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Historians

Also called Public historian, Academic historian, Research historian, Government historian, Oral historian

In brief

In May 2025, BLS counted 3,450 US historians, with median pay of $76,750 a year. Of the BLS counts since 2003, the highest was 3,700, in May 2008. The earliest figure here is an estimate of 2,500 from 1900, not measured the same way as today's count. The last full technology era for historians: Digital archives and open-access primary sources, 2005 to 2020.

What the sources say

Official outlook

BLS projects employment to grow 3% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Historians: jobs and pay since 1900 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-3093

Chicago

Future History. "Historians: jobs and pay since 1900." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-3093.

BibTeX

@misc{futurehistory_19_3093, author = {{Future History}}, title = {Historians: jobs and pay since 1900}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-3093}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
19001925195019752000now
2026
Known then as Historian / Historical Investigator (AHA era, university-centered)
Known then as Historian (academic and federal government expansion era)
Known then as Historian / Public Historian (diversification era, job market crisis, public history movement)
Known then as Historians (BLS SOC 19-3093)
Known today as Historians (BLS SOC 19-3093)
Projected to still be called Historians (BLS SOC 19-3093)
Data from the May 2025 survey
US Employment
3K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$76,750
Est.

≈ $74,782 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1884

The American Historical Association is founded at a meeting in Saratoga, New York on September 9, 1884 -- the first major step in organizing American historians as a self-governing profession. Herbert Baxter Adams, who had launched the first US historical seminar at Johns Hopkins four years earlier, is the driving organizational force. The AHA is chartered by an Act of Congress in 1889, becoming one of the few scholarly associations to hold a congressional charter. Its founding marks the formal transition of American historical work from an amateur gentlemanly pursuit to a credentialed university-centered profession.

Beat · 1942

World War II drives a significant expansion of federal historian employment. Historians were working for military branches in the 1930s and 1940s, but the wartime demand for organizational and operational history, intelligence analysis, and strategic documentation accelerates the formation of dedicated historical offices across the Army, Navy, State Department, and the Office of Strategic Services (predecessor to the CIA). William Langer, a Harvard historian, heads the OSS Research and Analysis branch and employs dozens of academic historians in government intelligence work. This wartime experience establishes the template for the federal "GS-170 Historian" classification that persists today.

Beat · 1971

At the AHA's annual convention in New York in December 1971, approximately 2,300 graduate students are registered on the Professional Register seeking academic positions. Approximately 155 jobs are available. The mismatch reflects a PhD production rate of roughly 1,200 new history doctorates per year built up through the 1960s university expansion -- a pipeline that made sense when university enrollment was growing rapidly and expected to continue growing but became catastrophic when enrollment growth slowed and new faculty hiring dried up. The 1970s job market crisis would permanently alter the profession's relationship to doctoral education and prompt the first serious discussions of non-academic career paths for historians.

Beat · 1979

The first reliable federal survey of former doctorate recipients (1979) finds history PhDs widely distributed outside the traditional four-year college and university jobs: more than a third are employed in two-year colleges, pre-collegiate teaching, government, historical societies, archives, and nonprofit or for-profit businesses. This survey is the first empirical documentation of a structural reality the profession had been slow to acknowledge: the academic job market will not absorb all the PhDs that history departments produce, and a substantial fraction of professional historians have always worked -- and will continue to work -- outside tenure-track faculty positions.

Beat · 2016

AHA job listings data shows that new faculty position postings have fallen 45% from the pre-recession peak of 1,064 advertisements in 2011-12 to 587 in 2015-16 -- the third consecutive annual decline. For the first time in 41 years, the number of jobs advertised with the AHA falls below half the number of PhDs conferred in the previous year. The AHA analysis flags that the current age profile of the tenured faculty means relatively few full-time positions will be opening through faculty retirement in the coming decade, extending the structural undersupply of academic positions. The employment picture for the narrow BLS "Historian" category has remained relatively stable precisely because it captures only the small number of people whose primary title is Historian -- not the broader academic market.

