That was genuinely a strong signal that whatever learning algorithm the brain uses isn't "magic", and probably can be approximated with the ML tools we have.

It also pointed at the possibility that the learning algorithms brain uses might be, like the paper has demonstrated, less compute-optimal and data-optimal than backprop - but far easier to implement in a localized, distributed fashion. Because backprop requires activation storage and global connectivity - and both "hot" data storage and connectivity of any kind are extremely expensive for the brain.