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.

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.

Sales experience on a resume can be a 'false positive.' When hiring SDRs, prioritize untrainable qualities like work ethic, mindset, and resilience over specific past roles. These character traits are a better predictor of long-term success than skills that can be taught.

Hiring managers often dismiss strong candidates by making snap judgments based on a resume. Focusing on the person behind the paper—their drive, skills, and potential—frequently reveals that the initially overlooked individual is the perfect fit for the role, according to executive search partner Mitch McDermott.

Aravind Srinivas intentionally avoids hiring candidates with established track records from large tech companies. He believes people hungry for their first major success are more motivated and better suited for a startup's intensity than those who may be less driven after a previous big win.

When hiring, focus on what a person has created, not their stated attributes or background. A great "invention" (a project, a piece of writing, code) is the strongest signal of a great "inventor." This shifts the focus from potential to proven output, as Charlie Munger advised.

When hiring for Zenly and Amo, the team prioritized a candidate's side projects over their experience at Meta or Apple. Side projects are the strongest signal of curiosity, ambition, and an entrepreneurial mindset—acting as a "Trojan horse" for getting noticed by top companies.

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performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.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