Beat · 2022

DeepMind publishes Ithaca in Nature (March 2022): an AI model for restoring, attributing, and dating ancient Greek inscriptions that achieves 62% accuracy on restoration tasks versus 25% for unaided expert epigraphers. Human experts using Ithaca as an assistant reach 72% -- the human-AI team substantially outperforms either alone. Ithaca is the most high-profile demonstration that AI can outperform domain experts on a bounded historian task. It is also the most precise example of what the AI augmentation picture for historians actually looks like: AI accelerates a specific, well-defined subtask (pattern-matching restoration of damaged text) while the historian retains the contextual judgment that gives a restored inscription scholarly meaning.

Beat · 2025

The Institute of Historical Research at the University of London publishes "Doing Historical Research in the Age of Generative AI" (blog.history.ac.uk, May 2025), documenting that AI tools fabricate archival citations, invent historical events, and misquote primary sources with high fluency -- and that historians cannot safely delegate fact-checking to AI. The same year, a Lancet preprint (May 2026) documents a sixfold increase in AI-hallucinated citations in academic papers between 2023 and 2025. For a discipline whose entire scholarly credibility rests on accurate citation of verifiable primary sources, these findings are directly load-bearing: the AI hallucination failure mode amplifies, rather than reduces, the value of the trained historian who can distinguish a real archival document from a plausible fabrication.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace historians? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+3%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 19-3093 (Historians): 3.8 thousand jobs in 2025, projected 3.9 thousand in 2035, a change of +0.1 thousand (+3.0%). Matrix employment counts all jobs in the occupation, including self-employed workers (5.5% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
50%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 19-3093.00 Historians. Shown: human-rated β = 0.5, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.316 and γ (all LLM-assisted) 0.684; GPT-4-rated β 0.474. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for historians

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereConduct systematic literature review and secondary source triage for book-length research projects and grant proposals (using JSTOR AI Research Tool for semantic discovery of secondary literature across 14,000+ participating institutions [no hallucinated citations

Conduct systematic literature review and secondary source triage for book-length research projects and grant proposals (using JSTOR AI Research Tool for semantic discovery of secondary literature across 14,000+ participating institutions [no hallucinated citations; searches real JSTOR corpus only], and Elicit for structured extraction of argument, methodology, and evidence type from 30-50 papers simultaneously) then independently assessing every AI-surfaced source for scholarly credibility, verifying that citations are real and accurately represent the cited work, and constructing a historiographical argument from verified secondary evidence.[10],[11]

Where your edge is

Use JSTOR AI Research Tool to run a semantic search on, say, "Atlantic slavery and capitalism 1820-1860": it will surface relevant articles from the JSTOR corpus by conceptual relevance rather than keyword matching, and generate short summaries that let you judge whether a paper is worth reading before opening the full text. Available at no added cost at 14,000+ JSTOR-participating institutions since July 2025; was prominently featured at AHA Annual Meeting January 2025. Use Elicit to extract structured data (central argument, primary vs. secondary evidence used, methodology, time period) from a batch of many articles you are deciding whether to cite: it compresses a three-hour bibliography sweep into minutes of review. Critical safeguard: every AI-surfaced citation must be independently verified as a real publication before you cite it. The Stat News / Lancet analysis (May 2026) documents a sixfold increase in AI-hallucinated academic citations between 2023 and 2025. JSTOR AI is safer than general-purpose LLMs for this task because it only retrieves actual JSTOR content, but even there, generated summaries require verification against the full text before quoting.

AI is sitting alongside you herePrepare historical reports, organizational histories, and policy-relevant historical analyses for government agencies, federal departments, think tanks, and organizations with institutional memory mandates: using AI tools to draft initial structure and background sections from supplied notes and verified data, then applying expert historical judgment to verify every factual claim, anchor every assertion to documented primary evidence, and ensure the narrative accurately represents the historical record without the anachronisms, inventions, or hallucinated citations that AI tools routinely introduce.

