Notion’s initial abstract vision of letting users build their own software failed to gain traction. The key insight was that users don't want to build tools; they want to accomplish tasks. By providing a familiar "wedge" like note-taking, Notion got into users' workflows before revealing its deeper, more powerful capabilities.
Cues' initial product was a specialized AI design agent. However, they observed that users were more frequently uploading files to use it as a knowledge base. Recognizing this emergent behavior, they pivoted to a more horizontal product, which was key to their rapid growth and product-market fit.
Startups often fail to displace incumbents because they become successful 'point solutions' and get acquired. The harder path to a much larger outcome is to build the entire integrated stack from the start, but initially serve a simpler, down-market customer segment before moving up.
Scribe started by building workflow automation, viewing documentation as a simple byproduct. Customers, however, found the automation only incrementally valuable but saw the documentation as a game-changing solution. Listening to this strong user pull led to the company's successful pivot.
Large enterprises don't buy point solutions; they invest in a long-term platform vision. To succeed, build an extensible platform from day one, but lead with a specific, high-value use case as the entry point. This foundational architecture cannot be retrofitted later.
Major tech successes often emerge from iterating on an initial concept. Twitter evolved from the podcasting app Odeo, and Instagram from the check-in app Burbn. This shows that the act of building is a discovery process for the winning idea, which is rarely the first one.
Figma learned that removing issues preventing users from adopting the product was as important as adding new features. They systematically tackled these blockers—often table stakes features—and saw a direct, measurable improvement in retention and activation after fixing each one.
Initially building a tool for ML teams, they discovered the true pain point was creating AI-powered workflows for business users. This insight came from observing how first customers struggled with the infrastructure *around* their tool, not the tool itself.