On Defensibility in the AI Era

In the AI era, anyone can spin up a flashy demo in a weekend. Core models that were once rare and expensive are now widely accessible. That reality forces founders to confront the uncomfortable question: what stops someone from cloning you tomorrow?

Defensibility still matters, but the sources have shifted. The old moats of network effects, distribution, brand, and workflow lock-in remain valuable, but how you build them has changed.

UX as a moat
The underlying AI model will be commoditised, but the way you wrap it will not be. The fastest path to defensibility is designing an experience that feels inevitable to the user, where every interaction is fluid, edge cases are handled gracefully, and the “how” disappears from view. That is much harder to replicate than an API call or a clever prompt.

Proprietary context
Data still drives advantage, but not all data is equal. Contextual data; rich, real-time, domain-specific information gathered from usage — can make your AI smarter in ways competitors cannot copy. Over time, this becomes the ingredient that improves relevance, accuracy, and trust.

Workflow embedding
The deeper your product is woven into a user’s daily work, the harder it is to remove. This is not just about integrations. It is about becoming the quiet backbone of how decisions are made, actions are tracked, and outcomes are achieved.

Compound defensibility
Early-stage companies cannot wait years for a moat to form. They need speed through distribution, brand buzz, and integration velocity in the early days, while steadily layering in stickier advantages like proprietary data, network effects, and switching costs.

In the AI era, defensibility is less about owning the algorithm and more about owning the ecosystem around it. Trust, embeddedness, and context create a gravitational pull that keeps users orbiting your product even when competitors can mimic the tech.