证券日报网讯 2月3日，中科创达在互动平台回答投资者提问时表示，公司与火山引擎的合作始于2024 年，从加入火山引擎汽车大模型生态联盟，到成立联合实验室、获HiAgent交付授权，合作不断升级。 公司依托火山引擎HiAgent与扣子平台，结合自身操作系统技术优势，构建起一套覆盖全链路的解决方 案。在智能汽车领域，公司推出端云协同的AI座舱解决方案，通过车端与云端大脑的实时联动实现了 500ms级语音反馈，多模态识别推荐等功能，其联合火山引擎基于火山方舟Mass平台开发的GUI Agent，可自主规划推理并执行UI交互操作，大幅提升座舱交互体验。 （文章来源：证券日报） ...

Core Insights - The article emphasizes that the current state of AI is primarily focused on financial returns, with a significant shift towards understanding its practical applications in business settings [5][6] - It highlights a collective realization that AI's role is often limited to enhancing existing processes rather than creating revolutionary new solutions [12][21] Group 1: AI in Consumer and Business Sectors - In the consumer sector, while AI tools like ByteDance's Douyin and DeepSeek have seen high user engagement, the willingness to pay for advanced services remains low, with a subscription rate of only 25% to 30% in AI education [5][8] - The business sector, however, is more pragmatic, with traditional industries actively seeking to integrate AI to solve specific cost-related challenges, such as reducing bad debt losses in finance or shortening drug development cycles in pharmaceuticals [8][9] Group 2: Challenges in AI Implementation - Many AI startups struggle to demonstrate effective delivery capabilities, as businesses demand integration with existing systems and cost efficiency that outperforms hiring interns [10][11] - The article points out a "productivity paradox," where AI's current applications often lead to increased production of low-value content rather than meaningful improvements [11][18] Group 3: Data and Automation Debt - A significant barrier to effective AI deployment is the "data debt," where many companies lack proper data governance and training, leading to fragmented and unreliable data systems [22][23] - The article also discusses "automation debt," particularly in traditional manufacturing, where outdated software and lack of integration hinder AI's potential [24][25] Group 4: Future of AI - By 2026, the article predicts a major transformation in AI applications, driven by a significant reduction in inference costs, potentially down to 1% of human labor costs, which would fundamentally change the business logic of AI [28] - The emergence of "agent" AI, capable of autonomously completing tasks, is anticipated, with companies needing to encapsulate industry-specific knowledge into software to maintain competitive advantages [30][32] - The article concludes that successful AI applications will seamlessly integrate into existing business processes, focusing on tangible problem-solving rather than abstract concepts [36]

Core Insights - The true challenge of intelligent agents is not just their ability to perform tasks but also their wisdom and execution capabilities, which require a deep understanding of user intent and actions [1] - The development of intelligent agents should not disrupt existing governance frameworks and commercial orders but should promote industry evolution through deep collaboration while ensuring safety and control [1] Group 1: Current Developments in AI Agents - Various exploration paths have emerged in the past year regarding whether AI can replace human tasks, with products attempting to operate through screen understanding and simulated actions, categorized as GUI Agents [3] - These products face significant challenges, including permission granting, accountability for errors, service invocation, and regulatory constraints [3] - Experts suggest that the authorization of intelligent agents should be scene-specific, with critical operations requiring secondary confirmation, and that not all scenarios should be authorized by third-party platforms [3] Group 2: Industry Perspectives on AI Implementation - OPPO's perspective indicates that while products like the Doubao phone positively impact the industry, they are not the final form of AI phones but rather a method to operate existing GUI interfaces [4] - The focus for phone manufacturers is on engineering and scalability, as any instability in system capabilities can lead to significant quality issues [4] - The future of intelligent agents is expected to shift towards A2A (Agent-to-Agent) collaboration models, with the core value for manufacturers lying in their long-term understanding of users rather than just model parameters [4] Group 3: Regulatory and Safety Considerations - The current GUI approach can activate the industry but should not be the sole focus; a more optimal evolution path that balances safety and development should be explored [5] - Apple's model is highlighted as a reference, establishing a collaborative mechanism between intelligent agents and apps through open APIs while ensuring safety boundaries [5] - The introduction of A2A mechanisms and market-based credit systems is suggested to improve authorization processes and manage potential risks associated with disruptive innovations [5]

