中国第一款面向用户研究的 AI Agent，用主持人的 AI"分身"提效百倍 出品｜虎嗅科技组 作者｜陈伊凡、李一飞 编辑｜苗正卿 头图｜视觉中国 "AI 原生 100" 是虎嗅科技组推出针对 AI 原生创新栏目，这是本系列的第「 39 」篇文章。 在《广告狂人》中，主人公唐• 德 雷珀 的每一句令人拍案叫绝的广告背后，都是对消费者的极致洞察。 现实世界，这类洞察基于冗长且大量的用户研究，抛开定量研究的部分，定性研究中包含了大量隐性知识 和人类经验，因此，在过去的几百年里，这个行业仍然保持最原始的运作方式，天花板明显，直到大模型 出现。 孙克强想改变的，就是这样一个老掉牙的行业。他是一家专注用户研究AI Agent产品开发的AI初创企业 Mizzen的创始人兼CEO。他想用AI，将用户研究的效率提升百倍。这也是中国第一款面向用户研究的AI Agent产品，是全球唯一一个将真人引入模型训练环节的产品。 与孙克强见面，是在Mizzen的第一代产品发布之后的一周。我们约了一次火锅，他兴奋地向我展示着用户 在小红书上自发的反馈，这么多"自来水"的好评，他始料未及。在和虎嗅交流之后，他还要见一位客户， 总之，业务是繁忙 ...

Core Insights - The article discusses the "Cinderella Glass Slipper Effect" in AI, highlighting that early users of AI models often exhibit higher retention rates compared to later users, which contrasts with traditional SaaS retention strategies [1][5][6]. Group 1: Traditional SaaS vs AI Retention - In traditional SaaS, the common approach is to launch a minimal viable product (MVP) and iterate quickly to improve user retention, but this often leads to high early user churn [4]. - The AI landscape is witnessing a shift where some AI products achieve high retention rates from their first users, indicating a new model of user engagement [5][6]. Group 2: Understanding the Cinderella Effect - The "Cinderella Glass Slipper Effect" suggests that when an AI model perfectly addresses a user's needs, it creates a loyal user base that integrates the model deeply into their workflows [7][8]. - Early adopters, referred to as the "foundational cohort," tend to remain loyal if the model meets their specific needs effectively [8][9]. Group 3: User Retention Dynamics - Retention rates serve as a critical indicator of a model's success, with early users' loyalty being a sign of a genuine breakthrough in capability [6][24]. - The window of opportunity for AI products to capture foundational users is short, often lasting only a few months, necessitating rapid identification and resolution of core user needs [6][22]. Group 4: Case Studies and Examples - The article provides examples of AI models like Google’s Gemini 2.5 Pro and Anthropic’s Claude 4 Sonnet, which demonstrate high retention rates among early users compared to later adopters [14][15]. - Models that fail to establish a unique value proposition often see low retention rates across all user groups, indicating a lack of product-market fit (PMF) [17][24]. Group 5: Implications for AI Companies - The "Cinderella Effect" emphasizes the need for AI companies to focus on solving high-value, unmet needs rather than creating broadly applicable but mediocre products [23][24]. - The competition in AI is shifting from merely having larger or faster models to effectively identifying and retaining users who find genuine value in the product [23][24].

Project Performance - TVL decreased by 89% [1] - $FRAG decreased by 97% to $1 million market cap [1] - The project failed to find Product-Market Fit (PMF), leading to user attrition [1] Fundraising - The project raised a $12 million Seed round from Robot Ventures, Hashed, BitGo, etc [1] Industry Analysis - The case highlights the flaws of the airdrop farming era [1]

Fundraising - MetaDAO raised $10 million from VCs including Paradigm and 6MV [1] Market Analysis - VCs purchased tokens directly on the open market at prices higher than those paid by early community supporters [1] Protocol Performance - Protocols are demonstrating product-market fit (PMF) and scaling transparently [1]

