In the evolving landscape of AI-native products, traditional SaaS strategies are proving inadequate. Else van der Berg, the interim product lead behind Wave, an AI-native terminal, shares insights from their journey. Launched in November 2023 with zero daily active users, Wave now boasts around 3,000 DAU and 12,000 GitHub stars. Van der Berg emphasizes that the typical SaaS go-to-market playbook doesn't fit AI-native products, leading them to break established rules. For instance, they prioritize growth over finding product-market fit and focus on a broader early customer profile rather than a narrowly defined ideal customer profile. Wave’s approach is distinct. Instead of waiting for a polished product, they aim to generate social distribution channels early on. Engaging influencers and potential users in the product's development is central to their strategy. Van der Berg notes that the AI market is saturated, making visibility essential. Traditional launch methods, like announcements on platforms such as Hacker News or Product Hunt, are insufficient. The goal is to create a robust distribution channel that can adapt as the product evolves. A relevant example is Genspark, which pivoted from an AI search engine to an AI Agentic Engine after recognizing shifts in user demands, resulting in $36 million in annual recurring revenue within weeks. The rapid pace of change in AI technologies demands that companies like Wave learn quickly. The expectations of users are also rising, often outpacing the capabilities of existing models. Van der Berg acknowledges that meeting these expectations is a challenge, as even simple requests can sometimes be beyond current AI capabilities. Their strategy emphasizes continuous adaptation and feedback from a wide array of users to refine the product. This ongoing learning process is critical for AI-native companies, where foundational models can shift dramatically, impacting both functionality and user satisfaction.