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.

Your first customers require obsessive, daily interaction to ensure the product works for them. Astronomer's founder spoke with their first managed Airflow customer four times a day for two months. This grueling process is essential for ironing out roadblocks and achieving product-market fit.

A genetic diagnostics machine was built to speed up patient diagnosis in hospitals. However, its biggest market turned out to be pharmaceutical companies needing to prove drug efficacy. This highlights how true product-market fit can be discovered accidentally in an adjacent, more lucrative market.

Founders who've built a product but aren't seeing traction should stop focusing on the product. Instead, they must leverage their market knowledge to find the real customer demand, even if it means scrapping prior work. This pivot can unlock massive growth, as seen with a startup that went 0 to $34M ARR.

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.

You've achieved product-market fit when the market pulls you forward, characterized by growth driven entirely by organic referrals. If your customers are so passionate that they do the selling for you, you've moved beyond just a good idea.

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.

TeamBridge initially built a scheduling tool, but customers revealed the real problem was workflows and automations stuck in spreadsheets *surrounding* the schedule. Pivoting to solve this deeper, systemic pain led to making more money in one month than the previous two years combined.