近期，关于抖音AI运动美女账号的讨论热度很高，很多人都对这个新兴领域感到好奇。这个方向真的能快速涨粉吗？新手操作难度大不大？今天，我将 以问答（Q&A）的形式，彻底打消你的所有疑虑，把这个项目的操作细节一次性讲清楚。 相信不少人和我一样，在寻找副业的过程中吃过亏、被割过韭菜，甚至花了不少冤枉钱。如今我找项目，基本都从"顺为网创"公众号获取，那里的教程详 细，实操性强。只要愿意动手，一个月多赚个五六千是完全可以实现的。 Q1：这种AI账号真的像传说中那样涨粉快吗？ A：是的，目前确实处在红利期。举个例子，有个对标账号，内容全是AI生成的虚拟美女运动日常，并非真人。仅仅发布了64条作品，粉丝数量就达到了 4.5万。这足以证明其涨粉效率。 Q2：我完全不会画画，怎么制作原创的AI图片来避免搬运？ A：完全不需要绘画基础。解决方法很简单：首先，找一个你认为会火的视频，截取一张图。然后，将这张截图上传到名为"星流"的AI工具中，输入你想 要的风格描述（即提示词），AI就能生成一张构图相似但风格全新的图片，原创问题就解决了。 Q3：如何保证AI生成的虚拟人物，每张图都是同一张脸？ A：这是一个核心问题，因为统一的形象才能 ...

来源：猎云网 10月10日，早期科技投资机构云启资本（下称"云启"）宣布发起年轻 AI 创业者专项支持计划—— Y Transformers 。该计划将联合中关村科学城、上海交大 工研院、MiniMax、火山引擎、亚马逊云科技、小宿科技、十字路口、WaytoAGI、S创、Wteam等10余家行业生态伙伴，设立专项资金，面向 "98后"AI创业 者，提供资金、资源与生态网络的多维度支持。 云启表示："Y Transformers并非扶持关系，而是与年轻创业者并肩作战。我们希望在他们尚未被广泛看见之时，就能提供最直接的支持。" 机制亮点：快、真、全 Y Transformers计划的运作机制突出"快、真、全、轻"： 快：单个项目在2周内完成投资决策； 真：投入规模达1亿元级别，资金与资源均为实打实的支持； 全：除资金外，还涵盖产品建议、市场连接、资源置换和社群陪伴。 聚焦年轻创业群体，直面最早期需求 Y Transformers计划重点关注正处于起步阶段的98后AI创变者，云启希望通过该计划，帮助这一群体更快获得"第一笔投资"和"第一批资源"，在创业初期即 形成良性发展。系列支持资源包括： 资金支持：提供真金白银 ...

云启长期坚持在创业最初期介入，与年轻团队共成长。 本文为IPO早知道原创 作者｜ Stone Jin 微信公众号｜ipozaozhidao 据 IPO早知道消息， 云启资本（下称 "云启"） 日前 宣布发起年轻 AI 创业者专项支持计划—— Y Transformers 。该计划将联合10 余家行业生态伙伴，设立专项资金，面向 "98后" AI 创业者， 提供资金、资源与生态网络的多维度支持。 具体来讲， Y Transformers 计划重点关注正处于起步阶段的 98 后 AI 创变者，云启希望通过该 计划，帮助这一群体更快获得"第一笔投资"和"第一批资源"，在创业初期即形成良性发展。 系列支 持资源包括： 资金支持：提供真金白银的早期投资，帮助年轻创业者在起点阶段解决资金瓶颈； 资源赋能：提供算力、模型接口、办公场地、等实用资源； 生态网络：联合知名投资机构、前沿创业者和活跃社群，构建交流与协作的多元平台。 @C叔 扫码添加C叔微信 加入【IPO早知道用户交流群】 参与资本市场讨论,获取第一 手价值讯息 推荐阅读 云启表示： "Y Transformers 并非扶持关系，而是与年轻创业者并肩作战。我们希望 ...

