I read the subtitle as

> The weird, rare, surprising patterns [that make data rich] slowly get smoothed out when an AI model trains on outputs from a previous model.

i.e., the patterns are responsible for making data rich, and they are slowly lost as each new generation model trains on the prior generation's output.

Or, if you'd prefer an analogy, we're using a copy machine to output new documents by taking the last copy spit out by the machine, adding some marks to it, and running it through the copier again. Over time, details present in much older copies blur and fade away in Nth generation copies.

It might be weird if you haven't read a lot of English. It's actually quite normal to say that process X is a way to make effect Y happen. "Makes your mout water" is more effective than "waters your mouth". "Makes your breath fresh and tolerable" is better than "freshens and tolerablerizes your breath". Etc.

Actually, what you are describing is what happens when LLM-generated prose cycles and then trains humans to use equally dull thinking.