Humans are trained on human language. LLMs are trained on human language.
Thus something that is easier for a human to understand is likely easier for an LLM to understand.
That higher level language with well named variables reads more comprehensibly than code:VERB with:PREPOSITION types:NOUN, intermixed:ADJECTIVE, stems:VERB from:PREPOSITION first:ADJECTIVE principles:NOUN too:ADVERB
Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo
versus
Buffalo:PN buffalo:N Buffalo:PN buffalo:N buffalo:V buffalo:V Buffalo:PN buffalo:N
I think the second one makes much more sense.
The majority of the time you can infer the type from reading well written code (to the extent that the shape of the type matters in the context of that piece of code)
x = y + z
Well that depends on the types of y and z, which themselves may depend on the types of other operands, which themselves may not be known until the program actually runs. All that inference takes a lot of thinking, which takes tokens, which cost money. Why not just write the types down? Although we call these things "inference engines" they're really pattern matching explicit tokens, so it's better to actually write down the types so they can be pattern matched than to figure them out at inference time.