So there is clearly still ways to go.
Since you brought up the brain, I will roll with some thoughts. Instead of thinking about a hunk of wet meat that contains a quadrillion synapses... wouldn't it make sense to break those into discrete, specialized and smaller hunks of wet meat of say 10 billion? Then we link those discrete units under a unified command which directs information to the correct specialized core and then the next until some arbitrary stopping point was reached. AKA: An ensemble of models.
My point; It seems that throwing more compute at individual problems has probably reached a kind of apogee in time and cost. PaLM, Bloom, GPT-NeoX and GPT-3 all live in silos. Cool story, but you can't use GPT-3 and PaLM at the same time without an absolute ton of overhead right now. At some point, I think there will be a unifying middleware that can utilize these models in there current form. That is, you will not need to train yet another model to combine the networks of one or more discrete models. Just a hunch.... could be wrong.