Assembled initially replaced a manual spreadsheet process. Their success came from understanding the spreadsheet was a symptom of deeper pains like headcount planning, real-time dashboards, and agent utilization. The real value was in solving these complex operational problems, not just digitizing a spreadsheet.

Metrics can be misleading. The founder's true "aha" moment for product-market fit came from solving a complex, real-world problem posed by a skeptical expert during a live demo. When the product solved in seconds what took the customer's team two weeks, it provided undeniable proof of value in a high-stakes environment.

Instead of pushing a failing product, the founders used their first two years to listen to the market. This 'doldrums' period was a strategic investment in discovery, allowing them to uncover the true customer need and pivot away from a sunk cost fallacy, leading to explosive growth.

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.

When a startup finally uncovers true customer demand, their existing product, built on assumptions, is often the wrong shape. The most common pattern is for these startups to burn down their initial codebase and rebuild from scratch to perfectly fit the newly discovered demand.

Technical founders often create a perfect solution to a real problem but still fail. That's because problem-solution fit is useless without product-market fit. An elegant solution that isn't plugged into the market—with the right GTM, pricing, and messaging—solves nothing in practice. It's unheard and unseen.

Astronomer's customers for their Clickstream product were more fascinated by its Airflow backend than the product's value proposition. This overwhelming interest validated their pivot to a managed Airflow service, revealing a hidden, more urgent market need.

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.

Product-market fit can be accidental. Even companies with millions in ARR may not initially understand *why* customers buy. They must retroactively apply frameworks to uncover the true demand drivers, which is critical for future growth, replication in new segments, and avoiding wrong turns.