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The company simplifies its hiring process down to two core traits. "Smart" means learning quickly, and "who care" translates to extreme ownership and a passion for impact. This focus allows them to build a high-density talent pool that can be deployed across any acquired product.

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Legora intentionally hires people with high learning velocity ("high Y slopes") over deep experience ("high Y intercepts"). In a rapidly evolving AI landscape, this ensures the team can scale their capabilities as exponentially as the company grows.

Prioritize hiring generalist "athletes"—people who are intelligent, driven, and coachable—over candidates with deep domain expertise. Core traits like Persistence, Heart, and Desire (a "PhD") cannot be taught, but a smart athlete can always learn the product.

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.

A powerful hiring heuristic is to identify candidates who, looking back at their career, were consistently the primary reason for success in different environments. This signals extreme ownership and an ability to reinvent themselves, making them highly valuable assets for a scaling company.

To maintain a collaborative, "no lanes" culture while scaling, Coya's CEO prioritizes hiring individuals with a strong sense of curiosity and a willingness to learn. This strategy counteracts the silo-building tendency of hiring narrow experts who may be less adaptable or open to cross-functional input.

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.

Sendbird updated its job descriptions for 'AI-first' roles to de-emphasize years of experience. Instead, they screen for high curiosity, agency, and energy, believing these traits are better predictors of success for employees who must constantly learn and build with new tools.

The "attitude vs. aptitude" debate is misleading. Hire the person with the smallest skill gap for the role. For complex roles, hire for intelligence (defined as rate of learning), as smart people can bridge any skill or attitude gap faster.

To scale a high-performing product team, hire individuals who exhibit the same level of ownership and love for the product as the original founders. This means prioritizing a blend of deep curiosity, leadership potential, and an unwavering commitment to execution over a simple skills checklist.

Zipline prioritizes innate characteristics—practical problem-solving, fast learning, low ego, and mission drive—over specific experience. By the time a new hire is onboarded, the job they were hired for has often changed, making adaptable traits far more valuable for success.