In today's job market, building a product or company on the side is no longer just "extra credit." Hiring managers view these projects as direct evidence of initiative, product thinking, and technical skill, sometimes weighing them as much as traditional work experience.

When assessing product managers for AI-centric roles, Freshworks' CPO Srini Raghavan prioritizes passion and hands-on experience over traditional interviews. He asks to see a candidate's Git repository to evaluate what they have built on their own initiative.

For roles leveraging new technologies like AI, where tools are nascent and constantly changing, competency is a fleeting metric. Instead, hire for curiosity. A curious mind will adapt, learn, and master new tools as they emerge, making them a more valuable long-term asset.

When recruiting talent, inquiring about an applicant's self-directed curiosity with AI can streamline hiring decisions. Daniel Priestley notes that he asks candidates how deep they are in the AI rabbit hole, hiring those who actively explore the tools.

A simple chat thread in ChatGPT is not a workflow. To truly assess if a candidate is a systems thinker, ask how they automate processes in their personal life. A good answer involves feedback loops and self-improving systems, revealing a deeper, more innate ability to leverage AI beyond simple queries.

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.

When hiring for Zenly and Amo, the team prioritized a candidate's side projects over their experience at Meta or Apple. Side projects are the strongest signal of curiosity, ambition, and an entrepreneurial mindset—acting as a "Trojan horse" for getting noticed by top companies.

Glean has updated its interview process to screen for "AI fluency" across all departments. They don't expect expertise. Instead, they test for curiosity and initiative by asking candidates how they've personally used AI, looking for a mindset that embraces new ways of working.

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.