My second hand experience says that they are very prone to firing people.
At first glance this seems good, because I've certainly worked places where dead weight was permitted and there seemed to be no getting rid of them.
The problem is: how do you know which people are good? I believe you can come to pretty accurate conclusions with the right people in place at each level of your hierarchy. However, one wrong person can completely skew your perception of who is doing good work.
I've also never seen a metrics-based review system that was actually designed for measuring humans. It must work really well on the robots that Google tests it on, but people don't map to 1-5 point scales or bell curves as well as you might think.
On a good team of engineers, I think a consensus generally builds when someone is not performing at the necessary level. Either you are constantly fixing their code, or they are slowing down the team because they cannot produce fast enough. Eventually this will bubble up to the manager, and they can then decide to shuffle them around, mentor them, put them on an improvement plan, or let them go.
Perhaps, if you work on a team of peers. I'm not speaking from personal experience in terms of managerial problems, but I have often been the only person doing my sort of work, which makes peer review challenging.
And I don't think that Netflix shuffles, mentors or warns people -- I think they just bounce you out of the company. It's been put to me as "they pay top of market, so they shouldn't have to deal with any personnel issues."
This is my issue with it as well. With perfect knowledge it sounds wonderful, but we all know how political situations can cloud people's judgment in an organizational context.
I mean think of it, have they literally never made a mistake here? Well, what if you're that mistake? Tough shit, here's 50 grand (made up number), go be unemployed now.
At first glance this seems good, because I've certainly worked places where dead weight was permitted and there seemed to be no getting rid of them.
The problem is: how do you know which people are good? I believe you can come to pretty accurate conclusions with the right people in place at each level of your hierarchy. However, one wrong person can completely skew your perception of who is doing good work.
I've also never seen a metrics-based review system that was actually designed for measuring humans. It must work really well on the robots that Google tests it on, but people don't map to 1-5 point scales or bell curves as well as you might think.
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