Traditional SaaS switching costs were based on painful data migrations, which LLMs may now automate. The new moat for AI companies is creating deep, customized integrations into a customer's unique operational workflows. This is achieved through long, hands-on pilot periods that make the AI solution indispensable and hard to replace.

When asked if AI commoditizes software, Bravo argues that durable moats aren't just code, which can be replicated. They are the deep understanding of customer processes and the ability to service them. This involves re-engineering organizations, not just deploying a product.

Malk, a retail-focused brand, built a Shopify site not for direct sales but to control messaging, connect with consumers, and gather data. Their site uses technology allowing users to add products to a local retailer's online cart. This creates a valuable, albeit incomplete, data point on purchase intent for a channel that traditionally offers none.

As AI makes building software features trivial, the sustainable competitive advantage shifts to data. A true data moat uses proprietary customer interaction data to train AI models, creating a feedback loop that continuously improves the product faster than competitors.

As return volumes rise, brands that make the process effortless and predictable will earn loyalty that can't be bought. This frictionless experience during a period of high customer anxiety builds a durable competitive moat. Every return also generates compounding data advantages for future forecasting and merchandising, further widening the gap.

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.

As AI commoditizes technology, traditional moats are eroding. The only sustainable advantage is "relationship capital"—being defined by *who* you serve, not *what* you do. This is built through depth (feeling seen), density (community belonging), and durability (permission to offer more products).

AI will fragment the customer journey across countless platforms, moving purchases away from brand-owned websites. Retailers must build systems to manage inventory and product information across this decentralized landscape, not just focus on perfecting their own site experience.

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.