Product-market fit is no longer a stable milestone but a moving target that must be re-validated quarterly. Rapid advances in underlying AI models and swift changes in user expectations mean companies are on a constant treadmill to reinvent their value proposition or risk becoming obsolete.

Since today's AI companies grow too fast to have multi-year renewal data, investors must adapt their diligence. The focus shifts from long-term retention to short-cycle retention and, crucially, deep product engagement. High usage is the best leading indicator of future stickiness and value.

SaaS playbooks for sales, marketing, and success were designed for annual product changes. AI-native products iterating every 30 days require a complete organizational rethink, as old go-to-market motions cannot keep pace with the product's rapid evolution.

The highest predictor of customer retention is an early success. Use AI in your onboarding to ask new clients, "What's the fastest, smallest win we can create for you?" Then, use automation to build and deliver that specific solution, ensuring immediate progress and long-term loyalty.

The current AI hype cycle can create misleading top-of-funnel metrics. The only companies that will survive are those demonstrating strong, above-benchmark user and revenue retention. It has become the ultimate litmus test for whether a product provides real, lasting value beyond the initial curiosity.

Unlike traditional software where PMF is a stable milestone, in the rapidly evolving AI space, it's a "treadmill." Customer expectations and technological capabilities shift weekly, forcing even nine-figure revenue companies to constantly re-validate and recapture their market fit to survive.

A one-size-fits-all onboarding process is ineffective. Customers have varying levels of technical proficiency; a power user may find excessive handholding annoying, while a novice needs it. The process must be flexible and tailored to the individual to avoid creating a frustrating experience.

Successful onboarding isn't measured by feature adoption or usage metrics. It's about helping the customer accomplish the specific project they bought your product for. The goal is to get them to the point where they've solved their problem and would feel it's 'weird to churn,' solidifying retention.

The conventional wisdom for SaaS companies to find their 'second act' after reaching $100M in revenue is now obsolete. The extreme rate of change in the AI space forces companies to constantly reinvent themselves and refind product-market fit on a quarterly basis to survive.