It's not just your bias, I too have found great success with a functional programming style, even from the earliest days of ChatGPT. (Not Haskell, but JS, which the models were always good at.)

I think the underlying reason is that functional programming is very conducive to keeping the context tight and focused. For instance, most logic relevant to a task tends to be concentrated in a few functions and data structures across a smallish set of files. That's all you need to feed into the context.

Contrast that with say, Java, where the logic is often spread across a deep inheritance hierarchy located in bunch of separate files. Add to that large frameworks that encapsulate a whole lot of boilerplate and bespoke logic with magic being injected from arbitrary places via e.g. annotations. You'd need to load all of those files (or more likely, simply the whole codebase) and relevant documentation to get accurate results. And even then the additional context is not just extraneous and expensive, but also polluted with irrelevant data that actually reduces accuracy.

A common refrain of mine is that for the best results, you have to invest a lot of time experimenting AND adapt yourself to figure out what works best with AI. In my case, it was gradually shifting to a functional style after spending my whole career writting OO code.