Unlike traditional SaaS where product-market fit meant a decade of stability, the rapid evolution of AI models makes today's PMF fleeting. Founders face the risk that their product could feel obsolete within a year, requiring constant innovation just to stay relevant in a rapidly changing market.

For AI products, the quality of the model's response is paramount. Before building a full feature (MVP), first validate that you can achieve a 'Minimum Viable Output' (MVO). If the core AI output isn't reliable and desirable, don't waste time productizing the feature around it.

The barrier to building AI products has collapsed. Aspiring builders should create a one-hour prototype to focus on the truly hard part: validating that they're solving a problem people actually want fixed. The bottleneck has shifted from technical execution to user validation.

Traditional SaaS development starts with a user problem. AI development inverts this by starting with what the technology makes possible. Teams must prototype to test reliability first, because execution is uncertain. The UI and user problem validation come later in the process.

Instead of starting with easy MVP features, PointOne built its complex AI time capture before manual entry. This strategy validates the core technical moat and riskiest assumption upfront, preventing wasted effort on a product that is ultimately not viable.

Sirian validated its market by securing five paid pilot agreements from large manufacturers based on its vision and understanding of customer pain points. This approach proved market demand and de-risked the venture before significant engineering investment, a powerful strategy for enterprise-focused founders.

The ease of AI development tools tempts founders to build products immediately. A more effective approach is to first use AI for deep market research and GTM strategy validation. This prevents wasting time building a product that nobody wants.

For deep tech startups aiming for commercialization, validating market pull isn't a downstream activity—it's a prerequisite. Spending years in a lab without first identifying a specific customer group and the critical goal they are blocked from achieving is an enormous, avoidable risk.

Many companies market AI products based on compelling demos that are not yet viable at scale. This 'marketing overhang' creates a dangerous gap between customer expectations and the product's actual capabilities, risking trust and reputation. True AI products must be proven in production first.