS-OEWS
20222,934,050$36,110BLS-OEWS
20233,008,300$37,660BLS-OEWS
20242,989,000$37,060BLS-OEWS
20252,950,280$40,240BLS-OEWS
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