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It's consistent with it.

Rich used to be very bullish on neural nets, then somewhat dismissive of them (due to the fragility/inadequacy of FCNs), and then increasingly enthusiastic as the renewed interest demonstrated that those problems could be overcome-- e.g., through better initialization, training, and (as you note) different architecture choices. His main concern was whether a method could keep working as more resources became available, as otherwise you would tautologically end up with something short of true artificial intelligence.

The important thing is that the technique can scale with increasing data or compute without hitting a hard or soft limit.