A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-27-3091-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot,
where humans remain in the loop, and what the labor market is doing. Built from observed
Claude.ai conversations mapped to O*NET tasks and from published research — measures of
usage and exposure, not advice or predictions that the job is going away.
Often handed to AI
Task areas most often handled directively in observed AI conversations —
candidates to delegate with light review.
Read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages. · 45.2%
Proofread, edit, and revise translated materials. · 19.6%
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible. · 2.2%
Task areas where a human was still judged necessary in a large share of observed
conversations — not a safety ruling, an observed-need signal.
Discuss translation requirements with clients and determine any fees to be charged for services provided. · 100.0% need a human
Proofread, edit, and revise translated materials. · 98.0% need a human
Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material. · 95.8% need a human
↔91st-percentile task overlap — yet
about 6,900 openings a year
(+1.7% projected, BLS), and
observed AI use leans 4022% copilot, not hand-off (AEI)
. What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is
actually being used in it, and where employment is headed. These are positions
within published studies — measures of exposure and usage, not predictions
that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows —
the percentile (this job's rank among all U.S. occupations with
data) is the comparable figure, and sizes the bars.
Measure
Rank vs all occupations
Percentile
Score
Overall AI exposure (Felten et al.) High
79th
1.1
LLM task exposure, γ (OpenAI / Eloundou) High
75th
0.9
AI assistant applicability (Microsoft) High
100th
0.5
OpenAI's exposure study scores tasks three ways: with a language model alone
(α 0.9), with simple added tooling
(β 0.9), and including AI-powered software
(γ 0.9). Higher means more of the job's
tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.4 ·
43rd percentile among occupations ·
Moderate
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic
Economic Index, 2026-01-15), these
task types came up most. These are shares of observed AI conversations —
not shares of the job, of worker time, or of what could be automated.
Read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages.
46.4%
Proofread, edit, and revise translated materials.
26.1%
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
8.7%
Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary.
2.9%
Check original texts or confer with authors to ensure that translations retain the content, meaning, and feeling of the original material.
1.5%
Follow ethical codes that protect the confidentiality of information.
0.9%
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 —
a labor-market forecast, not an AI-impact forecast.
Outlook
About average · +1.7% by 2034
Projected annual openings
6,900
Employment 2024 → 2034
75,300 → 76,600
“Annual openings” counts new jobs plus replacements for workers who leave the
occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08
occupation system, not US SOC. Bridged through the published (and approximate,
many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the
international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task
overlap, not automation, adoption, or jobs lost.
Interpreters and Translators sits at the 97th percentile of 427
occupations on the global GenAI task-exposure gradient
. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
Read the whole six-band gradient on the GenAI exposure gradient page.
The crosswalk is approximate: a US occupation can map to several international ones, and the
ILO scores describe the international occupation, not this exact US role.
Working with AI in this job
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic
Index, 2026-01-15. This is one AI
assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the
collaboration mix are model-rated estimates; figures below the sample floor are hidden.
Augmentation vs. automation
40.2% working with AI · 56.9% handed to AI
Most common way people use AI here
Directive · AI does it; you give the instruction
Typical AI autonomy
3.0 / 5
· higher = AI acts more independently
Used for work (vs. personal / coursework)
63.2%
What people delegate to AI
The role's most common tasks in AI conversations, each tagged with how people work
with the AI on it. “Usage” is the share of observed conversations, not of
the job.
Task
How
Usage
Read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages.
Directive
45.2%
Proofread, edit, and revise translated materials.
Directive
19.6%
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
Directive
2.2%
Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary.
Directive
0.8%
Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.
Directive
0.7%
Check original texts or confer with authors to ensure that translations retain the content, meaning, and feeling of the original material.
Iteration
0.6%
Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.
—
0.5%
Discuss translation requirements with clients and determine any fees to be charged for services provided.
—
0.4%
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful
read on the current boundary, not a guarantee.
Discuss translation requirements with clients and determine any fees to be charged for services provided.
100.0%
Proofread, edit, and revise translated materials.
98.0%
Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.
95.8%
Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary.
94.8%
Read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages.
93.5%
Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.
93.3%
What people most often hand AI here
Example prompts phrased from the tasks people most often delegate to AI in this
occupation (Anthropic Economic Index). Each shows the underlying measured task and its
share of observed AI use. They are suggested phrasings of real tasks — starting points,
not endorsed instructions.
Help me read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages.
From: Read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages. · 45.2% of measured AI use · directive
Help me proofread, edit, and revise translated materials.
From: Proofread, edit, and revise translated materials. · 19.6% of measured AI use · directive
Help me translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
From: Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible. · 2.2% of measured AI use · directive
Help me adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary.
From: Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary. · 0.8% of measured AI use · directive
Tasks
All 17 tasks O*NET lists for this occupation, ordered by importance.
Each links to its own page with AI-exposure and observed-use detail.
How characteristic each condition is of the job, on O*NET's 1–5 context scale
(higher = more present in day-to-day work). Each condition links to how it varies
across all occupations.
A considerable amount of work-related skill, knowledge, or experience is needed for these occupations. For example, an accountant must complete four years of college and work for several years in accounting to be considered qualified.
Preparation level
SVP (7.0 to < 8.0) — total schooling plus on-the-job experience.
Annual wages by percentile — U.S. (BLS OEWS). The light
band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line
is the median.
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for
the occupation, not an AI-impact forecast.
10th percentile
$35,630
25th percentile
$45,020
Median (50th)
$59,440
75th percentile
$80,020
90th percentile
$99,830
People employed
53,360
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is
the occupation's national median, not industry-specific.
Industries where this occupation is far more common than in the economy as a whole.
The location quotient is how many times more concentrated it is here (a value of 5
means five times its economy-wide share).
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.
▸Write a report on thisheadline · factoids · citation
Interpreters and Translators show 91st-percentile AI task overlap — and about 6,900 annual U.S. openings
Interpreters and Translators rank in the 91st percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
The occupation is projected to see about 6,900 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
BLS projects employment to be about average (+1.7%) from 2024 to 2034.BLS Employment Projections 2024–34
Median annual pay is $59,440, across about 53,360 U.S. workers.BLS OEWS (May 2024)
Of the AI use actually observed for this work, 40% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
Interpreters and Translators show 91st-percentile AI task overlap — and about 6,900 annual U.S. openings
• Interpreters and Translators rank in the 91st percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 6,900 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be about average (+1.7%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $59,440, across about 53,360 U.S. workers. (BLS OEWS (May 2024))
• Of the AI use actually observed for this work, 40% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2)
Source: Singulariki — "Interpreters and Translators". https://singulariki.com/roles/role-27-3091-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
Every line is built only from figures this page already shows and cites. AI task overlap means
what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not
hand-written opinion. See the full methodology for what each
measure does and does not mean.
O*NET 30.3U.S. Department of Labor / National Center for O*NET Development
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Interpreters and Translators." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-27-3091-00
APA
Singulariki. (2026). Interpreters and Translators. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-27-3091-00
BibTeX
@misc{singulariki-role-27-3091-00,
title = {Interpreters and Translators},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-27-3091-00}
}
Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.
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