Group 1 - The AI startup landscape in 2025 is characterized by divergent paths, focusing on either B-end or C-end applications, and whether to concentrate on domestic or global markets [1] - B-end applications are seen as having a mature business model with clear payment logic, particularly in the "cost reduction and efficiency enhancement" sector, making it a preferred area for investment [1][2] - C-end markets, despite challenges like payment difficulties, hold potential opportunities through continuous observation and rapid iteration, leveraging domestic talent and evolving model technologies [1] Group 2 - The technical characteristics of AI determine the landing logic in different scenarios, with a focus on customized development for complex enterprise environments [2] - Globalization is viewed as a crucial strategy to break competitive deadlocks, with faster growth opportunities concentrated overseas, supported by the global capabilities of Chinese product managers [2] - Chinese companies possess unique advantages in going global, combining strong AI technology capabilities with a complete supply chain system to create high-cost performance smart devices [2] Group 3 - The emergence of institutional incubation models empowers startups, with organizations like Innovation Works significantly reducing risks by investing in scarce directions 1.5-2 years ahead [3] - The dual drivers of technological iteration and market evolution are clarifying the AI entrepreneurial landscape, emphasizing the importance of precise demand insights and flexible strategy adjustments [3]

Core Insights - The AI industry is experiencing a dichotomy between the continuous release of application-level imagination and the uncertainties brought by technological iterations, making each entrepreneurial decision critical [1] - The maturity of the industry ecosystem provides a fertile ground for startups, with Beijing's Haidian District's AI core industry scale exceeding 280 billion yuan in 2024, growing at 30% annually, and accounting for 80% of the city's and a quarter of the nation's total [1] - There is a consensus in the industry that it is better to enter early but with precise predictions regarding timing, likening the current moment to the mobile internet turning point of 2011-2012 [1][2] Industry Dynamics - The differentiation of genuine demand is seen as a critical factor for entrepreneurial success, with AI value either creating new experiences or enhancing efficiency [2] - The industry is still in an early stage, similar to the early days of the internet, with expectations for more practical applications to emerge [2] - Four principles proposed for AI development include: algorithms must outperform humans, must exponentially enhance productivity, there must be a significant productivity gap for customers, and industry demand must be sufficiently deep [2] Market Trends - By 2025, AI entrepreneurship is expected to move beyond a focus on technical prowess, with a need to align AI functionalities with real-world scenarios to avoid market obsolescence [2] - The market is likely to witness price wars due to insufficient demand depth, emphasizing the importance of complex scenarios for sustaining technological advantages [2] - The core logic of AI entrepreneurship is becoming clearer, focusing on capturing industry explosion windows while anchoring on genuine demand [2]

Core Viewpoint - The article highlights the emergence of a new generation of founders born after 2000, particularly focusing on Axiom Math, an AI company founded by Carina Hong, which recently completed a $6.4 million financing round, achieving a post-money valuation of $300 million [3][6][10]. Company Overview - Axiom Math is positioned as an AI company aiming to create a self-improving superintelligent reasoning system that can solve complex mathematical problems and provide detailed reasoning steps for its solutions [6][10]. - The company intends to convert mathematical content from textbooks, papers, and journals into programmable knowledge, enabling AI to tackle mathematical problems and verify solutions [6][10]. Founder's Background - Carina Hong, the founder of Axiom Math, is a 24-year-old prodigy from Guangzhou with a remarkable academic background, including studies at MIT, Oxford, and currently pursuing a PhD at Stanford [12][13]. - Hong has received numerous accolades, including the Schaffer Mathematics Award and the Morgan Prize, and was awarded the Rhodes Scholarship, highlighting her exceptional capabilities in mathematics [12][13]. Team Composition - Axiom Math's core team consists of 10 full-time employees, including several experts from Meta, such as Shubho Sengupta, who has a strong background in AI and distributed training systems [9][10]. Market Context - The article notes a trend of young founders in the AI sector, with several startups led by individuals born after 2000 successfully raising significant funding, indicating a shift in the entrepreneurial landscape [15][17]. - Examples include companies like Sol a Solutions and Any sphere, which have also secured substantial investments, showcasing the growing influence of this demographic in the tech industry [15][16].

