Role Definition
| Field | Value |
|---|---|
| Job Title | Material Moving Workers, All Other |
| Seniority Level | Mid-level (1-3 years experience) |
| Primary Function | Catch-all BLS category (SOC 53-7199) for material moving workers not classified elsewhere — hand truckers, stock movers, warehouse labourers performing miscellaneous material handling tasks. Moves, loads, sorts, and stages materials using hand tools, carts, dollies, and basic powered equipment. Works in warehouses, distribution centres, freight yards, and manufacturing plants. Employment: ~27,700. |
| What This Role Is NOT | Not a Laborer/Hand Mover (SOC 53-7062 — larger, defined category, AIJRI 29.9). Not a Forklift Operator (SOC 53-7051 — powered equipment, AIJRI 26.1). Not a Stocker/Order Filler (SOC 53-7065 — retail-focused, AIJRI 26.0). Not a Packer/Packager (SOC 53-7064 — AIJRI 9.5). The "All Other" designation captures workers whose tasks span multiple sub-categories without fitting neatly into one. |
| Typical Experience | 1-3 years. No formal education required (O*NET Job Zone 1). On-the-job training. Physical fitness essential — lifting, bending, standing for extended periods. Median wage $41,690 (BLS 2024). |
Seniority note: Minimal seniority differentiation. Entry-level workers do identical tasks at slower pace. This role has less physical manipulation protection than named material mover categories because the "All Other" designation often captures workers in more automated, structured facilities where the generic tasks are most vulnerable.
Protective Principles + AI Growth Correlation
| Principle | Score (0-3) | Rationale |
|---|---|---|
| Embodied Physicality | 1 | Physical work in semi-structured environments, but tasks are generic — moving, transporting, staging. Warehouses are increasingly designed for robots with wider aisles, standardised racking, and AMR-compatible floors. The physical component is real but less specialised than trades or even the named hand laborer category. |
| Deep Interpersonal Connection | 0 | No human interaction beyond receiving work orders. Material is the counterpart, not people. |
| Goal-Setting & Moral Judgment | 0 | Follows instructions from WMS or supervisors. Zero strategic decision-making. "Move X from A to B" — the definition of a structured, deterministic task. |
| Protective Total | 1/9 | |
| AI Growth Correlation | -1 | Weak negative. AMRs and conveyor systems directly target generic material transport. More automation = fewer generic movers per facility. Not -2 because e-commerce growth is still creating new warehouse demand, offsetting per-facility headcount reduction — for now. |
Quick screen result: Protective 0-2 AND Correlation negative — almost certainly Red or borderline Yellow. The question is whether the "All Other" catch-all retains enough physical protection to stay Yellow.
Task Decomposition (Agentic AI Scoring)
| Task | Time % | Score (1-5) | Weighted | Aug/Disp | Rationale |
|---|---|---|---|---|---|
| Moving/transporting materials by hand (hand trucks, dollies, carts — internal transport between zones) | 30% | 4 | 1.20 | DISPLACEMENT | AMRs (Locus Robotics, 6 River Systems, Amazon Proteus) execute internal transport end-to-end. By end-2026, ~4.7M commercial warehouse robots will be installed globally. The generic "move materials from A to B" task is the first to go autonomous. |
| Loading/unloading trucks and containers (manual freight handling at dock doors) | 20% | 2 | 0.40 | AUGMENTATION | Variable trailer configurations, irregular freight, confined spaces. Boston Dynamics' Stretch targets this but is early deployment. Mixed freight in non-standard trailers still needs human hands. One of the last material moving tasks to automate. |
| Sorting, stacking, and organising stock/materials | 20% | 3 | 0.60 | AUGMENTATION | Automated sortation handles standardised packages at scale. AMRs deliver to put-away zones. But mixed-SKU stacking, irregular load palletising, and ad-hoc organisation still require human judgment and dexterity. Eroding 3-5 years. |
| Inventory scanning, documentation, and WMS updates | 15% | 5 | 0.75 | DISPLACEMENT | RFID, automated barcode scanning, and WMS integration handle inventory tracking. The human confirmation step is increasingly redundant. Drone inventory audits replacing manual cycle counts. |
| Equipment operation (hand trucks, pallet jacks, basic powered equipment) | 10% | 3 | 0.30 | AUGMENTATION | Autonomous pallet jacks exist but are early-stage. Ad-hoc manual equipment use in congested or irregular environments still requires human operators. Eroding as facilities standardise. |
| Safety compliance, housekeeping, and hazard response | 5% | 2 | 0.10 | NOT INVOLVED | Physical hazard response, facility cleaning, and OSHA compliance still human. IoT sensors flag issues, but response remains manual. |
| Total | 100% | 3.35 |
Task Resistance Score: 6.00 - 3.35 = 2.65/5.0
Displacement/Augmentation split: 45% displacement, 50% augmentation, 5% not involved.
