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.

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.

Vercel's hiring has fundamentally changed. Instead of hiring for specific tasks, they look for people who can build and manage agents to perform those tasks. A new key interview question is: "Walk me through how you would create the agent that solves the job that traditionally someone in your position would do."

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.

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.

To assess a candidate's ability to use AI as a thinking partner, have them solve a problem with an LLM. The key is observing their follow-up prompts and their ability to guide the AI step-by-step, rather than just accepting the initial output.

As AI renders cover letters useless for signaling candidate quality, employers are shifting their screening processes. They now rely more on assessments that are harder to cheat on, such as take-home coding challenges and automated AI interviews. This moves the evaluation from subjective text analysis to more objective, skill-based demonstrations early in the hiring funnel.

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.