A relevant paper came out 3 days ago talking about how AlphaGo used Bayesian hyperparameter optimization and how that improved performance: https://arxiv.org/pdf/1812.06855v1.pdf
It's interesting to set the OpenAI compute article's graph to linear scale so you can see that the compute that went into AlphaGo utterly dwarfs everything else. It seems like DeepMind is definitely ahead of nearly everyone else on the engineering effort and money they've put into scaling.
I just checked and seems it was fp32. I agree this makes it less impressive, I forgot to check that originally. I still think this somewhat counts as a software win, because working fp16 training required a bunch of programmer effort to take advantage of the hardware, just like optimization to make better use of cache would.
However, there's also a different set of same-machine datapoints available in the benchmark, where training time on a single Cloud TPU v2 went down from 12 hours 30 minutes to 2 hours 44 minutes, which is a 4.5x speedup similar to the 5x achieved on the V100. The Cloud TPU was special-purpose hardware being trained with bfloat16 from the start, so that's a similar magnitude improvement more clearly due to software. The history shows incremental progress down to 6 hours and then a 2x speedup once the fast.ai team published and the Google Brain team incorporated their techniques.
I went and checked and as far as I can tell they used the same 1024 batch size for the 12 and 6 hour time. The changes I noticed were better normalization, label smoothing, a somewhat tweaked input pipeline (not sure if optimization or refactoring) and updating Tensorflow a few versions (plausibly includes a bunch of hardware optimizations like you're talking about).
The things they took from fast.ai for the 2x speedup were training on progressively larger image sizes, and the better triangular learning rate schedule. Separately for their later submissions, which don't include a single-GPU figure, fast.ai came up with better methods of cropping and augmentation that improve accuracy. I don't necessarily think the 2x speedup pace through clever ideas pace is sustainable, lots of the fast.ai ideas seem to be pretty low hanging fruit.
I basically agree with the quoted part of your take, just that I don't think it explains enough of the apathy towards training speed that I see, although I think it might more fully explain the situation at OpenAI and DeepMind. I'm making more of a revealed preferences efficient markets kind of argument where I think the fact that those low hanging fruits weren't picked and aren't incorporated into the vast majority of deep learning projects suggests that researchers are sufficiently un-constrained by training times that it isn't worth their time to optimize things.
Like I say in the article though, I'm not super confident and I could be underestimating the zeal for faster training because of sampling error of what I've seen, read and thought of, or it could just be inefficient markets.