Reinstatement check (Acemoglu): Some new tasks — AMR fleet monitoring, exception handling, robotic system coordination. But these roles require fewer workers and different skills (technical literacy vs physical labour). The "All Other" worker is the least likely to transition into technical coordination roles. Limited reinstatement.
Evidence Score
| Dimension | Score (-2 to 2) | Evidence |
|---|---|---|
| Job Posting Trends | 0 | BLS projects modest growth for material moving broadly, but this "All Other" catch-all is tiny (27,700) and not tracked independently in job posting data. Generic warehouse labourer postings are stable, driven by turnover rather than growth. No clear directional signal. |
| Company Actions | -1 | Amazon deploying 750,000+ robots while restructuring warehouse workflow to reduce generic material handling roles. ~450,000 logistics robots sold in 2025 (500% increase from 2019). RaaS models (Locus, 6 River) making automation accessible to mid-market warehouses. No mass layoffs citing automation — e-commerce growth absorbs reductions through natural attrition. |
| Wage Trends | 0 | BLS median $41,690 (2024), up from $40,310 (2023). Modest real growth tracking inflation. Wages supported by labour shortage and physically demanding nature, not increasing role value. AMR TCO is 42% lower over five years, creating long-term wage pressure. |
| AI Tool Maturity | -1 | AMRs production-ready and deployed at scale for transport tasks. Automated sortation production-ready. Goods-to-person systems production-ready (Amazon Kiva/Proteus). But truck loading/unloading robots (Stretch) early deployment. Robotic manipulation for irregular items 3-5 years from broad deployment. Transport layer automated; manipulation layer lagging. |
| Expert Consensus | 0 | Consensus is "hybrid workforce" through 2030 — fewer generic movers per facility, humans retained for complex manipulation and exception handling. Nobody predicts full warehouse automation before 2030. But nobody says generic material moving roles are safe either. Mixed. |
| Total | -2 |
Barrier Assessment
Reframed question: What prevents AI execution even when programmatically possible?
| Barrier | Score (0-2) | Rationale |
|---|---|---|
| Regulatory/Licensing | 0 | No licensing required. OSHA safety rules apply equally to humans and robots. No regulatory barrier to automating generic material handling. |
| Physical Presence | 1 | Work is physical, but in semi-structured warehouse environments increasingly designed for robots. The "All Other" catch-all captures workers in more standardised settings than the named hand laborer category. Physical presence is real but the environment is converging on robot-friendly. Not the unstructured physicality of trades. |
| Union/Collective Bargaining | 0 | Most warehouse material movers are non-union, at-will employment. Some Teamsters coverage in freight terminals, but overall density is low. No significant collective bargaining barrier. |
| Liability/Accountability | 0 | Low stakes. Damaged goods are operational costs. No personal liability. Nobody goes to prison for a dropped pallet. No accountability barrier. |
| Cultural/Ethical | 0 | No cultural resistance to warehouse robots. Workers themselves prefer less repetitive heavy lifting. Industry actively embraces automation for safety and efficiency. |
| Total | 1/10 |
AI Growth Correlation Check
Confirmed -1 (Weak Negative). More warehouse automation = fewer generic material movers per facility. AMRs specifically target the core transport function that defines this catch-all category. Not -2 because e-commerce volume growth continues to create new warehouse demand — but the "All Other" worker is the first to be replaced when facilities upgrade. The correlation is negative and accelerating as AMR costs fall and RaaS models remove upfront capital barriers.
