Automation index
How much of a Climbing instructor’s job can AI already do?
A knot protocol comes earlier. Belaying so they do not actually fall do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Climbing instructor
19%
AI can already do 19% of this job.
#514 / 643
View rankingClimbing instructor
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 |
|---|---|---|---|
Knots, harness and the briefing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Wall, the panic and the one who freezes.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Belay, the factor and not getting distracted.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Rock, anchors and the one that is loose.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The group, the ego and not going too high.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
That they do not fall, and the knot tied.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- knots
- harness
- belay
- fall factor
- anchors
- the wall
- panic
- route briefing
Sources for this job· O*NET 39-9032.00· ESCO/ISCO 3422
IS WORK OVER?
ML engineer
29%
#64 / 643
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