← Risk register SOC 27-3091 · reviewed 2026-08-11

Interpreters and Translators

52,060 US workers · median $60,170/yr · Media

EXPOSED

Written translation — documents, subtitles, localization strings, marketing copy — is the single most thoroughly automated language task there is, and the market has already repriced it toward machine-translation post-editing at lower rates per word. Live interpreting holds up better: courtroom and hospital work demands physical or on-call presence, sworn accuracy, cultural mediation, and a certified human whose name goes on the record. The modal worker in this SOC straddles both, which is why the score lands on the COOKED/EXPOSED line rather than clearly above it.

10-year outlook: Written translation employment keeps contracting toward a smaller post-editing and certified-attestation tier, while court, medical, and sign language interpreting stay staffed by humans through the decade.

US employment, 2019–2025-11.6%
58,87052,060 workers

This fall is concentrated in 2020 and has not recovered since.

Median pay $51,830 → $60,170 -7.1% in real terms (nominal +16.1%, 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

+1.7%

Percentage only. The projection counts a different population from the 52,060 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 +1.7% 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.

~6,900 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.

LinguistTranslatorInterpreterFarsi LinguistDeaf InterpreterLegal TranslatorRussian LinguistArabic TranslatorCourt InterpreterBraille TranslatorEnglish TranslatorSpanish TranslatorBilingual SecretaryBraille TranscriberContract TranslatorLanguage TranslatorMedical InterpreterSpanish InterpreterCryptologic LinguistFreelance TranslatorLanguage InterpreterTechnical TranslatorBilingual InterpreterCommunity Interpreter

Score — 34/100 resistance

Holding it up: trust premium (9/20). Weakest point: task resistance (5/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 5 + 7 + 6 + 9 + 7 = 34. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 5/20

Core tasks are already automatable A 5 reflects that the bulk of billable volume in this SOC — contract translation, subtitle files, software strings, birth certificates, patent abstracts — now arrives as an MT draft to be post-edited, and even simultaneous interpreting has working speech-to-speech pipelines in conference and telehealth queues; the score is not 2 only because whispered courtroom simultaneous, ASL, and low-resource language pairs still require a live human ear.

Embodiment 7/20

Some physical or field component A 7 covers the interpreter who drives to the deposition, stands next to the patient during a pelvic exam, sits in the booth with a headset, or does relay work over VRI carts — real presence and travel, but nothing manipulated with the hands and no uncontrolled site conditions beyond a noisy courtroom or an ambulance bay, which is why it sits below the 13 line.

Liability shield 6/20

Certification preferred, not legally required Court certification (state or Administrative Office of the Courts) and CCHI/NBCMI medical credentials are gatekeepers for the good assignments and you swear an oath on the record, but no statute reserves the act of translating to a licensed person — an unsertified bilingual can and does take the work, and ATA certification is a marketing asset rather than a legal monopoly, which puts this at 6 rather than 12.

Trust premium 9/20

Some relationship component Court and hospital interpreters are booked through agency schedulers and language-access vendors who fill by availability and language pair, not by name, so the 9 credits the repeat-client relationships that literary translators, conference regulars, and long-term legal teams genuinely build — but most assignments end when the encounter does.

Judgment & accountability 7/20

Meaningful discretion A 7 reflects real calls made in the moment — flagging a register mismatch, deciding whether an idiom or a hedged consent phrase carries over, interrupting to request clarification on the record — bounded by canons of ethics that mandate completeness and prohibit editorializing, so you own accuracy but not the underlying decision, unlike the physician or judge in the room.

Scored twice. An independent second run returned 38/100 — EXPOSED, agreeing with the verdict above.

This score sits on a verdict boundary. At 34/100 it is one point from COOKED. Re-scoring moves results by a point or two, so here the score is more informative than the label.

Confidence: high · reviewed 2026-08-11 · how scoring works · 5 deployment reports on file

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: physical-presence, trust, licensure

How to future-proof this job

Training paths for your skill gaps: MIT OpenCourseWare — systems analysis and engineering free · edX — systems thinking and evaluation methods free to audit · Khan Academy — physics, chemistry and biology from the ground up free · Coursera — communication and interpersonal skills free to audit · Coursera — teaching and instructional design, audit free free to audit · Learning How to Learn — the most-taken course on Coursera, and 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 — 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.

