Core Viewpoint - The article discusses the significant impact of ByteDance's product launch event for its AI model "Doubao," highlighting its competitive edge in the AI landscape and the company's strategic direction towards becoming a leading technology firm in the cloud computing sector [5][23]. Group 1: Event Highlights - The launch event for Doubao 1.6 attracted considerable media attention, indicating the model's importance in the AI market [3][5]. - Doubao 1.6 has achieved top rankings in various international benchmarks, showcasing its advanced capabilities in complex reasoning and video generation [5][7]. Group 2: Strategic Insights - ByteDance's CEO Liang Rubo emphasized the company's commitment to long-term investment in AI and the importance of market feedback for technological advancement [7][10]. - The company aims to leverage its AI capabilities to enhance its cloud services, with a focus on releasing technological benefits to developers and enterprises [10][15]. Group 3: Financial Performance - Fire Mountain Engine's revenue has shown impressive growth, increasing from over 10 billion in 2021 to nearly 50 billion in 2023, with projections of exceeding 110 billion in 2024 [10][11]. - The company has maintained a healthy gross margin and effective cost control compared to competitors in the public cloud space [10][11]. Group 4: Market Positioning - ByteDance is positioning itself to compete aggressively in the AI cloud market, aiming to surpass established players like Baidu and Tencent by leveraging its extensive user base and computational resources [23][25]. - The company has opted for a self-research strategy in AI development, focusing on building a comprehensive ecosystem that includes servers, operating systems, and SaaS solutions [26][29]. Group 5: Technological Evolution - The article outlines the evolution of the internet through different eras, emphasizing the shift towards AI and the development of "Agents" that can autonomously execute tasks [13][14]. - Fire Mountain Engine's new security products aim to address challenges associated with AI models, such as model poisoning and data privacy [14][15]. Group 6: Future Outlook - The article suggests that the future of AI will involve interconnected Agents that can communicate and collaborate, enhancing software development processes [21][22]. - ByteDance's focus on self-research and technological innovation is seen as crucial for its transformation into a technology-centric company, moving beyond its image as merely an entertainment platform [30].

出品｜虎嗅黄青春频道 以下文章来源于黄青春频道 ，作者黄青春Youth 黄青春频道 . 看清流量迁徙的切面 字节跳动 CEO 梁汝波首次公开站台，给了豆包。 6 月 11 日，字节跳动旗下火山引擎开了一场发布会，现场数位拿着号码牌的媒体硬是因为主会场人 数爆满被拒之门外 20分钟，即便字节跳动公关竭力与现场安保交涉两轮，对讲机那头的负责人仍然 不为所动，严格遵守出一进一的规则，导致主会场内很多火山员工为了给媒体腾位置都被迫中途出会 场协同办公。 即便第三次交涉后虎嗅有幸进入内场，一番闪转腾挪仍被摩肩接踵的人群堵在了会场最后排的摄像臂 旁，仿佛挤进了一节北京早高峰地铁车厢，上一次如此夸张的阵仗还是年初春运赶高铁（不由感慨， 时代抛弃你的时候，连发布会都挤不进去）。 作者｜商业消费主笔 黄青春 头图｜视觉中国 为什么一场产品发布会搞得这么火爆？ 一方面，DeepSeek 凭一己之力掀翻了互联网，从微信到百度，从美团到小红书，国民级应用纷纷接 入 DeepSeek，唯独豆包至今依然坚持自研，且字节系大模型雨后春笋般冒出来，还能始终保持超高 的市场声量，自然会牵动着从业者乃至媒体、客户的神经。 比如，发布会上亮相的豆 ...

