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.

The VP of Search stated that technical interviews must now assess a candidate's ability to use AI coding assistants effectively. The goal is to measure not only problem-solving skills but also fluency with new tools that change how the job is performed, going beyond simply asking un-googleable questions.

Sierra transformed its hiring by replacing traditional coding challenges with real-world tasks. Candidates get a prompt and a $150 token budget to build an application using their preferred AI coding agents. This tests modern, AI-native problem-solving skills, not rote memorization or algorithm theory.

Dreamer's hiring process now evaluates an engineer's ability to work with and through AI coding agents. Beyond a basic coding screen, the main interview involves a project built using tools like Codex, testing the candidate's skill in prompting, reviewing, and orchestrating AI to be productive.

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.

Traditional product sense questions are being replaced. AI PM candidates should expect to solve problems live using AI tools or design complex AI-native systems. This shift assesses a candidate's hands-on "builder" capabilities and deep understanding of modern AI architecture.

With AI making code generation cheap, product taste is the key differentiator. In top AI teams, PMs are increasingly technical, using tools like Claude Code to build and iterate, making their role nearly identical to an engineer's.

Ramp requires all new hires, regardless of role, to be proficient with AI tools. The interview process for product managers now includes a practical session where candidates must build and present a functional product prototype using AI, demonstrating hands-on skill rather than just theoretical knowledge.

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.