Tiger Research:假如我是 Kaito 的创始人,面对 InfoFi 的变局会如何决策?

marsbitPublished on 2026-01-20Last updated on 2026-01-20

Abstract

X平台API政策的突变导致InfoFi生态系统在三天内崩溃,暴露了Web3项目过度依赖中心化平台的结构性风险。作为行业代表项目,Kaito面临彻底关停、转型赏金平台、采用韩国赞助博客模式、多平台扩张或转向MCN式KOL管理五种路径。InfoFi 2.0将转向更可控、重质量的模式,但需解决激励设计漏洞和代币价值证明等根本问题。未来需从“无需许可的规模化”转向“审核制的高质量合作”,并重新构建与代币持有者利益一致的可持续模型。

本报告由 Tiger Research 撰写。X 平台 API 政策的剧烈变动导致了 InfoFi 生态系统的瞬间崩溃。作为行业的领军项目,如果我是 Kaito 的创始人,在当前节点有哪些可行的转型路径?

核心观点

  • 三日内的生态瓦解: X 平台的政策调整在短短三天内摧毁了 InfoFi 生态,彻底暴露了 Web3 项目过度依赖中心化平台的结构性脆弱。
  • 五种生存路径: InfoFi 项目目前面临五种选择:彻底关停、转型赏金资助平台、采用韩国式赞助模式、多平台扩张或进化为 MCN 式的 KOL 管理模式。
  • InfoFi 2.0 的演进: 未来的模式将更加精致且可控,从“无需许可的规模化”转向“经过审核的高质量合作”。
  • 根本性挑战: 建立公平的激励补偿系统,并重新证明代币的内在价值,依然是行业必须跨越的鸿沟。

1. InfoFi 在三天内的“崩溃”

Source: X(@nikitabier)

1 月 15 日,X 平台产品负责人 Nikita Bier 发布了一则简短告示,明确表示不再允许通过奖励诱导用户发帖的应用在平台上运行。对于 InfoFi 赛道而言,这无异于下达了“死刑判决书”。

根据 Kaito 创始人 Yu Hu 披露的时间线,事件演变如下:

  • 1 月 13 日: Kaito 收到 X 平台的邮件,提示可能进行审查并要求澄清。
  • 1 月 14 日: X 平台发送正式法律通知,Kaito 当天即提交了法律回应。
  • 1 月 15 日: 官方声明公开发布,Kaito 与全行业同时获知了最终决定。

市场反应极其剧烈,$KAITO 价格大幅缩水。社区指责团队虽然声称早有预案,却未能提前预警。Kaito 随后发布紧急声明,解释称此前曾多次通过法律途径解决类似争议,因此误判了本次事件的协商空间。

教训: 一家中心化公司的单一决策,就在三天内终结了一个新兴的 Web3 类别。这种“生杀大权”掌握在他人手中的现状,令整个生态感到窒息。

2. 如果我现在是 InfoFi 创始人

这是否意味着 InfoFi 已经走入死胡同?像 Kaito 这样的项目已经在筹备接下来的发展方案。然而,当前需要的并非延续过去的旧路径,而是一个截然不同的“InfoFi 2.0”版本。

如果我是 Kaito 这样的 InfoFi 项目创始人,现在实际上有哪些可行的选择?通过审视这些潜在的前进路径,我们可以开始勾勒出 InfoFi 下一阶段的轮廓。

2.1 彻底关停

这是最简单也最直接的选择:在资金完全耗尽之前停止运营。实际上,许多中小型项目可能会就此进入“僵尸阶段”——基本处于不活跃状态,偶尔发布一些社交媒体动态,然后慢慢从大众视野中消失。

由于此前围绕 X 平台建立的“产品与市场契合度”(PMF)现已荡然无存,选择关停可能比为了寻找虚无缥缈的新方向而持续烧钱更为现实。如果项目手中还持有可用的数据资产,可以将这些资产出售给其他公司以收回部分残余价值。因此,大多数规模较小的 InfoFi 项目可能会选择这条道路。

