The early hiring funnel is now breaking on both ends

This has always been a problem: Candidate applies with an amazing resume but then flails when you ask them questions or “can’t remember”.

I can remember a few interviews where I asked candidates about something I read on their resume (which I study before every call) and they corrected me to explain that they did something different. Then I held up their resume and pointed to their exact words and they turned bright red while they tried to come up with a new explanation.

That was rare, though. You could catch a lot of little cases of stretching the truth, but it wasn’t common to feel like you were reading a resume that didn’t match the candidate.

What has changed in the age of AI is that more people are feeling more brazen about letting the AI speak for them. These situations are happening more frequently. You get the feeling that people are less shy about trying to cheat and manipulate because it feels like the AI is doing the cheating and writing the words, so it’s done at arm’s length.

I spend some time helping with resume reviews occasionally. It’s getting sad to see in the general discussion of the group when people go from elated that they got an interview for their dream job to embarrassed when the interviewers saw right through their AI written resume and ended the hiring cycle. I wonder if we’re seeing a peak in AI resume junk while everyone tries it out, but before it becomes common knowledge that an AI junk resume is a way to shoot yourself in the foot when applying to companies you actually want to work for.

Leetcode was always weak, but now that it is easy to cheat on it is a negative selector, because the cheaters do best. Leetcode was originally supposed to be done in-person on a whiteboard to assess a candidate's collaborative problem solving skills, but with remote interviewing it has evolved into writing passing code with minimal or no feedback.

The real problem is that engineering departments are now filled with leetcode grinders and cheaters, who all live in permanent fear of being replaced by AI, and so any candidate who doesn't fit that paradigm is a threat that must be eliminated at all costs.

Biases are a strange thing. “High performers” aren’t one homogenous group; take a staff engineer at a FAANG and plop them in a role at a startup or vice versa and you’ll find very quickly that high performers are a product of environment (IME). The people you need to ship something at a big company will sink your startup, and the people who will lead a startup to unicorn levels of success will flounder in frustration in a big corp.

Finding high performers is really hard, as you said it’s a filtering problem, and it’s very much based on vibes and feelings. Leetcode, take home tests, on site tests, discussions about projects all filter for specific things - some or many of which aren’t related to the job at hand. If we removed the “risk of leaving current job element” the only way to do it would be to give someone a 3 month trial and see if they’re a fit. Honestly you probably know in your gut by week 2 if it’s going to work or not.

We took a chance on a flash recruiting session our canton organized. 35 interviews in 2 hr 15 mins. Crazy. But excellent signal, because if you are looking for it, and give the candidate a hint to show it ("tell me a story about how you solved a computer problem for your self/friend/family/club"), you can find the candidates with a spark. And I would not have detected it from their CVs or cover letter alone.

More human connection. Less machines. There, I fixed it for you.