Core Viewpoint - The emergence of the Doubao mobile assistant has sparked significant interest in the smartphone industry, being recognized as a "truly AI phone" due to its autonomous cross-application capabilities, although it faces challenges regarding user privacy and data permissions [1][3][5]. Group 1: Doubao Mobile Assistant and Industry Response - The Doubao mobile assistant was launched at the end of 2025, creating a stir in the smartphone market due to its ability to operate autonomously across applications [1]. - Despite the initial excitement, the Doubao mobile assistant encountered restrictions in its application usage, leading to an announcement regarding adjustments to its AI operational capabilities [1][3]. - The assistant's features are not significantly different from previously demonstrated smart applications by other manufacturers, indicating that while technology is evolving, commercial application remains a challenge [5][6]. Group 2: Challenges and Legal Considerations - The core challenge for AI phones, including the Doubao assistant, lies in legal disputes over cross-application automation and whether it infringes on the interests of internet companies [6]. - Current compliance standards are inadequate for the demands of AI, which seeks to perform tasks across multiple applications, highlighting a "standard vacuum" in the industry [6][15]. - The need for a unified protocol and open ecosystem is emphasized, as the integration of multiple agents requires collaboration between smartphone manufacturers and third-party service providers [15]. Group 3: Technological Advancements and Market Dynamics - The rapid improvement of domestic open-source large models has provided a solid foundation for the iterative development of AI phones, shifting focus from cloud-based models to offline capabilities [3][11]. - The introduction of the AutoGLM model and its open-source nature marks a significant step towards "technological equality," potentially leading to an explosion of edge AI applications [10]. - The competition in AI phones is evolving from mere hardware specifications to a comprehensive race involving edge capabilities, ecosystem openness, and user experience [17]. Group 4: Future Prospects and Ecosystem Development - The ultimate competition in AI phones is fundamentally about ecosystem development, requiring breakthroughs in technology and the establishment of an open collaborative framework [15]. - The industry faces three main challenges: ecosystem openness, cross-application scheduling permissions, and user habit formation [15]. - The trend of software and hardware boundaries blurring is evident, with companies exploring AI hardware to enhance data collection and user interaction [16][17].

Core Insights - The launch of the Doubao mobile assistant in late 2025 has created significant waves in the smartphone industry, being recognized by users as a "truly AI phone" due to its autonomous cross-application operation capabilities [1][5] - However, the Doubao mobile assistant faced usage restrictions in multiple applications, leading to an official statement on December 5 regarding adjustments to its AI operation capabilities [1][3] - The challenges faced by Doubao reflect broader issues in the AI phone sector, particularly concerning user privacy and data rights, which require collaborative efforts from various stakeholders [3][7] Group 1: Technology and Development - The rapid improvement of domestic open-source large models has provided a solid foundation for the iterative development of AI phones, shifting focus from cloud-based models to offline capabilities [4][13] - The announcement of the open-sourcing of the AutoGLM model by Zhiyu on December 9 signifies a move towards "technological equality" in the mobile agent space, potentially lowering development barriers [3][12] - The evolution of AI phones is not just about technological advancements but also involves a complex interplay of patience, restraint, and ecosystem collaboration [4][17] Group 2: Market Dynamics and Competition - The Doubao mobile assistant's features are not significantly different from previously demonstrated smart applications by other manufacturers, indicating that the market is still in the early stages of realizing cross-application autonomous capabilities [5][6] - The competition in the AI phone market is fundamentally about ecosystem development, requiring a shift from traditional app models to a more integrated approach that allows for cross-application automation [17][21] - The emergence of AI agents is seen as a potential battleground for user engagement and control over data, with manufacturers needing to navigate the complexities of user habits and regulatory compliance [8][17] Group 3: Industry Challenges - The current landscape reveals a "standard vacuum" regarding user authorization and data rights, complicating the implementation of AI agents that require cross-application functionality [7][18] - The transition to AI agents necessitates a reevaluation of existing operational models, as traditional single-operation authorization methods are inadequate for the demands of AI-driven tasks [7][18] - The industry faces challenges related to memory and power consumption as the capabilities of end-side models expand, necessitating advancements in hardware to support these new demands [18][20] Group 4: Future Outlook - The AI phone competition is evolving from a focus on hardware specifications to a comprehensive race involving end-side capabilities, ecosystem openness, and user experience [21][22] - The integration of AI capabilities into hardware is expected to reshape traditional consumer electronics business models, moving towards subscription-based software services [20][22] - The developments surrounding the Doubao mobile assistant highlight the need for industry-wide collaboration to address the challenges of rights, interests, and standards in the evolving AI landscape [20][22]

