It's the exact same training process for both of your examples. I don't really see how you can claim books are not replicated, but that output from other LLMs is.
They have no choice but to train their own model to try and survive. They're paying API pricing for the top tier models but competing against subsidized subscriptions.
The problem is, what's ambiguous or precise is subjective. Your devil's advocate needs to reflect all of the possible readers, and that isn't possible.
There's a good reason we use jargon in professions, or more constrained and less ambiguous languages for maths/coding
>With RL, models no longer just learn what sounds correct based on patterns they've seen. They learn what words to output to be correct. RL is the process of forcing the pre-trained weights to be logically consistent.
How does Reinforcement Learning force the weights to be logically consistent? Isn't it just about training using a coarser/more-fuzzy granularity of fitness?
More generally, is it really solving the task if it's given a large number of attempts and an oracle to say whether it's correct? Humans can answer the questions in one shot and self-check the answer, whereas this is like trial and error with an external expert who tells you to try again.
The underlying feature of FIAT money creation is debt. And debt is a very natural thing (existing before money) that will just manifest in the crypto system instead.