As a rule: all high quality text is useful.

There's no "2022 split", and the "untouched by machines" bit came from the marketing blurb of a company offering book scanning services - not the AI labs themselves.

At the AI lab level: the book scanning seems to be driven by copyright concerns, not data contamination concerns. There was a concern about AI contamination, but there's no measurable performance loss from ingesting post-2022 data with minimal filtration, and some tests attribute small but persistent performance gains to post-2022 AI contamination. It's unclear where exactly do those gains come from.

Why is all high quality text useful? The "inverse problem" framing is that all text reflects the thinking behind it, somewhat, and by learning to reproduce it, LLMs implicitly learn to reproduce some of the thought process too. They don't just memorize the dry factual knowledge, but also learn how that knowledge fits together, and how to reason about that knowledge - both in the specific case and in general. And that "in general" then surfaces in an LLM's ability to generalize. Which is very desirable.