What even is an "AI chip"? What's the difference to a GPU? As long as nobody can explain that I have big doubts that it whould be more than a GPU+marketing. So no big deal if they don't provide one.
https://cloud.google.com/blog/products/gcp/an-in-depth-look-...
It boils down to:
CPUs: 10s of cores
GPUs: 1000s of cores
NNs: 100000s of cores
NNs have very simple cores (fused multiply-add and look-up table functions) but can run many of them in one cycle.
FTA: Because general-purpose processors such as CPUs and GPUs must provide good performance across a wide range of applications, they have evolved myriad sophisticated, performance-oriented mechanisms. As a side effect, the behavior of those processors can be difficult to predict, which makes it hard to guarantee a certain latency limit on neural network inference. In contrast, TPU design is strictly minimal and deterministic as it has to run only one task at a time: neural network prediction. You can see its simplicity in the floor plan of the TPU die.
Especially for mobile applications (most Arm customers), you pay extra energy for all that pipeline flexibility that isn't being used. A dedicated chip will save a bunch of power.
Another comment mentioned cores and I don't think that's a good way of looking at it, as in most ways a TPU is back to very "few" but hyper-specialized "cores". There is essentially no parallelism in a TPU or neural processor -- you feed it three matrixes and it gives you the result. You move on to the next one.