The point is that google is trying to hire "only the best". Let's say that "the best" are 1% of applicants (to make it simple).
Now, imagine that google's interview process, optimised to reduce the false positive rate [1] to 0% as it purportedly is, rejects 10% of applicants that should be hired (i.e. it has a 10% "false negative rate").
How would you guarantee that this rejected 10% does not include the 1% that are "the best"? You can't find out because you've already ditched them, so you can't exactly compare them to the ones you hired. You can find out which of the ones you hired are "the best" but only compared to your other hires. There's no guarantee that you don't end up hiring mediocre people, just by consistently failing to hire the actual best every time.
How likely is it that you'll ditch the 1% by chance? If you consistently reject 10% of candidates you actually should hire, then it's one out of ten, I'd say.
So it depends on how high is google's "false negative rate". If it's as high as 50% they may well end up rejecting half of the people they're trying to hire. The google SRE user above mentions "many false-negatives". That sounds like worse than 50%.
So, to answer your question with another question: what happens if you consistently miss most of the group you are trying to hire, week after week?
_______________________
[1] Normally people look at true positive rate and true negative rate, the former being the proportion of all positive results that are correct, and accordingly for the latter. "False positive" is just the complement of "true positive".
Also, note that a process may have a high TPR and high TNR at the same time, so a high TNR on its own is no guarantee of a good-quality process.
A huge amount of time/energy/money wasted in interviewing way too many people in a way too deep recruiting process.