I blame the ML engineers who work on these recommendation systems. They chase simplistic objectives like CTR, time spent, and so on, which can be gamed by this kind of content. This creates huge positive feedback loops in which popular content becomes even more popular and forms “metas,” while models train on clickstream data they themselves have influenced. They could try to fix this, but they won’t, because no one is asking them to

Very much intertia bias that hamstrings discoverability