Core Insights - The article emphasizes the importance of AI productization and how businesses should focus on integrating AI into user tasks rather than merely adding AI features to existing products [2][3][17] - It highlights the concept of "tenfold speed change" brought by AI, which has made intelligent supply more accessible, faster, and cheaper, thus creating new opportunities for businesses [2][10] Group 1: Understanding AI Productization - AI productization is about identifying opportunities where supply and demand intersect, known as Product Market Fit (PMF) [2][3] - Companies should focus on how AI can help users complete tasks more efficiently rather than just adding AI to their products [3][4] Group 2: Identifying Opportunities - Businesses should analyze the entire user task process to identify pain points where AI can provide assistance [5][8] - The focus should be on optimizing user experiences by integrating AI into the task rather than the product itself [7][10] Group 3: Levels of Implementation - The first level of implementation involves using AI to enhance existing processes, making them more efficient for users [10][11] - The second level suggests creating entirely new processes that leverage AI, rather than merely optimizing old ones [12][13] - The third level focuses on expanding market access by lowering service costs and barriers for previously underserved user groups [13][14] Group 4: Future Opportunities - Companies should consider how to design infrastructure for AI, anticipating a future where AI performs many tasks traditionally done by humans [14][15] - The article suggests that the real opportunity lies in helping AI find tasks to perform, thus creating value for users [17][21]

Core Viewpoint - The article emphasizes the importance of AI productization for businesses, focusing on how to identify opportunities and optimize user tasks rather than merely adding AI features to existing products [3][39]. Group 1: Understanding AI Productization - AI productization is not just about integrating AI into products but understanding how to leverage AI to help users complete tasks more efficiently [3][9]. - The concept of "tenfold change" in supply due to AI advancements highlights that AI has made intelligent supply ten times better, faster, and cheaper [4][5]. - Businesses should focus on the overlap between supply and demand to identify new opportunities in the AI landscape [3][6]. Group 2: Finding Opportunities in AI Productization - Companies should analyze the entire user task process to identify pain points where AI can provide assistance, rather than just enhancing existing products [12][19]. - For example, in job searching, AI can streamline the process by generating tailored resumes and matching candidates with suitable positions, rather than just improving job boards [16][17]. - The focus should be on helping users achieve their goals more easily, rather than simply adding AI features to products [19][40]. Group 3: Implementing AI Solutions - The first level of implementation involves using AI to enhance existing processes, making them more efficient for users [21][22]. - The second level suggests creating entirely new processes that leverage AI, rather than just optimizing old ones [23][24]. - The third level focuses on expanding market access by lowering barriers for previously underserved user groups, such as those with disabilities [26][28]. Group 4: The Essence of AI Productization - The core of AI productization lies in identifying tasks that AI can perform effectively, thus creating value for users [39][42]. - Companies should focus on understanding user needs and the steps they find burdensome, aiming to alleviate these pain points through AI solutions [44]. - The ultimate goal is to help AI find tasks that can assist users, ensuring that these services are valuable and worth paying for [44].