Automation index
How much of a Game designer’s job can AI already do?
A GDD and an economy table come earlier. Playing it until it hurts, and cutting a system, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Game designer
26%
AI can already do 26% of this job.
#199 / 623
View rankingGame designer
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 |
|---|---|---|---|
GDD, flows and economy tables.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Leveling, quests and tutorial copy.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Playtest, telemetry and reading frustration.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Align art, engineering and production.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Cut systems and defend the feel.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Decide what the game is, and is not.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- design interfaces
- prototypes
- figma
- GDD, flows and economy tables
- Leveling, quests and tutorial copy
- Align art, engineering and production
- Cut systems and defend the feel
- Decide what the game is, and is not
- game designer
- diseñador de videojuegos
Sources for this job· O*NET 15-1255.00· 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