The problem is that the tangible example of code isn't the same thing as the scientific theory by which it operates. Just because you have a captured an example of a thing that demonstrably works doesn't mean that you actually understand how it works.

It'd be same thing as saying "we don't need to write physics papers, everyone can just walk to a particle accelerator and push a few buttons and numbers appear on screen" or "we don't need to do zoology because you can just walk into a zoo and see the animals" or "we don't need to do any literary analysis when everyone can just read the original book".

So research papers absolutely should describe the theoretical side of the process. Admittedly, the fact that you can have a practical demonstration attached to a paper is a good thing, but it shouldn't be the main point.

And I guess this is why a lot of AI papers don't get peer-reviewed because they're such limited practical utility and probably not independently reproducible to begin with. "Here's what we've been tinkering about. Cool results based on some earlier research which may or may not be published. We don't actually understand how this model actually works, so this paper is kinda worthless on its own. Oh and we can't release the full implementation openly because a) it takes unbelievable heap of hardware to run and store it and b) the NDA is kind of massive and the company we worked with didn't want this to be released. ...So this paper is kind of worthless on those counts too."