To be clear, I had read the whole post, I was just being terse since the emphasis seemed to be so heavily on an apples to bananas comparison (I believe 100% of the results cited in the prose, many in bold, are with mismatched precision and batch size), with minimal articulation of the many axes of nuance here. Precision isn't defined at all in the LSTM case, and could easily be the cause of the failure of the TPU run to converge where the GPU runs do. To a non-expert audience I think the end result is confusing and misleading.
Also, while I certainly agree that the performance/dollar comparison is highly relevant to customers at a given instant, that may only tell us that Google is subsidizing this hardware now that they've deployed it, and/or that, lacking serious competition, NVIDIA has been building crazy margins into their P100/V100 prices. In understanding fundamental technological tradeoffs, and even the limits of what the pricing in a more competitive market could be, it is relevant to compare performance per unit of hardware resources (mm^2, die/package, watt, GB of HBM, etc.)
In short, these comparisons are hard, and there is no one which tells a complete story. I pushed back because, while the post includes some nuance, it brushes a great deal under the rug and focuses primarily on problematic comparison
(Further disclosure: I'm at least the third person in this sub-thread with some Google Brain/Cloud affiliation. I am speaking in my independent academic voice. I also think TPUs are great, having them publicly available now is great, and competition and diversity of architectural approach in accelerators is great. I appreciate the effort of the authors, but think the subtlety of these comparisons requires serious care.)