These are all terrible arguments: the 100x slowdown for a vaguely relevant algorithm, the power of evolution, the power of more people working on backprop, the esimates of brain compute themselves. The point is, nonlocality of backprop makes relevance of its compute parity with evolved learning another terrible argument, and the 100x figure is an anchor for this aspect usually not taken into account when applying estimates of brain compute to machine learning.
These are all terrible arguments: the 100x slowdown for a vaguely relevant algorithm, the power of evolution, the power of more people working on backprop, the esimates of brain compute themselves. The point is, nonlocality of backprop makes relevance of its compute parity with evolved learning another terrible argument, and the 100x figure is an anchor for this aspect usually not taken into account when applying estimates of brain compute to machine learning.