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They're not; you want what Google is calling a "TPU" and what NVidia is calling a "Tensor Core GPU" - operations on ridiculously coarse data types at ridiculously high speeds. Science+engineering simulations want FLOPS on 64-bit numbers, and video games want 32-bit, but AI is happy with 8-bit and doesn't even seem picky about whether you use 4 or 5 or 7 bits of that for mantissa.
I'd guess a cap on FLOPs (well, OPs, on whatever datatype) and another on memory bandwidth would work for the current software paradigm, for "serial" (as much as you can call a chip with 64k multipliers "serial") runs ... except that neural nets parallelize really well, and there's probably still a lot of room to improve interconnect bandwidth and latency, and if you do that well enough then at some point you don't care so much if there's a cap on serial execution speed. Human latency is enormous; no need to beat it by too many orders of magnitude.
The depressing bit is that the "hardware" side of the pair might be "just reuse the existing hardware with this new super efficient software". Even if the initial cap is low enough that we can't get to an AI smart enough to "foom" itself, if there are clever and heretofore unseen software improvements possible (and that's the safe way to bet) then human researchers will hit on them themselves eventually.
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