Prepare historical reports, organizational histories, and policy-relevant historical analyses for government agencies, federal departments, think tanks, and organizations with institutional memory mandates: using AI tools to draft initial structure and background sections from supplied notes and verified data, then applying expert historical judgment to verify every factual claim, anchor every assertion to documented primary evidence, and ensure the narrative accurately represents the historical record without the anachronisms, inventions, or hallucinated citations that AI tools routinely introduce.[6],[5]

Where your edge is

Federal government historians (National Archives, State Department, Department of Defense, NASA, and agency historical offices) produce the authoritative institutional record: accuracy is a legal and policy requirement, not a stylistic preference. AI can draft background sections and section structures from your notes and outlines, and can accelerate the writing phase for routine descriptive sections. The non-delegatable work: every factual claim in a government historical report must be cited to a verified, accessible primary source; every date, name, and organizational fact must be independently confirmed; and the interpretive framing of contested historical events must reflect the actual state of the evidence, not an AI's training data. It is a records-integrity failure. Use AI for structure and drafting velocity; retain independent verification of every empirical claim.

AI is sitting alongside you hereBuild and manage digital history projects and public-facing online archives: using Omeka or Omeka S for collection organization, metadata generation, and digital exhibit design

Build and manage digital history projects and public-facing online archives: using Omeka or Omeka S for collection organization, metadata generation, and digital exhibit design; applying AI-assisted Dublin Core metadata tagging for batch-uploaded historical photographs, documents, and objects; designing interpretive narratives for digital exhibitions that translate archival research for general public audiences without the jargon of academic historiography; ensuring that all metadata accurately represents the provenance and content of original source materials.[12],[6]

Where your edge is

Omeka (Roy Rosenzweig Center for History and New Media) is the dominant platform for public historians building digital collection websites, virtual archives, and online exhibitions, deployed by state humanities councils, historical societies, university special collections, and local history organizations. AI tools accelerate two specific Omeka workflows: (1) batch metadata generation (use ChatGPT to draft Dublin Core descriptions for a collection of hundreds of digitised Civil War photographs given a template and a set of supplied facts [date range, donor provenance, geographic region], then review and correct each entry against the actual image; this is significantly faster than writing hundreds of descriptions from scratch; [2] interpretive narrative drafting) use ChatGPT or NotebookLM to generate a first-draft exhibition essay from notes you have written, then rewrite it for voice, accuracy, and audience appropriateness. The critical constraint: every factual claim in the metadata and exhibit copy must be independently verified against primary sources before publication, AI-generated exhibit labels that hallucinate facts about historical objects or people are a professional and institutional reputation risk.

Where historians go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

History Teachers, Postsecondary

Historians with active research programs and publication records, particularly those holding PhDs and affiliated with academic institutions or research centers, are the natural pipeline for postsecondary history faculty positions. The core competencies (archival research, scholarly writing, historiographical argument) are identical; the adjustment is adding teaching and student advising to a research identity rather than substituting one for the other. The CRI delta is slightly negative because postsecondary history teaching has its own AI exposure in the lecture prep, bibliography generation, and essay assessment domains, but the human-advantage moat from archival expertise and primary-source interpretation transfers directly. The academic job market for tenure-track historians is highly constrained (MLA / AHA job reports document continued decline), so this pivot is less accessible than its Low difficulty implies: the barrier is supply-side, not skill-based.

