Reading the comments here suggests that it would be cool to use both of the chips together. If it is true that spiking does well with unsupervised learning, then you could feed a bunch of input to a spiker, then scan it for components that could be mostly mimicked with a convolutional chip and reroute the inputs/outputs. (Yeah, the interconnect would suck.) The point is not to come up with some magic better-performing hybrid, but rather to explore an intermediate point in the design space of augmenting/replacing actual neurons with silicon. The IBM chip isn't that close to biology, but it's closer than a convolutional network, and a convolutional network is a much smaller step than a general purpose processor. We might learn about some simple augmentations that are likely to work in practice.

Also, the whole "airplanes don't flap their wings" analogy can be taken too far. Little flying things are qualitatively different from big flying things. You'll notice that a lot of small artificial flying things are flapping, and the biggest natural fliers tend to glide a lot. There are other reasons why nature didn't evolve large fliers. (Although I'm willing to believe some large fliers may occasionally have hot gases shooting out of their back ends, I do not believe propulsion is their purpose.)