Core Insights - Plaud is aggressively hiring and aims to expand its team to enhance its AI hardware capabilities, reflecting its growth trajectory and market potential [2][9] - The company reported over $100 million in earnings last year, with projections to exceed $200 million this year, indicating strong financial performance and market demand [3][4] - Plaud's product, a $150 recording card, has sold to over 1 million users globally, showcasing its success in the AI hardware startup space [4] Group 1: Business Model and Market Position - Plaud's business model is not heavily reliant on external financing, as it has established itself as a leading AI hardware startup [4] - The company emphasizes the importance of product-market fit (PMF), which has driven its rapid growth, achieving a fourfold increase in sales within a year [5][18] - The competitive landscape is evolving, but Plaud remains focused on delivering cutting-edge intelligence to its users, rather than being distracted by slower competitors [6][9] Group 2: Product Development and User Engagement - The company is iterating on its product offerings, moving from a simple recording device to a more comprehensive work companion that integrates various functionalities [58][70] - New features like "Press to Highlight" allow users to mark important moments during recordings, enhancing the value of the captured information [44][46] - Plaud aims to align AI capabilities with user intentions, ensuring that the technology not only records but also understands and processes user needs effectively [47][56] Group 3: Future Directions and Market Strategy - The company plans to expand its presence in the Chinese market, recognizing the significant opportunity presented by a large user base [68] - Future product iterations will focus on integrating advanced AI capabilities, with an emphasis on context and user interaction [70][74] - Plaud is committed to maintaining a strong engineering team to support its ambitious goals in the AI hardware space, prioritizing talent that can drive innovation [78][79]

Market Trends - Prediction markets have proven product-market fit (PMF) beyond elections [1] - Betting volumes are surging in prediction markets [1] - Investors are flooding into the prediction market space [1] - New approaches, including information perps and Telegram bots, are entering the prediction market [1] Growth Strategies - The report explores what strategies will actually work to maximize volume growth in prediction markets [1]

Solana Focus - Solana is for founders relentlessly focused on finding Product-Market Fit (PMF) [1] - Solana encourages founders to pivot and iterate repeatedly [1]

Industry Focus - Solana is suitable for founders relentlessly focused on finding Product-Market Fit (PMF) [1] - Companies should be prepared to pivot multiple times [1]

Core Insights - ClickUp has announced its ARR has surpassed $300 million, continuing its All-In-One approach in the AI era, aiming to integrate various productivity tools into a single platform [1] - The concept of "ambient AI" is introduced, suggesting that future AI tools will be deeply integrated and personalized within work environments, enhancing productivity without requiring manual operation [2] - Several AI companies have reported significant growth, with Databricks achieving a valuation over $100 billion and an ARR of $4 billion, while ElevenLabs' ARR has doubled to $200 million in eight months [6][7] Group 1 - ClickUp's ARR has reached $300 million, maintaining its All-In-One model for productivity tools [1] - The company aims to create AI-driven workflows that automate repetitive tasks and enhance project management [1] - The founder emphasizes the shift towards "ambient AI," which will personalize user experiences and integrate seamlessly into workflows [2] Group 2 - Databricks completed a $1 billion Series K funding round, with AI products contributing $1 billion to its ARR [6] - ElevenLabs' valuation increased to $6.6 billion, with its ARR growing from $100 million to $200 million [7] - Cognition, after acquiring Windsurf, announced a $400 million funding round, achieving a valuation of $10.2 billion and an overall ARR of $150 million [7] Group 3 - Fal, an AI Infra product, achieved over $100 million in ARR with a monthly growth rate of 40%, highlighting the importance of product-market fit (PMF) [8] - The company experienced a significant transformation through four stages to establish its current position as a generative media platform [8] - The rapid growth of these AI companies is attributed to the release of major models and the evolving landscape of AI applications [9]

Volume & Revenue - Company is about to clear $14 million in volume as the second quarter begins [1] - This volume is notable considering the company charges fees on its platform [1] Product-Market Fit (PMF) - The company's Product-Market Fit (PMF) is described as "pretty insane" [1]