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Anthropologists and archeologists

Also called Anthropologist, Cultural anthropologist, Archeologist, Archaeologist, Bioarcheologist

In brief

In May 2025, BLS counted 8,990 US anthropologists and archeologists, with median pay of $70,770 a year. Of the BLS counts since 2003, the May 2025 count is the highest. The earliest figure here is an estimate of 1,800 from 1930, not measured the same way as today's count. The last full technology era for anthropologists and archeologists: AI-assisted qualitative coding and literature synthesis, 2016 to 2022. AI-assisted coding and transcription tools reduced the junior-level research assistant hours devoted to mechanical processing of fieldwork data.

What the sources say

Official outlook

BLS projects employment to grow 6% 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). Anthropologists and archeologists: jobs and pay since 1930 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-3091

Chicago

Future History. "Anthropologists and archeologists: jobs and pay since 1930." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-3091.

BibTeX

@misc{futurehistory_19_3091, author = {{Future History}}, title = {Anthropologists and archeologists: jobs and pay since 1930}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-3091}}, 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.

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19001925195019752000now
2026
Known then as Government Ethnologist / Field Archaeologist (Bureau of Ethnology era)
Known then as Anthropologist / Archaeologist (academic professionalization era)
Known then as Anthropologist / Archaeologist / Cultural Resource Specialist (CRM expansion era)
Known then as Anthropologists and Archeologists (BLS SOC 19-3091)
Known today as Anthropologists and Archeologists (BLS SOC 19-3091)
Projected to still be called Anthropologists and Archeologists (BLS SOC 19-3091)
Data from the May 2025 survey
US Employment
9K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$70,770
Est.

≈ $68,956 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1879

Congress established the Bureau of American Ethnology under John Wesley Powell at the Smithsonian, creating the first significant paid employment for anthropologists in the United States. Before this, the discipline had no institutional home and practitioners were largely self-funded gentlemen scholars or museum employees.

Beat · 1902

The American Anthropological Association was founded on December 29, 1902, at the University of Pennsylvania, formalizing professional identity for the discipline. Franz Boas argued for restricting membership to 40 practicing professionals; W. J. McGee, the founding president, successfully advocated for an open membership policy. The AAA today has over 10,000 members and remains the primary professional society.

Beat · 1949

Willard Libby published the first radiocarbon dates, giving archaeologists an independent chronometric tool that transformed how sites could be dated. The technique was rapidly adopted across American archaeology through the 1950s, fundamentally changing the evidentiary basis of archaeological argument.

Beat · 1966

The National Historic Preservation Act, signed October 15, 1966, transformed the profession by creating legally mandated cultural resource management work that would employ more archaeologists than academia within two decades. Section 106 required federal agencies to assess the effects of their projects on historic properties, generating a permanent compliance industry.

Beat · 1990

NAGPRA, the Native American Graves Protection and Repatriation Act, was signed on November 16, 1990. The law required federally funded museums and institutions to inventory and repatriate Native American human remains, funerary objects, and sacred items to lineal descendants and culturally affiliated tribes. It profoundly reshaped the ethics and practice of American archaeology and bioanthropology, creating ongoing repatriation compliance work and permanently changing the relationship between archaeologists and Native American communities.

Beat · 2019

The CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, Ethics) were formalized by the International Indigenous Data Sovereignty Interest Group and the Research Data Alliance. These principles directly govern how AI-generated or AI-processed research data involving indigenous communities must be handled, and are now relevant to the growing use of AI tools in anthropological fieldwork involving indigenous populations.

Beat · 2022

Google DeepMind published Ithaca in Nature in March 2022, demonstrating that AI could restore damaged ancient Greek inscriptions with 62% standalone accuracy and boost expert historian accuracy from 25% to 72% when used collaboratively. Ithaca was trained on 78,608 inscriptions from the I.PHI dataset and was made publicly available via Google Arts and Culture. The paper was widely cited as a model for human-AI collaboration in humanities research.

