神译局是36氪旗下编译团队，关注科技、商业、职场、生活等领域，重点介绍国外的新技术、新观点、新风向。 编者按: 拒绝"AI 套壳"的平庸：要么成为大模型的"供货商"，要么去发明那些只有 AI 才能实现的全新生意。文章来自编译。 在过去的八年里，我先后在 Andreessen Horowitz 以及我现在创办的 Worldbuild 担任投资人，见证了同样模式的不断重演。软件行业曾陷入一个同 质化的时代，其发展路径往往由驾轻就熟的融资套路决定，而非真正的创新。同样的单位经济效益，同样的增长曲线，同样的 C 轮及更远的发展 路径。创始人往往为了融资里程碑而非构建可持续发展的业务进行优化，导致许多公司以过高的估值筹集了过多的资金。 随着生成式人工智能的兴起和宽松货币政策的终结，那个时代已经落幕。作为一名投资人，我感到非常兴奋；AI 终于开启了自移动革命以来久违 的真正创新潜力。然而，我看到一些创始人仍在为营销或金融领域构建专业的 AI 产品，就好像他们还在打造过去十年的那种订阅制软件工具一 样。那些仍套用旧框架的人，即将犯下一个巨大的错误。 在这个时代，为了实现足以吸引风险投资人的成果（即数十亿美元规模的退出），当 ...

Core Viewpoint - The article discusses Rich Sutton's critical perspective on large language models (LLMs), suggesting they may not align with the principles outlined in his work "The Bitter Lesson" and highlighting their limitations in learning from real-world interactions [1][3][22]. Group 1: Limitations of LLMs - Sutton argues that LLMs have significant flaws, particularly their inability to learn from ongoing interactions with the environment [3][21]. - He emphasizes that true intelligence should emerge from continuous reinforcement learning through dynamic interactions, rather than relying on extensive pre-training and supervised fine-tuning [3][4][22]. - The reliance on human knowledge and data in LLMs may lead to a lack of scalability and potential failure to meet expectations, as they are fundamentally limited by the biases present in the training data [24][25][26]. Group 2: Alternative Perspectives on Intelligence - Experts in the discussion, including Suzanne Gildert and Niamh Gavin, express skepticism about achieving pure reinforcement learning, suggesting that current systems often revert to imitation learning due to the difficulty in defining universal reward functions [7][11]. - The conversation highlights the need for systems that can autonomously learn in new environments, akin to how a squirrel learns to hide nuts, rather than relying solely on pre-existing data [8][10]. - There is a consensus that while LLMs exhibit impressive capabilities, they do not equate to true intelligence, as they lack the ability to explore and learn from their environment effectively [33][35]. Group 3: The Future of AI Development - The article suggests that the AI field is at a crossroads, where the dominance of certain paradigms may hinder innovation and lead to a cycle of self-limitation [28][29]. - Sutton warns that the current trajectory of LLMs, heavily reliant on human imitation, may not yield the breakthroughs needed for genuine understanding and reasoning capabilities [22][24]. - The discussion indicates a shift towards exploring more robust learning mechanisms that prioritize experience and exploration over mere data absorption [28][30].

Windsurf 团队的联合创始人 Anshul Ramachandran 最近发布了一篇关于 Agent 的科普文章，对于现下被广泛讨论，且经常被误用混淆的各种 Agent 概念 进行了辨析，同时对 Agent 系统的核心构成进行了拆解。如果你想要通过一篇全面地了解关于 Agent 的基础情况，这是一篇相当不错的资料。 以下为《What is an Agent？》全文内容，Founder Park 进行了编译和适当的调整。 Founder Park 正在搭建开发者社群，邀请积极尝试、测试新模型、新技术的开发者、创业者们加入，请扫码详细填写你的产品/项目信息，通过审核 后工作人员会拉你入群～ 进群之后，你有机会得到： 欢迎来到 2025 年，这一年 「Agent」 一词的使用频率极高，其含义也变得相当宽泛。在日常交流中，人们基于各自的理解 confidenty 地使用这 个词，反而使其原本清晰的含义逐渐模糊。 如果你是一名开发者，正在构建与 Agent 相关的解决方案，那么本文可能并不适合你。本文更适合以下几类人群： 在会议、讨论或日常对话中听到他人提及 AI Agent 时心存疑惑的朋友，或许你对 Agen ...