文章探讨了初创公司在产品定位中常见的问题，指出创始人往往不了解目标用户的真实需求，且传统的“创造新类别”等定位方法已失效 1。为此，作者建议创业公司应围绕用户真正想要的东西进行定位，通过了解用户的愿望、期望结果和具体要求来制定有效的市场信息 1。
该文章作者曾参与Temporal、Runlayer、Console、Restate、Pydantic、Rerun、Netlify和Pinecone等100多家初创公司的工作，并在Pinecone帮助创建并推广了“向量数据库”概念 1。基于对工程师、AI研究人员、安全和IT领导者等超过1000名买家的采访，作者以Slack、Wiz、Kumo等公司为例说明了这种方法的有效性 1。例如，Slack从早期就围绕“完成工作”的愿望进行定位，而非“现代聊天”或“对话式工作平台”等类别 1。在Kumo的案例中，数据科学家平均每三个月才能用数据和预测模型改善业务一次，Kumo通过了解数据科学家希望“让工作重新变得有意义”的深层愿望，将定位从预测建模转向预测AI，实现20倍速度提升 1。
A recent article highlights the common pitfalls startup founders face when positioning their products, arguing that traditional methods such as creating entirely new categories are losing their effectiveness 1. The author, who has worked with over 100 startups including Temporal, Runlayer, Console, Restate, Pydantic, Rerun, Netlify, and Pinecone, suggests that companies should instead build their market positioning around what users genuinely desire 1. These insights are backed by extensive industry experience, as the author helped create and popularize the concept of the "vector database" at Pinecone and has interviewed more than 1,000 buyers, ranging from engineers and AI researchers to security and IT leaders 1.
To demonstrate the effectiveness of this user-centric approach, the article cites companies such as Slack, Wiz, and Kumo 1. Slack successfully positioned itself early on around the fundamental desire to "get work done" rather than relying on category labels like "modern chat" or "conversational work platform" 1. Similarly, Kumo achieved a 20x speed increase by shifting its positioning from predictive modeling to predictive AI after uncovering the deep-seated wish of data scientists to "make work meaningful again" 1. The article notes that prior to this strategic shift, data scientists in the Kumo case study were able to improve their business using data and predictive models only once every three months on average 1.
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