What you'd add
  • · Course design and syllabus construction: building a 14-week undergraduate or graduate course with primary-source assignments, AI-resilient assessments, and scaffolded writing instruction
  • · Teaching methods for historical thinking: facilitating document analysis exercises, Socratic seminars, oral history projects, and paleography labs that build evidence-based interpretation skills
  • · Student advising at undergraduate and graduate levels: guiding thesis topic selection, archive identification, IRB compliance for oral history, and academic career strategy
  • · AI-resilient assessment design: replacing unobserved take-home essays with primary-source critique workshops, Transkribus paleography labs, in-class analytical writing on unseen document excerpts
  • · Academic departmental governance: curriculum committee participation, promotion and tenure review, faculty hiring processes, and AHA disciplinary compliance expectations
What it takesMost of your skills carry over
Tools of the era

How has the work of historians changed since 1825?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Archival seminar and Quellenkritik (Ranke's source-critical method, 1825)

    Leopold von Ranke's historical seminar at the University of Berlin, begun in 1825, established the foundational technology of the historical profession: systematic Quellenkritik (source criticism). Students were trained to locate, authenticate, transcribe, and critically evaluate primary documents rather than simply reading previous authors. This was not a physical technology but a methodological one -- the seminar replaced the dilettante's library with the archive as the historian's workshop. Herbert Baxter Adams imported it to Johns Hopkins in 1880, where he trained the first generation of American professional historians in the same documentary method. The technique remains the core of historical training today, over two centuries after Ranke first taught it.

  • American Historical Review and peer-reviewed journal infrastructure (AHA era, 1884)

    The founding of the American Historical Association in 1884 and the launch of the American Historical Review in 1895 established the peer-reviewed journal as the historian's primary communication technology. This was the mechanism by which the profession governed itself: claims had to survive expert review, and careers were made or ended by publication in recognized journals. The journal infrastructure created the citation economy that defines historical scholarship to this day -- and which now collides with AI's hallucination failure mode, since a discipline built on verified citations cannot tolerate fabricated ones.

  • Microfilm reader and photocopy machine (mass archival reproduction, 1960s)

    The microfilm reader, widely adopted in research libraries through the 1950s and 1960s, was the first technology to allow historians to work with reproductions of archival documents held in distant repositories. Before microfilm, research required physical travel to the archive and hand-transcription of documents. Microfilm collections -- Foreign Office records, newspaper archives, papal registers, plantation ledgers -- allowed a scholar in Iowa to read documents held in London, Rome, or Havana. The photocopier (Xerox 914, 1959) amplified this: historians could produce working copies of archival documents for annotation. These technologies did not change what historians did, but they dramatically changed the geographic and financial constraints under which they did it.

    Effect on the work

    Microfilm and photocopying reduced the cost of archival research substantially, allowing historians at non-elite institutions to undertake large-scale primary source projects that would previously have required a research fellowship at a European archive. This contributed to the expansion of the profession in the 1960s and the democratization of historical research beyond the Ivy League.

  • CD-ROM databases and digitised newspaper archives (JSTOR 1995, ProQuest Historical Newspapers)

    JSTOR launched in 1995 with the stated goal of providing electronic access to academic journals, beginning with backruns of several journals and expanding rapidly. ProQuest's Historical Newspapers product digitised the full runs of major US newspapers (New York Times from 1851, Chicago Tribune, Atlanta Constitution) and made keyword search across millions of pages available at research libraries. For historians, these tools transformed the literature review phase of research: finding every scholarly article citing a specific person, event, or concept went from a weeks-long manual index search to a minutes-long database query. The digitisation of newspapers was even more transformative for social and political historians, enabling research questions that required scanning thousands of issues.

    Effect on the work

    Database search and digitised archives are estimated to have cut the literature review phase of a major historical project compared to the card-catalogue and printed index era. This productivity gain was not reflected in employment growth -- it freed time for deeper archival work rather than expanding the profession.

  • Digital archives and open-access primary sources (Google Books, Internet Archive, HathiTrust, FRASER)

    Google Books launched its mass digitisation program in 2004 in partnership with major research libraries; HathiTrust Digital Library was established in 2008 to archive and provide access to the resulting corpus; the Internet Archive's Wayback Machine and digital text collections provided a parallel open-access repository. The Federal Reserve Bank of St. Louis's FRASER archive digitised decades of federal economic reports, BLS bulletins, and Congressional hearings. For historians, these platforms moved a substantial fraction of the pre-1923 (public domain) printed record online and searchable -- including newspapers, government documents, periodicals, and published diaries -- while FRASER opened up twentieth-century primary sources in economic and labor history that had previously required archival travel. The digital archive era did not replace the physical archive but dramatically expanded the domain of work that could be done remotely.