JobZone Composite Score (AIJRI)
| Input | Value |
|---|---|
| Task Resistance Score | 2.65/5.0 |
| Evidence Modifier | 1.0 + (-2 x 0.04) = 0.92 |
| Barrier Modifier | 1.0 + (1 x 0.02) = 1.02 |
| Growth Modifier | 1.0 + (-1 x 0.05) = 0.95 |
Raw: 2.65 x 0.92 x 1.02 x 0.95 = 2.3624
JobZone Score: (2.3624 - 0.54) / 7.93 x 100 = 23.0/100
Zone: RED (Green >=48, Yellow 25-47, Red <25)
Sub-Label Determination
| Metric | Value |
|---|---|
| % of task time scoring 3+ | 75% |
| AI Growth Correlation | -1 |
| Sub-label | Red — AIJRI <25, Task Resistance 2.65 >= 1.8, so not Imminent |
Assessor override: None — formula score accepted. The score sits 2.0 points below the Yellow boundary, reflecting the genuine vulnerability of this catch-all category. Compared to the named Laborer/Hand Mover (29.9) and Forklift Operator (26.1), the "All Other" category scores lower because it captures the least specialised workers in the most automatable settings.
Assessor Commentary
Score vs Reality Check
The score (23.0) sits 2.0 points below the Red/Yellow boundary — placing this catch-all below the named material moving roles it neighbours. This is honest. The "All Other" designation captures workers whose tasks are generic enough that they don't warrant a specific BLS classification — and generic tasks are precisely what AMRs and automated systems target first. The named Laborer/Hand Mover (29.9) benefits from higher Embodied Physicality (2/3 vs 1/3) because that category captures workers handling diverse, irregular freight. The "All Other" catch-all, by contrast, often captures workers in more routine, standardised material handling — exactly the tasks AMRs execute best.
What the Numbers Don't Capture
- The catch-all conceals enormous variation. SOC 53-7199 includes workers doing anything from pushing carts in a modern fulfilment centre (functionally Red Imminent) to staging irregular construction materials at a building site (functionally Yellow). The AIJRI score captures the average, but individual risk depends entirely on the specific facility and task mix.
- The turnover-masks-displacement dynamic. Warehouse material moving roles have among the highest turnover rates in the economy (~40-45% annually). When AMRs eliminate positions, the headline is "labour shortage persists" not "robots taking jobs." Displacement is invisible until the music stops.
- Small employment base amplifies risk. At 27,700 workers, this is a tiny category. Small categories can be absorbed into automation faster than large ones — there's no critical mass of workers to slow the transition politically or organisationally.
Who Should Worry (and Who Shouldn't)
Workers in large e-commerce fulfilment centres and modern distribution centres should worry most. These facilities have the capital, the standardised layouts, and the volume to justify AMR deployment. If your daily work is "move totes from A to B in a facility with wide aisles and flat floors," that is the first task that goes autonomous. Workers handling irregular, heavy, or hazardous materials in older facilities have more time — 3-5 years rather than 1-2. The single biggest factor is facility type. A material mover in a 2024-built Amazon fulfilment centre is functionally in Red Imminent territory. A material mover at a regional building materials yard handling irregular freight has genuine Yellow-level protection. The AIJRI score captures the blended average.
What This Means
The role in 2028: Significantly fewer generic material movers per facility. AMRs handle routine internal transport. Automated sortation handles standardised packages. The surviving version of this work involves exception handling — irregular freight, hazmat, returns processing, and tasks requiring human dexterity in unstructured sub-environments within the warehouse. Fewer positions, different skills.
Survival strategy:
- Specialise. Move from generic material handling into a named category with more protection — forklift certification (AIJRI 26.1), dock operations, cold chain, or hazmat handling
- Learn warehouse technology. AMR coordination, WMS proficiency, automated system monitoring. The "robot fleet coordinator" is the surviving version of this work
- Target skilled trades. Physical fitness, safety awareness, and equipment familiarity provide a foundation for apprenticeship pathways with far stronger AI protection
Where to look next. If you're considering a career shift, these Green Zone roles share transferable skills with material moving:
- Electrician (AIJRI 82.9) — Physical fitness, safety compliance, and equipment familiarity translate to electrical apprenticeship. Strong demand from AI infrastructure buildout
- Maintenance & Repair Worker (AIJRI 53.9) — Equipment operation, facility knowledge, and mechanical aptitude transfer directly. Many material movers already do basic equipment upkeep
- Construction Laborer (AIJRI 53.2) — Physical endurance, safety protocols, and material handling experience provide a direct foundation. Unstructured physical environments offer far stronger AI protection than warehouses
Browse all scored roles at jobzonerisk.com to find the right fit for your skills and interests.
Timeline: 1-3 years for significant AMR displacement at large e-commerce and logistics facilities. 3-5 years for mid-market warehouse adoption via RaaS models. 5-7 years for broad automation penetration including SMEs. Driven by AMR cost economics (42% five-year OPEX reduction), labour shortage dynamics, and facility modernisation pace.
