The reason Jeff Dean cares is that his team's improvement compared to standard EDA tools was marginal at best and may have overfitted to a certain class of chips. Thus, he is defending his research because it is not widely accepted. Open source code has been out for years and in that time the EDA companies have largely done their own ML-based approaches that do not match his. He attributes this not to failings in his own research but to the detractors at these companies not giving it a fair chance.

The guys at EDA companies care because Google's result makes them look like idiots when you take the paper at face value, and does advance the state of the art a bit. They have been working hard for marginal improvements, and that some team of ML people can come in and make a big splash with something like this is offensive to them. Furthermore, the result is not that impressive and does not generalize enough to be useful to them (and competent teams at these companies absolutely have checked).

The fact that the result is so minor is the reason that this is so contentious.