Instead of teams building their own merchant analysis tools, Stripe created a centralized "Merchant Intelligence" service. This AI agent crawls the web, generates merchant embeddings, and serves insights to diverse teams like risk, credit, and sales, eliminating duplicated effort and creating massive internal leverage.

The key for enterprises isn't integrating general AI like ChatGPT but creating "proprietary intelligence." This involves fine-tuning smaller, custom models on their unique internal data and workflows, creating a competitive moat that off-the-shelf solutions cannot replicate.

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.

AI tools drastically reduce the time and expertise needed to enter new domains. This allows startups to pivot their entire company quickly to capitalize on shifting investor sentiment and market narratives, making them more agile in a hype-driven environment where narrative alignment attracts capital.

The true enterprise value of AI lies not in consuming third-party models, but in building internal capabilities to diffuse intelligence throughout the organization. This means creating proprietary "AI factories" rather than just using external tools and admiring others' success.

To ensure the "triumph of ideas, not the triumph of seniority," Sequoia uses anonymized inputs for strategic planning and initial investment votes. This forces the team to debate the merits of an idea without being influenced by who proposed it, leveling the playing field.

For over a decade, Sequoia has systematically asked top operators, 'Who are your five smartest peers?' By tracking responses in a proprietary CRM, they've built a talent map that functions like a 'PageRank for people.' This system allows them to assess engineering team quality deep within organizations, providing a unique diligence advantage.

A key competitive advantage wasn't just the user network, but the sophisticated internal tools built for the operations team. Investing early in a flexible, 'drag-and-drop' system for creating complex AI training tasks allowed them to pivot quickly and meet diverse client needs, a capability competitors lacked.

Beyond automating data collection, investment firms can use AI to generate novel analytical frameworks. By asking AI to find new ways to plot and interpret data inputs, the team moves from rote data entry to higher-level analysis, using the technology as a creative and strategic partner.