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.

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.

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.

Ditch traditional coding interviews. To hire for the AI era, ask candidates to record a full-screen video of themselves using AI agents to build a feature. This reveals their true skill: how they prompt, manage, and debug AI, which is more valuable than their ability to write code from scratch.

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.

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.

To avoid wasting significant capital on an underperforming developer, vet candidates by hiring them for a small, isolated test project first. Use platforms like Upwork for this initial trial to confirm their skills and work ethic before committing to a larger, more expensive build.

For roles where skills are difficult to assess in standard interviews, Clay implements a 2-3 week paid "work trial." This allows the company to evaluate a candidate's actual performance and fit on real tasks before extending a full-time offer, de-risking the hiring process for complex positions.

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.