For a start, the whole "3 million applied to Google in 2020 and only 20,000 were hired" is for the whole of Google. Not just developers. And it sounds like a lot but really it only works out at 150 people per role ... McDonalds probably gets more than that in any large city with relatively high unemployment numbers. It also assumes that all 3 million candidates were actually suitable for the roles. Having hired plenty of devs in the past, I can be sure that many probably shouldn't have been applicants. Using the raw, unfiltered number is dubious.
Ignoring all that though, the article assumes that the way FAANG companies hire is optimal. Given the number of stories about people who are really good being turned away that's probably not the case. For a company like Google that's not a problem; they can afford false negatives. They don't really affect the outcomes for the company given it's size. Other companies, and especially startups, can't afford the same false negatives. If you're a startup and you hire the wrong person it's going to have a significant impact on the probability of success.
Copying what FAANG companies do rather than working out the best way to filter the candidate pool for what you actually need is a really bad idea.
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[ 0.25 ms ] story [ 24.8 ms ] threadFor a start, the whole "3 million applied to Google in 2020 and only 20,000 were hired" is for the whole of Google. Not just developers. And it sounds like a lot but really it only works out at 150 people per role ... McDonalds probably gets more than that in any large city with relatively high unemployment numbers. It also assumes that all 3 million candidates were actually suitable for the roles. Having hired plenty of devs in the past, I can be sure that many probably shouldn't have been applicants. Using the raw, unfiltered number is dubious.
Ignoring all that though, the article assumes that the way FAANG companies hire is optimal. Given the number of stories about people who are really good being turned away that's probably not the case. For a company like Google that's not a problem; they can afford false negatives. They don't really affect the outcomes for the company given it's size. Other companies, and especially startups, can't afford the same false negatives. If you're a startup and you hire the wrong person it's going to have a significant impact on the probability of success.
Copying what FAANG companies do rather than working out the best way to filter the candidate pool for what you actually need is a really bad idea.