Automation index
How much of a Screenwriter’s job can AI already do?
A bible and a treatment come earlier. Knowing which scene cuts, which joke lives, and what the showrunner wants do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Screenwriter
26%
AI can already do 26% of this job.
#203 / 623
View rankingScreenwriter
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 |
|---|---|---|---|
Bibles, treatments and sequence breakdowns.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 48% | +9.6 |
Dialogue, variants and fast rewrites.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 40% | +8 |
Writers’ room and showrunner notes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Fit to budget, locations and cast.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Pitch, sale and studio relationship.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
Decide tone, cut and what is not written.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Bibles, treatments and sequence breakdowns
- Dialogue, variants and fast rewrites
- Writers’ room and showrunner notes
- Fit to budget, locations and cast
- Pitch, sale and studio relationship
- Decide tone, cut and what is not written
- guionista
- screenwriter
- script
Sources for this job· O*NET 27-3043.00· ESCO/ISCO 2641
IS WORK OVER?
ML engineer
29%
#64 / 623
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