Core Insights - The emergence of Agents marks a significant shift in the AI landscape, transitioning from large models as mere tools to self-scheduling intelligent entities [1][2] - The Agent sector is rapidly gaining traction, with a consensus forming around its potential, yet many products struggle to deliver real user value, often repackaging old demands with new technologies [2][3] - The true challenges for Agents lie not in model capabilities but in foundational infrastructure, including controllable operating environments, memory systems, context awareness, and tool utilization [2][3] Group 1: Market Dynamics - The Agent market is characterized by a supply overflow and unclear demand, prompting a need to identify genuine problems and opportunities within this space [2][3] - Successful Agents must evolve from initial Copilot functionalities to fully autonomous systems, leveraging user data and experience to transition effectively [9][19] - Coding is viewed as a critical domain for achieving AGI, with the potential to capture a significant portion of the value in the large model industry [11][25] Group 2: Product Development and User Experience - A successful Agent must create a verifiable data environment, allowing for reinforcement learning from clear rewards, particularly in structured fields like coding [26][27] - The design of AI Native products should consider both human and AI needs, ensuring a dual mechanism that serves both parties effectively [31][32] - User experience metrics, such as task completion rates and user retention, are essential for evaluating an Agent's effectiveness and potential [30][31] Group 3: Business Models and Commercialization - The trend is shifting from cost-based pricing to value-based pricing models, with various innovative approaches emerging, such as charging per action or workflow [36][41] - Future commercial models may include paying for the Agent itself, akin to employment contracts, which could redefine the relationship between users and AI [42][43] - The integration of smart contracts in the Agent ecosystem presents a unique opportunity for establishing economic incentives based on task completion [42][43] Group 4: Future of Human-Agent Collaboration - The concepts of "Human in the loop" and "Human on the loop" highlight the evolving nature of human-AI collaboration, with a focus on asynchronous interactions [43][44] - As Agents become more capable, the nature of human oversight will shift, allowing for higher automation in repetitive tasks while maintaining human intervention for critical decisions [44][45] - The exploration of new interaction methods between humans and Agents is seen as a significant opportunity for future development [45][46] Group 5: Infrastructure and Technological Evolution - The foundational infrastructure for Agents includes secure environments, context management, and tool integration, which are crucial for their operational success [56][57] - The demand for Agent infrastructure is expected to grow significantly as the number of Agents in the digital world increases, potentially reshaping cloud computing [61][62] - Key technological advancements anticipated in the next few years include enhanced memory capabilities, multi-modal integration, and improved context awareness [63][64]

Core Insights - The article discusses the significant role of Volcano Engine in promoting the large-scale adoption of AI Agents, emphasizing its innovative pricing strategies and technological advancements [1][3][4]. Pricing Strategy - Volcano Engine has introduced a tiered pricing model for its new Doubao 1.6 model, which reduces costs significantly for enterprises, with a 63% decrease in expenses compared to previous models [6][7]. - The pricing for the 0-32K input range of Doubao 1.6 is set at 0.8 yuan per million tokens for input and 8 yuan for output, making it one-third the cost of its predecessor [6][7]. Technological Advancements - Doubao 1.6 supports multi-modal capabilities and is designed to enhance operational efficiency, allowing for tasks such as hotel bookings and data organization from receipts [9][10]. - The newly launched Seedance 1.0 pro model can generate high-quality videos at a low cost, with each 5-second 1080P video costing only 3.67 yuan [11][12]. Market Impact - Doubao models are currently utilized by 9 out of the top 10 global smartphone manufacturers, 80% of mainstream automotive brands, and over 70% of systemically important banks [14]. - The daily token usage for Doubao models has surged to over 16.4 trillion, reflecting a 137-fold increase since its initial launch [13]. Future Outlook - Volcano Engine aims to maintain a rapid development pace, with plans to release at least one major version of its models annually, driven by clear and substantial market demand [14][15].

Core Viewpoint - The event highlighted ByteDance's commitment to AI development, particularly through its product "Doubao," which has shown significant advancements in various AI capabilities and aims to establish a strong presence in the AI cloud market [4][5][30]. Group 1: Event Highlights - The product launch event for "Doubao" attracted significant media attention, indicating its importance in the industry [2][4]. - ByteDance's CEO Liang Rubo publicly supported "Doubao," emphasizing the company's long-term investment in AI and its strategic importance for the company's growth [7][29]. - The event showcased "Doubao 1.6-thinking," which excelled in complex reasoning and multi-turn dialogue tests, positioning it among the top global models [5][34]. Group 2: Financial Performance - ByteDance's revenue from its cloud services has shown impressive growth, with projections of nearly 50 billion yuan in 2023 and over 110 billion yuan in 2024, reflecting a doubling trend year-on-year [12][15]. - The company aims to achieve over 230 billion yuan in revenue by 2025, potentially surpassing competitors like Baidu [15]. Group 3: Market Position and Strategy - ByteDance's "Doubao" model has captured a significant market share, with a reported 46.4% in the Chinese public cloud model market, outperforming its closest competitors [34]. - The company is focusing on self-research and development rather than external investments, aiming to build a comprehensive ecosystem that includes servers, operating systems, and SaaS [37][41]. Group 4: Technological Advancements - The introduction of "Doubao 1.6" is part of a broader strategy to enhance AI capabilities, with a focus on reducing operational costs by 63% for enterprises [22][24]. - ByteDance is positioning itself as a technology company rather than just an entertainment platform, with a goal to lead in AI and cloud services [43][44].

