Instead of focusing solely on a candidate's current skills, Figma's CEO looks for their 'slope,' or their trajectory of rapid learning and improvement. This is assessed by analyzing their history of decision-making and growth mindset, betting on their future potential rather than just their present abilities.

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 frequent hiring error is choosing candidates because you believe they possess "magical knowledge" from their specific background that will solve all problems. These hires often fail by rigidly applying an old playbook. Prioritize adaptable, curious problem-solvers over those with seemingly perfect but ultimately static domain expertise.

Senior leaders now value candidates who ask excellent questions and are eager to solve problems over those who act like they know everything. This represents a significant shift from valuing 'knowers' to valuing 'learners' in the workplace.

The most promising hires are often high-agency individuals constrained by their current environment—'caged animals' who need to be unleashed. Look for candidates who could achieve significantly more if not for their team or organization's limitations. This is a powerful signal of untapped potential and resourcefulness.

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.

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performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.

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.