I'm not even a hardware or AI person, and even I could have told you ASICs would make way more sense than GPUs or FPGAs for machine learning. It's all about data locality. Fetching memory is the most costly thing a GPU does, and for ML (DNNs) there no big need for global access memory 99% of the time.
Anyone the casually follows AI knows that people have been talking about making DNN ASICS for some time. It was all a matter of time and $$$$$$
There is no doubt FB is working on them too. Which is why Google is finally publicaly saying that "we did it first ;)"
That's kind of what Movidius says, too, about its Myriad 2 VPU, which is kind of a GPU (SIMD-VLIW) with larger amounts of local memory combined with hardware accelerators.
> Google is finally publicaly saying that "we did it first ;)"
Makes sense. In that respect yeah, they probably wanted to keep it under wraps to avoid Facebook/others from getting a timeline estimate out of it and jump ahead.
Obviously details and plans how it was done is where all the good stuff is, so that being hidden is understandable.