Interpreters and Translators
In short: today's best AI models could do about 43% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 66% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 31% of the time, stays with people.
Interpret oral or sign language, or translate written text from one language into another.
52,060 people do this job in the US. $60,170 a year.
Reviewed on October 19, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on3 h
- A person checks AI's work14 h
- Desk work AI can't do yet10 h
- Stays with people13 h
Interpreters and Translators, now: of a 40-hour week, AI could take on 3 hours, 14 hours become checking its work, 10 hours are desk work it can't do yet, and 13 hours stay with people.
The largest pieces: Compile terminology (about 4 h, a person checks); Check technical terms in translations (about 2 h, a person checks); Gather context for a translation (about 2 h).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 69%
- 0%
- 7%
- 24%
- 0%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 43% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 1%. With Claude Mythos Preview (early), measured in April 2026, 26%. Today's estimate: 43%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.98% went to this job's tasks in May 2026, the 26th largest of the 447 jobs that showed up at all; 5 of its 17 tasks showed up. Within each of Anthropic's methods, never across a change: 0.62% to 0.91% (released February and March 2025); 0.68% to 0.77% (August 2025 to February 2026); 0.94% to 0.98% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.62% | 26 of 708 | 11 of 17 |
| Released March 2025 | 0.91% | 19 of 703 | 13 of 17 |
| The week of Aug 4, 2025 (released September 2025) | 0.68% | 28 of 655 | 9 of 17 |
| The week of Nov 13, 2025 (released January 2026) | 0.76% | 25 of 672 | 11 of 17 |
| The week of Feb 5, 2026 (released March 2026) | 0.77% | 27 of 677 | 12 of 17 |
| April 2026 (released June 2026) | 0.94% | 28 of 434 | 5 of 17 |
| May 2026 (released June 2026) | 0.98% | 26 of 447 | 5 of 17 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates almost none of this job's working time as physical work, so robots do not reach it.
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
31% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Interpret speech live, the in-person work, 12%
- Listen to speakers to interpret, the in-person work, 7%
- Adapt interpreting to the student's level, the in-person work, 4%
- Guide foreign-language tourists, the in-person work, 2%
- Keep information confidential, the in-person work, 2%
- Train and supervise other translators, the conversation, 1%
What becomes checking the machine
58% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 8% is desk work that is small if wrong, which can be handed over with lighter checks.
- Localize software and its documents, costly if wrong, 26%
- Compile terminology, costly if wrong, 10%
- Check technical terms in translations, costly if wrong, 6%
- Translate written materials, costly if wrong, 5%
- Proofread and edit translations, costly if wrong, 4%
- Resolve conflicts of meaning, costly if wrong, 3%
What to learn
- Briefing a model and checking its drafts. About 69% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
- The conversations. About 31% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 17 tasks from .
| Task | ||||
|---|---|---|---|---|
| Adapt software and accompanying technical documents to another language and culture.Translating the software's text and manuals and adapting them to the culture.Done more than yearly by most who do it. | 28% | 2.1 work days3.3 h to 1.7 work weeks | Documents 100% | 15%Half by Oct 2027 |
| Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.Rendering what is said into the other language as it is spoken, in person or over a link.Done hourly or more by most who do it. AI observed on this task. | 12% | 50 min25 min to 3.3 h | In person 80%, conversation 20% | Stays with people |
| Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.Building the glossary of legal, medical or technical terms before translating.Done more than weekly by most who do it. | 10% | 1.7 h25 min to 4.2 h | Documents 100% | 97%Half by now |
| Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.Hearing the speaker live, in the room or through a headset, and working out the meaning.Done hourly or more by most who do it. | 7% | 38 min13 min to 2.5 h | In person 80%, conversation 20% | Stays with people |
| Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.Reading the translation through for terms used right and the same way throughout.Done hourly or more by most who do it. | 6% | 50 min17 min to 2.5 h | Documents 100% | 100%Half by now |
| Compile information on content and context of information to be translated and on intended audience.Reading up on the subject, the setting and the audience before translating.Done hourly or more by most who do it. | 6% | 50 min17 min to 1.7 h | Documents 100% | 100%Half by now |
| Read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages.Reading the source text and writing it in the other language.Done more than yearly by most who do it. AI observed on this task. | 5% | 3.3 h50 min to 1.7 work days | Documents 100% | 71%Half by now |
| Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary.Interpreting at the level each student can follow, worked out with the teaching team.Done daily by most who do it. AI observed on this task. | 5% | 1.3 h25 min to 3.3 h | In person 80%, documents 20% | 20%Desk part half by now |
| Identify and resolve conflicts related to the meanings of words, concepts, practices, or behaviors.Working out what a word or custom means in each language, on the page or in the room.Done hourly or more by most who do it. | 4% | 25 min13 min to 1.7 h | Documents 70%, in person 30% | 70%Desk part half by now |
| Proofread, edit, and revise translated materials.Reading the translation through and fixing it.Done daily by most who do it. AI observed on this task. | 4% | 1.3 h25 min to 3.3 h | Documents 100% | 99%Half by now |
| Follow ethical codes that protect the confidentiality of information.Working to the code of confidentiality while interpreting and translating.Done hourly or more by most who do it. | 4% | 13 min4 min to 38 min | Documents 50%, in person 50% | 50%Desk part half by now |
| Check original texts or confer with authors to ensure that translations retain the content, meaning, and feeling of the original material.Reading the translation beside the source, with questions to the author by call or note.Done more than weekly by most who do it. | 3% | 50 min13 min to 1.7 h | Documents 60%, conversation 40% | 60%Desk part half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Speech-Language Pathologists54% now, 56% by 2028
- Foreign Language and Literature Teachers, Postsecondary49% now, 73% by 2028
- Speech-Language Pathology Assistants44% now, 44% by 2028
- Adult Basic Education, Adult Secondary Education, and English as a Second Language Instructors66% now, 66% by 2028
- English Language and Literature Teachers, Postsecondary28% now, 59% by 2028
- Tutors23% now, 23% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Interpreters and Translators is within reach of AI now?
- An estimated 43% of the working time is within reach of AI models now and 66% by the end of 2028, on METR's long-run pace at four in five (57% to 68% across the three paces the site shows). Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Interpreters and Translators come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Compile terminology (97% of the task's time now); Check technical terms in translations (100% of the task's time now); and Gather context for a translation (100% of the task's time now).
- Which tasks of Interpreters and Translators stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Interpret speech live; Listen to speakers to interpret; and Guide foreign-language tourists. Together such tasks are about 22% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Interpreters and Translators learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are briefing a model and checking its drafts; reviewing work you did not do yourself; and the conversations. By the end of 2028 on the long-run pace at four in five, about 31% of the working time stays with people, 58% becomes checking a model's work and 8% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Interpreters and Translators?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.98% went to this job's tasks, the 26th largest share of the 447 jobs that showed up; 5 of its 17 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 43%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Interpreters and Translators?
- Anthropic's robot exposure rating (September 2026) finds almost none of the working time is physical work, so robots do not reach it.
- How is this estimated?
- From the 17 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on October 19, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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