Core Viewpoint - The emergence of Agents marks a significant shift in the AI landscape, transitioning from large models as mere tools to self-scheduling intelligent entities, creating new opportunities and challenges in the industry [1][2]. Group 1: The Rise of Agents - Agents have become the second major trend in the tech industry following large models, with a consensus forming around their potential [2]. - Despite the surge in consumer-facing products, many projects struggle to create a sustainable user value loop, often falling into the trap of applying new technology to old demands [2][3]. - The true barriers to the practical application of Agents lie in foundational infrastructure, including controlled operating environments, memory systems, context awareness, and tool invocation [2][3]. Group 2: Opportunities and Challenges - The conversation aims to uncover the real issues and opportunities within the Agent space, focusing on product forms, technical paths, business models, user experiences, and infrastructure construction [2]. - The transition from "Copilot" to "Agent" can be gradual, starting with user data collection and experience enhancement before evolving into fully automated solutions [9][19]. Group 3: Coding as a Key Area - Coding is viewed as a critical domain for achieving AGI, as it provides a clean, verifiable data environment conducive to reinforcement learning [24][25]. - The ability to code is seen as a universal skill that enables AI to build and create, potentially capturing a significant portion of the value in the large model industry [26][47]. Group 4: Evaluating Agents - A good Agent must create an environment that fosters a data feedback loop, with verifiable outcomes to guide optimization [27]. - Key metrics for assessing an Agent's effectiveness include task completion rates, cost efficiency, and user engagement metrics [30][31]. Group 5: Business Models and Market Trends - There is a shift from cost-based pricing to value-based pricing in the Agent market, with various models emerging, such as charging per action, workflow, or result [36][41]. - The trend of bottom-up adoption in organizations is becoming more prevalent, allowing products to gain traction without traditional top-down sales processes [35]. Group 6: Future of Human-Agent Collaboration - The concepts of "Human in the loop" and "Human on the loop" are explored to define the evolving relationship between humans and Agents, emphasizing the need for human oversight in critical decision-making [43][44]. - As Agents become more integrated into workflows, the nature of human interaction with these systems will evolve, presenting new opportunities for collaboration [45]. Group 7: Infrastructure and Technological Evolution - The foundational infrastructure for Agents includes secure execution environments, context management, and tool integration, which are essential for their effective operation [56][60]. - Future advancements in AI will likely focus on multi-agent systems, where different Agents collaborate to complete tasks, leading to a more interconnected digital ecosystem [53]. Group 8: The Role of Major Players - Major tech companies are beginning to differentiate their strategies in the Agent space, with some focusing on specific applications like coding while others leverage broader capabilities [54]. - The competition among giants like OpenAI, Anthropic, and Google is intensifying, with each company exploring unique paths to capitalize on the Agent trend [55].

Core Viewpoint - The year 2025 is anticipated to be a breakthrough year for Agents, significantly enhancing the capabilities of large models and transforming human-computer interaction across various platforms, particularly in multi-task automation [1]. Group 1: Agent Development and Platforms - The emergence of the first general-purpose Agent product, Manus, has garnered unprecedented attention, with major internet companies and startups focusing on Agents as a key area of AI competition [2]. - At the recent Force 2025 conference, Agents were highlighted alongside the latest version of the Doubao large model series [3]. - The conference's main forum showcased a new paradigm for AI cloud-native Agent development, emphasizing how Agents can reshape productivity [4]. - The Doubao platform has evolved into a "full lifecycle platform," addressing diverse development and tuning needs for Agents in the era of large models [5]. Group 2: Doubao Platform Features - The Doubao development platform enables low-code Agent development, allowing users with no coding experience to quickly build and deploy Agents across various channels [8]. - The platform empowers Agent development through four main aspects: intelligent IDE, application IDE, a rich set of plugins and workflow templates, and enterprise-level security capabilities [9]. - The application IDE, set to launch in 2024, will allow developers to create GUI-based applications using drag-and-drop features [10]. - Pre-configured Agent templates facilitate rapid deployment of functional Agents, such as smart customer service assistants and educational assistants [12]. Group 3: Eino Framework - Eino, a Go language-based LLM application development framework, draws inspiration from open-source communities and emphasizes simplicity, scalability, reliability, and effectiveness [13]. - Eino standardizes core modules for Agent development, enabling seamless integration with both open-source and closed-source models [14]. - The framework supports flexible orchestration capabilities for complex task decomposition and multi-tool collaboration [15]. - Over 300 systems have been developed internally at ByteDance using Eino, with a GitHub star count of 4.3k, indicating growing interest among developers [16]. Group 4: Agent Lifecycle Management - The Doubao platform establishes a comprehensive Agent lifecycle system encompassing development, evaluation, online observation, and optimization [16]. - The evaluation phase includes quantifying Agent performance to ensure it meets standards, while the observation phase involves real-time data collection and analysis [19]. - Developers can analyze user queries and behavior to adjust Agent performance, identifying and addressing issues through a robust observation system [20]. - The platform supports flexible evaluation set management, allowing developers to create and manage evaluation sets easily [22]. Group 5: Doubao Space - Doubao Space, launched in April, serves as a collaborative platform for high-quality Agents, facilitating efficient task resolution through expert collaboration [25]. - Users can leverage Doubao Space for market analysis, academic guidance, and expert support, with capabilities continuously expanded through the MCP protocol [26]. - The Doubao platform is expected to become foundational infrastructure for Agent development in the era of large models [27].

