Share and intensity of work current AI systems can materially affect.
SOC 19-3093
Historians AI displacement risk
Historians gather evidence from archives, diaries, and records, then judge its authenticity and significance. Handwritten-text recognition and AI summarization now transcribe and digest documents at scale, which expands the searchable archive while putting a premium on source criticism, the discipline's core skill.
Likely potential for exposed tasks to move to software after workflow integration.
AI summarization flattens exactly what historians are trained to catch: bias, provenance, and what a source does not say. Interpretation, exhibit and publication judgment, and archival discovery of un-digitized material remain stubbornly human work.
Distribution
Where Historians sits across 620 tracked roles
Displacement pressure 36 — higher than 61% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
21 O*NET task statements matched to SOC 19-3093. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $76,750 (May 2025, US national). The latest BLS row matched SOC 19-3093.
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 Historians
SOC 19-3093 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 36/100 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 Historians
The current evidence import matched 21 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 tasks21
SOC19-3093
- Core task / ID 3718
Gather historical data from sources such as archives, court records, diaries, news files, and photographs, as well as from books, pamphlets, and periodicals.
- Core task / ID 3717
Organize data, and analyze and interpret its authenticity and relative significance.
- Core task / ID 3724
Prepare publications and exhibits, or review those prepared by others, to ensure their historical accuracy.
- Core task / ID 3728
Organize information for publication and for other means of dissemination, such as via storage media or the Internet.
- Core task / ID 3720
Conduct historical research as a basis for the identification, conservation, and reconstruction of historic places and materials.
- Core task / ID 20744
Conserve and preserve manuscripts, records, and other artifacts.
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
Gather historical data from archives and records
Exposure 54, automation 28%, augmentation 62%.
O*NET evidence: Gather historical data from sources such as archives, court records, diaries, news file... (ID 3718)
Analyze authenticity and significance of sources
Exposure 38, automation 16%, augmentation 60%.
O*NET evidence: Organize data, and analyze and interpret its authenticity and relative significance. (ID 3717)
Prepare publications and exhibits
Exposure 58, automation 32%, augmentation 68%.
O*NET evidence: Prepare publications and exhibits, or review those prepared by others, to ensure their ... (ID 3724)
Conserve and preserve manuscripts and artifacts
Exposure 24, automation 8%, augmentation 40%.
O*NET evidence: Conserve and preserve manuscripts, records, and other artifacts. (ID 20744)
TaskExposureAutomationAugmentation
Gather historical data from archives and records5428%62%
Analyze authenticity and significance of sources3816%60%
Prepare publications and exhibits5832%68%
Conserve and preserve manuscripts and artifacts248%40%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateDigital Humanities Specialist
Training horizon: 3-6 months. Skill overlap 66. Wage preservation signal 108.
- Build digital collections
- Run transcription pipelines
- Teach verification methods
role redesign
ModerateArchivist
Training horizon: 6-12 months. Skill overlap 70. Wage preservation signal 104.
- Own collection policies
- Digitize priority holdings
- Serve researcher requests
Comparison guides
Compare the next move before you commit
3-6 months
Historians to Digital Humanities Specialist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Historians into Digital Humanities Specialist.
6-12 months
Historians to Archivist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Historians into Archivist.
What the AI risk score means for Historians
The displacement pressure score for Historians is 36. 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. Prepare publications and exhibits carries 32% automation pressure, while Prepare publications and exhibits carries 68% 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: $76,750 (May 2025, US national). Employment context: Archive-based research role meeting handwritten-text recognition. Typical education: Master or doctoral degree typical.
Wage vulnerability is 34, 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.
- Moderate displacement pressure
- HTR opens unsearched archives
- Summarization raises demand for source critics
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Historians, 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.
Archival research
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.
Document analysis
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.
Historical writing
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.
Records preservation
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 Digital Humanities Specialist, such as build digital collections.
- By 90 days, compare internal openings and external postings for Digital Humanities Specialist or Archivist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Historians
Will AI replace Historians?
Historians gather evidence from archives, diaries, and records, then judge its authenticity and significance. Handwritten-text recognition and AI summarization now transcribe and digest documents at scale, which expands the searchable archive while putting a premium on source criticism, the discipline's core skill. 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 Historians work are most exposed to AI?
Prepare publications and exhibits and Gather historical data from archives and records show the strongest automation pressure in this model. Prepare publications and exhibits and Gather historical data from archives and records are better treated as AI-augmented work.
What should Historians learn next?
Start with Archival research, Document analysis, Historical writing. The most practical adjacent paths in this model are Digital Humanities Specialist and Archivist.
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