Your product's core abstractions determine whether new features compound into a moat or just add to a feature list. Here's how to get it right.

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A product's data model—the core concepts and objects it prioritizes—determines whether new features create compounding advantages or just add to a feature list. Companies like Slack (persistent channels), Notion (blocks), Figma (shared canvas), and Rippling (employee records) succeeded by choosing non-obvious data models that became impossible for competitors to replicate without rebuilding from scratch. As AI commoditizes code execution, the data model becomes the primary moat. Horizontal tools innovate on how products are built, while vertical tools succeed by elevating the right domain objects. The key is identifying the atomic unit of work in your domain and ensuring every new feature strengthens that central concept.

Your data model is your destiny

<p>For life in general, too, your browser history is your destiny; who you follow decides your future. And now, for LLMs, what you talk to bots is your future.</p>


<p>The comparisons are interesting, but the article is a major oversell. How is notion blocks Vs Google documents a comparison? Also for someone not familiar with one, the explanations are way to short. Interesting Products, no deeper insight.</p>
