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Stripe's hiring criteria have evolved. Beyond craft, they now prioritize two key traits: a deep curiosity about AI's impact and "agency"—the proactive drive to build and experiment without being asked. They look for candidates with a "fire under their belly" who will push the company forward.

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With AI trivializing the mechanical act of writing code, the most valuable traits for emerging engineers are no longer just technical proficiency. Instead, employers will seek demonstrated agency (the drive to build), taste (knowing what to build), and a commitment to quality.

As AI handles technical tasks, the value of hard skills diminishes. The most crucial employee traits become "human" qualities: buying into the company vision, emotional intelligence, and self-awareness. These are the new competitive advantages in talent acquisition.

Theoretical knowledge is now just a prerequisite, not the key to getting hired in AI. Companies demand candidates who can demonstrate practical, day-one skills in building, deploying, and maintaining real, scalable AI systems. The ability to build is the new currency.

AI tools are so novel they neutralize the advantage of long-term experience. A junior designer who is curious and quick to adopt AI workflows can outperform a veteran who is slower to adapt, creating a major career reset based on agency, not tenure.

In the fast-evolving world of AI, the most valuable trait in a designer is a deep-seated curiosity and the self-direction to learn and build independently. A designer who has explored, built, and formed opinions on new AI products is more valuable than one with only a perfect aesthetic.

When building core AI technology, prioritize hiring 'AI-native' recent graduates over seasoned veterans. These individuals often possess a fearless execution mindset and a foundational understanding of new paradigms that is critical for building from the ground up, countering the traditional wisdom of hiring for experience.

Lovable prioritizes hiring individuals with extreme passion, high agency, and autonomy—people for whom the work is a core part of their identity. This focus on intrinsic motivation, verified through paid work trials, allows them to build a team that can thrive in chaos and drive initiatives from start to finish without supervision.

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.

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.

In rapidly evolving fields like AI, pre-existing experience can be a liability. The highest performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.