High inference costs can be acceptable if they drive product virality and competitive advantage. The article highlights a shift in thinking among successful AI companies. Instead of viewing inference costs as a burden, firms like Cursor, Lovable, and Replit treat them as investments in growth. These companies prioritize creating a product that sells itself, reducing reliance on traditional sales and marketing strategies. For instance, Cursor reached over $1 billion ARR in just 24 months, largely due to an engaging user experience that prompted users to evangelize the product. Companies need to choose between two distinct paths: an inference-first model, which accepts lower gross margins but relies on product virality, or a sales-first approach, which depends on traditional sales methods and higher margins. The former allows for rapid growth with minimal sales costs, while the latter often leads to increased expenses as companies try to maintain sales teams amid high inference costs. For many traditional SaaS businesses, this presents a significant challenge as they struggle against AI-native competitors that can invest in making their products more appealing without incurring substantial marketing costs. An alternative strategy exists for companies that can create an exceptionally valuable AI product that commands high prices—ranging from $50,000 to over $100,000. This pricing model shifts the focus from reducing costs to providing overwhelming value. As businesses increasingly seek to replace human labor with AI, the return on investment becomes apparent, making even high inference costs manageable. This approach allows companies to thrive without getting caught in the trap of balancing high inference and marketing expenses.