You're missing the boat entirely. Think about how an AI model is trained: It reads a section of text (one context size at a time), converts it into tokens, then increases a floating point value a little bit or decreases it a little bit based on what it's already associated with the previous token.

It does this trillions of times on zillions of books, articles, artificially-created training text (more and more, this), and other similar things. After all of that, you get a great big stream of floating point values you write out into a file. This file represents the a bazillion statistical probabilities, so that when you give it a stream of tokens, it can predict the next one.

That's all it is. It's not a database! It hasn't memorized anything. It hasn't encoded anything. You can't decode it at all because it's a one-way process.

Let me make an analogy: Let's say you had a collection of dice. You roll them each, individually, 1 trillion times and record the results. Except you're not just rolling them, you're leaving them in their current state and tossing them up into a domed ceiling (like one of those dice popper things). After that's all done you'll find out that die #1 is slightly imbalanced and wants to land on the number two more than any other number. Except when the starting position is two, then it's likely to roll a six.

With this amount of data, you could predict the next roll of any die based on its starting position and be right a lot of the time. Not 100% of the time. Just more often than would be possible if it was truly random.

That is how an AI model works. It's a multi-gigabyte file (note: not terabytes or petabytes which would be necessary for it to be possible to contain a "memorized" collection of millions of books) containing loads of statistical probabilities.

To suggest its just a shitty form of encoding is to say that a record of 100 trillion random dice rolls can be used to reproduce reality.