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.

As AI handles technical tasks, uniquely human skills like curiosity, empathy, and judgment become paramount. Leaders must adapt their hiring processes to screen for these non-replicable soft skills, which are becoming more valuable than traditional marketing competencies.

When entering a new domain, don't feign expertise. Build trust faster by openly admitting "I don't know" and explicitly leaning on stakeholders for their subject matter knowledge. This positions you as a partner who values their expertise, rather than an outsider imposing solutions.

Unlike previous generations who respected positional authority, Gen Z grants influence based on connection and trust. They believe the best idea should win, regardless of who it comes from. To lead them effectively, managers must shift from exercising control to building connection, acting as mentors rather than gatekeepers.

In the fast-evolving world of AI, the most valuable trait in a designer is a deep-seated curiosity and the self-direction to learn and build independently. A designer who has explored, built, and formed opinions on new AI products is more valuable than one with only a perfect aesthetic.

True growth and access to high-level opportunities come not from feigning knowledge, but from openly admitting ignorance. This vulnerability invites mentorship and opens doors to conversations where real learning occurs, especially in complex fields like investing, which may otherwise seem like a "scam."

The pace of change in AI means even senior leaders must adopt a learner's mindset. Humility is teachability, and teachability is survivability. Successful leaders are willing to learn from junior colleagues, take basic courses, and admit they don't know everything, which is crucial when there is no established blueprint.

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performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.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