Reading very charitably: you can say that at the moment a lot of the AI's advantages seem to come from: (a) having read approximately everything, and (b) being willing and able to try out lots and lots of approaches without tiring or getting bored. You don't need to have superhuman intelligence, yet, when superhuman persistence still yields so much low hanging fruit.
If I want to understand some mathematics, my first step is always translating it to code. Modern code (excluding array languages) no longer has a culture of minimizing character count, and omitting "obvious" steps is not possible. LLMs are based on an architecture (transformers) that was originally designed for natural language translation, so it doesn't surprise me that they're good at this.
You need to think of mathematical writing as being both symbols/words and also a picture. You're meant to see patterns in what is in front of you. The short names are necessary for the picture. Without them you can't see it. It's like you've zoomed into the individual pixels; the information is all there but you'll never make sense of it that way. You're meant to read the "zoomed out" view where you have short placeholders as names.