When hiring, a candidate with high passion for the subject matter but low experience is more valuable than an experienced candidate with low passion. Skills are teachable, but genuine enthusiasm for the mission is not. This framework helps resolve the common hiring dilemma between potential and polish.

When building a team for a novel venture, prioritize curious qualities over pure credentials. Look for collaborators who are passionate, resilient, and 'iconoclastic'—comfortable challenging the status quo. Also seek out people with diverse outside interests, as they can draw unique connections and avoid narrow thinking.

Zipline defines its top talent as "heat-seeking missiles for pain." These are people who proactively identify critical business or product problems, rally the necessary resources to solve them with maniacal urgency, and operate with an "it's not not my job" mentality.

Early-stage startups thrive on rapid iteration. Seek hires who can 'get shit done at an incredible clip' and make decisions at '100 miles per hour,' even if some are wrong. These individuals, often 'rough around the edges,' are more valuable than candidates with perfect paper pedigrees from large tech companies.

In a rapidly changing environment like an AI startup, raw competence is insufficient. The most valuable trait is "extreme ownership"—the proactive drive to see a problem through from start to finish, regardless of formal roles or responsibilities.

The ideal employee isn't just a problem-solver but is actively addicted to finding the hairiest, most painful issues in the business or for customers and surgically eliminating them. This proactive pain-seeking mindset is the most important signal in hiring for a fast-growing company.

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.

Aravind Srinivas intentionally avoids hiring candidates with established track records from large tech companies. He believes people hungry for their first major success are more motivated and better suited for a startup's intensity than those who may be less driven after a previous big win.

For cutting-edge AI problems, innate curiosity and learning speed ("velocity") are more important than existing domain knowledge. Echoing Karpathy, a candidate with a track record of diving deep into complex topics, regardless of field, will outperform a skilled but less-driven specialist.