Challenge the 'hire slow' mantra. Hiring is an intuitive guess, so act quickly. Once a person is in the organization, their performance is a known fact, not a guess. This clarity allows for faster decisions—both in removing underperformers and, crucially, in accelerating the promotion of superstars ahead of standard review cycles.

Before hiring for a critical function, founders should do the job themselves, even if they aren't experts. The goal isn't mastery, but to deeply understand the role's challenges. This experience is crucial for setting a high hiring bar and being able to accurately assess if a candidate will truly up-level the team.

Organizational success depends less on high-profile 'superstars' and more on 'Sherpas'—generous, energetic team players who handle the essential, often invisible, support work. When hiring, actively screen for generosity and positive energy, as these are the people who enable collective achievement.

Over a long career, great leaders accumulate a "snowball of talent"—A-players who follow them from one venture to the next. This becomes a powerful litmus test when hiring executives: if they have no network of past colleagues eager to join them, it's a major red flag about their leadership ability or the quality of their past teams.

To clarify difficult talent decisions, ask yourself: "Would I enthusiastically rehire this person for this same role today?" This binary question, used at Stripe, bypasses emotional ambiguity and provides a clear signal. A "no" doesn't mean immediate termination, but it mandates that some corrective action must be taken.

To scale hiring efficiently, eliminate ambiguity. Each interviewer must make a definitive 'yes' or 'no' decision. If an interviewer is 'not sure' after their session, they are the problem, not the candidate. This prevents endless interview loops and forces clear, decisive judgment.

The "attitude vs. aptitude" debate is flawed. Instead, hire the person with the smallest skill deficiency relative to the role's requirements. For a cashier, attitude is the harder skill to train. For an AI researcher, technical aptitude is. The key question is always: is it worth our resources to train this specific gap?

Don't be paralyzed by the fear of making a bad hire. View hiring as an educated guess. The real knowledge comes after they've started working. Firing isn't a failure, but the confirmation of a mismatched hypothesis. This reframes hiring from a high-stakes decision to an iterative process of finding the right fit.