And yet, when I ask them how they are tracking AI’s effectiveness, especially with regard to degrading skill sets, lowered creativity/effectiveness in solutions to complex/edge problems, slowed dev velocity, and increasing levels of needless code complexity (with associated ballooning of LoC) and gratuitous hallucinations breeding bugs like meal worm farms, all I get are crickets. Or worse, deer-in-the-headlight looks. They’re all wildly unaware of the downsides that are slowly being confirmed by science.

Frankly, I feel that I am lucky that I’ve chosen a sabbatical to deal with my parent’s EoL issues. The chance that this will extend into the popping of the AI bubble appears to be non-trivial. By the time I start looking in earnest again, AI might not be a critical employment benchmark anymore.

Or one of my projects will become profitable and I won’t have to deal with all that bullshite.

One interesting direction I’ve been exploring is evaluating candidates on how they think with AI, not whether they avoid it. Things like debugging AI-generated code, verifying assumptions, identifying hallucinations, choosing when to trust the model, etc. These are the actual bottlenecks today, not LeetCode puzzles.

We built an internal tool that looks at this “cognitive intelligence” part instead of raw coding speed, and the signals have been much more predictive than traditional interviews. I feel like more companies will eventually move toward this kind of evaluation because banning AI in interviews makes less sense every day.