← Risk register SOC 25-4011 · reviewed 2026-08-11

Archivists

7,970 US workers · median $64,550/yr · Education

EXPOSED

The description layer of archival work — writing finding aids, generating metadata, transcribing handwritten documents, subject indexing, and answering routine reference queries — is exactly what current AI does at usable quality, and backlogs are the main reason institutions will adopt it fast. What resists is the physical side: rehousing fragile paper, monitoring environmental conditions, handling deteriorating film and magnetic media, and doing on-site appraisal of a donor's basement. Appraisal decisions (what enters the permanent record and what is destroyed), provenance judgment, restricted-records and privacy determinations, and donor negotiation are consequential calls no one will hand to a model unsupervised — but they're a thin slice of a small occupation with no licensure protecting it.

10-year outlook: Description backlogs get cleared by AI and headcount stays flat or falls slightly, with the surviving roles concentrated in appraisal, donor relations, conservation, and digital preservation infrastructure.

US employment, 2019–2025+21.5%
6,5607,970 workers

Dipped in 2020, then grew past where it started.

Median pay $53,950 → $64,550 -4.3% in real terms (nominal +19.6%, 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

+3.8%

Percentage only. The projection counts a different population from the 7,970 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.

Exposed, but growing

AI can already do a lot of these tasks, and the BLS still expects +3.8% 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.

~1,100 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 — 23 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.

ArchivistRegistrarRecord ClerkFilm ArchivistImage ArchivistRecords ManagerState ArchivistDocument ScannerMuseum ArchivistDigital ArchivistProject ArchivistRecords AssociateRecords SpecialistArchives SpecialistReference ArchivistProcessing ArchivistUniversity ArchivistAccessioning ArchivistDigitization AssistantDigital Asset ArchivistRecords Management SpecialistArchives Technician (Archives Tech)Document Management Technician (Document Management Tech)

Score — 43/100 resistance

Holding it up: embodiment (11/20). Weakest point: liability shield (4/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 9 + 11 + 4 + 8 + 11 = 43. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 9/20

Mixed — a routine tier and a judgment tier DACS-compliant finding aids, EAD encoding, series-level description, and accession registers are structured text generation that models already produce at draft quality, and what pulls this to 9 rather than 4 is the irreducible work of opening 200 unlabeled boxes to establish original order and physically arranging series before any description can exist.

Embodiment 11/20

Some physical or field component Rehousing brittle nitrate negatives, reformatting open-reel audio and U-matic tape on legacy decks, reading hygrothermograph data in stacks, and appraising a donor's flood-damaged garage put real hands on real objects — but most of the week is still spent at a workstation in a climate-controlled reading room, which is why this sits at 11 and not with the field techs at 16.

Liability shield 4/20

No licence, no signature requirement No state licenses archivists; the Academy of Certified Archivists' CA credential is voluntary, rarely required outside federal and large university postings, and carries no personal legal exposure — your institution's counsel, not you, answers for a wrongful FOIA release or a botched deed of gift.

Trust premium 8/20

Some relationship component Donor cultivation on a family papers collection can run years and the relationship genuinely closes the deal, but the vast majority of your output — finding aids, catalog records, digitized items — is consumed by researchers who never learn your name, holding this to 8.

Judgment & accountability 11/20

Meaningful discretion Appraisal is irreversible: deciding a records series has no enduring value means it is destroyed, and restricted-records calls under FERPA, HIPAA, or a 50-year donor closure have no lookup table — but retention schedules, institutional collecting policies, and a supervising director constrain most of those calls, which keeps this at 11 rather than in the high teens.

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, judgment

How to future-proof this job

Where to go deeper on what this job runs on: Khan Academy — reading and vocabulary, all levels, free free · Coursera — active listening and communication skills free to audit · Purdue OWL — the standard reference for professional writing free · Toastmasters — public speaking practice at local clubs worldwide low · Coursera — critical thinking and logic, audit free free to audit · MIT OpenCourseWare — full course materials across every department, free free

All 35 skills ranked by how many jobs they open →

Where this experience transfers — nothing clears the bar

No occupation passed every test: close enough to archivists 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:

Historians EXPOSED 36/100 (-7) · 77% overlap
History Teachers, Postsecondary EXPOSED 47/100 (+4) · 74% overlap
Librarians and Media Collections Specialists EXPOSED 42/100 (-1) · 73% overlap

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.

What would move this occupation up is the other direction, and on this page it's the more useful one.

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 57/100, still EXPOSED.

5 specific changes that would raise this score
  • already happening task resistance +3

    Task-mix shift: once auto-generated finding aids, OCR/HTR transcription and subject indexing are done by machine, the residual day is appraisal, provenance reconstruction of ambiguous accessions, restriction/privacy review under FERPA/HIPAA/state privacy law, and born-digital forensics (disk imaging, format migration, chain-of-custody documentation) — genuinely two-tiered work, and the judgment tier is what remains

  • plausible judgment accountability +4

    If federal/state records-retention regimes make a named archivist personally responsible for disposition authorization — e.g. NARA records-schedule certifications and state records-commission approvals requiring an identified human appraiser to attest that AI-suggested destruction was independently reviewed — appraisal and disposition move from advisory to accountable, and the surviving job is the call, not the description

  • plausible embodiment +2

    If institutional mandates on born-digital and audiovisual carriers (obsolete magnetic media, nitrate/acetate film, proprietary optical formats) push reformatting deadlines, the hands-on capture and condition-triage work grows as a share of the role; this is fragile, non-standard physical handling that does not template

  • plausible trust premium +2

    Only in provenance-contested and repatriation work: if museum/archive acquisition due diligence under NAGPRA, Indigenous data-sovereignty protocols, or looted-property claims requires a named human researcher's provenance opinion that funders and claimants will accept, buyers pay for the person's judgment specifically

  • unlikely liability shield +3

    Narrow route only: if courts or evidence rules tighten authentication of archival records (FRE 901/902 self-authentication for digital records) so that a certifying archivist's attestation of chain of custody carries personal exposure — as records managers already sign in litigation-hold contexts — a thin signature requirement appears. No licensure body exists (SAA's Academy of Certified Archivists credential is voluntary and confers no legal standing), so this stays small

The limit. Structural ceiling is low. 7,970 workers, no licensure, and employers are mostly budget-constrained public institutions and universities whose primary motive for AI adoption is clearing description backlogs cheaply — the same fiscal pressure that drives adoption also prevents any trust premium. The judgment tier is real but thin, so task-mix shift raises resistance per surviving worker while reducing headcount. Realistic composite ceiling around mid-50s, and that is compatible with fewer positions.

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 42 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

New York-Newark-Jersey City, NY-NJ 760 $77,450 +20%
Washington-Arlington-Alexandria, DC-VA-MD-WV 670 $84,590 +31%
Los Angeles-Long Beach-Anaheim, CA 490 $70,630 +9%
Seattle-Tacoma-Bellevue, WA 310 $78,530 +22%
Baltimore-Columbia-Towson, MD 230 $76,440 +18%
Boston-Cambridge-Newton, MA-NH 230 $78,220 +21%
San Francisco-Oakland-Fremont, CA 180 $74,320 +15%
Chicago-Naperville-Elgin, IL-IN 160 $57,930 -10%

Best paid

Bridgeport-Stamford-Danbury, CT 80 $119,190 +85%
San Jose-Sunnyvale-Santa Clara, CA 110 $97,550 +51%
Washington-Arlington-Alexandria, DC-VA-MD-WV 670 $84,590 +31%

Percentages are against this occupation's national median of $64,550. 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 43. 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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