Automation index
How much of a Animal breeder’s job can AI already do?
A pedigree comes earlier. A healthy litter placed well do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Animal breeder
21%
AI can already do 21% of this job.
#413 / 633
View rankingAnimal breeder
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 |
|---|---|---|---|
Lines, seasons and the mating.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Birth, litter and the one that will not feed.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Health, vaccines and the breed standard.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Home, the contract and the one that is not for that.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The fashion, the demand and not over-breeding.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
A healthy litter, and not placing with just anyone.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- breeding lines
- seasons
- litters
- breed standard
- placement contracts
- litter vaccines
- births
- not over-breed
Sources for this job· O*NET 45-2021.00· ESCO/ISCO 6121
IS WORK OVER?
Prompt engineer
34%
#21 / 633
View rankingPrompt 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 |
|---|---|---|---|
Design prompts, tools and chains.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 58% | +13.9 |
Evals, datasets and regressions.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Integrate with product and safety limits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to the business about a good output.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Watch hallucinations, bias and cost.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 20% | +2.8 |
Decide what the model gets, and what it does not.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 16% | +1.9 |
| Total | 100% | 34% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design prompts, tools and chains
- Evals, datasets and regressions
- Integrate with product and safety limits
- Talk to the business about a good output
- Watch hallucinations, bias and cost
- prompt engineer
- ingeniero de prompts
- llm engineer
Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2512