Automation index
How much of a Anthropologist’s job can AI already do?
A transcript and a code come earlier. Being in the field and not projecting too much do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Anthropologist
23%
AI can already do 23% of this job.
#287 / 633
View rankingAnthropologist
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Transcribe, code and write.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Field, interviews and observation.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Archive, literature and research design.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Ethics, consent and communities.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Teaching, projects and outreach.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not extracting from people without giving anything back.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Transcribe, code and write
- Field, interviews and observation
- Archive, literature and research design
- Ethics, consent and communities
- Teaching, projects and outreach
- antropologo
- anthropologist
Sources for this job· O*NET 19-3091.00· ESCO/ISCO 2632
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511