The purpose of quirky interview questions has evolved. Beyond just assessing personality, questions about non-work achievements or hypothetical scenarios are now used to jolt candidates out of scripted answers and expose those relying on mid-interview AI prompts for assistance.

Tools like Final Round AI provide candidates with live, verbatim answers to interview questions based on their resume and the job description. This development undermines the authenticity of remote interviews, creating a premium on face-to-face interactions where such tools cannot be used covertly.

To gauge an expert's (human or AI) true depth, go beyond recall-based questions. Pose a complex problem with multiple constraints, like a skeptical audience, high anxiety, and a tight deadline. A genuine expert will synthesize concepts and address all layers of the problem, whereas a novice will give generic advice.

A common hiring mistake is prioritizing a conversational 'vibe check' over assessing actual skills. A much better approach is to give candidates a project that simulates the job's core responsibilities, providing a direct and clean signal of their capabilities.

For high-level leadership roles, skip hypothetical case studies. Instead, present candidates with your company's actual, current problems. The worst-case scenario is free, high-quality consulting. The best case is finding someone who can not only devise a solution but also implement it, making the interview process far more valuable.

Ineffective interviews try to catch candidates failing. A better approach models a collaborative rally: see how they handle challenging questions and if they can return the ball effectively. The goal is to simulate real-world problem-solving, not just grill them under pressure.

As AI renders cover letters useless for signaling candidate quality, employers are shifting their screening processes. They now rely more on assessments that are harder to cheat on, such as take-home coding challenges and automated AI interviews. This moves the evaluation from subjective text analysis to more objective, skill-based demonstrations early in the hiring funnel.

Since AI assistants make it easy for candidates to complete take-home coding exercises, simply evaluating the final product is no longer an effective screening method. The new best practice is to require candidates to build with AI and then explain their thought process, revealing their true engineering and problem-solving skills.

Traditional hiring assessments that ban modern tools are obsolete. A better approach is to give candidates access to AI tools and ask them to complete a complex task in an hour. This tests their ability to leverage technology for productivity, not their ability to memorize information.