Founders must consider their sales motion (e.g., PLG vs. enterprise sales-led) when designing the product. A product built for one motion won't sell effectively in another, potentially forcing a costly redesign. This concept extends "product-market fit" to "product-market-sales fit."

Unlike traditional software development, AI-native founders avoid long-term, deterministic roadmaps. They recognize that AI capabilities change so rapidly that the most effective strategy is to maximize what's possible *now* with fast iteration cycles, rather than planning for a speculative future.

The idea that startups find product-market fit and then simply scale is a myth. Great companies like Microsoft and Google continuously evolve and reinvent themselves. Lasting success requires ongoing adaptation, not resting on an initial achievement.

The moment you find product-market fit is not a time to celebrate; it's a signal that competitors will soon flock to your space. The founder’s immediate reaction was paranoia and an urgent need to build a moat, raise capital, and scale aggressively. The discovery of 'gold' means you must instantly shift from exploration to defense.

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.

PMF isn't a fixed state achieved once. It's a continuous process that must be re-evaluated at every stage of growth—from $1M to $1B. A company might have PMF for one scale but not for the next, requiring a constant evolution of strategy and product.

Having paying customers doesn't automatically mean you have strong product-market fit. The founder warns against this self-deception, describing their early traction as a "partial vacuum"—good enough to survive, but not to thrive. Being "ruthlessly honest" about this gap is critical for making necessary, company-defining pivots.

Successful AI products follow a three-stage evolution. Version 1.0 attracts 'AI tourists' who play with the tool. Version 2.0 serves early adopters who provide crucial feedback. Only version 3.0 is ready to target the mass market, which hates change and requires a truly polished, valuable product.

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.