Prototypes and products serve different purposes in the product development cycle, yet many product creators, particularly those lacking engineering backgrounds, struggle to grasp this distinction. The article highlights a growing trend where new generative AI-based prototyping tools enable more people to actively participate in product design. While this democratizes the process, it can lead to misconceptions about the complexity involved in turning prototypes into market-ready products. High-fidelity prototypes can create the illusion that transitioning to a fully functional product is straightforward, which is misleading. The complexity of actual products often far exceeds that of simple prototypes, encompassing numerous use cases and intricate business logic. For enterprise-class solutions, the operational demands multiply, requiring reliability, performance, and compliance with security standards. While some product teams may work on less complex internal tools, customer-facing products demand rigorous standards. The article warns that some tool providers may overstate their capabilities, leading to unrealistic expectations about what these prototyping tools can achieve. The author differentiates between tools designed for prototyping, like Lovable and Figma Make, and those aimed at building commercial-quality products, such as Claude Code. Each serves distinct needs: one focuses on learning through prototyping, while the other emphasizes delivering a product that can generate revenue. There’s skepticism about whether code-generation tools can bridge the gap from prototype to complex product within a few years. Current research hasn’t provided convincing solutions, and the inherent limitations of spoken language as a specification hinder progress. Understanding these distinctions is vital for product creators to navigate their roles effectively.