作者：吴炳见 今年AI应用的投资明显活跃了很多，而套壳这个词，正在从贬义词，成为中性词，甚至褒义词。 主要是市场出现了百亿美金的套壳案例。 什么样的壳有价值？如果把模型能力看成水位线，有的壳是柱子，模型能力涨上来后，柱子就没了。有的壳是船，模型能力提升后，水涨船高。 所以，做船不做柱子。 两个月前，我有个内部分享，这里写一部分出来，讨论下套壳的事。 我们回顾下过去两年多 AI 应用发生过什么，这是 A16Z 发布的AI 应用 Top100 榜单。 | | · D 330 | | | | Gen Al Web Products, by Unique Monthly Vi | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | ChatGPT | 11 | Kimi | 21. | CINITHI | 31 | P. Photoroom | 41 | Monica | | 2 | deepseek | IS | 6 Hailuo Al | 22. | IIElevenLabs | 32. | Moescape Al | ...

Core Viewpoint - Volcano Engine, as a latecomer in the AI and cloud computing sector, is leveraging its rapid iteration of the Doubao model and full-stack AI cloud-native capabilities to accelerate its breakthrough in the market [1][2]. Group 1: Market Position and Competitive Advantage - Volcano Engine's ambition extends beyond just tools; it aims to dominate the AI era's core battlefield, which is the Agent [2]. - The company has achieved a significant market share of 46.4% in the large model service market, far exceeding its competitors [3]. - The scale advantage is highlighted by its internal operations for Douyin and Toutiao, making it one of China's largest cloud service providers, thus reducing costs and offering high-cost performance multi-cloud services [2][3]. Group 2: Technology and Product Development - Volcano Engine emphasizes a "public cloud first" and "AI first" strategy, learning from competitors to avoid pitfalls [4]. - The company is innovating in cost reduction through technology, optimizing existing resources, and enhancing AI service pricing [5]. - It aims to lower the technical barriers for small and medium enterprises by optimizing models and resource scheduling, allowing even small startups to experiment with AI at low costs [6]. Group 3: Client Services and Digital Transformation - Volcano Engine focuses on providing leading products and "accompanying services" to assist B-end clients in their digital transformation [7]. - The company collaborates with clients to co-create solutions, such as hosting AI workshops and exploring AI applications in various industries [7]. Group 4: Ecosystem Development and Future Goals - The company is forming industry alliances and incubating innovative enterprises to promote the growth of AI services in China [8]. - Future goals include deepening collaborations in retail and finance sectors and fostering more AI-first enterprises to build a mutually beneficial ecosystem [8]. Group 5: Industry Trends and Future Outlook - The industry is transitioning from PC and mobile internet to the AI era, with a shift in development paradigms towards intelligent agents [10]. - Volcano Engine is positioned as an "AI cloud," focusing on AI-native technologies and leveraging its internal service experience to assist enterprises in their AI transformation [10]. - The demand for computing power remains strong, with a focus on optimizing model efficiency and enhancing the value of AI rather than merely increasing computing scale [12].

Core Viewpoint - The company believes that scale is crucial for success in the cloud computing industry, and it aims to be a leading player in the AI cloud market, leveraging its technological advantages and market opportunities [4][6][8]. Group 1: Company Performance and Market Position - Volcano Engine has achieved a significant market share, accounting for 46.4% of the domestic cloud model invocation volume, surpassing its closest competitors combined [4][17]. - The daily token processing volume of the Doubao model has increased fourfold to 16.4 trillion since December, indicating rapid growth and adoption in the AI sector [4][26]. - The company set an ambitious revenue target of 100 billion yuan for 2021, which was significantly higher than its competitors at the time, reflecting confidence in its growth potential [5][14]. Group 2: Technological Innovations and Offerings - Volcano Engine has introduced several new services and tools tailored for AI agents, including MCP services, prompt tools, and a reinforcement learning framework, aimed at reducing operational costs and enhancing scalability [5][22]. - The company has innovated its pricing model based on input length, significantly lowering costs to encourage widespread adoption of AI agents [5][23]. - The focus on AI and agent development is seen as a transformative shift in cloud computing, moving from traditional app-based models to more autonomous, self-executing agents [25]. Group 3: Future Outlook and Market Strategy - The company anticipates that the market for AI cloud services will expand by at least 100 times, positioning itself to maintain a leading role in this growing sector [5][14]. - The strategy includes enhancing the capabilities of the Doubao model and ensuring that it meets the evolving needs of clients, particularly in terms of performance and cost-effectiveness [19][28]. - The company emphasizes the importance of vertical optimization and collaboration across departments to ensure that its AI offerings remain competitive and effective [29][30].