If you go with simple, straightforward data structures you often get adequate performance at the macro level. In my experience, getting really good performance often involves being clever with data structures. It depends how far you want to push performance.
I’ve done a lot of work optimising text CRDTs. There, the simple data structure is (essentially) a list which contains metadata for each character. But you’re constantly scanning and inserting into the list. You can improve it in two ways: first, make each list item store the metadata for a connected span of characters. Second, use a b-tree for fast insertion. Make the b-tree store aggregate metadata in internal nodes. That gets you orders of magnitude better performance - O(n) per keystroke to O(log n). RLE gets ~10x lower ram utilisation. It’s only the obvious data structure when you’ve thought about the problem a lot. And you have to write your own btree - there are no libraries for this. At least, none I have found.
> I'm saying that you shouldn't sacrifice simplicity and readability for the sake of performance until you are sure it's necessary
It really depends on the domain. If you’re making a note taking app, you probably get good enough performance by doing the obvious thing. If you’re making a browser, database, llm inference engine or 3d game engine, it pays to think about perf from the start. But my impression is that most people on this site aren’t doing that sort of thing.