（原标题：父母给孩子讲题，为什么容易急眼？） 经常辅导孩子学习的家长会发现，自己在讲题时特别容易急眼。 哪怕平时脾气再好的人，讲着讲着，就搂不住火了。 等冲孩子发完脾气，又会陷入自责和内疚，责怪自己没控制好情绪。 还有人会对孩子、对自己感到失望，那种强烈的挫败感，真的很让人无力。 成年人在看孩子的学习内容时，自带大量的基本常识和成熟的逻辑框架，但这些都是孩子还不具备的。 家长着急，"这么简单的问题怎么都搞不明白？！"可对孩子来说，可能大人以为的常识，全部都是他的 新知。 他没有能力按照大人的逻辑去思考，而是需要通过关联自己当前有限的经验，来学习和理解新知识。 这就是典型的"知识的诅咒"。 一个人在掌握某种知识后，就很难想象自己或别人在没有这个知识时候的状态，容易高估别人的理解的 能力，带来误解和冲突，算是一种常见的认知偏差。 就好像已经大学毕业，甚至拥有硕博学位的家长，也不会记得自己上小学时的认知水平，更无法理解孩 子此刻的蒙圈。 另外，白天上班已经很累了，晚上回到家不仅不能休息，还要辅导孩子写作业，身心俱疲之下，父母也 很容易没有耐心。 一些容易焦虑的家长，很可能还会从孩子"一道题不会"，马上就关联到"成绩 ...

Core Insights - The article discusses the challenges faced by companies in implementing AI, highlighting that while many have adopted AI technologies, only a small percentage see significant economic value from them [1][3][14] - It identifies three major misconceptions that lead to failures in AI implementation: repeating past mistakes from the internet era, ignoring hidden costs of AI efficiency, and focusing too much on technology rather than human factors [5][14][25] Group 1: Misconceptions in AI Implementation - Misconception 1: Companies often treat AI as a quick-fix tool similar to past IT solutions, failing to integrate it deeply into their business processes [5][6][7] - Misconception 2: There is a tendency to overlook the hidden costs associated with AI, such as trial and error expenses and the need for effective workflows [14][15] - Misconception 3: Many organizations prioritize technology over the human element, neglecting the importance of employee engagement and understanding in successful AI deployment [25][26][27] Group 2: Strategies for Effective AI Integration - Companies should focus on understanding and validating existing workflows before deploying AI, ensuring that the technology complements and enhances these processes [11][12][19] - It is crucial to activate employees and encourage them to become co-creators with AI, rather than passive users, to maximize the technology's potential [27][30][32] - Organizations need to foster an environment that promotes learning and sharing of successful AI applications among employees to drive collective engagement and innovation [30][33]

Core Viewpoint - The article emphasizes the importance of understanding market dynamics from a grassroots perspective rather than a high-level, luxury viewpoint, suggesting that true insights come from observing everyday consumer behavior and environments [2][8][32]. Group 1: Market Understanding - The article discusses the concept of "灯下黑" (blind spots), indicating that professionals often overlook critical market insights while focusing on high-level analysis [3][5]. - It highlights the disconnect between luxury experiences and the realities of the broader market, suggesting that frequent travelers may lose touch with the average consumer's preferences and behaviors [8][10]. - The author argues that true market understanding requires immersion in everyday environments, such as local neighborhoods, rather than just high-end commercial areas [15][17]. Group 2: Consumer Behavior - The article illustrates how consumer preferences can be misjudged, using the example of airplane meals being sold to a demographic that may not have experienced flying [4][6]. - It points out that many consumers prioritize practicality and affordability over luxury, as evidenced by the popularity of budget travel options and local dining experiences [24][26]. - The discussion includes the observation that many consumers are willing to spend on experiences that are perceived as valuable, even if they seem mundane to industry insiders [28][31]. Group 3: Industry Insights - The article critiques the reliance on traditional market theories, suggesting that businesses often miss opportunities by not adapting to the realities of consumer behavior [21][22]. - It emphasizes the need for businesses to engage with the actual market conditions and consumer sentiments rather than relying solely on data from high-end environments [12][14]. - The author notes that understanding the local market dynamics is crucial for successful investment and business strategies, as many assumptions made by analysts can be misleading [18][30].