Knowledge is learning relationships by decontextualizing information into generalized components. Application of knowledge is recontextualizing these components based on the problem at hand.
This is essentially just compression and decompression. It's just that with prior compression techniques, we never tried leveraging the inherent relationships encoded in a compressed data structure, because our compression schemes did not leverage semantic information in a generalized way and thus did not encode very meaningful relationships other than "this data uses the letter 'e' quite a lot".
A lot of that comes from the sheer amount of data we throw at these models, which provide enough substrate for semantic compression. Compare that to common compression schemes in the wild, where data is compressed in isolation without contributing its information to some model of the world. It turns out that because of this, we've been leaving a lot on the table with regards to compression. Another factor has been the speed/efficiency tradeoff. GPUs have allowed us to put a lot more into efficiency, and the expectations that many language models only need to produce text as fast as it can be read by a human means that we can even further optimize for efficiency over speed.
Also, shout out to Fabrice Bellard's ts_zip, which leverages LLMs to compress text files. https://bellard.org/ts_zip/