When WCM refreshed its portfolio, the new holdings initially lagged behind the old ones as the market snapped back. This created a "lonely" period of intense self-doubt and internal questioning. This highlights the emotional difficulty of sticking with a process change before results validate the decision.
Industry specialists can become trapped in an "echo chamber," making them resistant to paradigm shifts. WCM found their generalist team structure was an advantage, as a lack of "scar tissue" and a broader perspective allowed them to identify changes that entrenched specialists dismissed as temporary noise.
Identifying a company's stated values is insufficient. WCM's research evolved to analyze the social mechanisms that reinforce desired behaviors, turning values into a "cult." They found that many companies espouse the same behaviors, but only the best have the rituals and systems to make them stick.
WCM realized their portfolio became too correlated because their research pipeline itself was the root cause, with analysts naturally chasing what was working. To fix this, they built custom company categorization tools to force diversification at the idea generation stage, ensuring a broader set of opportunities is always available.
While long-term focus is a virtue, investment managers at WCM warn it can become an excuse for inaction. During periods of significant market change, blindly "sticking to your knitting" is a liability. Recognizing when to sensibly adapt versus when to stay the course is a critical and nuanced skill.
The well-intentioned question "How can I help?" puts the burden on the receiver to delegate. A far more valuable trait is proactively identifying needs and simply taking action—a "just do" mentality. This demonstrates a deeper understanding of team goals and removes cognitive load from leaders.
WCM avoids generic AI use cases. Instead, they've built a "research partner" AI model specifically tuned to codify and diagnose their core concepts of "moat trajectory" and "culture." This allows them to amplify their unique edge by systematically flagging changes across a vast universe of data, rather than just automating simple tasks.