I see this said a lot, but in reality it just depends on how the network is trained and how it's prompted. For example, you don't get dumber because you read children's books, you just get better at understanding what makes a good children's book. It's only if reading children's books comes at the cost of reading other content that you might be dumber as a result.
Similarly, an AI doesn't automatically get dumber because it encounters dumb content. It's only if you're training exclusively on dumb content that it doesn't know what quality looks like that you'd have problems.
Broad training sets (ideally pruned of as much junk as possible) and RLHF in theory should condition the network to reproduce quality content and not simply the lowest common denominator of what's found on the internet.
And assuming all that fails there's nothing stopping researchers from just using past datasets with improved architecture going forward. I mean you'd have to wonder why on Earth OpenAI would even release GPT-5 if it's worse than GPT-4...
There's just no scenario here in which what you're saying would actually play out in reality. One way or another companies will ensure the next iteration of their LLMs are better than their previous.