Automation index
How much of a Actor’s job can AI already do?
A self-tape and a script breakdown come earlier. Hitting a mark on a set, and actually crying, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Actor
17%
AI can already do 17% of this job.
#595 / 643
View rankingActor
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 |
|---|---|---|---|
Break down the text, mark and memorise.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Self-tapes and casting materials.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Perform on set, stage or in voice-over.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 6% | +1.3 |
Rehearse with director and cast.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Care for body, voice and emotional continuity.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Promotion, agents and life between jobs.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
| Total | 100% | 17% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Break down the text, mark and memorise
- Self-tapes and casting materials
- Perform on set, stage or in voice-over
- Rehearse with director and cast
- Promotion, agents and life between jobs
- actor
- actriz
- actor de cine
- actor de teatro
Sources for this job· O*NET 27-2011.00· ESCO/ISCO 2655
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