Automation index
How much of a VFX artist’s job can AI already do?
A previz comes earlier. Integrating the shot without seeing the trick do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
VFX artist
27%
AI can already do 27% of this job.
#133 / 623
View rankingVFX artist
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 |
|---|---|---|---|
Previz, tracking and the asset.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Comp, light and the edge that shows.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Sim, smoke and the one that has no weight.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Review, notes and the one that comes back again.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The deadline, the farm and not faking the render.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That the trick does not show, and delivering the shot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- previz
- tracking
- comp shots
- sims
- reviews
- render farm
- assets
- edges
Sources for this job· O*NET 27-1014.00· ESCO/ISCO 2166
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