EXPOSED
This is a catch-all bucket of hands-on material handling — feeding conveyors, moving product between stations, operating specialized movers, tending hoppers and chutes, staging loads — none of which language AI can touch directly. The exposure comes from mechanization rather than chatbots: autonomous mobile robots, automated conveyance, and palletizing cells are being installed exactly where the work is repetitive and the floor layout is predictable. What holds is the messy, variable, non-standard handling in older plants, ports, construction sites, and mixed-load facilities, where the cost of engineering a robot exceeds the wage.
This fall is concentrated in 2020 and has not recovered since.
Median pay $31,770 → $41,800 +5.3% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+4.3%
Percentage only. The projection counts a different population from the 23,480 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +4.3% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~3,100 openings a year on average, including replacing people who leave.
RiderDumperFlumerHaulerLanderPackerStakerStorerStowerCarrierDragmanDraymanDropperHeadmanHitcherMovemanPick UpRackmanWheelerZanjeroSilo ManTeamsterTruckmanYarn Man
The BLS uses Material Moving Workers, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Clearing a jammed chute by hand, re-slinging an awkward load that won't sit on a pallet, or shifting product through a plant whose aisles were laid out in 1962 are all still cheaper to do with a person than to engineer around — but the repetitive half of the day (feeding a conveyor at fixed intervals, cycling totes between two fixed stations) is exactly what AMRs and palletizing cells already do at scale, which is why this sits at 13 and not 17.
Hands-on in uncontrolled environments The whole job is body: lifting, pushing, walking loads across uneven ground, dust, cold storage, ports and construction laydown yards where the surface changes weekly and there is no fixed fixture to bolt a robot to — 16 rather than 20 only because a good share of these workers are inside a warehouse with a roof and a concrete floor.
No licence, no signature requirement Nothing here requires a state licence — a powered industrial truck operator needs OSHA 1910.178(l) employer-provided training and rigging may need a qualified-person sign-off, but the employer owns that certification and the employer eats the citation, so there is no personal credential standing between you and replacement.
Executes defined procedures on defined inputs Sequencing, routing and lift decisions run off pick tickets, load plans, tag weights and posted capacity charts, and anything genuinely ambiguous — an unstable stack, an overweight lift, a damaged sling — is escalated to a lead or rigging supervisor rather than called by you, which is a 4 rather than a 10.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (13/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (2/20) is whether the law requires a licensed human to sign. Trust premium (3/20) is whether buyers specifically pay for a person. Judgment and accountability (4/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 9 of this occupation's 38 points (24%).
Embodiment (16/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 49/100, still EXPOSED.
Task-mix shift within the bucket: as AMRs and palletizing cells absorb flat-floor, uniform-load moves, the residual jobs concentrate in unstructured settings — port break-bulk, construction site staging, foundry and scrap handling, older multi-level plants with freight elevators and no reliable floor markings. The remaining work is more irregular, not less, which raises measured embodiment even as headcount falls.
Two tiers do exist here: repetitive conveyor-feeding versus judgment-heavy irregular handling. As the first tier is engineered out, what remains is the tier robots cannot cost-justify — but note this raises the score of the surviving job, not the number of jobs.
OSHA or state rules requiring a designated human spotter/attendant during mobile robot operation in mixed pedestrian-vehicle zones — the pattern already set by ANSI/ITSDF B56.5 safety standards for driverless industrial trucks and by warehouse-safety bills (e.g. Washington's HB 1762, New York's warehouse worker protection act) that regulate automated pacing. If attendance is mandated rather than optional, part of this workforce becomes a compliance requirement rather than a cost line.
Role redefinition into robot-fleet tending: exception handling, load-integrity calls, rigging and load-securement decisions on non-standard freight. Where employers formalize a 'lead materials handler' who signs off on load stability or hazmat staging under 49 CFR segregation rules, the call becomes owned rather than incidental.
The limit. No realistic route to a trust premium — buyers of freight and production throughput do not pay extra for human hands, and the work is invisible to the end customer. Liability gains are also capped: even mandated spotters are low-wage and one attendant can cover many machines, so a rule that raises the shield score can coexist with sharp employment decline.
| Los Angeles-Long Beach-Anaheim, CA | 3,030 | $37,530 -10% |
| Riverside-San Bernardino-Ontario, CA | 1,340 | $38,070 -9% |
| San Diego-Chula Vista-Carlsbad, CA | 940 | $36,550 -13% |
| San Francisco-Oakland-Fremont, CA | 920 | $44,410 +6% |
| New Orleans-Metairie, LA | 840 | $46,360 +11% |
| New York-Newark-Jersey City, NY-NJ | 740 | $61,540 +47% |
| Sacramento-Roseville-Folsom, CA | 650 | $38,250 -8% |
| Dallas-Fort Worth-Arlington, TX | 610 | $43,010 +3% |
| Boston-Cambridge-Newton, MA-NH | 80 | $81,700 +95% |
| Denver-Aurora-Centennial, CO | 220 | $65,110 +56% |
| Virginia Beach-Chesapeake-Norfolk, VA-NC | 70 | $62,730 +50% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 38. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.