Switching from an established competitor is difficult due to high friction like data migration. New market entrants should exclusively target "greenfield" customers who have not yet adopted any solution, as they represent the path of least resistance for gaining initial traction and market validation.

Scaling readiness is a sequential, two-step process. First, achieve Product-Market Fit, defined by customer retention. Only then should you focus on Go-to-Market Fit, defined by profitable unit economics. Scaling before proving both leads to failure.

PMF is not a one-time achievement; it is a moving target that changes as a company scales, competitors emerge, and user needs evolve. Teams, especially in large organizations, must continuously re-run PMF surveys to avoid complacency and ensure the product remains essential.

Fat Llama's founder learned that strong user demand doesn't equal Product-Market Fit. His first company had users who loved the service for three years, but it took a full year *after* their Series A to make the unit economics work. True PMF requires both aspects.

Obsessing over creating a new market category is often a mistake. Data shows the vast majority of successful public tech companies compete within established categories. It's more effective to get "invited to the party" by using a known category label and then winning with a sharp, differentiated value proposition.

Dara Khosrowshahi argues that entrepreneurs over-index on Total Addressable Market (TAM), which he sees mainly as a fundraising tool. The real focus should be on proving product-market fit and solid unit economics in a small, defensible niche. Once that's established, you can expand into adjacent markets.

Unlike traditional SaaS, achieving product-market fit in AI doesn't guarantee a viable business. The high cost of goods sold (COGS) from model inference can exceed revenue, causing companies to lose more money as they scale. This forces a focus on economical model deployment from day one.

Unlike a failed feature launch, business viability risks (e.g., wrong pricing, changing market) kill products slowly. By the time the damage is obvious, it's often too late. This makes continuous monitoring of the business model as critical as testing new features.

Founders mistakenly define product-market fit by revenue or customer numbers. A better definition is achieving a high retention rate, proving customers get long-term value. This prevents scaling a business that can't retain its customers.