← Risk register SOC 51-4022 · reviewed 2026-08-11

Forging Machine Setters, Operators, and Tenders, Metal and Plastic

8,930 US workers · median $49,030/yr · Production

EXPOSED

The work is hands-on and hot: mounting and aligning dies, adjusting ram stroke and heat settings, loading billets, gauging forged parts, clearing jams — none of which a language model touches. The real threat isn't AI writing but capital investment in servo presses, robotic billet handling, and automated inspection, plus continued offshoring of forge work; that has already shrunk this occupation to under 9,000 US jobs. Setup skill and in-process judgment (reading a scale-covered part, hearing a press go off-tune) are the durable part; tending an already-dialed-in press is not.

10-year outlook: Employment keeps sliding as forge lines automate and consolidate, but the setup-and-troubleshoot core stays human in most US shops through the 2030s — the tending-only jobs are the ones that disappear.

US employment, 2019–2025-45.3%
16,3208,930 workers

Part 2020 shock, part continued decline in the years since.

Median pay $39,670 → $49,030 -1.1% in real terms (nominal +23.6%, 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

-18.9% 8,800 → 7,200 on the projections basis

Exposed, and shrinking

Both signals point the same way: the tasks are largely automatable and the BLS projects -18.9% 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.

~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.

ForgerDropperExpanderKnucklerUpsetterBlacksmithCage MakerDie ForgerDie HolderDie SetterForge HandForgesmithNut FormerDrop ForgerHammersmithHeat ReaderRivet MakerSpike MakerForge TenderHeavy ForgerLever TenderHammer DriverHammer RunnerSpring Fitter

Score — 39/100 resistance

Holding it up: embodiment (16/20). Weakest point: trust premium (2/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 12 + 16 + 2 + 2 + 7 = 39. · 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 Die changeover, shimming to hit tolerance, and dialing in stroke and induction heat on a part that has been run before are still manual and non-scripted, but once the press is dialed in the tending cycle — load billet, cycle, transfer, gauge every tenth part — is exactly what robotic loaders and in-line gauging already do in the newer forge shops, so roughly half the job is scheduled to leave and half isn't.

Embodiment 16/20

Hands-on in uncontrolled environments You are within arm's reach of 2,000°F billets, scale, hammer noise and hydraulic oil mist, aligning dies inside the press bolster and clearing a stuck part from a hot die — a 16 rather than 19 only because the press itself is fixed in place and the work comes to you rather than you working in the open field.

Liability shield 2/20

No licence, no signature requirement No state licence gates running a forging press; the shop needs a crane or forklift card and OSHA lockout/tagout training at most, and if a part cracks in service the answer sits with the metallurgist and the quality system, not with your signature.

Trust premium 2/20

Anonymous artifact production The forging leaves your cell as a numbered lot in a tote with a heat number on the tag, and the customer receiving it knows the part spec and the PPAP, not your name.

Judgment & accountability 7/20

Meaningful discretion You decide whether a part with heavy scale or a slightly short fill goes to the scrap bin or through, and when to pull the press because the stroke sounds off — real calls that cost the shop money, but bounded by the process sheet, the go/no-go gauge, and a foreman who signs off on scrapping a run.

This occupation has already been through one. Headcount fell 51.2% between 2017 and 2025 — 18,300 to 8,930 — while the median wage held roughly flat in real terms ( -2.7% 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: medium · 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 — engineering and procurement courses, auditable without paying free to audit · Apprenticeship.gov — electrical, HVAC, plumbing and fitting programs that pay while you learn paid to train · Khan Academy — mathematics, arithmetic through calculus free · Coursera — teaching and instructional design, audit free free to audit · Apprenticeship.gov — paid, employer-sponsored trade apprenticeships, searchable by trade and ZIP paid to train · MIT OpenCourseWare — full course materials across every department, free 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.

Riggers SAFE · 70/100 · you already have ~80% of the skill profile

Skills to close: Equipment Selection

Sheet Metal Workers SAFE · 67/100 · you already have ~77% of the skill profile

Skills to close: Installation, Mathematics, Instructing, Equipment Selection

Control and Valve Installers and Repairers, Except Mechanical Door SAFE · 68/100 · you already have ~74% of the skill profile

Skills to close: Equipment Selection, Installation, Repairing, Active Learning

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 53/100, still EXPOSED.

4 specific changes that would raise this score
  • already happening task resistance +4

    As robotic billet handling and automated inspection absorb the tending tier, the surviving job becomes die setup, alignment, first-article qualification and troubleshooting off-tune presses — a genuine two-tier occupation where the routine tier is what gets bought away. Watch for job postings shifting from 'press operator' to 'die setter / forge setup technician' at the same plants.

  • plausible liability shield +4

    Safety-critical forgings (aerospace, defense, nuclear, pressure-containing) already require documented process control under AS9100/NADCAP and Nadcap AC7114 heat-treat and forging audits; if a customer or prime (e.g., a Nadcap-accredited flow-down) names a qualified setup operator who must personally sign the process-parameter and first-article record, rather than only an engineer or quality manager, sign-off attaches to this role. Also watch DFARS/NAVSEA forging traceability requirements naming operator certification.

  • plausible judgment accountability +4

    If plants formalize the operator's authority to stop production on a suspected die crack, lap, or underfill — a documented stop-work authority in the quality manual or union contract, as exists in some USW-represented plants — the consequential-call ownership becomes explicit rather than informal.

  • plausible embodiment +2

    Little headroom: die changes in a hot forge bay, scale-covered part handling, and jam clearing already resist automation, and the remaining automation gains are in handling rather than setup. Score could nudge up only as the residual role concentrates on manual die/tooling work.

The limit. Trust premium has no realistic route: buyers of forgings pay for metallurgical certs and dimensional conformance, never for a human having run the press. The binding constraint here is capital and offshoring, not AI capability, so a higher register score would not by itself mean more jobs — the occupation can become harder to automate and still keep shrinking.

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 27 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

Los Angeles-Long Beach-Anaheim, CA 510 $59,320 +21%
Detroit-Warren-Dearborn, MI 380 $61,980 +26%
Nashville-Davidson--Murfreesboro--Franklin, TN 240 $44,460 -9%
Houston-Pasadena-The Woodlands, TX 230 $36,730 -25%
New York-Newark-Jersey City, NY-NJ 200 $38,580 -21%
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 190 $63,880 +30%
Dallas-Fort Worth-Arlington, TX 180 $44,900 -8%
Columbus, IN 140 $52,060 +6%

Best paid

San Francisco-Oakland-Fremont, CA 40 $67,230 +37%
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 190 $63,880 +30%
Detroit-Warren-Dearborn, MI 380 $61,980 +26%

Percentages are against this occupation's national median of $49,030. 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 39. 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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Kept current

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