Court Reporters and Simultaneous Captioners
The routine layer of this job is compressing.
A substantial share of this occupation's activities overlaps with what AI already does well. Expect the routine layer to compress first, and the human-anchored layer to gain weight and value.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Use verbatim methods and equipment to capture, store, retrieve, and transcribe pretrial and trial proceedings or other information. Includes stenocaptioners who operate computerized stenographic captioning equipment to provide captions of live or prerecorded broadcasts for hearing-impaired viewers.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Record information about legal matters. | 26.8% | Not observed 0.00 |
| Examine materials or documentation for accuracy or compliance. | 15.5% | Moderate 0.51 |
| Prepare legal or regulatory documents. | 12.2% | Not observed 0.00 |
| Perform administrative or clerical activities. | 11.3% | Moderate 0.58 |
| Provide information or assistance to the public. | 8.8% | High overlap 0.78 |
| Evaluate the quality or accuracy of data. | 6.6% | Moderate 0.45 |
| Discuss legal matters with clients, disputants, or legal professionals or staff. | 6.4% | Not observed 0.00 |
| Process forensic or legal evidence. | 6.4% | Not observed 0.00 |
| Process digital or online data. | 6.0% | Moderate 0.51 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is examine materials or documentation for accuracy or compliance; the most important activity AI is not observed doing is record information about legal matters. The plan below is built around exactly that split.
Move first: compress the routine before it compresses you.
Next 30 days
- Take the exposed layer yourself. Your most AI-overlapped activity, Examine materials or documentation for accuracy or compliance, is exactly what a well-tooled colleague will accelerate. Be that colleague first.
- Time-box the routine. Measure how long your repeatable tasks actually take; that's the budget AI adoption will compete against, and the time you will reinvest.
Next 90 days
- Double down where humans hold. Grow the share of your week spent on work like Record information about legal matters, measured as outside AI's current reach, and take on more of it explicitly.
- Build one AI-assisted workflow end-to-end for your team and own its quality. Being the local standard-setter compounds.
The longer game
- Position at the boundary. The valuable seat in partially-exposed occupations is the translator: someone fluent in both the domain and the tools. Certifications matter less than a demonstrated workflow portfolio.
- Recheck yearly. Elevated-band occupations move: new tools shift specific activities fast. The data vintage is printed at the top of this page.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.