2.2 基于赏金的资助平台

如果无法再访问 X 的 API,另一种选择是回归到更早期的业务模式:KOL 直接申请相关活动,在提交内容并经过人工审核后获得奖励。

Source: Scribble

Scribble 为代表的模式就是一个典型例子。项目方以赏金形式发布资助任务,KOL 创建并提交内容供平台审核,审核通过后获得报酬。这是一种“先提交、后审核”的模式,而非依赖 API 的实时追踪。

这种结构可以作为开放平台进行扩展:平台仅提供撮合中介和基础设施,而各个项目方管理自己的活动。随着参与的项目增多,KOL 池也会随之扩大;而 KOL 基础的增长,反过来又为项目方提供了更多选择。其缺点在于 KOL 面临极大的不确定性,如果提交的内容被拒绝,投入的时间和精力就付诸东流。在经历多次失败后,优质的 KOL 很可能会离开平台。

2.3 韩国式赞助博客模式

韩国的赞助博客模式遵循的是“先选择、后管理”的方法,而非事后审核。像 Revu 这样的机构使用这种模式已经超过十年。

其流程非常清晰:项目方设定目标的参与人数并发布活动,申请人提交申请后,项目方根据粉丝数量、过往表现等数据挑选合适的 KOL。被选中的 KOL 会收到明确的创作指南,内容发布后由运营人员进行审核。如果不符合标准则要求修改,如果错过截止日期则会受到相应处罚。

在这种模式下,KOL 可以有效避免无用功。一旦被选中,只要遵循指南,报酬基本是有保障的。与基于赏金的系统不同,这里不存在完成工作后被无故拒绝的风险。从项目方的角度来看,由于只选择了预先审核过的参与者,质量控制也会变得更加容易。

2.4 多平台扩张

如果 X 平台不再是一片沃土,接下来的选择必然是转向 YouTube、TikTok 和 Instagram。在 Web3 领域,目前已经出现了超越 X 平台的强烈推动力。主流观点认为,真正的增长需要从由加密原生用户主导的平台,转向拥有更广泛受众的大众化渠道。

这一路径的主要优势在于拥有比 X 平台大得多的潜在用户基础,尤其是在东南亚和拉丁美洲等新兴市场,TikTok 和 Instagram 具有极强的影响力。同时,每个平台运行的算法各不相同,即使其中一个渠道受到限制,整体运营也可以继续维持。

然而,这种权衡带来的挑战是运营复杂度的剧增。在 X 平台上,通常只需要审核基于文本的帖子;而在 YouTube 上,内容的长度和制作质量至关重要;在 TikTok 上,视频的前三秒就决定了表现;而在 Instagram 上,则必须评估 Story 的执行力和格式质量。这需要针对特定平台的专业知识,甚至需要开发全新的内部工具。由于各平台的 API 政策和数据收集方法迥异,这实际上几乎相当于从头开始重建整个项目。此外,政策风险依然如影随形,任何平台都可能像 X 平台那样突然改变规则。不过,将活动分散到多个平台确实可以显著减少对单一平台的依赖,对于较大规模的项目来说,这是唯一能提供实质性可扩展性的选择。

2.5 MCN 式的 KOL 管理

在 Web2 的 MCN(多频道网络)模式中,KOL 的品牌价值至关重要。在 Web3 领域,这种影响力甚至更具决定性:叙事驱动资本,意见领袖的一句评论就可能直接影响代币价格。

成功的 InfoFi 项目通常已经形成了一批活跃且忠诚度高的 KOL 群体,这些创作者通过数月在平台上的深度参与而成长。项目方可以保留这一群体,并将其转向数据驱动的管理模式,而不是从头开始寻找创作者。这与依赖持续发现新人的传统 Web2 MCN 有所不同。

