If a human brain does its magic with 10 petaflops, and you have 1 petaflop, you should be able to make an equivalent to the human brain that runs at 1/10th of the speed but never sleeps. In other words, once you've reached the same order of magnitude it doesn't matter.
On the other hand, Kurzweil's math really comes down to an argument that the brain is using about 10 petaflops for inference, but it also is changing weights and doing a lot more math and optimization for training (which we don't completely understand). It may (or may not) take considerably more than 10 petaflops to train at the rate humans learn. And remember, humans take years to do anything useful.
Further, 10 petaflops may be enough math, but it doesn't mean you can store enough information or flow enough state between the different parts "of the model."
These are the big questions. If we knew the answers, IMO, we would already have really slow AGI.
I don't know where that "computationally sufficient" line is. It'll always be fuzzy (because you could have a very slow, but smart entity). And before we have a working AGI, thinking about how much computation we need always comes down to back of the envelope estimations with radically different assumptions of how much computational work brains do.
But I can't rule out the idea that current architectures have enough processing to do it.