Beat · 2024

The Vesuvius Challenge awarded its $700,000 grand prize in February 2024 to a team that recovered 2,000 characters from a previously unread Herculaneum papyrus using TimeSformer and related AI models applied to X-ray computed tomography scans. The text proved to be a philosophical work by Philodemus on the topic of pleasure. The achievement demonstrated that AI could recover readable text from physically destroyed documents that had been sealed since 79 AD, with implications for the broader field of ancient document recovery.

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 anthropologists and archeologists? 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
+6%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 19-3091 (Anthropologists and archeologists): 10.2 thousand jobs in 2025, projected 10.8 thousand in 2035, a change of +0.6 thousand (+6.0%). Matrix employment counts all jobs in the occupation, including self-employed workers (8.9% 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)
51.7%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 19-3091.00 Anthropologists and Archeologists. Shown: human-rated β = 0.517, 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) 0.293 and γ (all LLM-assisted) 0.741; GPT-4-rated β 0.44. 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 anthropologists and archeologists

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 research synthesis using AI tools: use Elicit to identify relevant papers on a research question without perfect keyword matching and generate structured research matrices

Conduct systematic literature review and research synthesis using AI tools: use Elicit to identify relevant papers on a research question without perfect keyword matching and generate structured research matrices; use Consensus to surface peer-reviewed agreement across published studies on specific anthropological hypotheses; upload curated PDF corpora to NotebookLM for synthesis across fieldwork reports, grey literature, and archival sources; and write the resulting literature review with explicit citation of AI-assisted search methods in research methods sections.[19],[20],[21]

Where your edge is

AI literature synthesis tools have compressed systematic review timelines significantly: Elicit finds relevant papers without perfect keyword matching across tens of thousands of records in minutes; Consensus surfaces peer-reviewed consensus levels on specific questions. For anthropology and archaeology, these tools are particularly valuable given the fragmented literature landscape (area journals, regional conference proceedings, CRM grey literature, multi-language publication norms). The residual human skill is critical evaluation of sources: distinguishing a peer-reviewed excavation report from a CRM Phase I with limited quality control, evaluating whether a study's methodology is appropriate for your research question, and synthesizing across heterogeneous evidence types (historical documents, material culture analysis, skeletal data, ethnohistoric accounts) that AI tools cannot integrate without human guidance. Always verify AI-surfaced citations against the primary sources before citing them.

AI is sitting alongside you hereConduct drone photogrammetry documentation of excavation trenches and archaeological landscapes: plan and execute drone flights over active excavations using DroneDeploy automated flight planning

Conduct drone photogrammetry documentation of excavation trenches and archaeological landscapes: plan and execute drone flights over active excavations using DroneDeploy automated flight planning; process imagery in PIX4Dcloud to generate centimeter-accurate orthomosaics, digital surface models, and dense point clouds; use PIX4Dcatch for close-range photogrammetric capture of individual artifacts, features, and stratigraphic sections; and integrate the 3D deliverables into GIS-based site records and published site plans.[8],[9]

Where your edge is

Drone photogrammetry has compressed 3D site documentation from multi-day theodolite and total-station workflows to same-day deliverables with superior spatial completeness: it is now standard practice in research and CRM archaeology. Your competitive edge is in acquisition quality (camera settings, flight overlap, ground control point placement, lighting conditions for artifact photogrammetry) and in the interpretive use of the resulting point clouds and meshes, detecting subtle cut-and-fill relationships in a dense point cloud, identifying ploughmarks from differential reflectance, or using the DSM to reconstruct a site formation process. Proficiency in Pix4D and open-source alternatives (OpenDroneMap, Metashape) is now a baseline hiring requirement for field archaeologist positions in CRM.

AI is sitting alongside you hereApply AI-assisted qualitative coding to ethnographic interview transcripts and field notes: use Otter.ai to auto-transcribe recorded fieldwork interviews with speaker identification and timestamps

Apply AI-assisted qualitative coding to ethnographic interview transcripts and field notes: use Otter.ai to auto-transcribe recorded fieldwork interviews with speaker identification and timestamps; import transcripts into ATLAS.ti AI Lab or MAXQDA AI Assistant; apply AI-suggested code generation to flag thematic clusters across the corpus; review and validate AI-proposed codes against the theoretical framework; and use visual network analysis tools to map conceptual relationships across coded segments.[13],[14],[18]

