Anthropologists and Archeologists
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
Study the origin, development, and behavior of human beings. May study the way of life, language, or physical characteristics of people in various parts of the world. May engage in systematic recovery and examination of material evidence, such as tools or pottery remaining from past human cultures, in order to determine the history, customs, and living habits of earlier civilizations.
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 |
|---|---|---|
| Research historical or social issues. | 25.9% | Moderate 0.57 |
| Direct scientific or technical activities. | 10.6% | Not observed 0.00 |
| Collect information about patients or clients. | 9.4% | Not observed 0.00 |
| Train others on operational or work procedures. | 9.3% | Not observed 0.00 |
| Present research or technical information. | 7.8% | High overlap 0.75 |
| Teach academic or vocational subjects. | 7.7% | High overlap 0.62 |
| Develop research plans or methodologies. | 7.2% | Not observed 0.00 |
| Record images with photographic or audiovisual equipment. | 6.0% | Not observed 0.00 |
| Maintain operational records. | 4.2% | Not observed 0.00 |
| Prepare proposals or grant applications. | 4.1% | Not observed 0.00 |
| Develop systems or practices to mitigate or resolve environmental problems. | 3.9% | Not observed 0.00 |
| Advise others on legal or regulatory matters. | 3.9% | High overlap 0.68 |
"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 research historical or social issues; the most important activity AI is not observed doing is direct scientific or technical activities. 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, Research historical or social issues, 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 Direct scientific or technical activities, 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.