作者 | 华卫 小红书，似乎突然成了 AI 产品的"发迹"地。 今年开始，越来越多 AI 领域创业者选择在这里完成产品从 0 到 1 的冷启动。AI 创作工具 Flowith 的 CMO 亲自在小红书高频率更新各类场景的使用体 验，在小红书上收获大量粉丝，被用户们自发安利；专门做给老外的 AI 风水应用 OCTA，从灵感过程诞生到发起内测群聊，通通在小红书完成；瞄 准"拖延症"群体的 AI 效率神器 plancoach，开发者把小红书当成了能和用户实时互动的产品日志和个人随感，一条内测招募视频都爆了 10 万 + 的浏览 量。 创始人们纷纷以个人身份入驻，来分享自己的创业思考和产品灵感。公司团队则充分利用小红书的社交属性，下场招募志同道合的合作伙伴。投资人们 亦主动出击，亲自来此寻找不错的 AI 项目。 近期，在小红书平台，还刮起一阵奇妙的"Ask me anything"风。平时在企业里扛技术、在高校里钻研究的 AI 精英们齐刷刷地发帖，介绍自己职位身份 的同时积极向外链接。 | | 大家好,我是陈恺。 | | 我是邓瑞恒Ryan | | --- | --- | --- | --- | | 大家好,我是阿岛 ...

Core Insights - The entrepreneurial paradigm in the AI era differs significantly from that of the mobile internet era, emphasizing the need for a more targeted approach to user acquisition and product development [2][7][8] Group 1: User Acquisition and Market Fit - In the AI era, startups should focus on identifying "prosumers," who have a better understanding of technology and are willing to invest time and money into products that add value to their lives [7][10] - The previous strategy of aggressively acquiring users through free offerings is less applicable; instead, a more selective approach is necessary to find the right users to engage with [8][14] - Startups must consider how to convert the capabilities of large models into product features that attract initial users and create a sustainable competitive advantage [7][11] Group 2: Cost Management and Efficiency - The cost structure in AI entrepreneurship is evolving, with the marginal cost of acquiring users now being a significant concern, as each additional user incurs additional inference costs [29][36] - The inference costs of large models have decreased by over 90% in the past two years due to advancements in hardware and model optimization [29][30] - Entrepreneurs are encouraged to prioritize building a loyal user base before focusing on cost reduction and efficiency improvements [32][36] Group 3: Product Development and Innovation - The focus should be on enhancing productivity and efficiency through AI, with an emphasis on creating products that significantly improve operational capabilities [15][17] - Successful entrepreneurs are those who understand both the technical aspects of AI and the human elements of user needs, allowing them to create products that resonate with their target audience [21][22] - The ability to adapt and innovate in response to user feedback and market demands is crucial for maintaining a competitive edge [49][50] Group 4: Funding and Financial Strategy - Some startups are choosing to operate without external funding, relying on strong cash flow and profitability to sustain growth, which allows for greater control over their business direction [25][27][28] - Entrepreneurs are advised to have a clear understanding of their financial needs and the purpose of any funding they seek, rather than pursuing investment for its own sake [28][36] Group 5: Competitive Landscape and Barriers to Entry - The concept of a "moat" in the AI era is evolving; it is not solely about user scale but also about the comprehensive capabilities that a startup can offer [44][46] - Startups must leverage their industry knowledge and optimize their offerings to differentiate themselves from competitors, including larger firms [44][46] - The ability to effectively acquire users and maintain engagement is becoming increasingly challenging, necessitating innovative strategies for user retention and growth [45][46]

