「锦秋AI实验室」 而谁还停留在"懂了点，又好像没懂透，反正先瞎操作一波"的阶段？ 这是一档专注于探索和评测AI产品在实际场景中应用效果的栏目。 我们正在 用AI 解锁100个效率场景。 下一个场景会是什么？ 以前以为"语音输入"只是给懒人用的：张嘴说两句，手机替你打字。 直到真的开始用它写长文、回微信、记灵感、开会做纪要——才发现，语音输入法的核心根本不是"省事"，而是： 它到底能不能把我说的"人话"，变成 能能让信息接收者听懂 的 "人话"。 我们也被这些"转写翻车"折磨过。 所以这次我们决定认真测一测： 7 款 AI语音输入法，5个真实场景，统一题库，一轮一轮地比。 我们想知道： 在语音输入这件事上，谁真的听懂了"帮我把我说的话打出来"？ * 需要说明的是 ，我们此系列的测评以年轻普通用户的实用视角和审美进行测评，于 AI 产品持有相对积极的评价态度。 这里也插播一下未来的测评预告： 近期我们还将会进行 AI 小游戏制作、 AI 知识库、 AI 画布、 AI 陪伴类产品的测评。如果你对这些 AI 产品方向的测评感兴趣，也欢迎私信或者 评论区告诉锦秋基金（微信公号：锦秋集；微信 ID ： jqcapita ...

Core Viewpoint - The article discusses the recent emergence of input methods as a strategic entry point for major tech companies, highlighting the competitive landscape and the potential for monetization through AI integration and user data collection [5][9][26]. Group 1: Input Method Market Dynamics - ByteDance's Doubao input method has launched, featuring a simple interface and strong voice recognition capabilities, which sets it apart from competitors [5][6]. - Major tech companies, including WeChat and Baidu, are re-entering the input method market, indicating a renewed interest in this seemingly basic tool that serves as a critical flow entry point for apps [9][11]. - Input methods are described as "bug-level" entities in the app ecosystem, acting as intermediaries that capture user needs before they reach other applications [11][12]. Group 2: Strategic Importance of Input Methods - Historically, input methods have been used to drive traffic and capture user data, with examples from the PC era where Sogou's search candidate feature redirected users to its own search engine [12][15]. - In the AI era, the potential of input methods has expanded, allowing for real-time translations and information retrieval without switching apps, enhancing their value as a traffic conduit [15][20]. - The competitive landscape is intensifying, with companies like Baidu and Sogou integrating AI features into their input methods to enhance user experience and maintain relevance [17][19]. Group 3: Monetization Strategies - Companies are exploring various monetization avenues, including advertising within AI responses, similar to past search engine practices [20][22]. - Input methods can serve as hooks to draw users into proprietary ecosystems, facilitating deeper engagement with the company's services [23]. - User data collected through input methods can be utilized for product optimization and training AI models, representing a hidden value stream for companies [24][25]. Group 4: Future Outlook - The article suggests that the current trend of tech giants revisiting input methods reflects a broader strategy to leverage AI across foundational services, indicating a cyclical nature in tech development [26][28]. - The high ROI associated with input methods makes them an attractive business opportunity for large companies, reinforcing their strategic importance in the digital landscape [26].

真正的转折，其实发生在我开始高频使用各种 AI App 的这两年。 第一次真正觉得「语音输入这件事好像值得重视」，是各个 AI App 里那个「语音转文字」按钮变得越来越好用的时候。这些 App 里的语音转写，明显比 传统输入法里的语音要聪明得多：它不仅能听清我在说什么，还能自动加上标点，帮我把一些口语化的表达整理得比较书面，甚至在我说得磕磕绊绊的时 候，最后呈现出来的那一段文字读起来仍然是顺的。 如果几年前有人跟我说，「你以后写稿可能不怎么需要键盘了」，我大概会把这句话当成一句玩笑。那时候我正处在对机械键盘的迷恋期，研究轴体、键 帽、键程，购入过 Cherry、Filco、NiZ、Keychron、3D 打印分体式键盘。甚至为了提高打字效率，专门学习过双拼输入法。 我的注意力都放在消费的快感上，很少认真想过这样一个问题： 敲键盘，真的是输入的最优解吗？ 主流的 AI 几乎都覆盖了语音转文字功能｜图片来源：极客公园 更关键的是，它和后面的 AI 是连在一起的——我说完一句话，看到的不只是干巴巴的转写结果，而是 AI 根据这段话给我的反馈和回答。那一刻我第一 次有了一个直观的感受：语音不再只是一个「替代键盘的输 ...

