Automation index
How much of a DJ’s job can AI already do?
A setlist and a recut come earlier. Reading a floor at 3 a.m., and not killing the dance, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
DJ
20%
AI can already do 20% of this job.
#448 / 633
View rankingDJ
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 |
|---|---|---|---|
Prep crates, edits and setlists.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Promo, contracts and riders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 30% | +4.8 |
Play live and read the floor.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 8% | +1.9 |
Room sound, desk and technical faults.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
Venue, talent buyer and crowd.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
Brand, touring and own catalogue.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 20% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Prep crates, edits and setlists
- Promo, contracts and riders
- Play live and read the floor
- Room sound, desk and technical faults
- Venue, talent buyer and crowd
- Brand, touring and own catalogue
- pinchadiscos
- disc jockey
Sources for this job· O*NET 27-2042.00· ESCO/ISCO 2652
IS WORK OVER?
ML engineer
29%
#64 / 633
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