Why are you optimizing for zero false-positives? That's what I'd expect from an early startup, where every employee is critical, not from a large company.
My assumption would be that Google has a continuous stream of high-quality applicants to choose from, so they don't really lose much with false negatives, because they will simply end up hiring a different qualified candidate for the position. Every false positive on the other hand brings down the overall quality of their workforce.
Part of the company culture is that we value always being able to expect that everybody you encounter is a strong engineer who will do sensible things when presented with data.
You never go into an encounter with a new person or team being unsure of whether they're going to be difficult. You never have to avoid dealing with "that guy". You get to trust everybody that you meet.
It doesn't just improve overall quality, it makes it a better place to work. Good engineers are happier in this environment, and there is pretty near universal agreement from people who have experienced the results that this is a thing worth preserving.
There are plenty of things about the hiring process which get enthusiastic internal debate, criticism, and data-driven analysis. This is not one of them. This is a thing which we really like.
(Full disclosure: I have gone through the interview process twice, failed the first time, passed the second.)
You can get all the same benefits by firing people who don't work out. Optimizing for zero false positives mean you miss a lot of people who would have been great but were rejected in the name of zero false-positives. Startups can't handle having someone bad be hired early on, because even if they're fired later they've still done a lot of damage. Big companies don't have to worry about that.
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?
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[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.
