This problem can't really be avoided once we begin using AI to write, understand, explain, and disseminate information for us. It'll be writing more than blogs and SEO pages.

How long before we start readily using AI to write academic journals and scientific papers? It's really only a matter of time, if it's not already happening.

From “known good” sources of knowledge, we can generate an infinite amount of content. We can add more “known good” knowledge to the model by generating content about that knowledge and training on it.

I agree there will be many issues keeping up with what “known good” is, but that’s always been an issue.

That's my entire point -- AI only generates content right now, but it will also be the source of content for training purposes soon. We need a "known good" human knowledge-base, otherwise generative AI will degenerate as AI generated content proliferates.

Crawling the web, like in the case of the OP, isn't going to work for much longer. And books, video, and music are next.

That is training on content.

The future will have models pre-trained on content and tuned on corpuses of knowledge. The knowledge it is trained on will be a selling point for the model.

Think of it this way - if you want to update the model so it knows the latest news, does it matter if the news was AI generated if it was generated from details of actual events?

“It’s ok bro, another model will fix, just please, one more ~layer~ ~agent~ model”

It’s all fun and games until you can’t reliably generate your base models anymore, because all your _base_ data is too polluted.

Let’s not forget MS has a $10bn stake in the current crop of LLM’s turning out to be as magic as they claim, so I’m sure they will do anything to ensure that happens.

My point is about the inevitable future when _those_ models start to struggle.

The phi approach doesn’t seem like breaking the ouroboros, it just feels like inserting another model/snake into the loop.

I don’t think that we are going to get big improvements in LLMs without architecture improvements that need less data, and the current generation of models appears to be good enough at creating content from data/knowledge to train any future architectures we have with better synthetic datasets. Fortunately we have already seen examples of both of these “in the lab” and will probably see commercially sized models using some of the techniques in the coming months.