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Core Viewpoint - Traditional SaaS applications like CRM and ERP systems will not be replaced but will evolve to serve as the infrastructure for AI Agents, which will enhance the importance of data definition and interpretation within enterprises [2][10][15] Group 1: The Role of AI Agents - AI Agents will not eliminate traditional software systems; instead, they will necessitate a clearer separation between how tasks are performed and the sources of facts [2][10] - The effectiveness of AI Agents is contingent upon their ability to access and understand the correct data from various systems, highlighting the need for accurate and structured input data [2][9] - The emergence of AI Agents creates significant entrepreneurial opportunities for companies that can help businesses manage and structure their unstructured data [3][10] Group 2: Data Management Challenges - A significant portion of enterprise knowledge (80%) exists in unstructured data, which is becoming increasingly difficult to manage [2] - The complexity of data definitions within organizations leads to discrepancies in key metrics like Annual Recurring Revenue (ARR), complicating the role of AI Agents in providing accurate information [7][11] - The traditional approach of consolidating data into warehouses has only partially succeeded, as operational teams still rely on individual systems for real-time transactions [8][10] Group 3: Evolution of Systems - CRM and ERP systems will transition from user-centric interfaces to machine-oriented APIs, allowing AI Agents to interact with these systems programmatically [12][15] - The core value of enterprise systems lies in their ability to encapsulate chaotic data, which will remain essential despite changes in interface and interaction methods [13][15] - The demand for a clear, authoritative source of truth will only increase as AI Agents become more prevalent in business processes [14][15] Group 4: Future of Data Infrastructure - The combination of data warehouses, semantic layers, and governance tools will form the foundation for AI Agent workflows, evolving beyond traditional reporting systems [10][12] - The valuation of AI platforms will increasingly depend on their ability to define and manage facts, rather than just their user interfaces [14][15] - Companies that can create exceptional AI Agent experiences based on reliable data sources will have a competitive advantage in the evolving landscape [15]

Core Insights - Data is a critical risk point for startups, even if it may not serve as a competitive moat [1] - Different types of user data, AI-generated content, and other data categories have varying legal risks and processing requirements [2] - For companies expanding overseas, prioritizing compliance risks is essential due to frequent litigation and infringement disputes [3] Group 1: Workshop Details - The workshop will feature partners Zheng Wei and Sun Qimin from Beijing Xingye Law Firm, focusing on compliance and high-risk issues faced by AIGC startups during international expansion [4] - The event is scheduled for December 18 at 8 PM and will be held online [5] - Participation is limited and requires a screening process for registration [6] Group 2: Data Usage and Compliance - During model training, it is crucial to determine which types of data, such as synthetic data, copyrighted content, and user behavior data, can be utilized [8] - Different data types, including code, images, and audio/video, present unique infringement risks that need to be addressed [8] - Questions regarding ownership of AI-generated content, as well as the delineation of data usage rights and intellectual property for ToB and ToC applications, are critical [10] - Companies must also consider how to manage cross-border data transfer, local storage, and data isolation when expanding internationally [10]

Core Insights - Data is a critical risk point for startups, even if it may not serve as a competitive moat [1] - Different types of user data, AI-generated content, and various media have distinct legal risks and processing requirements [2] - For companies expanding overseas, prioritizing compliance risks is essential due to frequent litigation and infringement disputes [3] Group 1: Workshop Details - The workshop features partners Zheng Wei and Sun Qimin from Beijing Xingye Law Firm, focusing on compliance and high-risk issues faced by AIGC startups during international expansion [4] - The event is scheduled for December 18 at 8 PM and will be held online [5] - Participation is limited and requires a screening process for registration [6] Group 2: Key Discussion Topics - During the model training phase, it is crucial to determine which types of data, such as synthetic data, copyrighted content, and user behavior data, can be utilized [8] - There are specific considerations regarding infringement risks associated with different data types, including code, images, and audio/video [8] - Clarification is needed on the ownership of AI-generated content and the delineation of data usage rights and intellectual property for ToB and ToC applications [10] - Companies must address cross-border data transmission, local storage, and data isolation when expanding their products internationally [10]