Special Education Teachers, Preschool SAFE · 82/100 · you already have ~68% of the skill profile

Skills to close: Systems Analysis, Systems Evaluation, Science, Social Perceptiveness

Secondary School Teachers, Except Special and Career/Technical Education SAFE · 69/100 · you already have ~58% of the skill profile

Skills to close: Instructing, Learning Strategies, Science, Systems Analysis

Anthropologists and Archeologists EXPOSED · 51/100 · you already have ~56% of the skill profile

Skills to close: Science, Systems Analysis, Active Learning, Systems Evaluation

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 52/100, still EXPOSED.

6 specific changes that would raise this score
  • already happening liability shield +5

    Federal or state court rules explicitly barring machine translation for record proceedings and requiring a credentialed interpreter's oath — e.g. state judiciary language-access plans (California, New Jersey, Washington) codifying that only a court-certified interpreter may render testimony, plus USCIS/asylum and Title VI hospital rules requiring a qualified human interpreter's attestation rather than an AI relay. Similar for sworn/certified translations for immigration, adoption, and patent filings where a translator's signed certificate of accuracy carries perjury exposure.

  • already happening task resistance +4

    Task-mix shift as raw-draft translation disappears entirely: what remains is legally-consequential certified translation, simultaneous conference and court interpreting under time pressure, ASL and low-resource-language work with thin training data, and transcreation where the deliverable is a judgment call about register and cultural risk rather than a rendering. This is a genuine two-tier occupation and the bottom tier is already largely gone.

  • plausible liability shield +3

    Malpractice insurers and hospital accreditation bodies (Joint Commission language-access standards) requiring documented human interpreter involvement for consent, discharge, and psychiatric encounters after an adverse-event case traced to machine translation.

  • plausible judgment accountability +3

    Formalization of the interpreter's affirmative duty to intervene — codes of ethics (NCIHC, RID, court interpreter codes) already require flagging miscommunication, cultural framing, and interpreter conflict; if court rules or hospital policy make the interpreter the named party responsible for halting a proceeding or encounter when comprehension fails, the role owns a consequential call.

  • plausible trust premium +2

    Buyer-side backlash in specific niches: literary publishers and author contracts prohibiting MT (Authors Guild model clauses, translator credit-on-cover campaigns), and audiovisual union agreements on subtitle quality after high-profile bad-subtitle incidents on streaming platforms. Narrow, prestige-segment only — it does not reach the commercial document market.

  • unlikely embodiment +1

    On-site presence requirements for court, deposition, medical, and conference work if remote-interpreting platform standards tighten (some judiciaries restrict video remote interpreting for jury trials). Marginal — the trend runs the other way toward remote.

The limit. The ceiling is structural: the certified-interpreter tier that carries the liability and judgment is a minority of this SOC's headcount, and gains there do not lift the written-translation majority. Realistic top is mid-50s for the courtroom/medical interpreter, while document translators stay near the current score regardless of what happens to interpreting rules.

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 167 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

Houston-Pasadena-The Woodlands, TX 2,530 $44,490 -26%
New York-Newark-Jersey City, NY-NJ 2,520 $87,590 +46%
Phoenix-Mesa-Chandler, AZ 1,720 $50,070 -17%
Boston-Cambridge-Newton, MA-NH 1,660 $75,120 +25%
Washington-Arlington-Alexandria, DC-VA-MD-WV 1,630 $94,510 +57%
Los Angeles-Long Beach-Anaheim, CA 1,480 $69,460 +15%
Dallas-Fort Worth-Arlington, TX 1,280 $56,920 -5%
Tampa-St. Petersburg-Clearwater, FL 1,170 $49,470 -18%

Best paid

San Jose-Sunnyvale-Santa Clara, CA 300 $101,530 +69%
Washington-Arlington-Alexandria, DC-VA-MD-WV 1,630 $94,510 +57%
Bridgeport-Stamford-Danbury, CT 60 $93,960 +56%

Percentages are against this occupation's national median of $60,170. 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

The score above is about what the work exposes. This is reporting about real deployments in this occupation — the difference between "could be automated" and "somebody automated it."

DeepL · Warhorse Studios · Duolingo · State of New Jersey

6 of 7 reported cases, with sources

1 more in the dispatch

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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