以下文章来源于卫夕指北 ，作者卫夕 卫夕指北 . 一个看完你会置顶的科技互联网公众号，作者卫夕，每周一篇深度文章剖析互联网、广告、营销相关的 底层逻辑！ 最近我在Twitter上看到了一篇非常精彩的论文，它全局、真切地研究了AI对工作的冲击。 我看了非常有感触，也分享给大家。 本文来自微信公众号： 卫夕指北 ，作者：卫夕，公众号"卫夕指北"出品人，专注科技、广告、AI底 层逻辑，题图来自：AI生成 后台不少读者朋友给我留言，说能不能聊聊AI抢工作这件事。 这其实也反映了一种普遍的焦虑情绪，即大家隐约感觉AI会对工作造成冲击。 但它具体是怎么把一个办公室白领的饭碗给干掉的，很多人并没有真实的体感。 论文来自哈佛大学，由两位经济学博士生Seyed M. Hosseini和Guy Lichtinger操刀。 而他们的导师是劳动经济学的重量级大咖拉里·卡茨 （Larry Katz） 。 因此，论文是严谨而有分量滴。 论文没有任何情绪渲染，就是用冰冷、庞大的真实数据，剖析了2023年以来美国就业市场的AI冲击 具体是如何发生的。 作为读研的时候也被写经济学论文折磨过的学术逃兵，在我看来，这篇论文很厉害的地方不是结论。 ...

Core Viewpoint - The article discusses the concept of Context Scaling as a crucial step towards achieving Artificial General Intelligence (AGI), emphasizing the need for AI to understand and adapt to complex and ambiguous contexts rather than merely increasing model size or data volume [2][21]. Summary by Sections Evolution of Large Models - The evolution of large models is summarized in three acts: 1. The first act focuses on the success of model scaling, where data and parameters are stacked to compress knowledge, leading to the emergence of models like ChatGPT and MOSS [6]. 2. The second act involves post-training optimization, enhancing decision-making capabilities through methods like reinforcement learning and multi-modal approaches, exemplified by models such as GPT o1/o3 and DeepSeek-R1 [6][7]. 3. The third act, Context Scaling, aims to address the challenges of defining context to improve model capabilities, particularly in complex and nuanced situations [8][21]. Context Scaling - Context Scaling is defined as the ability of AI to understand and adapt to rich, complex, and dynamic contextual information, which is essential for making reasonable judgments in ambiguous scenarios [8][9]. - The concept of "tacit knowledge" is introduced, referring to the implicit understanding that humans possess but is difficult to articulate, which AI must learn to capture [11][12]. Three Technical Pillars - Context Scaling is supported by three key capabilities: 1. Strong Interactivity: AI must learn from interactions, understanding social cues and cultural nuances [14][15]. 2. Embodiment: AI needs a sense of agency to perceive and act within its environment, which can be tested in virtual settings [16]. 3. Anthropomorphizing: AI should resonate emotionally with humans, understanding complex social interactions and cultural sensitivities [17]. Challenges and Integration - The article highlights that Context Scaling is not a replacement for existing scaling methods but rather complements them by focusing on the quality and structure of input data [18]. - It also redefines the environment for reinforcement learning, moving beyond simple state-action-reward loops to include rich contextual information [20]. Conclusion - The exploration of Context Scaling aims to unify various technological paths under the core goal of contextual understanding, which is seen as essential for navigating the complexities of the real world and a potential key to achieving AGI [22].