  • AI handwritten text recognition + LLM research tools (Transkribus, JSTOR AI, NotebookLM, Ithaca)

    Transkribus (READ-COOP SCE), active in historical research communities since around 2018 and substantially upgraded in 2022-2025, applies trained handwritten text recognition (HTR) models to archival manuscript corpora -- turning weeks of paleographic transcription into hours of expert review and correction at a Character Error Rate of 5-10% on trained models. JSTOR's AI Research Tool, launched July 2025, enables semantic search across 14,000+ institutional subscriptions without the hallucination risk of general-purpose LLMs. Google's NotebookLM allows historians to upload digitised primary source collections and query them with source-linked summaries. DeepMind's Ithaca model (Nature, 2022) achieves 62% accuracy on ancient Greek inscription restoration versus 25% for unaided expert epigraphers. These tools represent the first generation of AI that materially augments archival productivity rather than simply speeding up search. The structural constraint remains: AI tools fabricate archival citations with high fluency (Stat News/Lancet, May 2026: AI-hallucinated citations in academic papers increased sixfold between 2023 and 2025), which means the historian's source-verification role is amplified rather than replaced.

    Effect on the work

    The Institute of Historical Research (May 2025) explicitly documents that AI tools fabricate archival citations, invent historical events, and misquote primary sources -- and that historians cannot delegate fact-checking to AI. The professional consensus is that AI augments productivity in transcription, literature triage, and corpus synthesis while structurally amplifying the value of the human expert who can authenticate, contextualize, and verify what the AI produces.

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Historians employment and pay by year

On record since1884
Latest US employment3,450 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$76,750 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+3% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
19002,500n/aEst., Wikipedia Researched contextReport
19408,000n/aEst., AHA Researched contextReport
197119,000n/aEst., AHA Perspectives on History Researched contextReport
1975n/a$14,500Est., AHA Perspectives on History Researched contextReport
20001,880$39,860BLS OEWS 2000 Official statisticReport
20032,350$41,880BLS OEWS May 2003 Official statisticReport
20042,350$44,490BLS OEWS May 2004 Official statisticReport
20052,850$44,400BLS OEWS May 2005 Official statisticReport
20063,090$48,520BLS OEWS May 2006 Official statisticReport
20073,600$50,790BLS OEWS May 2007 Official statisticReport
20083,700$54,530BLS OEWS May 2008 Official statisticReport
20093,620$51,050BLS OEWS May 2009 Official statisticReport
20103,320$53,520BLS OEWS May 2010 Official statisticReport
20113,190$52,370BLS OEWS May 2011 Official statisticReport
20123,340$52,480BLS OEWS May 2012 Official statisticReport
20133,200$55,180BLS OEWS May 2013 Official statisticReport
20143,220$55,870BLS OEWS May 2014 Official statisticReport
20153,010$55,800BLS OEWS May 2015 Official statisticReport
20162,950$55,110BLS OEWS May 2016 Official statisticReport
20173,060$59,120BLS OEWS May 2017 Official statisticReport
20183,040$61,140BLS OEWS May 2018 Official statisticReport
20193,040$63,680BLS OEWS May 2019 Official statisticReport
20202,770$63,100BLS OEWS May 2020 Official statisticReport
20212,910$63,940BLS OEWS May 2021 Official statisticReport
20223,120$64,540BLS OEWS May 2022 Official statisticReport
20233,040$72,890BLS OEWS May 2023 Official statisticReport
20243,140$74,050BLS OEWS May 2024 (employment) Official statisticBLS OEWS May 2024 (pay) Official statisticReport
20253,450$76,750BLS OEWS May 2025 Official statisticReport
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