Taxi Drivers and Chauffeurs
Transportation & Material MovingAI exposure
- Data source: BLSPublished: 2026-08
Moderate· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.000
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.28
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 73% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Show 16 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Provide passengers with information about the local area and points of interest or give advice on hotels and restaurants.53-3041 | 0.002843.8 | —0 |
Determine fares based on trip distances and times, using taximeters and fee schedules, and announce fares to passengers.53-3041 | 0.002132.8 | —0 |
Communicate with dispatchers by radio, telephone, or computer to exchange information and receive requests for passenger service.53-3041 | 0.001523.4 | 0.005759.1 |
Drive taxicabs, limousines, company cars, or privately owned vehicles to transport passengers. | —0 | 0.002222.6 |
Pick up passengers at prearranged locations, at taxi stands, or by cruising streets in high traffic areas. | —0 | 0.001718.3 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Communicate with dispatchers by radio, telephone, or computer to exchange information and receive requests for passenger service.O*NET Task ID 23759 | 1.0 |
Complete accident reports when necessary.O*NET Task ID 23760 | 1.0 |
Determine fares based on trip distances and times, using taximeters and fee schedules, and announce fares to passengers.O*NET Task ID 23761 | 1.0 |
Notify dispatchers or company mechanics of vehicle problems.O*NET Task ID 23764 | 1.0 |
Provide passengers with information or advice about the local area, points of interest, hotels, or restaurants.O*NET Task ID 23769 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page