Model collapse
Model collapse is when AI models get worse because they're trained on text written by other AI models, losing the rare and varied parts.
Model collapse is what happens when AI models are trained on text that earlier AI models wrote, again and again. Each round loses a little of the variety in real human writing. The rare facts, unusual views and less common names fade first, and over enough rounds the models get blander and less accurate.
It's a long-term risk for the whole field, as more of the web is written by AI. It also says something useful about what kind of content will be worth more.
How it happens
A model learns the patterns in its training text. It's best at the common ones and weaker at the rare ones. When its own output becomes the next model's training text, the rare patterns are already thin. The next model learns them even less well, and the one after that less again.
A 2024 study in Nature showed this happening when models were trained over several generations on their own output.
Think of photocopying a photocopy. Each copy looks fine on its own, but the fine detail goes first, and after enough copies you can't read the small print.
What it means for brands
Small brands are the small print. If models learn more from AI-written text and less from original sources, the brands that were only mentioned now and then are the ones that fade.
For example, if hundreds of AI-written "best coffee" articles all repeat the same five big names, a model trained on them learns those five even more firmly. Fernhill Coffee, mentioned on a few real review sites, gets harder to find in the pattern.
What to do about it
Original material becomes more valuable: your own data, real reviews, first-hand testing and named experts. Keep publishing things only you can say, and get mentioned by real people on sites the platforms read. Publishing piles of AI-written pages adds to the problem without helping you stand out.