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.

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.

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.

Traditional 90-day onboarding is useless when your product's value proposition changes up to 12 times a year. The most strategic function is now "everboarding"—continuously re-engaging and re-educating users on new capabilities to drive adoption and prevent churn in a rapidly evolving product environment.

In the previous SaaS era, emulating giants like Salesforce was a common but flawed strategy for startups. In the new AI era, there is no playbook at all, forcing founders to rethink go-to-market strategies from first principles rather than copying incumbents.

The historical advantage of being first to market has evaporated. It once took years for large companies to clone a successful startup, but AI development tools now enable clones to be built in weeks. This accelerates commoditization, meaning a company's competitive edge is now measured in months, not years, demanding a much faster pace of innovation.

Fal treats every new model launch on its platform as a full-fledged marketing event. Rather than just a technical update, each release becomes an opportunity to co-market with research labs, create social buzz, and provide sales with a fresh reason to engage prospects. This strategy turns the rapid pace of AI innovation into a predictable and repeatable growth engine.

The rapid pace of AI makes traditional, static marketing playbooks obsolete. Leaders should instead foster a culture of agile testing and iteration. This requires shifting budget from a 70-20-10 model (core-emerging-experimental) to something like 60-20-20 to fund a higher velocity of experimentation.

In AI-native companies that ship daily, traditional marketing processes requiring weeks of lead time for releases are obsolete. Marketing teams can no longer be a gatekeeper saying "we're not ready." They must reinvent their workflows to support, not hinder, the relentless pace of development, or risk slowing the entire company down.