When hiring, top firms like McKinsey value a candidate's ability to articulate a deliberate, logical problem-solving process as much as their past successes. Having a structured method shows you can reliably tackle novel challenges, whereas simply pointing to past wins might suggest luck or context-specific success.

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.

Pete Najarian's successful trading firm prioritized hiring aggressive, smart individuals who could execute under pressure, regardless of their academic background. Many of their top traders "barely got out of high school," proving that in high-stakes environments, practical skills can trump traditional credentials.

Instead of hiring well-rounded generalists, Wealthsimple's design leadership looks for a unique, standout strength (a 'spike') in every candidate. This creates a more diverse and high-performing team, akin to a sports team with specialized player roles.

As AI handles linear problem-solving, McKinsey is increasingly seeking candidates with liberal arts backgrounds. The firm believes these majors foster creativity and "discontinuous leaps" in thinking that AI models cannot replicate, reversing a long-standing trend toward STEM and business degrees.

When hiring, Brookfield seeks people who are "nerdy" in their intellectual curiosity. The firm values individuals intrinsically motivated to dissect and solve complex problems that others have failed to crack, prioritizing this trait over any specific background or stereotype.

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performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.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