Post-AI dev interview practices debated

AI coding help is now part of day-to-day development work, so the old interview playbook is under pressure. An Ask HN discussion asked how teams are adapting their developer interviews when candidates can use AI tools to write, explain, and debug code. The thread drew 39 points and a substantive set of practitioner responses, which makes it a useful snapshot of a question many hiring managers are now facing: what are you actually trying to measure? You can read the original discussion.

For a business owner, this is not really about whether AI is “allowed” in an interview. It is about defining the skills the role requires. If a developer will use AI on the job, an interview that bans it completely may test an artificial version of the work. On the other hand, letting a tool do everything tells you little about the candidate’s judgement, ability to spot bad output, or capacity to understand a system when something breaks.

The practical response is to separate coding output from engineering judgement. Ask candidates to explain trade-offs, review AI-generated code, diagnose a problem, or describe how they would validate an answer before shipping it. Those are the habits that matter when tools can produce code quickly.

This is the kind of thing we build into an AI command centre, helping leaders track where AI is changing roles, processes, and the signals they use to make decisions.