Core Viewpoint - Data is not necessarily a moat for products, but it is a risk point that startups must take seriously [1] Group 1: Legal Risks and Compliance - Different types of user data, AI-generated content, and various media have distinct legal risks and processing requirements [2] - For companies expanding overseas, it is crucial to prioritize compliance risks, especially given the frequency of lawsuits and infringement disputes [3] - The workshop features partners from Beijing Xingye Law Firm discussing how AIGC startups can navigate compliance and high-risk issues during international expansion [4] Group 2: Data Usage and Rights - During the model training phase, it is essential to determine which types of data, such as synthetic data, copyrighted content, and user behavior data, can be used [8] - There are specific considerations regarding infringement risks for different types of data, including code, images, and audio/video [8] - Questions arise about the ownership of AI-generated content and how to define data usage rights and intellectual property for ToB and ToC applications [10] Group 3: Cross-Border Data Management - Companies must understand how to manage cross-border data transmission, local storage, and data isolation when expanding their products internationally [10]

21世纪经济报道记者肖潇 北京报道 输入法这个沉寂多年的老赛道，今年下半年又起了波澜。11月末，字节跳动的豆包输入法结束内测，正式上架安卓和苹果应用商店；大模型六小虎的智谱， 最近也在AutoGLM中推出了自己的小凹语音输入法。 从覆盖面来说，输入法很容易被人忘记是最"国民级"的日常场景。在2020年QuestMobile的中国移动互联网年度大报告里，第三方输入法App的活跃用户规模 已达8.82亿，渗透率高至89.5%。而过去两年里，不管是老玩家搜狗，还是后来者微信，都在输入法场景里持续强化AI功能。 但任何一款输入法都绕不开隐私话题。"输入法是目前打破App壁垒，获取全局信息最自然的方式，这种long context（上下文信息）是很可怕的。"一位AI语 音从业者向21记者这样形容。如果说过去输入法的最大挑战是商业化，那么在大模型时代，它的角色正在转变：不再追求直接变现，而是为AI输入更多上 下文记忆。 数据隐私问题随之变得更关键。AI输入法产品现在发展到什么了程度？围绕输入法的隐私担忧，是会被 AI 推得更深，还是有机会被缓解？ 最近一次激起国内AI输入法水花的是字节跳动。11月24日，字节产品线上多了 ...

Core Insights - Goldman Sachs highlights that while Apple has not made significant progress in Apple Intelligence, major Chinese smartphone brands have integrated OS-native AI assistants powered by self-developed LLMs and/or third-party LLMs [1] Group 1: ByteDance's Doubao - The newly released Doubao smartphone assistant has achieved system-level integration through collaboration with smartphone OEC manufacturers, reflecting ByteDance's intention to expand into mobile internet infrastructure and the smartphone ecosystem [1] - Doubao's recent input method product further emphasizes this strategic direction [1] Group 2: Xiaomi's AI Strategy - Xiaomi's substantial investment in GUI has positioned its AI assistant, Super Xiao Ai, among the top three OS-native AI assistants in China, alongside OPPO and Huawei [1] - The penetration rate of Super Xiao Ai among Xiaomi smartphone monthly active users (MAU) is reported to be 71% [1] Group 3: Jumpshare's Developments - Jumpshare has recently open-sourced its GUI intelligent agent, Gelab, which can be deployed locally and has achieved state-of-the-art (SOTA) performance in several benchmark tests [1] - The company has previously collaborated with ZTE to develop a smartphone assistant aimed at elderly users and has engaged in AI smartphone feature partnerships with domestic manufacturers such as Honor and OPPO [1]