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.
The result is minor AND Google spent a (relative) lot of money to achieve it (especially in the eyes of the new CFO). Jeff Dean is desperately trying to save the prestige of the research (in a very insular, Google-y way) because he wants to save the 2017-era economically-not-viable blue sky culture where Tensorflow & the TPU flourished and the transformer was born. But the reality is that Google’s core businesses are under attack (anti-trust, Jedi Blue etc), the TPU now has zero chance versus NVidia, and Google is literally no longer growing ads. His financing is about to pop in the next 1-2 years.
What makes you say TPU has zero chance against growing NVIDIA?
If anything, now is the best time for TPU to grow and I'd say investing in TPU gave Google an edge. There is no other large scale LLM that was trained on anything but NVIDIA GPUs. Gemini is the only exception. Every big company is scrambling to make their own hardware in the AI era while Google already has it.
Everyone I know who worked with TPUs loves how well they scale. Sure Jax has a learning curve but it's not a problem, especially given the performance advantages it gives.
Besides the many CAPEX-vs-OPEX tradeoffs that are completely unavailable due to not being able to buy physical TPU pods, there are inherent Google-y risks e.g. risk of the TPU product and/or support getting killed or fragmented / deprecated (very very common with Google), your data & traffic must also be locked in to Google’s pricing, and you must indefinitely put up with / negotiate with Google Cloud people (in my experience at multiple companies: worst customer support ever).
Google does indeed lock in their own ROI with deciding to not compete with AMD / Graphcore etc, but that also rooflines their total market. If they were to come up with a compelling Android-based Jetson-like edge product, and if demand for said product eclipses total GPU demand (robotics explosion?) then they might have a ramp to compete with NVidia. But the USB TPUs and phone accelerators today are just toys. And toys go to the Google graveyard, because Googlers don’t build gardens they treat everything like toys and throw them away when they get bored.
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.