@nixCraft I remember the feeling when I suddenly realised "Holy carp! These guys are paying me to solve logic puzzles!" I couldn't imagine a better job.
Of course the last twenty years they have been trying to turn tech into "easily replaceable monkey turns the handle, yet another nearly identical code unit comes out".
@renef @nixCraft there's a high cost to the team a new hire is joining, as they (try to) train and integrate them professionally and integrate them socially. When they're then fired, the former wasted time and effort stays wasted, and there's a sharp cut to the team's morale as someone they've come to know as a person is reduced to a statistic.
So even assuming (correctly, in almost all cases) that the company doesn't care about the individual, there's a business impact beyond just having paid someone for nothing for a few months.
(We are, in the context of "how do we interview", talking about someone who just isn't good (enough) at the job, not someone who is severely toxic in some way.)
When I'm asked to conduct coding interviews, or to evaluate the set of interviews done as part of the hiring committee, the real question is never "can this candidate write code to do X in 30 minutes on a whiteboard, in a fundamentally adversarial situation". That's not useful information, because that is not and will never be the job.
The real questions are: presented with an underspecified problem, do they gather information about the constraints? When they make assumptions, do they say so? When producing whiteboard-code in what they say is their preferred programming language, does it look a bit like that language? Do they attempt any sort of structure/factoring, or is it just stream-of-consciousness code dumping? Is whichever data structures and/or algorithms they pick at least plausible for what they say they're going to do?
It doesn't matter what their solution is, whether it's optimal, whether it would even work. What does matter is whether they think about the question at all before trying to write down a solution top to bottom, and their seniority decides the bar for what category of thoughts I expect them to have.
Come to think of it, I've been interviewing for over 20 years, trying to establish "are you smarter than a brainless-by-definition LLM".
@nixCraft i don't know what kind of jobs you had the privilege of working in but that does not match my experience.
Most jobs are rather dull, even in IT. Just complex enough that they cannot be fully automated. And a lot of jobs are even bullshit jobs (Graeber).
And even if your job isn't dumb most probably your manager is and you need to watch out for not being smarter than him/her/it.
Am i too cynical? I really don't think so.
@nixCraft computers are powerful tools for those who can control them. Companies with cheaters will be worse off since they can not compete.
It’s self regulating
But that 2 weeks means you've told other candidates you're not interested, taken down job postings, you have to go through all the paperwork to establish them as employee (tax forms, insurance, that stack of onboarding paperwork), and have to pay severance if you fire them. And you're out those two weeks that a non-fraudulent employee could have been productive.
Now you have to re-open the job-listings, re-screen candidates (some of whom you might have to apologize with a "we're sorry we rejected you, but the person we thought was better turned out to be a fraud"), and do all the onboarding paperwork again.
So there's certainly value in determining fit up front.
I wish I had a good answer from the candidate side of things. I do contract work so it's very different from trying to get a traditional W2 (or whatever the equivalent is in other countries) job. For me, most work I've gotten has come from either knowing somebody who can get my resume in front of the right people, or from my publicly helping folks (mailing lists, Reddit, fediverse, formerly-Twitter, utilities I've shared on GitHub, and my blog).
And the AI rubbish is bidirectional—inept AI-assisted candidates applying to positions, and inept AI-assisted interviewers/HR systems ingesting the firehose of applications. The whole landscape is 💩