← Risk register SOC 41-9012 · reviewed 2026-08-11

Models

3,780 US workers · median $48,470/yr · Sales

COOKED

The modal model's deliverable is a still image for catalog, e-commerce, or advertising — and that image is exactly what generative models now produce at usable commercial quality, without call sheets, day rates, or travel. What survives is physical: live runway and trunk shows, fit modeling on real garments during sampling, in-person brand activations, and the small tier of named talent whose recognizable identity is the product being licensed. No license, no sign-off requirement, and almost no decision authority means there is no regulatory or accountability moat to slow substitution.

10-year outlook: Within ten years most catalog and e-commerce modeling work is generated rather than shot, and paid bookings concentrate into live events, fit work, and a smaller set of licensable named faces.

US employment, 2019–2025+62.9%
2,3203,780 workers

Dipped in 2020, then grew past where it started.

Median pay $28,350 → $48,470 +36.8% in real terms (nominal +71.0%, 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

-0.5%

Percentage only. The projection counts a different population from the 3,780 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 -0.5% 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.

~1,200 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.

ModelArt ModelFit ModelHat ModelHand ModelInfluencerMale ModelNude ModelAgent ModelFloor ModelSales ModelFigure ModelRunway ModelStudio ModelClothes ModelFashion ModelFitness ModelModel BuilderSpirits ModelArtist's ModelArt Class ModelFine Arts ModelFreelance ModelLife Drawing Model

Score — 31/100 resistance

Holding it up: embodiment (13/20). 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: 7 + 13 + 1 + 7 + 3 = 31. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 7/20

Mixed — a routine tier and a judgment tier Holding a pose under strobes for an e-commerce still is now the single most reproducible thing a diffusion model does, and the reason this sits at 7 rather than 3 is the residual work that can't be rendered — walking a 20-look show on a live runway, standing for a fitter pinning a sample at a size-8 block, and appearing at a store opening where the crowd is the point.

Embodiment 13/20

Hands-on in uncontrolled environments Fit modeling means your actual measurements are the tool and you spend hours on a fitting stand in an unheated sample room; runway and location shoots put you outdoors, on cobblestones in unbroken shoes, changing backstage on a schedule you don't set — that's real physical exposure, but it stops short of 17+ because nobody is asking you to carry loads or operate equipment.

Liability shield 1/20

No licence, no signature requirement There is no state license, no board, no continuing-education requirement to call yourself a model; agency representation is a contract, not a credential, and the brand's legal exposure for the campaign sits with the client and photographer, not with you.

Trust premium 7/20

Some relationship component A 7 reflects the real but narrow relationship layer — a booker who knows your fit history, a designer who rebooks you every season, a photographer who works fast with you — while acknowledging that catalog clients hire off a digital and would not notice a substitution, unlike the top tier where the name on the contract is the licensed asset.

Judgment & accountability 3/20

Executes defined procedures on defined inputs The call sheet, stylist, art director, and photographer decide the look, the pose, and the frame; your discretion is micro-adjustment within someone else's direction, and no consequential decision rests on your read of an ambiguous situation.

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: OSHA Outreach Training — the 10- and 30-hour cards most employers ask for low · edX — operations management and process monitoring courses free to audit · Apprenticeship.gov — industrial maintenance and millwright programs paid to train · Apprenticeship.gov — paid, employer-sponsored trade apprenticeships, searchable by trade and ZIP paid to train

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.

Refuse and Recyclable Material Collectors EXPOSED · 55/100 · you already have ~72% of the skill profile

Skills to close: Operation and Control, Operations Monitoring, Equipment Maintenance, Repairing

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 — EXPOSED.

4 specific changes that would raise this score
  • already happening embodiment +3

    Task-mix shift toward the irreducibly physical: fit/sampling modeling on live pattern iterations, runway, and in-person activations become a larger share of remaining paid days as flat catalog work is absorbed. This raises the score for the surviving job without any new law, but it also shrinks headcount.

  • plausible liability shield +4

    AI-disclosure and consent statutes that make brands legally exposed for synthetic bodies: California AB 2602 (2024) already voids contracts allowing digital replicas without specific consent, and the proposed federal NO FAKES Act plus New York's fashion-worker and digital-replica rules point at a regime where any AI-generated human likeness in an ad requires a documented, consented, compensated real person of record. If the EU AI Act's synthetic-content labeling plus a mandatory 'synthetic model' disclosure on e-commerce imagery lands, brands may keep a signed human release in the chain for every campaign image.

  • plausible trust premium +4

    Retailer or platform policy that bans undisclosed AI imagery and markets 'real bodies only' — Levi's 2023 backlash over AI models is the template, and a size-inclusivity or advertising-standards rule (UK ASA-style, or a Fashion Workers Act amendment) requiring that garment fit be shown on a real wearer would make human-shot editorial a paid brand signal rather than a cost line.

  • plausible task resistance +3

    Fit modeling as a distinct judgment tier — verbal feedback on drape, seam pressure, and range of motion feeding pattern correction is a sensor-and-report function generative models cannot supply; if brands formalize fit-model panels with standardized measurement contracts, that tier is defensible.

The limit. Even with every lever, this stays low-to-mid: the modal deliverable is a still image, the surviving work concentrates into a small named-talent and fit-model tier, and 3,780 counted workers can shrink faster than any dimension rises. Disclosure law protects likeness rights, not the volume of bookings.

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 5 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 400 $109,820 +127%
Chicago-Naperville-Elgin, IL-IN 50 $53,700 +11%
Rochester, NY 50 $208,000 +329%
Anchorage, AK 40 $81,520 +68%
Washington-Arlington-Alexandria, DC-VA-MD-WV 30 $47,170 -3%

Best paid

Rochester, NY 50 $208,000 +329%
Los Angeles-Long Beach-Anaheim, CA 400 $109,820 +127%
Anchorage, AK 40 $81,520 +68%

Percentages are against this occupation's national median of $48,470. 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 31. 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

Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.