Share and intensity of work current AI systems can materially affect.
SOC 27-3091
Translators AI displacement risk
Written translation is the single most machine-exposed language task: neural and AI translation now produce usable drafts instantly. Post-editing, literary and marketing nuance, certified translation, and terminology governance are where the work is moving.
Likely potential for exposed tasks to move to software after workflow integration.
Volume-driven general translation rates are under severe pressure. Translators who pivot to post-editing quality ownership, specialization, or transcreation retain pricing power.
Distribution
Where Translators sits across 620 tracked roles
Displacement pressure 72 — higher than 94% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
17 O*NET task statements matched to SOC 27-3091. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $60,170 (May 2025, US national). The latest BLS row matched SOC 27-3091.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Translators
SOC 27-3091 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 72/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Translators
The current evidence import matched 17 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks17
SOC27-3091
- Core task / ID 9326
Follow ethical codes that protect the confidentiality of information.
- Core task / ID 9328
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
- Core task / ID 9335
Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.
- Core task / ID 9333
Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.
- Core task / ID 9332
Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy.
- Core task / ID 9330
Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Translate written documents
Exposure 92, automation 68%, augmentation 54%.
O*NET evidence: Read written materials, such as legal documents, scientific works, or news reports, and... (ID 9331)
Localize software and technical content
Exposure 84, automation 56%, augmentation 60%.
Edit machine-translated drafts
Exposure 76, automation 46%, augmentation 70%.
O*NET evidence: Proofread, edit, and revise translated materials. (ID 9329)
Build terminology and style resources
Exposure 60, automation 32%, augmentation 64%.
TaskExposureAutomationAugmentation
Translate written documents9268%54%
Localize software and technical content8456%60%
Edit machine-translated drafts7646%70%
Build terminology and style resources6032%64%
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
HighLocalization Project Manager
Training horizon: 3-6 months. Skill overlap 66. Wage preservation signal 118.
- Manage vendor and MT workflows
- Track quality scorecards
- Coordinate multilingual releases
role redesign
HighMachine Translation Post-Editor
Training horizon: 1-3 months. Skill overlap 80. Wage preservation signal 104.
- Define quality thresholds
- Log recurring MT errors
- Certify high-stakes output
Comparison guides
Compare the next move before you commit
3-6 months
Translators to Localization Project Manager
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Translators into Localization Project Manager.
1-3 months
Translators to Machine Translation Post-Editor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Translators into Machine Translation Post-Editor.
What the AI risk score means for Translators
The displacement pressure score for Translators is 72. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Translate written documents carries 68% automation pressure, while Edit machine-translated drafts carries 70% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $60,170 (May 2025, US national). Employment context: Written-language role at the center of machine translation adoption. Typical education: Bachelor's degree common.
Wage vulnerability is 58, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Very high machine substitution pressure
- Post-editing replaces raw translation
- Specialization protects rates
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Translators, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Post-editing quality judgment
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Subject-matter specialization
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Terminology management
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Transcreation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Localization Project Manager, such as manage vendor and mt workflows.
- By 90 days, compare internal openings and external postings for Localization Project Manager or Machine Translation Post-Editor and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Translators
Will AI replace Translators?
Written translation is the single most machine-exposed language task: neural and AI translation now produce usable drafts instantly. Post-editing, literary and marketing nuance, certified translation, and terminology governance are where the work is moving. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Translators work are most exposed to AI?
Translate written documents and Localize software and technical content show the strongest automation pressure in this model. Edit machine-translated drafts and Build terminology and style resources are better treated as AI-augmented work.
What should Translators learn next?
Start with Post-editing quality judgment, Subject-matter specialization, Terminology management. The most practical adjacent paths in this model are Localization Project Manager and Machine Translation Post-Editor.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics