[D] Is content discovery becoming a bottleneck in generative AI ecosystems?

I’ve been thinking about an emerging structural issue in generative AI.

Model quality is improving rapidly.

Creation cost is decreasing.

Inference is becoming cheaper.

But discovery mechanisms haven’t evolved at the same pace.

As generative systems scale, the amount of produced content increases superlinearly. Ranking, filtering and relevance models often remain engagement-driven rather than quality-driven.

From a machine learning perspective, I’m curious:

Do we see discovery and relevance modeling becoming the next major bottleneck in generative ecosystems?

Specifically:

– Are current ranking systems fundamentally misaligned with user value?

– Is engagement still the right optimization objective?

– Could smaller, curated relevance models outperform large engagement-optimized feeds?

Would appreciate perspectives from people working on recommender systems or ranking models.