Automation index
How much of a Writer’s job can AI already do?
A first chapter and a synopsis can be farmed out. Choosing the book, lasting three years, and signing it cannot.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Writer
27%
AI can already do 27% of this job.
#151 / 623
View rankingWriter
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 |
|---|---|---|---|
Research, document and outline plots.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 45% | +9.9 |
Draft chapters and variants.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 38% | +7.6 |
Rewrite with an editor and your own taste.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Readings, fairs and reader relations.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
Contracts, agent and book promotion.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
Find the voice and decide what ships.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Research, document and outline plots
- Draft chapters and variants
- Rewrite with an editor and your own taste
- Readings, fairs and reader relations
- Contracts, agent and book promotion
- Find the voice and decide what ships
- escritor
- escritora
- novelista
- writer novelist
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