[runtime_blues]

But it will. Because it doesn't matter if it's fundamentally better than a good recruiter if it's orders of magnitude cheaper. If you can have it pursue far more leads, maybe the outcomes are going to be the same or better. And if you used it to replace a bad or a mediocre recruiter? In any case, you might not care: hiring is a crapshoot anyway, and AI is saving you millions of dollars.

You want to weed out people who are clearly unqualified, but that's not rocket science. Beyond that, every company has a different hiring bar, a different process... and approximately zero data that their approach works better than anybody else's. Interview performance is a poor predictor of job performance. Whether the bar is high or comparatively relaxed, around 70% of the people you hire will be good, and the rest will underperform, leave after a couple of months, have difficult personalities, and so forth.

[godelski]

This is the only "pro-AI" comment that I've seen that makes sense. This isn't too different from the non-CS interviewing strategy. Try to make interviewing cheap and accept that it is a noisy (and biased) process. This also makes it easier to let bad candidates ("false hires") cheap replace.

As I see it, you're trying to optimize: p(X|F,C) > T, F=filter, and C=cost, and T=threshold. Treating this as a probabilistic problem seems important. So reducing C is valuable, even if F is not as good.

[wiseowise]

> This also makes it easier to let bad candidates ("false hires") cheap replace.

Not in Europe. It’s almost impossible to fire someone in a reasonable time in Europe.