An effective remote hiring funnel weeds out unserious candidates efficiently. After an initial skills test, request a one-minute video introduction—most won't bother. For the final candidates, replace interviews with a paid, task-based trial to assess real-world skills and work ethic before speaking to them.

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.

In an era where AI can assist with coding challenges, 10X's solution is to make their take-home assignments exceptionally difficult. This approach immediately filters out 50% of candidates who don't even respond, allowing for a much faster and more focused interview process for the elite few who pass.

With LLMs making remote coding tests unreliable, the new standard is face-to-face interviews focused on practical problems. Instead of abstract algorithms, candidates are asked to fix failing tests or debug code, assessing their real-world problem-solving skills which are much harder to fake.

Rather than creating assessments that prohibit AI use, hiring managers should embrace it. A candidate's ability to leverage tools like ChatGPT to complete a project is a more accurate predictor of their future impact than their ability to perform tasks without them.

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.

To scale hiring efficiently, eliminate ambiguity. Each interviewer must make a definitive 'yes' or 'no' decision. If an interviewer is 'not sure' after their session, they are the problem, not the candidate. This prevents endless interview loops and forces clear, decisive judgment.

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.