Is Prediction the same as Compression? Just came across this transcript that states:

The principle is that prediction is the same thing as compression. And
what that means is that whenever you have a prediction algorithm, you
can also get a correspondingly good compression algorithm for data you
already have, and vice versa.

Even though the article gives some examples, I would like to know some answers from this community on the question: Is Prediction the same as Compression?
Fundamentally, yes. Prediction can be used to compress, and compression is a form of prediction. More generally, any learned pattern can be used to compress and vice versa.
This rabbit hole goes a lot deeper, for example I'd suggest A Tutorial Introduction to the Minimum Description Length Principle as a thorough introduction into trying to learn and choose models using compression.