[visarga]

> ways to abstract "discoverability"

Combine "search" with "learning". Or content generation with content validation, and retrain on the clean outputs. Or, run many simulations such as AlphaGo, and learn from the outcomes.

In general the idea is to use lots of compute to generate interesting and hard to come by training data for the next iteration. This approach is necessary because we have exhausted most of the good training data and need a path forward. You can't copy money, but you can copy the model and data.