Nothing wrong with LLM textual content per se. Current LLMs simply cannot produce sufficient quality of it. And the problem is that P(garbage|LLM) is so high that P(LLM) means you're going to extract a small amount of content from it.
However, there is another problem: first-party content becomes unreliable. "I looked at X and when I did Y, I noticed that a dropped by b%". Vast amounts of such content can be generated that are now plausible. Humans were not so good at producing sufficiently convincing information here, but LLMs are, so my heuristics required to filter down input to manually review are no longer effective.
For my part, I try to write human-directed content when intended for humans, but I have a large machine-curated knowledge base of machine-written content that I consume through a machine intermediary. In some future, I'm sure that machines will perform better at communication, and so the writing will improve, but the provenance of first-party information cannot be solved now. I hope someone figures out a new mechanism to transmit facts to others interested in those.
However, there is another problem: first-party content becomes unreliable. "I looked at X and when I did Y, I noticed that a dropped by b%". Vast amounts of such content can be generated that are now plausible. Humans were not so good at producing sufficiently convincing information here, but LLMs are, so my heuristics required to filter down input to manually review are no longer effective.
For my part, I try to write human-directed content when intended for humans, but I have a large machine-curated knowledge base of machine-written content that I consume through a machine intermediary. In some future, I'm sure that machines will perform better at communication, and so the writing will improve, but the provenance of first-party information cannot be solved now. I hope someone figures out a new mechanism to transmit facts to others interested in those.