Private equity firms often hire commercial leaders based on past roles and industry experience, which may not fit the current needs of the business. This leads to hiring "the memory, not the moment," resulting in poor performance for organic growth initiatives.

To combat their own bias for "proven" playbooks, private equity firms should require potential search partners to propose two creative, non-obvious candidate archetypes as part of the sales process. This forces the search firm to demonstrate innovative thinking beyond simply matching a rigid scorecard.

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.

Companies create impossible job descriptions seeking perfect candidates ('purple squirrels') who have already done the exact job. A better strategy is to identify high-aptitude individuals ('brown squirrels') from undervalued talent pools and invest in training them to fill specific needs, bridging the gap between academia and industry.

Jane Street, which outperforms Wall Street giants, built its success by hiring brilliant problem-solvers with no required finance background. Their interview process tests raw intelligence with brain teasers, proving that hiring for a flexible, analytical mindset can be more valuable than hiring for pre-existing, role-specific skills.

To combat the private equity industry's low success rate with CXO appointments, Speyside Equity uses a two-axis framework. It evaluates executives on their ability to achieve results (the Y-axis) and their personality and competencies to do it the 'right way' (the X-axis), effectively creating a 'no jerks' filter.

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.

At the start of a tech cycle, the few people with deep, practical experience often don't fit traditional molds (e.g., top CS degrees). Companies must look beyond standard credentials to find this scarce talent, much like early mobile experts who weren't always "cracked" competitive coders.

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.