Core Viewpoint - The article discusses the extreme work culture among young AI entrepreneurs in Silicon Valley, highlighting their dedication to work at the expense of personal well-being and social life, driven by the desire for success and financial freedom [2][12][22]. Group 1: Work Culture and Lifestyle - Many young founders in Silicon Valley are adopting an extreme work ethic, often working over 90 hours a week and sacrificing sleep and social activities [2][5][12]. - The office has become a multifunctional space for these entrepreneurs, serving as their workplace, dining area, and even sleeping quarters [6][8]. - The trend of "sleeping in the office" is prevalent, with some entrepreneurs using makeshift sleeping arrangements to maximize work time [6][8]. Group 2: Investment Landscape - The investment landscape for AI startups has seen fluctuations, with global private investment in AI startups totaling approximately $96 billion in 2023, a decrease of nearly 20% from $103.4 billion in 2022 [13][17]. - Despite the decline in total investment, the number of AI startups receiving funding has increased, with 1,812 companies securing financing in 2023, a 40.6% rise from the previous year [17][18]. - The average funding amount per startup has decreased, indicating a more competitive environment where only those with strong capabilities can secure significant investments [18][20]. Group 3: Competitive Environment - The AI startup ecosystem is characterized by intense competition and a lack of differentiation among many new entrants, leading to a reliance on basic models and applications [21]. - Investors are increasingly cautious, preferring to fund established companies or those with clear competitive advantages, which has led to a "winner-takes-all" dynamic in the market [21][22]. - The pressure to succeed is compounded by the rapid pace of technological advancement, with many entrepreneurs feeling a sense of urgency to capitalize on fleeting opportunities [27][28]. Group 4: Motivations and Aspirations - The drive for financial success and the allure of becoming a "unicorn" motivate many young entrepreneurs to endure extreme working conditions [23][25]. - The current AI boom is likened to the internet bubble of the late 1990s, with many seeing it as a chance to achieve life-changing wealth [24][25]. - There is a pervasive fear of missing out (FOMO) among entrepreneurs, pushing them to work tirelessly to secure their place in the rapidly evolving AI landscape [26][27]. Group 5: Future Outlook - The article suggests that the most successful AI companies may not emerge from the most extreme work cultures but rather from teams that balance ambition with sustainability [31][32]. - The ongoing struggle and dedication of these young entrepreneurs are noted as significant contributions to the evolving narrative of the AI industry [32][33].

Core Viewpoint - The article emphasizes that the key difference between the AI era and the mobile internet era is that leading large model companies pursue general intelligence rather than being limited to specific vertical applications. This shift poses risks for companies that merely build applications on top of existing models without deeper integration [2][3]. Group 1: AI and Education - The education sector is highlighted as a field where the complexity of industry know-how and long-term user data can provide a competitive edge against large model companies [3][11]. - Current large model companies face challenges in unit economics, driving them to seek new monetization paths by extending their capabilities into various scenarios [2][3]. - The article discusses the importance of addressing learning motivation, suggesting that game design principles can enhance student engagement and retention [5][9]. Group 2: Learning Mechanisms - The article outlines several cognitive challenges that affect attention and learning, such as limited resources, cognitive fatigue, and external distractions [6]. - Effective educational materials are designed with a gradual increase in difficulty, which is difficult for large models to replicate due to the nuanced understanding required [8][11]. - Traditional educational methods often lack immediate feedback mechanisms, which can be improved through technology [9][11]. Group 3: AI's Role in Language Learning - AI has the potential to revolutionize language education by providing personalized learning experiences and real-time feedback, which traditional methods struggle to offer [18][22]. - The article suggests that language learning is a "low-hanging fruit" for AI applications, as it can significantly enhance efficiency and effectiveness in teaching [23][26]. - The ability of AI to simulate real-life conversations can help learners overcome barriers in practical language use, addressing the gap between knowledge and application [26][27]. Group 4: Future of Education Companies - The ideal future for education companies involves minimizing the need for extensive service and sales teams by leveraging AI for these functions [34][33]. - AI can provide personalized learning paths and planning, which can build trust with parents and reduce the need for traditional sales tactics [32][33]. - The article concludes that the focus should be on how AI can better solve core user problems rather than merely enhancing existing models [36].