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.

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Product-led models create deep loyalty and organic demand, providing a stable business foundation. Marketing-led models can scale faster but risk high customer churn and rising acquisition costs if the product doesn't resonate, leading to business volatility. An ideal approach blends both strategies for sustainable scale.

Don't just collect feedback from all users equally. Identify and listen closely to the few "visionary users" who intuitively grasp what's next. Their detailed feedback can serve as a powerful validation and even a blueprint for your long-term product strategy.

To keep growth aligned with product, foster a shared culture where everyone loves the product and customer. This isn't about formal meetings, but a baseline agreement that makes collaboration inherent. When this culture exists, the product team actively seeks marketing's input, creating a unified engine.

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.

Winning accolades like Product of the Day/Week/Month provides an initial user spike but doesn't guarantee product-market fit. True PMF is indicated by sustained, accelerating organic word-of-mouth growth, not a launch-driven bump that later flattens out.

Great PMs excel by understanding and influencing human behavior. This "people sense" applies to both discerning customer needs to build the right product and to aligning internal teams to bring that vision to life. Every aspect, from product-market fit to go-to-market strategy, ultimately hinges on understanding people.

The rapid evolution of AI makes traditional product development cycles too slow. GitHub's CPO advises that every AI feature is a search for product-market fit. The best strategy is to find five customers with a shared problem and build openly with them, iterating daily rather than building in isolation for weeks.