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.

Theoretical knowledge is now just a prerequisite, not the key to getting hired in AI. Companies demand candidates who can demonstrate practical, day-one skills in building, deploying, and maintaining real, scalable AI systems. The ability to build is the new currency.

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.

To make a hire "weird if they didn't work," don't hire for potential or vibe. Instead, find candidates who have already succeeded in a nearly identical role—selling a similar product to a similar audience at a similar company stage. This drastically reduces performance variables.

To de-risk hiring and upskill your team, use a "consult-to-teach" model. An expert or agency is hired for a short-term contract to execute a task for the first 30 days, then spend the next 30 days training your full-time employee to take over.

To assess a product manager's AI skills, integrate AI into your standard hiring process rather than just asking theoretical questions. Expect candidates to use AI tools in take-home case studies and analytical interviews to test for practical application and raise the quality bar.

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 build an AI-native team, shift the hiring process from reviewing resumes to evaluating portfolios of work. Ask candidates to demonstrate what they've built with AI, their favorite prompt techniques, and apps they wish they could create. This reveals practical skill over credentialism.

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.