Without sounding crass:

1. It works already (IE it's already in use)

2. It works really well (or else they wouldn't be using it so broadly)

3. Considering how long this was said to be in development, it also likely means they are working on the next big improvement before these guys have even gotten the current one working.

IBM's TrueNorth chip is taking a much more neuromorphic design approach by trying to approximate networks of biological neurons. They are investigating a new form of computer architecture away from the classic Von Neumann model.

TPUs are custom ASICs that speed up math on tensors i.e. high-dimensional matrices. Tensors feature prominently in artificial neural networks, especially the deep learning architectures. While GPUs help accelerate these operations, they are optimized first and foremost for video rendering/gaming applications -- compute-specific features are mostly tacked on. TPUs are optimized solely for doing ML-related computations.

They are already using it in production and have been for over a year? That is a huge difference compared to what people are working on and building. They beat Lee Sedol using this hardware. Over 100 teams at Google are using machine learning. This hardware already accelerates their projects.