Where your edge is

AI transcription and coding tools have materially cut the mechanical burden of qualitative data processing: Otter.ai transcription replaces hours of manual transcription per interview; ATLAS.ti and MAXQDA AI-suggested codes surface thematic patterns across large corpora without full manual reading of every line. However, a January 2025 arxiv study found that LLMs fall below human performance on reliable ethnographic text annotation features: they produce plausible-sounding codes that miss nuance, cultural context, and the researcher's theoretical framework. Your irreplaceable contribution is theory-driven interpretation: distinguishing a code that is analytically interesting from one that is descriptively obvious, recognizing the conceptual relationship between surface-level observations and underlying cultural logics, and constructing a coding scheme that is defensible to peer review. Rigorously validate AI-suggested codes against a sizeable share of transcripts before trusting the AI's pattern attribution.

Where anthropologists and archeologists go next

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

What each move takes

Applied cultural anthropologists with ethnographic research skills have a well-documented pivot into user experience (UX) research, consumer insights, and market research roles, often branded as "corporate anthropology" or "design anthropology." The methodological toolkit transfers directly: participant observation, semi-structured interviewing, thematic coding, and ethnographic report writing are the core methods of qualitative market research and UX ethnography. Major technology companies (Microsoft Research, Intel Labs, IDEO, Google ATAP) have historically recruited cultural anthropologists specifically for their ethnographic skill sets. The transition is lateral in methodology but represents a shift in institutional context (corporate client timelines vs. academic research norms) and output form (actionable business insights vs. peer-reviewed publications). LinkedIn data (2026) shows "UX Researcher" and "Consumer Insights Analyst" as the two most common destination titles for anthropology-to-industry pivots.

What you'd add
  • · UX research methods: usability testing, think-aloud protocols, diary studies, contextual inquiry, the UX-specific vocabulary mapped onto anthropological participant observation
  • · Survey design and quantitative synthesis: combining qualitative ethnographic data with survey data in a mixed-methods market research framework; familiarity with Qualtrics and SurveyMonkey
  • · Data visualization for business audiences: presenting qualitative findings to non-academic stakeholders using Figma, Miro, or Dovetail; insight mapping and affinity diagramming at business speed
  • · AI-assisted qualitative synthesis: proficiency in Dovetail AI (purpose-built UX research repository with AI tagging), ATLAS.ti AI Lab, and MAXQDA AI for high-velocity research sprints
  • · Product development lifecycle basics: Agile/Scrum research integration; research sprint planning; translating ethnographic findings into design recommendations within 2-week sprint cycles
What it takesMost of your skills carry over
Tools of the era

How has the work of anthropologists and archeologists changed since 1879?

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

  • Field notebooks, photography, and artifact typology (Bureau of Ethnology era)

    The founding generation of American professional anthropologists worked entirely by hand: field notebooks recorded observations in prose and sketch, glass-plate and later film photography documented people, sites, and objects, and artifact typology was established through physical comparison of specimens arranged in museum drawers. John Wesley Powell hired artists and photographers to accompany BAE ethnographers into the field. The field notebook remained the primary data capture tool for decades. Photography, introduced to archaeology by the 1880s, transformed the documentation of sites but required fieldworkers to develop their own plates, a chemical process conducted in improvised darkrooms in the field.

    Effect on the work

    Photography accelerated publication timelines (field images could accompany printed reports) and created a documentary record that outlasted any individual observer, but it did not change the labor structure of fieldwork. The core bottleneck was not data capture but data analysis: typological comparison of artifact collections required human experts with years of regional knowledge.

  • Radiocarbon dating (1949) and systematic stratigraphic excavation methods

    Willard Libby published the first radiocarbon date in 1949, and the technique was rapidly adopted by archaeologists worldwide. For the first time, organic materials from excavations could be dated independently of artifact typology. This transformed archaeological practice: chronological arguments that had required decades of comparative typological work could now be tested with a laboratory date. Systematic stratigraphic methods, codified by researchers including Mortimer Wheeler in the 1940s and adopted in American CRM practice by the 1960s, transformed excavation from treasure-hunting into evidence-collection. The Wheeler-Kenyon box grid system, requiring rigorous balks and section drawings, became the standard framework for documenting site formation processes.

