The venture narrative focuses on 'slope' (rapid growth) but often misses the value of 'area under the curve' companies. These startups, like Figma, may have a slower growth story as they build deep moats. This long-term focus can create more durable value than high-slope businesses with weaker defensibility.

The founders initially feared their data collection hardware would be easily copied. However, they discovered the true challenge and defensible moat lay in scaling the full-stack system—integrating hardware iterations, data pipelines, and training loops. The unexpected difficulty of this process created a powerful competitive advantage.

Glean spent years solving unsexy enterprise search problems before the AI boom. This deep, unglamorous work, often dismissed in the current narrative that credits AI for its success, became its key competitive advantage when the category became popular.

Even against other "Excel-based" FP&A tools, Datarails won deals by letting customers connect their existing spreadsheets without rebuilding them. This dramatically lowered the adoption barrier and made the learning curve immediate for finance teams with complex legacy models, creating a powerful competitive edge.

While most business users view Excel as a necessary evil, Datarails found finance professionals are different. They see Excel proficiency as a core skill and part of their professional identity. By offering a solution that supercharges their existing Excel workflows instead of replacing them, Datarails found its product-market fit.

Creating a basic AI coding tool is easy. The defensible moat comes from building a vertically integrated platform with its own backend infrastructure like databases, user management, and integrations. This is extremely difficult for competitors to replicate, especially if they rely on third-party services like Superbase.

True defensibility comes from creating high switching costs. When a product becomes a system of record or is deeply integrated into workflows, customers are effectively locked in. This makes the business resilient to competitors with marginally better features, as switching is too painful.

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.

Drawing from Verkada's decision to build its own hardware, the strategy is to intentionally tackle difficult, foundational challenges early on. While this requires more upfront investment and delays initial traction, it creates an immense competitive barrier that latecomers will struggle to overcome.