Sentience as an emergent property of sufficiently complex brains is the exact opposite of "supernatural".
But that is just my opinion.
But, from a best-of-all-possible-worlds perspective, surprising coincidences that are necessary to observe coincidences and label them as surprising aren't crazy. At least not more crazy than the fact that slightly adjusted physical constants would prevent the universe from existing.
Well, I wouldn't say impossible: just that BMI's are probably first. Then probably wetware/bio-hardware sentience, before silicon sentience happens.
My point is the mechanisms for sentience/consciousness/experience are not well understood. I would suspect the electro-chemical reactions inside every cell to be critical to replicating those cells functions.
You would never try to replicate a car never looking under the hood! You might make something that looks like a car, seems to act like a car, but has a drastically simpler engine (hamsters on wheels), and have designs that support that bad architecture (like making the car lighter) with unforeseen consequences (the car flips in a light breeze). The metaphor transfers nicely to machine intelligence: I think.
These can be problem words, the same way that "quantum" and "energy" can be problem words, because they get used in a way that's like magic words that don't articulate any mechanisms. Lots of complex things aren't sentient (e.g. our immune system, the internet), and "emergent" things still demand meaningful explanations of their mechanisms, and what those mechanisms are equivalent to at different levels (superconductivity).
Whether or not AI's being networked together achieves sentience is going to hinge on all kinds of specific functional details that are being entirely skipped over. That's not a generalized rejection of a notion of sentience but of this particular characterization as being undercooked.
I’m not immediately convinced the brain is more complicated, based on raw numbers.
There's no reason to expect self awareness to emerge from stacking enough Lego blocks together, and it's no different if you have GPT-based neural nets instead of Lego blocks.
In nature, self awareness gives a strong evolutionary advantage (as it increases self-preservation) and it has been independently invented multiple times in different species (we have seen it manifest in some species of fishes for instance, in addition to mammals and birds). Backpropagation-based training of a next-token predictor doesn't give the same kind of evolutionary advantage for self-awareness, so unless researchers try explicitly to make it happen, there's no reason to believe it will emerge spontaneously.