This assumes that our processes and algorithms don't get more targeted or improve. The rate of new approach discovery is staggering. For every problem, some combination of approaches will more efficiently pre-process and understand the training data.

The article also ignores training vs running tradeoffs. Training a model once may be extremely resource intensive, but running the resulting model on millions of devices can be negligible while having huge value add.