EXPOSED
Nothing in this job is text or screen work, so language AI barely touches it — the threat is mechanical, not conversational. Automated pouring cells, robotic ladles, and continuous-casting lines already replace manual pourers in high-volume foundries; what survives is short-run, odd-geometry, and repair-oriented casting in small job shops where fixturing a robot costs more than the order. No license protects the role and no customer pays for a relationship with the pourer, so the only real moat is the messy, hot, variable physical environment.
Part 2020 shock, part continued decline in the years since.
Median pay $38,620 → $51,810 +7.3% 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
-4.7%
Percentage only. The projection counts a different population from the 4,560 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -4.7% 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.
BusherCasterHeaderLadlerMelterPourerLadlemanTin PourerIron PourerBrass PourerIngot CasterIngot HeaderLadle FillerLadle PourerLadle PullerSteel PourerBillet HeaderMetal HandlerLadle OperatorAluminum PourerCasting OperatorBull Ladle TenderBuggy Ladle TenderLadle Car Operator
Holding it up: embodiment . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier Hand-ladling a 200-lb bull ladle into odd-geometry sand molds, skimming dross by eye, and judging pour rate off the fluidity of a specific alloy heat is not yet codified for a robot in a 12-order job shop, but tilt-pour machines, autopour units on green-sand lines, and continuous casters have already taken the repeat-volume work — which is why this sits at 13 rather than 17.
Hands-on in uncontrolled environments You work within arm's length of metal at 2,600°F in leathers and a face shield, walking uneven foundry floor between molds, handling shanks, tapping cupolas, and reacting to steam explosions and runouts — an uncontrolled thermal environment that only stops short of 20 because the pour itself happens at a fixed station rather than out in the field.
No licence, no signature requirement No state licenses a pourer; the plant's furnace and crane certifications, OSHA 1910.252 compliance, and any foundry-engineering sign-off sit with the supervisor and the metallurgist, so a bad heat becomes a scrap ticket rather than your personal legal exposure.
Executes defined procedures on defined inputs Pour temperature, alloy charge, and mold sequence come off the job ticket and the pyrometer, and you call it when the metal looks wrong or a mold is unsafe to pour, but those are in-the-moment shop-floor decisions with a supervisor and a metallurgist above you rather than ambiguous high-stakes calls you own.
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 (2/20) is whether buyers specifically pay for a person. Judgment and accountability (6/20) 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 10 of this occupation's 40 points (25%).
Embodiment (17/20) 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 52/100, still EXPOSED.
Task-mix shift: this occupation genuinely has two tiers — repetitive production pouring versus judgment pouring (reading melt surface, skim decisions, deciding to scrap a heat, adjusting pour speed as a mold vents). If the production tier is automated out, the residual headcount is the tier that requires reading an uninstrumented melt in real time.
Continued concentration of remaining work in short-run, one-off, and repair casting (art bronze, ornamental restoration, replacement parts for obsolete equipment) where fixturing a robotic ladle for a single mold costs more than the order; each job has different mold geometry, gate placement, and pour rate judged by eye and sound. Watch whether small job-shop foundries (under ~20 employees) hold share as high-volume lines fully automate.
Formalization of the pourer's authority to abort a heat or reject a mold under safety rules — e.g. an OSHA molten-metal emphasis program or an insurer-required written stop-work authority naming the pour operator (parallel to steel-industry mutual insurer loss-control requirements after runout/explosion claims). Would make the call a documented, owned decision rather than an informal one.
Aerospace/defense and nuclear casting already runs under NADCAP AC7004/AS9100 and NAVSEA tech pubs; a further requirement that a named, certified melt/pour operator sign the heat record for safety-critical castings — rather than a metallurgist signing off downstream — would attach personal certification to the pour itself. Watch NADCAP heat-treat/casting audit criteria revisions.
Only route is art and architectural bronze, where the foundry (and sometimes the named pour crew) is part of provenance for editioned sculpture. This is a few hundred workers at most and does not generalize to industrial casting.
The limit. Realistic ceiling is roughly the low-50s, and it is almost entirely embodiment plus task-mix residue, not institutional protection. The mechanism that shrinks this occupation — robotic ladles and continuous casting in high-volume plants — is capital-cost driven and unaffected by anything a licensing board could do. A higher score here would coexist with a much smaller headcount: the surviving job gets harder to automate while there are fewer of them.
| Chicago-Naperville-Elgin, IL-IN | 320 | $61,680 +19% |
| Cleveland, OH | 150 | $63,040 +22% |
| Dallas-Fort Worth-Arlington, TX | 150 | $58,240 +12% |
| Los Angeles-Long Beach-Anaheim, CA | 120 | $44,020 -15% |
| Milwaukee-Waukesha, WI | 100 | $53,100 +2% |
| Indianapolis-Carmel-Greenwood, IN | 80 | $47,750 -8% |
| Allentown-Bethlehem-Easton, PA-NJ | 70 | $49,780 -4% |
| Grand Rapids-Wyoming-Kentwood, MI | 70 | $37,560 -28% |
| Boston-Cambridge-Newton, MA-NH | 40 | $66,120 +28% |
| Columbus, OH | 40 | $66,040 +27% |
| Cleveland, OH | 150 | $63,040 +22% |
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 40. 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.