← Risk register SOC 51-9198 · reviewed 2026-08-11

Helpers--Production Workers

165,700 US workers · median $39,070/yr · Production

EXPOSED verdict contested

Almost nothing in this job is text or screen work, so language AI barely touches it — the threat is mechanization: conveyors, automated feeders, palletizers, and cobots that load, unload, and stage materials for machine operators. What protects the role today is messy, varied hand work (untangling jams, hand-sorting mixed parts, cleaning equipment in tight spaces, fetching odd-shaped stock) that is expensive to engineer around at low wages. There is no license, no signature, no client relationship, and very little discretion, so when a plant does invest in automation the position disappears rather than shrinks.

10-year outlook: Employment keeps eroding through the 2030s as plants add automated feeding and palletizing; the survivors are the helpers who converted into operators, setup techs, or maintenance assistants.

US employment, 2019–2025-45.3%
303,030165,700 workers

This fall is concentrated in 2020 and has not recovered since.

Median pay $29,100 → $39,070 +7.4% in real terms (nominal +34.3%, less ~25% US inflation over the period)

The job count is not the verdict

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

-8.9% 168,500 → 153,500 on the projections basis

Exposed, and shrinking

Both signals point the same way: the tasks are largely automatable and the BLS projects -8.9% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.

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.

~23,600 openings a year on average, including replacing people who leave.

One email if this score changes. Watch as many occupations as you like from the same address — no account, and nothing is sent on a schedule, only when a verdict actually moves.

Also known as — 24 job titles this covers

Titles reported by people doing this work, from the US Department of Labor's O*NET survey. If your job title is here, this page is about your work even though the name doesn't match.

LacerRakerWaxerWiperBedderBrinerBusherDipperFolderHelperPairerPickerPinnerPourerRackerShakerShaverSifterWasherBrusherChopperClamperCleanerCreeler

Score — 34/100 resistance

Holding it up: embodiment (15/20). Weakest point: liability shield (1/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 12 + 15 + 1 + 2 + 4 = 34. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 12/20

Mixed — a routine tier and a judgment tier At 12 the job sits just above the midpoint because the predictable half — moving totes to a line, stacking finished cases, feeding blanks into a press — is already handled by conveyors and palletizers in modernized plants, while the unpredictable half (reaching into a jammed sealer, hand-separating parts that came off-spec, sweeping and wiping down a machine's tight interior between runs, hunting down an odd-length piece of stock in the back) still has no cheap engineering answer, which is why it isn't a 6 but also can't reach the high teens.

Embodiment 15/20

Hands-on in uncontrolled environments A 15 reflects that essentially every duty happens standing on a plant floor with your hands on material — lifting 50-lb boxes, positioning stock, clearing scrap — in conditions that vary by shift, product run, and how the last operator left the station, though it stops short of the high end because you work inside one building with fixed machines rather than on a roof, in a trench, or in traffic.

Liability shield 1/20

No licence, no signature requirement A 1 is right because nothing you do requires a credential — no forklift certification is assumed in the base job, no inspection stamp, no signature on a batch record; the machine operator or line lead owns the output, and the only paperwork touching you is the OSHA log and the employer's own training sign-off.

Trust premium 2/20

Anonymous artifact production At 2, the work is interchangeable by design: helpers are assigned to whichever line is short that morning, temp agencies fill the slots for weeks at a time, and no customer, patient, or client ever knows which person staged their material — the only relational value is the shift supervisor knowing you show up and don't damage stock.

Judgment & accountability 4/20

Executes defined procedures on defined inputs The 4 is not zero because you decide when a jam is safe to clear yourself versus when to stop the line and call the operator, and you catch obvious defects moving past you, but the standard is a posted work instruction and a lockout-tagout procedure rather than your own call, and anything ambiguous goes up the chain.

The verdict on this page is contested. We scored this occupation twice, independently, and the two runs disagreed: 34/100 — EXPOSED and 31/100 — COOKED. Combining them doesn't settle it — the answer changes depending on how the halfway points are rounded, which is a property of our arithmetic and not of the job. Rather than pick the side that suits us, we've left the original verdict in place and flagged it. Read the dimension scores below and the reasoning attached to each; on this page they carry the information, and the one-word label does not.

This occupation has already been through one. Headcount fell 58.8% between 2017 and 2025 — 402,140 to 165,700 — while the median wage held roughly flat in real terms (+ 14.9% after inflation). A job being commoditised usually loses pay along with headcount. One that shrinks by half while pay holds is leaving a specialist core behind, and the score above was assigned from the occupation title without sight of that history — so it may be describing the job this used to be rather than the people still doing it. Why this is a known limit.

Confidence: high · reviewed 2026-08-11 · how scoring works

What this job involves — and which parts are yours

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.

