Initial data suggested the market for design tools was too small to build a large business. Figma's founders bet on the trend that design was becoming a key business differentiator, which would force the market to expand. They focused on building for the trend, not the existing TAM.

Pursuing large "whale" customers for early validation is risky because they often come with heavy demands that can derail the product vision. Instead, seek out innovative, mid-level companies who are early adopters. They provide better feedback, and building traction with them opens doors to larger clients later.

Early-stage investors shouldn't be deterred by a small current market size. The key is assessing the potential for rapid growth and future scale. Many massive companies emerged from markets that initially appeared small, proving that market creation and expansion are critical variables.

Never start a business without first validating demand by securing commitments from at least three initial clients. This strategy ensures immediate revenue and proves product-market fit from day one, avoiding the common trap of building a service that nobody wants to buy.

Flock Safety was dismissed by VCs because its initial market of neighborhood associations seemed too small. This perception of a small TAM acted as a moat, deterring competition and allowing them to build a foundation to later expand into much larger government contracts.

In the earliest stages, the goal isn't a profitable P&L but proving people want your product. Spot & Tango's founder hand-delivered orders at a loss, prioritizing demand validation over unit economics, which could be optimized later.

Instead of waiting for a working product, the founders invested in a conference booth with just screenshots. This early, public validation test, though risky, attracted two crucial prospects who became their first customers. This demonstrated market demand before the product was fully built, a move many founders would avoid.

Before scaling a service business like chandelier cleaning, the founder was advised to quantify the opportunity. This means building a spreadsheet to model the total addressable market: number of homes/hotels, likely frequency of service, and cost per service. This data-driven approach determines if the market is large enough to support growth.

Instead of starting with a scalable platform, Decagon built bespoke, perfect solutions for its first few enterprise customers. This validated their ability to solve the core problem deeply. Only after proving this value did they abstract the common patterns into a platform.