For reference, I think a common approximation is one token being 0.75 words.
For a 100 page book, that translates to around 50,000 tokens. For 1 mil+ tokens, we need to be looking at 2000+ page books. That's pretty rare, even for documentation.
It doesn't have to be text-based, though. I could see films and TV shows becoming increasingly important for long-context model training.
What about the role of synthetic data?
Synthetic data requires a discriminator that can select the highest quality results to feed back into training. Training a discriminator is easier than a full blown LLM, but it still suffers from a lack of high quality training data in the case of 1M context windows. How do you train a discriminator to select good 2,000 page synthetic books if the only ones you have to train it with are Proust and concatenated Harry Potter/Game of Thrones/etc.