MCN 式的结构意味着建立正式的合同关系,而非松散的选择性参与。凭借积累的历史数据和已建立的关系,平台可以在 Web3 生态系统中发挥更强的影响力,并以此谈判更好的商业交易。对于 InfoFi 项目来说,这需要一套强大的管理系统,数据将成为核心资产。如果可以通过数据精准引导 KOL,并为项目方提供专业且数据驱动的 GTM(市场切入)策略,这种模式将提供持久的竞争优势。

3. InfoFi 2.0

InfoFi 生态的这次崩溃给 Web3 世界留下了两个深刻的教训:

  1. 去中心化的讽刺: 许多 Web3 项目深度依赖于中心化的 X 平台,而 X 的一个决定就足以摧毁整个系统。
  2. 激励设计的局限性: 奖励机制虽然成功吸引了大量参与者,但缺乏有效的方法来控制内容质量。垃圾内容的泛滥,给 X 平台提供了干预的明确理由。

Source: X(@nikitabier)

这是否意味着 InfoFi 的路已经走到了尽头?

并非完全如此。少数找到了“产品与市场契合度”的项目,可能会通过改变业务形式生存下来。它们可以转向多平台扩张、策划精品活动或转型为 MCN 式管理。

InfoFi 2.0 可能会变得规模更小、更可控且更注重内容质量。它将从开放、无需许可的平台,转向经过严格审核的专业网络,其形态将更接近于一种结合了本地 GTM 努力和线下广告等组件的整合营销平台。

然而,根本性的问题依然摆在桌面上。来自 Tiger Research House 的 Joel Mun 指出:一旦引入奖励机制,参与者不可避免地会寻找利用系统漏洞的方法,这使得公平的激励结构极难设计。这种投机行为会导致低质量内容的产生,并产生可能破坏平台的负面反馈循环。

此外,研究员 David 提出了一个更本质的问题:他认为 InfoFi 代币的价值维持,在过去更多是依赖于质押空投预期和对某种叙事的信念,而非基于平台的实际表现。如今,这两者都失去了相关性。这引出了一个直接的疑问:投资者未来为什么要购买 InfoFi 代币?

为了让 InfoFi 2.0 能够真正生存下去,这些问题必须得到明确且有说服力的回答。如果一个项目无法与其代币持有者的利益保持一致,就无法实现真正的可持续发展。

原文链接

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Related Questions

QX平台的政策调整对InfoFi生态系统造成了什么影响?

AX平台在1月15日宣布不再允许通过奖励诱导用户发帖的应用运行,这导致InfoFi生态系统在三天内迅速崩溃。Kaito等项目的代币价格大幅下跌,暴露了Web3项目过度依赖中心化平台的结构性脆弱性。

QInfoFi项目目前面临哪些可行的转型路径?

AInfoFi项目目前有五种主要选择:彻底关停、转型为基于赏金的资助平台、采用韩国式赞助博客模式、进行多平台扩张(如YouTube、TikTok等),或进化为MCN式的KOL管理模式。

QInfoFi 2.0的核心特征是什么?

AInfoFi 2.0将更加注重内容质量和可控性,从“无需许可的规模化”转向“经过审核的高质量合作”。其形态可能更接近整合营销平台,结合多平台策略和线下广告等组件,规模更小但更精致。

QInfoFi生态崩溃揭示了哪些根本性问题?

A崩溃揭示了两个根本问题:一是Web3项目对中心化平台的过度依赖,导致单一决策即可摧毁整个生态;二是激励设计存在局限性,奖励机制易引发投机行为,难以控制内容质量,且代币价值缺乏实际表现支撑。

QKaito项目在事件中的具体时间线是怎样的?

AKaito在1月13日收到X平台的审查提示邮件,14日收到正式法律通知并当天提交回应,15日X平台公开发布政策调整声明,Kaito与全行业同时获知最终决定,导致其代币价格大幅缩水。

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What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

770 Total ViewsPublished 2025.01.14Updated 2025.01.14

What is AGENT S

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