NeuraLink was supposed to learn the mapping, yes, but on the assumption that the mapping was relatively stable, unless I am completely mistaken.
What we learn in this article is that the mapping changes a lot, and quickly.
This is an important difference, as previously it could be enough to converge slowly towards the right mapping, but now it has to be done quickly in order to become good enough before starting again a retraining on it.
(To sum up my remark: the retraining has to be fast enough to not become obsolete before it can be used, given the "representation drift".)
You are conflating low-level I/O (like eyes and NeuraLink) with high-level reasoning.
Assume NeuraLink transmits an encoded signal that there's a dog in front of you. Without "representation drift" (or rather its underlying mechanism) I don't think your brain would ever notice that signal has anything to do with dogs. The wire is inserted into a random place, it is not designed to be repositioned to fire up your "dog" neurons it needs to send "dog" signal.
As for the assumption of mapping being stable, there's no such thing at all. First and foremost the brain itself must learn to interpret signals from the wire and send them back. Any mechanism on wire's side would mostly be to establish a feedback loop for the brain to be able to learn to do that (which it will do by rearranging its internal structure).
The presentation with pigs was all about exporting the brain's representation for olfactory stimuli. They were not demonstrating learning to control cursors.
So now I understand why the quiproquo, sorry.
I agree with you that in this situation (cursor controlling) nothing is changed regarding the learning task for the brain.
What we learn in this article is that the mapping changes a lot, and quickly.
This is an important difference, as previously it could be enough to converge slowly towards the right mapping, but now it has to be done quickly in order to become good enough before starting again a retraining on it.
(To sum up my remark: the retraining has to be fast enough to not become obsolete before it can be used, given the "representation drift".)