Good point. What I'm thinking is probably more like individual taste. An AI trained is kind of a collection of different tastes. What would be interesting is to see is if a "collective" taste that would yield good products on its own. Design by committee often creates things that have conflicting points of view. Can a trained AI do better than design by committee? It's obviously not a committee and not an individual (at least in the human sense). Is there something inherent in following general principles that makes it difficult to have a specific point of view in the creation of a product? My thinking is it's probably possible to train AI to have, say, an "Apple taste" and make good Apple-like products but that taste might not be suitable for particular products or needs. A "Linux taste" might be better for some things, but not for others, and so on. If we did get to that point, then there'd still be a need for a choice to be made about what particular "taste hat" to put on, and likely an AI can be trained to do even that. Definitely an interesting thing to ponder!

I'm not convinced that "more training" is a silver bullet here because "for you and the people you learned from" is a subset—potentially a tiny one—of the world of potential training data.

Even with newer model generations larger efforts still seem to drift back towards "default choices" vs "steered specific aims."

In many areas, "good" taste is not particularly good anymore when it's everything's default style.

The edge then becomes finding the niches that (a) can be better served w/ non-defaults and (b) have economic characteristics that make it worth catering towards those.

I think the days of one-size-fits-all huge-scale-and-ROI SaaS swiss army knives are quite numbered, but I think the replacement world could be a much more vibrant ecosystem with a much more varied set of players.