Tier 1 is default alive and thriving. They don't need the smartest people. They need people who do not screw up. Someone who's willing to spend 3-6 months cramming interview questions is also someone who is unlikely to drop the ball.
Startups are the opposite. They are default dead. If they hire good enough people, they die. They need exceptional, hungry people. The kind of people who abhor busywork. Startups will pull much lower quality on average and paying higher rates drastically increases the odds of death. So their hope is to get lucky on an exceptional junior. They're also more willing to fire fast instead of doing some odd layoff down the line.
Basically Tier 1 will bias towards minimal false positives, and startups will bias towards minimal false negatives. Most companies are somewhere in the middle, default dead in 30 years or so.
Tier 1 also has a huge funnel and they hire lots of people so they need it. A lot of junk gets into the funnel because of the sheer size of it. They need that 1 out of 300, so they just layer enough filters to hit that rate.
Tier 2 companies who need to filter 1 out of 50 should not be adopting Tier 1 practices. Startups probably have like 10 applicants and 6 of them can't do FizzBuzz. But they still need to filter, even if sometimes they end up with 0.
> Someone who's willing to spend 3-6 months cramming interview questions is also someone who is unlikely to drop the ball.
I don't believe this at all, they're unlikely to not comply and execute arbitrary commands. Which at least in my experience is orthogonal to whether they're able to actually problem solve or build good things.
This sounds good but I don't think it lines up with reality at parts.
I don't think there's any correlation between "willing to spend 3 months cramming LC Hards" and "doesn't screw up on the job." The speed at which you can figure out time complexity in your head, or how clean your maze solver is on the first try has nothing to do with your skill or ability in managing services in AWS or implementing a Figma design or making sure you're utilizing cache correctly.
> Most companies are somewhere in the middle, default dead in 30 years or so.
Uh, no? What's the evidence supporting this? I haven't seen any organizational literature or research suggesting you have "Tier 1 tech" on one extreme, "Tech startups" on the other extreme, and "every other company" in the middle.
I think the general thrust of your point with regard specifically to Tier 1 tech is spot on - keep on layering more and more filters until you go from 500 applicants to a handful, then make offers to those people. My sibling comment from yesterday says basically the same thing, that it's not "ok," but TC and competition are so high they can get away with it. Home Depot or Kaiser Permanente or JP Morgan can't, despite having their own set of unique and pretty interesting technical problems.
The startup stuff doesn't line up with my experience though.
Startups are the opposite. They are default dead. If they hire good enough people, they die. They need exceptional, hungry people. The kind of people who abhor busywork. Startups will pull much lower quality on average and paying higher rates drastically increases the odds of death. So their hope is to get lucky on an exceptional junior. They're also more willing to fire fast instead of doing some odd layoff down the line.
Basically Tier 1 will bias towards minimal false positives, and startups will bias towards minimal false negatives. Most companies are somewhere in the middle, default dead in 30 years or so.
Tier 1 also has a huge funnel and they hire lots of people so they need it. A lot of junk gets into the funnel because of the sheer size of it. They need that 1 out of 300, so they just layer enough filters to hit that rate.
Tier 2 companies who need to filter 1 out of 50 should not be adopting Tier 1 practices. Startups probably have like 10 applicants and 6 of them can't do FizzBuzz. But they still need to filter, even if sometimes they end up with 0.