Core Insights - The article discusses the challenges faced by product managers in developing AI products, highlighting three main limitations that need to be addressed for successful product positioning [1] Group 1: Types of AI Product Technologies - AI products can be categorized into two types based on their technology implementation: API-based and deployed AI models. Native AI products can utilize both, but they require a fundamental redesign of the product's interaction framework [2][4] - Native AI products must leverage a vector database for data management, which necessitates a shift from traditional relational databases to non-relational structures [4][5] Group 2: Product Development Challenges - The development of native AI products requires breaking through existing product design frameworks, allowing for AI-driven interactions rather than fixed functionality [3][6] - A significant challenge is the need for resource allocation from management to support new product lines, as many AI projects fail due to insufficient backing or unrealistic expectations [6][7] Group 3: Team Dynamics and Learning - There is a notable learning curve for teams, with over 60% of product managers reportedly lacking experience with advanced AI models, which can hinder development efforts [7] - The culture within large tech companies often promotes a competitive environment that encourages continuous learning and adaptation, which is crucial for the successful development of AI products [8]