As several people pointed out, the arguments made in this article are severely weakened by the fact that we do not know what fraction of this complexity is essential for brain function and what fraction is unnecessary complexity caused by evolutionary history and biological constraints.
In addition, we should also keep in mind that a digital AI can have many 'unfair advantages' compared to human brains. Besides lacking the obvious biological constraints, digital AIs are not necessarily limited to pure neural networks. AIs can be made up of hybrid systems in which neural networks have access to web-scale knowledge bases, extremely fast and prcecise databases for writing and reading arbitrary data and storing them indefinitly, APIs for running computations that would be difficult to implement in neural networks, and so on. The neural network part of the AI could evolve and learn with having zero-latency, high-bandwith access to several such resources and learn how to make optimal use of them.
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As several people pointed out, the arguments made in this article are severely weakened by the fact that we do not know what fraction of this complexity is essential for brain function and what fraction is unnecessary complexity caused by evolutionary history and biological constraints.
In addition, we should also keep in mind that a digital AI can have many 'unfair advantages' compared to human brains. Besides lacking the obvious biological constraints, digital AIs are not necessarily limited to pure neural networks. AIs can be made up of hybrid systems in which neural networks have access to web-scale knowledge bases, extremely fast and prcecise databases for writing and reading arbitrary data and storing them indefinitly, APIs for running computations that would be difficult to implement in neural networks, and so on. The neural network part of the AI could evolve and learn with having zero-latency, high-bandwith access to several such resources and learn how to make optimal use of them.