Automation index
How much of a Musician’s job can AI already do?
A backing track and a score generate. Playing live, tuning to a room, and lasting a concert do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Musician
21%
AI can already do 21% of this job.
#429 / 633
View rankingMusician
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 |
|---|---|---|---|
Compose, arrange and produce demos.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Scores, desk rehearsal and setlists.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 30% | +4.8 |
Play in concert, session or pit.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Rehearse with the band and lock the live show.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Contracts, touring and promotion.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Lessons, commissions and keeping the instrument.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Compose, arrange and produce demos
- Scores, desk rehearsal and setlists
- Play in concert, session or pit
- Contracts, touring and promotion
- musico
- musician
- instrumentista
- concertista
Sources for this job· O*NET 27-2042.00· ESCO/ISCO 2652
IS WORK OVER?
Climatologist
27%
#140 / 633
View rankingClimatologist
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 |
|---|---|---|---|
Series, homogenise and the gap.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Models, scenarios and the uncertainty.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Report, the headline and not selling the easy one.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Field, the station and the one that moves.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The politician, the figure and not staying quiet.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Attribute, and not selling the headline.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
- climate series
- homogenise
- scenarios
- uncertainty
- headlines
- stations
- climate reports
- attribution
Sources for this job· O*NET 19-2021.00· ESCO/ISCO 2112