I had noticed that Google's ML teams are doing a ton of Python, and at the same time the overall support for Python in prod was pretty low.

This has lead to some interesting situations over the last year. Especially as "move fast and break things" Google Labs style demos meet "everything deployed must comply with a thousand horizontal mandates relating to SLAs, logging, production best practices, etc".

An astonishing number of things used every day at Google are unfunded "community commons" projects. This sort of used to work in the past when the company and the employees had a different relationship.

As Google gradually changes that relationship to a more conventional one, they will see more and more conventional results.