Automation index
How much of a Taxi driver’s job can AI already do?
The route is already an app. Traffic, the 3 a.m. passenger, and knowing when not to stop stay yours.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Taxi driver
18%
AI can already do 18% of this job.
#590 / 643
View rankingTaxi driver
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 |
|---|---|---|---|
Navigate, take payment and log shifts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Find the passenger and read the city.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Drive safely in all kinds of traffic.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Care, luggage and passenger languages.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Car maintenance and shifts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Licences, radio and zone knowledge.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 22% | +3.1 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- drive
- route
- load goods
- Navigate, take payment and log shifts
- Find the passenger and read the city
- Drive safely in all kinds of traffic
- Care, luggage and passenger languages
- Car maintenance and shifts
- Licences, radio and zone knowledge
- taxista
Sources for this job· O*NET 53-3054.00· ESCO/ISCO 8322
IS WORK OVER?
ML engineer
29%
#64 / 643
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