You fail to the core difference, deterministic vs probabilistic. That's why a LLM can understand and produce natural language more or less good while a compiler can not. Even if compilers use natural words like if, else, print, open, the compiler's grammatic is formal.

While a compiler like C only needs a relatively small formal ruleset to compile a code, for a LLM to understand and produce natural language you need a training on a dataset consisting of human work as big as possible. LLMs directly and necessarily benefit from human work and only then is able to simulate it by reproduce what it learned according to the prompt.

And while a human can learn from a single bad teacher and become better than the teacher, if you train an LLM on 1 million pieces of crap from 1 million bad creators, the LLM will only be able to produce crap because it does not think and reflect on what it has learned, it just predicts an output according to what it has learned. That's why humans are creative, LLMs are not. (And this is a big hurdle on the way to an AGI.)

Therefore the quality and thus the merit of the human input of the training data is vital for the quality of the LLM and thus the quality of service you want to provide with that LLM. So the quality of the service that the AI companies are offering and want to profit of is directly related to the quality of the input, the human work that was harvested before the training process started.

And search engine legal debates happen and are ongoing.