I take it as a given that some kind of computation in the brain is responsible for human intelligence, so the only question is how difficult is it to replicate that computation? There are hints that it might not be as difficult as it first appears:

- in general, brains are not evolved specifically for intelligence but rather proprioception. ie. in most animals, brains are primarily a control system for the body.

- the marginal difference between a human and the closest primate is small in absolute terms (apes also have a necortex), but the difference in intelligence is huge. This implies that large parts of the brain are just scaffolding and is not necessarily required for "intelligence"

- human brains are subject to extreme design constraints - it must work on almost no energy, redundant enough to function even with half of it missing, and compact enough to fit through a narrow opening. These things must have a design cost.

- human brains use neurons because they are the only tools available to an eukaryotic organism. From a de novo perspective, there's no reason to believe that this is the only or best way to approach the problem.

If we add this up, it seems to me that computer-based AI has the potential to be more efficient than human brains (in terms of flops) and may ultimately not require much computation at all in comparison.

We still don't know enough about the brain to guess at the properties of the algorithm that the brain is running. But it's precisely because we don't know that we can't rule out that it's something simple. Given that extremely simple algorithms, scaled up, are already superhuman on a number of ML benchmarks, I think there is a good chance that general cognition is something tractable.