Group 1 - The core message emphasizes the importance of not wasting time on problems that are likely to resolve themselves due to technological advancements or the passage of time [2][12][38] - AI products face two types of challenges: "transitional problems" that will be automatically resolved with the next model update, and "eternal challenges" that will persist regardless of AI advancements [3][4] - The case of Granola illustrates the strategy of focusing on improving product quality rather than fixating on a temporary limitation, which was later resolved with the release of GPT-4 [5][9][10] Group 2 - The article discusses the broader implications of resource allocation, highlighting the need for a comprehensive perspective that includes spatial, temporal, and probabilistic views [27][29][30] - It suggests that recognizing which problems will disappear over time is a crucial skill in the rapidly evolving AI landscape [37][38] - The proposed "three-step filtering method" for problem management includes categorizing issues, focusing resources on core challenges, and regularly reviewing which problems have resolved themselves [42][43][44]