Since modern AI is so new, no one has more than a few years of relevant experience. This levels the playing field. The best hiring strategy is to prioritize young, AI-native talent with a steep learning curve over senior engineers whose experience may be less relevant. Dynamism and adaptability trump tenure.

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.

In highly dynamic and unstructured startup environments, hiring for high potential ("slope") is more effective than hiring for deep experience ("intercept"). Experienced hires from structured companies often perceive the environment as chaotic and fail to adapt, whereas high-slope individuals see it as normal and thrive.

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.

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.

Carvana's hiring philosophy avoids candidates with deep industry experience because they often believe things must work as they always have. Instead, they seek highly motivated individuals with less experience to avoid legacy thinking and build new systems from scratch.

A CRO found 80% of hires based on deep industry experience failed. He now prioritizes candidates with a winning attitude, openness to change, and a history of solving complex problems in adjacent fields, valuing coachability over a seemingly perfect resume.

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.