In fact the most successful early precursor AGI we have - the atari deepmind agent - is a pure ANN.

People have been using ANNs for reinforcement learning tasks since at least the TD-Gammon system with varying success. The Deepmind Atari agent is bigger and the task is sexier, but calling it an early precursor AGI seems far fetched.

Consider the case of extreme hydrocephaly - where fluid fills in the center of the brain and replaces most of the brain and squeezes the remainder out to a thin surface near the skull. And yet, these patients can have above average IQs. Optimal dynamic wiring can explain this - the brain is constantly doing global optimization across the wiring structure, adapting to even extreme deformations and damage. How does evolved modularity explain this?

I suppose that the network topology of these brains is essentially normal, isn't it? If that's the case, then all the modules are still there, they are just squeezed against the skull wall.

This is nonsense - language processing develops in general purpose cortical modules, there is no specific language circuitry.

If I understand correctly, damage to Broca's area or Wernicke's area tends to cause speech impairment.
This may be more or less severe depending on the individual, which is consistent with the evolved modularity hypotheses: genetically different individuals may have small differences in the location and shape of the brain modules.

Under the universal learning machine hypothesis, instead, we would expect that speech impairment following localized brain damage to quickly heal in most cases as other brain areas are recruited to the task. Note that there are large rewards for regaining linguistic ability, hence the brain would sacrifice other abilities if it could. This generally does not happen.

In fact, for most people with completely healthy brains it is difficult to learn a new language as well as a native speaker after the age of 10. This suggests that our language processing machinery is hard-wired to a significant extent.