AI already does these at usable quality

These still need a person

Active moats on the surviving side: embodiment, physical-presence

How to future-proof this job

Training paths for your skill gaps: Coursera — decision making under uncertainty free to audit · Apprenticeship.gov — electrical, HVAC, plumbing and fitting programs that pay while you learn paid to train · Coursera — quality control and inspection courses, auditable free free to audit · Coursera — work planning and personal productivity free to audit · Apprenticeship.gov — paid, employer-sponsored trade apprenticeships, searchable by trade and ZIP paid to train · Apprenticeship.gov — industrial maintenance and millwright programs paid to train · MIT OpenCourseWare — circuits, systems and diagnostic method, free and ungated free · Purdue OWL — the standard reference for professional writing free

All 35 skills ranked by how many jobs they open →

Where this experience transfers — occupations you could move toward

Computed from U.S. Dept. of Labor O*NET skill and knowledge profiles: high overlap with what you already do, a materially higher resistance score, no large jump in required training, and no licence you would have to start a new pipeline to get. Targets that pay meaningfully less, that are themselves COOKED, or whose own headcount is falling are excluded — a move into a shrinking trade is not an escape.

Welders, Cutters, Solderers, and Brazers EXPOSED · 62/100 · you already have ~87% of the skill profile

Skills to close: Judgment and Decision Making, Installation, Quality Control Analysis, Time Management

Refractory Materials Repairers, Except Brickmasons EXPOSED · 65/100 · you already have ~87% of the skill profile

Skills to close: Repairing, Equipment Maintenance, Troubleshooting, Time Management

Refuse and Recyclable Material Collectors EXPOSED · 55/100 · you already have ~84% of the skill profile

Skills to close: Equipment Maintenance, Writing

What would move this back up — beyond any one person

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 45/100, still EXPOSED.

4 specific changes that would raise this score
  • already happening embodiment +2

    Plant product mix shifting toward high-mix/low-volume or seasonal short runs (e.g. contract packaging, food co-packers) where changeover frequency makes fixed conveyor/palletizer lines uneconomic and the helper's job becomes almost entirely unstructured manual handling in varying cells; also confined-space and wet-sanitation cleaning tasks that cobots cannot enter under current safety standards

  • plausible task resistance +3

    Task-mix shift after the plant automates staging and palletizing: what remains for the surviving headcount is jam clearing, exception handling on mixed/damaged parts, and machine-tending judgment — effectively a lower-tier operator role. This raises the score for the remaining jobs while cutting the number of jobs, so it is not protection at the occupation level

  • plausible liability shield +3

    OSHA machine-guarding and robot-safety enforcement (ANSI/RIA R15.06 risk assessments) plus food-safety rules (FSMA, USDA HACCP) requiring a trained, named human to perform and log sanitation and lockout/tagout verification steps that cannot be delegated to automated equipment; some union contracts already specify bargaining-unit staffing for these logged checks

  • plausible judgment accountability +3

    Formal designation of helpers as the recorded first-line quality/foreign-object inspection point under a customer audit scheme (SQF, BRCGS) or as trained authorized employees under 29 CFR 1910.147, making their sign-off on line clearance consequential

The limit. There is no realistic route to a trust premium — buyers never see this worker and no one pays extra for human material handling. Even with every lever above, the occupation stays exposed: the mechanism of loss is capital investment in fixed automation eliminating positions outright, and none of these levers slows that once payback pencils out.

These are conditions, not forecasts — what would have to happen, not what will. Specific rules, cases and bills are named so you can go and check whether they exist and where they stand; verify before relying on any of them. Nothing here is legal or financial advice.

Where this work is, and what it pays there

BLS metro figures for 314 areas. The verdict above does not change by city — the rubric judges what the work involves, not where it happens — but pay and headcount do, and the national median hides a very wide range.

Most of these jobs

Dallas-Fort Worth-Arlington, TX 8,640 $37,890 -3%
Houston-Pasadena-The Woodlands, TX 8,120 $37,290 -5%
Los Angeles-Long Beach-Anaheim, CA 6,810 $42,490 +9%
New York-Newark-Jersey City, NY-NJ 4,780 $40,970 +5%
Chicago-Naperville-Elgin, IL-IN 3,710 $41,480 +6%
St. Louis, MO-IL 2,290 $40,190 +3%
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 2,190 $40,580 +4%
Seattle-Tacoma-Bellevue, WA 2,080 $46,980 +20%

Best paid

Reading, PA 340 $54,550 +40%
Albany, OR 200 $52,290 +34%
Des Moines-West Des Moines, IA 200 $52,050 +33%

Percentages are against this occupation's national median of $39,070. Counts are jobs in that metro, not vacancies. Metros where the BLS suppressed the cell are absent rather than shown as zero.

Who is actually doing this — nobody, on the record

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 34. 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.

Read that as a gap in the reporting we can see, not proof of absence — the dispatch runs on English-language feeds and misses plenty. If you know of a case, tell us, or add a field report from inside the job.

Quick take — do you do this job?

Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.

Self-reported and unverified — a sentiment signal, not a survey. One response per person per occupation; you can change your answer.

Field reports — what people say has changed

No field reports yet. A written account takes a paragraph rather than a tap, goes to an editor before it appears, and is the one thing on this page the rubric cannot produce on its own.

File a field report

Concrete beats general: a tool that arrived, a task that moved, a headcount decision you watched happen. Don't include anything that identifies you or your employer if that would put you at risk.

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