"The brain does it" is not in itself a reason to do the same in AI.

Sometimes, what the brain does is genuinely a good solution to a given task - one that's good regardless of whether your neural network is of artificial or biological variety. But sometimes, what the brain does is an evolutionary kludge, or a hack that works around one of the "being made of flesh" issues - of which there are a great many.

We have known examples of both - and a few known features that might go either way.

The brain does have some known useful features that we are yet to plunder - usually because we know they're there somewhere but not how they work. We don't know how the brain stabilizes online learning, for example. Or what low k-complexity priors and data augmentation processes does it use to enable its sample efficiency.

But a two-system split? Useless by itself. Splitting a network in two is easy - but if we don't know what that split does, what it buys us, what useful bias does it impart? We're just adding complexity. See: the investigation into HRMs, and how the "hierarchical" part proved to be a lot less meaningful than anticipated.