  Explaining the behaviour of intelligent systems will get increasingly and
perhaps intractably challenging as models grow in size and complexity. We may
not be able to expect an explanation for every prediction made by a brain-scale
model, nor can we expect explanations to remain objective or apolitical. Our
functionalist understanding of these models is of less advantage than we might
assume. Models precede explanations, and can be useful even when both model and
explanation are incorrect. Explainability may never win the race against
complexity, but this is less problematic than it seems.
