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.

A person's past rate of growth is the best predictor of their future potential. When hiring, look for evidence of a steep learning curve and rapid progression—their 'slope.' This is more valuable than their current title or accomplishments, as people tend to maintain this trajectory.

When hiring, prioritize a candidate's speed of learning over their initial experience. An inexperienced but rapidly improving employee will quickly surpass a more experienced but stagnant one. The key predictor of long-term value is not experience, but intelligence, defined as the rate of learning.

Ramp's hiring philosophy prioritizes a candidate's trajectory and learning velocity ("slope") over their current experience level ("intercept"). They find young, driven individuals with high potential and give them significant responsibility. This approach cultivates a highly talented and loyal team that outperforms what they could afford to hire on the open market.

Snowflake's hiring philosophy for the AI era prioritizes adaptability over specific, perishable skills. Recognizing that today's tools will be obsolete tomorrow, they screen for lifelong learners by asking questions like, 'How do you advance your craft?' rather than focusing on current tool proficiency.

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performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.

Dropbox's founders built their team using a first-principles approach, prioritizing exceptional talent even when candidates lacked traditional pedigrees or direct experience for a role. This strategy of betting on the person's potential over their polished resume proved highly effective for scaling.

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.