EXPOSED
Food batchmakers weigh, mix, cook and blend ingredients to fixed formulas, monitor kettle and mixer gauges, and record batch data — work that language AI can't do directly but that industrial process automation has been eating for decades, with sensors and PLCs now handling dosing, temperature holds, and batch logging. The physical handling of ingredients, hoses, filters, and messy cleanup in wet, hot plant conditions is the real moat, and it's a moat against robots, not chatbots. No personal licensure and no customer relationship; discretion is mostly limited to sensory checks and adjusting a batch that's off-spec.
Dipped in 2020, then grew past where it started.
Median pay $30,790 → $42,290 +9.9% 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
+6.9% 173,500 → 185,400 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +6.9% 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.
~24,200 openings a year on average, including replacing people who leave.
MakerMixerJuicerBatcherBlenderChurnerColorerKneaderPicklerSpinnerWet MixerBran MixerCake MakerCompounderLard MixerPie FillerSoup MixerBatch MakerBatch MixerBrine MakerCandy MakerCheesemakerChili MakerChocolatier
Holding it up: embodiment . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier Automated dosing systems, jacketed kettles with PLC temperature holds, and inline batch logging already cover the weigh-mix-cook-record sequence in large plants, but hand-charging minor ingredients from sacks and totes, swapping filters, hooking transfer hoses, and hand-tasting a batch keep this at 11 rather than down near the fully-scripted assembly jobs.
Hands-on in uncontrolled environments The shift is spent on a wet, hot production floor lifting 50-lb ingredient bags, climbing to mixer hatches, dragging CIP hoses, and scraping out kettles between runs — uncontrolled enough to sit at 13, though it's still one fixed plant with fixed equipment rather than a changing outdoor site.
No licence, no signature requirement No state licence attaches to the batchmaker; the plant's HACCP plan, the PCQI, and the QA lab carry the food-safety accountability, and the 3 reflects only the food handler card or in-house GMP/allergen training the employer requires.
Executes defined procedures on defined inputs Formulas, cook times, and hold temperatures come off a spec sheet with deviations escalated to QA, so the 6 covers the real but narrow calls — judging viscosity or colour by eye, deciding a batch needs another minute or a rework — inside a documented procedure someone else owns.
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 (11/20 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 (3/20) 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 (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 12 of this occupation's 36 points (33%).
Embodiment (13/20) 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.
No occupation passed every test: close enough to food batchmakers on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
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 48/100, still EXPOSED.
Product-mix shift toward small-batch, allergen-segregated, and artisanal SKUs (cheese, fermentation, craft chocolate, cultured dairy) where changeover cleaning, hose swaps, and open-vessel handling in wet/hot conditions defeat fixed automation economics; FSMA 204 traceability and allergen changeover validation add manual verification steps between runs
Task-mix shift as PLCs absorb dosing, temperature holds, and batch logging, leaving the residual role as off-spec troubleshooting, sensory/organoleptic judgment, fermentation and dough-development calls that no in-line sensor reliably reads, plus line startup/shutdown and deviation investigation under HACCP
FSMA Preventive Controls rule already requires a named, trained individual to monitor and sign Critical Control Point records and initiate corrective action; if plants formally designate batchmakers as CCP monitors and deviation decision-makers (rather than QA-only), and SQF/BRC audits test that person's independent judgment, the role owns consequential recall-relevant calls
Signature accountability on batch and CCP records under FDA Part 11 / FSMA recordkeeping — where a named operator's initials are the audit trail in a recall investigation. This is documentary, not licensure; no realistic route to personal licensure or personal liability for this SOC
The limit. Trust premium is omitted: buyers are food manufacturers and retailers purchasing on cost, spec, and audit score. There is no consumer-visible batchmaker and no plausible route to one; 'handmade' labeling premiums accrue to the brand, not the occupation, and rarely change staffing. Even with all levers, the occupation stays capital-substitutable — a new depositor or CIP system removes headcount regardless of judgment content.
| Chicago-Naperville-Elgin, IL-IN | 8,490 | $47,660 +13% |
| New York-Newark-Jersey City, NY-NJ | 6,860 | $38,790 -8% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 4,490 | $45,020 +6% |
| Dallas-Fort Worth-Arlington, TX | 4,190 | $38,950 -8% |
| Los Angeles-Long Beach-Anaheim, CA | 4,130 | $38,930 -8% |
| Boston-Cambridge-Newton, MA-NH | 3,840 | $37,010 -12% |
| Houston-Pasadena-The Woodlands, TX | 3,210 | $33,830 -20% |
| Seattle-Tacoma-Bellevue, WA | 3,010 | $42,370 +0% |
| Cedar Rapids, IA | 480 | $77,920 +84% |
| Battle Creek, MI | 270 | $75,370 +78% |
| Harrisonburg, VA | 200 | $66,330 +57% |
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 36. 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.