The Secret Behind “Unleavable” Apps

In today's competitive landscape, product success hinges on more than just engaging user experiences; it's about building an intelligence that becomes indispensable to the user. While many founders focus on dopamine loops and addictive micro-interactions—what's termed "layer one" design—the true differentiator for apps dominating in the coming years lies in their ability to turn user activity into an unexportable, personalized intelligence. This goes beyond simple data storage; it's about products that learn and get smarter about you with every interaction, creating a "stored value" that makes switching feel like starting over with a stranger. This concept, dubbed the "intelligence trap," means that every session trains an AI model that cannot be transferred, exported, or easily rebuilt elsewhere. Midjourney, an AI image generation platform, exemplifies this by building a "personalization profile" of a user's visual taste. Despite lacking traditional funding or marketing, Midjourney achieved significant revenue by creating a trained submodel of individual aesthetic preferences. It learns whether a user prefers warm or cool tones, maximalist or minimalist compositions, and photo-realism or painterly styles. Every image generated, variation selected, or mood board curated further refines this submodel. This means that switching to a competitor like DALL-E requires starting from scratch, as the user's unique creative fingerprint resides solely on Midjourney's servers. The "IKEA effect" principle, where people value things they helped create more, is at play here, as users actively contribute to the intelligence that serves them. To apply this, products should map their investment loops to ensure every core user action specifically enhances the product for that individual, design for cumulative personalization, and measure how much better the product performs for long-term users compared to new ones. The Oura Ring, a physical health wearable, demonstrates the intelligence trap through body intelligence. While regulations like the EU data act mandate data export, this only provides raw data (CSVs) and not the AI model that has learned a user's body rhythms over years. Oura collects vast amounts of sleep architecture data, heart rate variability trends, respiratory rate patterns, and temperature baselines. Its AI advisor uses this comprehensive data to provide personalized health recommendations, far beyond generic advice. This creates an "intelligence lock-in" where the trained model, specific to the user, lives within the product. Oura maintains high retention rates even with a monthly subscription for data that users' own bodies generated. For other products, this means identifying data that can compound into personalized intelligence, making that intelligence visible to users (e.g., showing how many nights their body has been tracked), and asking what a user would have to rebuild from scratch if they left for a competitor. If the answer is "nothing much," an intelligence layer is missing. Finally, Ramp, a spend management platform for businesses, showcases this principle in corporate spending. Ramp's AI agents make millions of autonomous decisions based on a company's financial logic, which is encoded through automation rules, card policies, vendor categorizations, and integrations with accounting software. This accumulated "decision intelligence" takes years to build. While competitors like Brex also offer strong engagement, Ramp's investment loops, where every transaction automatically trains the AI, make the exit door heavier. The key here is for products to actively learn from user decisions, not just store them. For businesses, this involves identifying repeated user decisions and ensuring the product learns from them to make subsequent decisions easier, counting integration roots (as every external system depending on the product makes switching painful), and automating the deposit of value so users train the AI without conscious effort. In summary, the most successful products are moving beyond mere engagement to build personalized intelligence. Midjourney built an aesthetic profile, Oura an intelligence around the body, and Ramp around corporate financial logic. The core lesson is that engagement gets users in the door, but investment—in the form of irreplaceable, compounding intelligence—keeps them there long-term. The critical question isn't whether users can export their data, but whether they can export the intelligence their product has built from it. This gap is where the powerful "intelligence trap" resides.