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
That is a great question. IMO, major LLM players currently have a large enough user base to generate training data from their users (questions and user provided answers, corrections, etc). So, if StackOverflow dies, it will become harder to keep up with closed source models
