When interviews and references leave you with ambiguity about a candidate, move beyond conversation. Invite them to analyze or work on a live investment or project. Observing their thought process and how they "underwrite" a decision provides unparalleled clarity on their actual abilities.

Be prepared for live prototyping rounds in senior AI PM interviews. Interviewers provide an IDE and expect you to build an idea. They evaluate your problem-solving process, how you collaborate with the AI, and your ability to navigate trade-offs, not just raw coding skill.

To find talent capable of managing an AI stack, traditional interviews are insufficient. A better test is to provide candidates with platform credits (e.g., Replit) and challenge them to build a functional agent that automates a real business task, proving their practical skills.

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.

To truly assess collaboration and skill, Linear replaces traditional final interviews with a 2-5 day paid project. Candidates work on real, shippable tasks, allowing Linear to evaluate not just the final output but also the collaborative process and communication style.

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 avoid hypothetical interview questions, Zipline makes its hiring process as applied as possible. This includes pair programming, collaborative design sessions, and even offering paid 1-2 week work trials. This "work together" approach quickly reveals a candidate's true fit and capabilities.

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.

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.