Arts, Design, Entertainment, Sports, and Media · United States (national)
Interpreters and Translators
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is highly exposed to automation, and meaningful change looks likely over the next 5 to 10 years.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 72.0 to 96.0
Physical / roboticsrange 6.9 to 30.9
When the change is likely
Partial, 5 to 10 yearslocal demand growing
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Use your 5 to 10 year runway
Change here is gradual. Build resilient skills steadily and watch the signals rather than waiting for a shock.
2
Lean into the human edge
The exposed parts are routine and cognitive. Invest in judgment, client relationships, and complex problem-solving that AI still cannot do.
3
Pivot toward Speech-Language Pathology Assistants
It is skill-adjacent and less exposed (22.1 vs 84). A lateral move now beats a forced one later.
4
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
5
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
Keep the monitor on
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Which of my tasks are exposed
Follow ethical codes that protect the confidentiality of information.
84.0
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
84.0
Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.
84.0
Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.
84.0
Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy.
84.0
Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.
84.0
Identify and resolve conflicts related to the meanings of words, concepts, practices, or behaviors.
50.0
Compile information on content and context of information to be translated and on intended audience.
50.0
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Follow ethical codes that protect the confidentiality of information.
Task: Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
Local demand is growing
Lower-risk adjacent roles
Speech-Language Pathology Assistants
22.1
Tutors
37.1
Speech-Language Pathologists
39.0
Adult Basic Education, Adult Secondary Education, and English as a Second Language Instructors
42.6
Foreign Language and Literature Teachers, Postsecondary
56.7
English Language and Literature Teachers, Postsecondary
57.1
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Interpreters and Translators to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 84.0, robotics 18.9.
The timeline comes from the higher axis (84.0), shifted for local demand (growing), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.