To hire for traits over background, Mark Kosaglo suggests testing for coachability directly. Run a skill-based roleplay (e.g., discovery), provide specific feedback, and then run the exact same roleplay again. The key is to see if the candidate can actually implement the coaching, not just if they are open to receiving it.

Treat interviews as evidence-gathering sessions, not snap judgments. Ask broad questions like 'How did you grow your product?' and listen for signals of desired traits. Use a scorecard with concrete examples to assess candidates against criteria like being data-driven, thereby reducing personal bias.

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.

In AI PM interviews, 'vibe coding' isn't a technical test. Interviewers evaluate your product thinking through how you structure prompts, the user insights you bring to iterations, and your ability to define feedback loops, not your ability to write code.

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.

Ditch standard FANG interview questions. Instead, ask candidates to describe a messy but valuable project they shipped. The best candidates will tell an authentic, automatic story with personal anecdotes. Their fluency and detail reveal true experience, whereas hesitation or generic answers expose a lack of depth.

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.

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.