    Effect on the work

    Radiocarbon dating increased the rate at which archaeological chronologies could be established and reduced the career specialization required for dating. Sites that had previously required an expert in a single regional artifact tradition to date could now be dated independently, allowing generalist field archaeologists to produce datable results.

  • Cultural Resource Management compliance framework (NHPA 1966, NEPA 1969, ARPA 1979)

    The National Historic Preservation Act, signed into law on October 15, 1966, required federal agencies to consider the effects of their projects on historic properties through the Section 106 review process. NEPA (1969) added environmental impact assessment requirements that encompassed archaeological resources. The Archaeological Resources Protection Act (1979) strengthened criminal penalties for artifact looting and established permitting requirements for excavation on federal land. Together these laws transformed the profession more than any technological change: prior to NHPA, most archaeologists worked in academia. After its passage and enforcement, private-sector CRM firms emerged to conduct the compliance surveys, Phase I pedestrian surveys, Phase II site evaluations, and Phase III data recovery excavations that federal and state agency projects now required. Employment in the profession roughly doubled between 1970 and 1990.

    Effect on the work

    The CRM sector created the first large, stable non-academic employment base for archaeologists. It also bifurcated the profession: academic archaeology focused on theoretical and interpretive questions; CRM archaeology focused on systematic documentation within regulatory frameworks. By the 1980s CRM archaeology employed more archaeologists than academia, a structural shift that has persisted.

  • GIS mapping and GPS field recording (early adoption phase)

    Geographic Information Systems entered archaeological practice in the early 1990s, initially on workstations at universities and large CRM firms. GPS receivers capable of sub-meter accuracy became practical field tools by the mid-1990s, replacing hand-drawn sketch maps and allowing artifact and feature positions to be recorded as georeferenced coordinates that could be imported directly into GIS databases. Early adopters used ArcView and ArcGIS for site predictive modeling, survey planning, and report map production. The GIS era compressed the time between field data collection and finished report maps from weeks to days and enabled spatial analyses (viewshed modeling, catchment analysis, site density mapping) that were previously impractical at landscape scale.

    Effect on the work

    GIS adoption eliminated the technical drafting role that had existed in larger CRM firms and research projects: the cartographer who produced hand-drawn site maps was replaced by the GIS technician who produced digital maps. Overall headcount was not reduced but skill requirements shifted, with GIS competency becoming a baseline requirement for archaeologist positions by 2000.

  • LiDAR airborne survey and drone photogrammetry (mainstream adoption)

    Airborne LiDAR reached archaeological practice around 2005 with pioneering Maya forest studies in Belize and Honduras that penetrated jungle canopy to reveal settlement patterns invisible from the ground. By 2010, LiDAR was being routinely commissioned by CRM firms for Phase I survey planning on large infrastructure projects. Drone photogrammetry (using Pix4D, Agisoft Metashape, and similar tools) became affordable for field teams around 2012-2015, compressing 3D site documentation from multi-day total-station surveys to same-day drone flights. Together these technologies expanded the spatial scale at which a single archaeologist could document a landscape while reducing the time required for precision recording.

    Effect on the work

    LiDAR survey and photogrammetry reduced the field crew hours required for large-area reconnaissance and site documentation. A survey that in 2000 required a team of ten walking pedestrian transects for a week could in 2015 be augmented by LiDAR-processed data that identified candidate features before fieldwork, focusing ground effort on the highest-priority targets. The effect was productivity amplification rather than workforce reduction: CRM projects could deliver more complete documentation within the same budget.

  • AI-assisted qualitative coding and literature synthesis (ATLAS.ti, MAXQDA, Otter.ai)

    Qualitative data analysis software had existed since the 1980s (ATLAS.ti launched in 1993), but the AI-assisted coding and summarization features added from around 2016 onward transformed how cultural anthropologists managed large fieldwork corpora. Otter.ai and similar automatic transcription tools eliminated the manual transcription step that had consumed several hours per interview hour. ATLAS.ti's AI Lab and MAXQDA's AI Assistant introduced automated code suggestions across interview transcript corpora, reducing the time required for initial thematic analysis. For archaeologists, AI-enhanced literature review tools (Elicit, Consensus, Google Scholar Alerts) compressed systematic review timelines on grant applications and site background reports.

