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.
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As platforms like AlphaSense automate the grunt work of research, the advantage is no longer in finding information. The new "alpha" for investors comes from asking better, more creative questions, identifying cross-industry trends, and being more adept at prompting the AI to uncover non-obvious connections.
Historically, private equity was pursued for its potential outperformance (alpha). Today, with shrinking public markets, its main value is providing diversification and access to a growing universe of private companies that are no longer available on public exchanges. This makes it a core portfolio completion tool.
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.
Acknowledging venture capital's power-law returns makes winner-picking nearly impossible. Vested's quantitative model doesn't try. Instead, it identifies the top quintile of all startups to create a high-potential "pond." The strategy is then to achieve broad diversification within this pre-qualified group, ensuring they capture the eventual outliers.
The key to effective portfolio entrepreneurship isn't random diversification. It's about serving the same customer segment across multiple products. This creates a cohesive ecosystem where each new offering benefits from compounding knowledge and trust, making many things feel like one thing.
The dominant VC narrative demands founders focus on a single venture. However, successful entrepreneurs demonstrate that running multiple projects—a portfolio approach mirrored by VCs themselves—is a viable path, contrary to the "focus on one thing" dogma.
AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.
The increased volatility and shorter defensibility windows in the AI era challenge traditional VC portfolio construction. The logical response to this heightened risk is greater diversification. This implies that early-stage funds may need to be larger to support more investments or write smaller checks into more companies.
Large tech conferences often foster consensus views, leading VCs to chase the same deals. A better strategy is to attend smaller, niche events specific to an industry (e.g., legal tech). This provides an information advantage and helps develop a unique investment perspective away from the herd.
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.