    Effect on the work

    AI-assisted coding and transcription tools reduced the junior-level research assistant hours devoted to mechanical processing of fieldwork data. Graduate students who previously spent weeks manually transcribing interviews before analysis could reach the analytical stage faster. This did not reduce employment but changed the task composition of research roles toward higher-skill interpretation earlier in the project cycle.

  • Deep learning for site detection, skeletal analysis, and ancient text recovery (ArchAI, Ithaca, ADAF)

    The years from 2022 onward represent the steepest technology adoption curve the profession has experienced. Three distinct AI capabilities arrived in rapid succession: ArchAI and ADAF (deep CNNs for automated detection of archaeological features from LiDAR data, achieving over 90% accuracy) transformed landscape survey by replacing weeks of manual hillshade interpretation with automated candidate detection; Ithaca (Google DeepMind, Nature 2022) transformed ancient text restoration by boosting expert accuracy from 25% to 72% on damaged Greek inscriptions; and the Vesuvius Challenge (2024 grand prize: $700,000 for recovering 2,000 characters from a carbonized Herculaneum papyrus using TimeSformer) demonstrated that AI can recover text from physically destroyed documents at scale. The net effect is a genuine productivity multiplier on data-intensive tasks, concentrated in the laboratory and remote-sensing phases of work, while field judgment and community-embedded practice remain definitionally human.

    Effect on the work

    AI adoption in LiDAR analysis and skeletal processing is expected to reduce the junior analyst hours required for screening-phase tasks in CRM and research archaeology. The Journal of Computer Applications in Archaeology (2025) documented hundreds of ML-archaeology publications in years (January 2023 to September 2024), 12% more than all of 2021-2022 combined, indicating a rapidly accelerating research front. Whether this translates into reduced headcount or expanded project scope per team is an open empirical question for the late 2020s.

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Anthropologists and Archeologists employment and pay by year

On record since1879
Latest US employment8,990 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$70,770 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+6% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 26 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
19301,800n/aEst., Wikipedia Researched contextReport
19704,200$12,000Est., National Park Service (employment) Researched contextEst., IPUMS USA (pay) Researched contextReport
19906,200$28,000Est., NPS Archeology Program (employment) Researched contextEst., BLS Occupational Outlook Handbook (pay) Researched contextReport
20034,550$40,650BLS OEWS May 2003 Official statisticReport
20044,510$43,890BLS OEWS May 2004 Official statisticReport
20054,790$45,910BLS OEWS May 2005 Official statisticReport
20064,960$49,930BLS OEWS May 2006 Official statisticReport
20075,250$53,080BLS OEWS May 2007 Official statisticReport
20085,230$53,910BLS OEWS May 2008 Official statisticReport
20095,570$53,460BLS OEWS May 2009 Official statisticReport
20105,100$54,230BLS OEWS May 2010 Official statisticReport
20115,370$56,070BLS OEWS May 2011 Official statisticReport
20126,060$57,420BLS OEWS May 2012 Official statisticReport
20136,560$58,360BLS OEWS May 2013 Official statisticReport
20147,040$59,280BLS OEWS May 2014 Official statisticReport
20156,980$61,220BLS OEWS May 2015 Official statisticReport
20166,470$63,190BLS OEWS May 2016 Official statisticReport
20176,120$62,280BLS OEWS May 2017 Official statisticReport
20186,020$62,410BLS OEWS May 2018 Official statisticReport
20196,720$63,670BLS OEWS May 2019 Official statisticReport
20207,180$66,130BLS OEWS May 2020 Official statisticReport
20216,650$61,910BLS OEWS May 2021 Official statisticReport
20227,350$63,940BLS OEWS May 2022 Official statisticReport
20237,720$63,800BLS OEWS May 2023 Official statisticReport
20248,070$64,910BLS OEWS May 2024 Official statisticReport
20258,990$70,770BLS OEWS May 2025 Official statisticReport
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