⑥Adobe Firefly新增基于提示词的视频编辑功能； ⑦OpenAI据悉正与亚马逊洽谈超100亿美元投资，并使用亚马逊的人工智能芯片； ⑧Alphabet旗下自动驾驶公司Waymo洽谈新一轮融资，估值或超1000亿美元。 ③腾讯大模型团队架构调整，前OpenAI研究员姚顺雨任要职； ④小米开源MiMo-V2-Flash大模型； ⑤阶跃星辰宣布GUI Agent全面升级，包括云端模型Step-GUI全量上新，并开放GUI-MCP协议； AI进化速递 | OpenAI推出GPT Image 1.5 ①OpenAI推出全新图像生成模型GPT Image 1.5； ②腾讯混元世界模型1.5正式发布； ...

每经AI快讯，阶跃星辰官微12月17日宣布GUI Agent全面升级，包括云端模型Step-GUI全量上新，任务 场景突破200+，支持超长步骤推理，并开放GUI-MCP协议，短至10分钟即可部署一台AI手机。 ...

Financial Performance - The company achieved a revenue of 1.848 billion CNY in the recent quarter, representing a growth of 42.87% year-on-year [4] - Net profit attributable to shareholders reached 70.568 million CNY, an increase of 48.26% compared to the previous year [4] - For the first three quarters, total revenue was 5.148 billion CNY, up 39.34% year-on-year, with net profit of 229 million CNY, reflecting a growth of 50.72% [4] Product and Technology Development - The company is focusing on AI integration across various sectors, including smartphones, smart vehicles, and smart hardware, with a goal to transition from traditional operating systems to AI-based operating systems (AIOS) [4] - AIOS is designed to revolutionize the OS landscape by integrating traditional interfaces with AI capabilities, enabling new forms of interaction such as generative UI and multi-modal experiences [4] - The company is actively developing AIOS for automotive applications, exemplified by the launch of the Drip OS platform in collaboration with Geely at the IAA Mobility 2025 [4][6] AIOS Strategy and Implementation - AIOS is structured into four levels: M1 (prototype), M2 (small-scale), M3 (expandable), and M4 (mass production), with a focus on effective AI strategy execution [5] - The Drip AIOS leverages Qualcomm's Snapdragon Ride platform, achieving real-time operation of 7B large models in vehicles, enhancing the AI cockpit experience [5][6] - The company is expanding its AIOS applications to include AI glasses and other IoT devices, aiming for a comprehensive AI ecosystem [5] Market Expansion and Collaboration - The company is enhancing its overseas automotive ecosystem, facilitating the entry of foreign brands into China and integrating with local middleware and software ecosystems [5][7] - Strategic partnerships with chip manufacturers like Qualcomm and NVIDIA are pivotal for the development of AIOS and related technologies [6][9] - The company is also collaborating with major AI model firms to optimize applications in smart automotive environments, enhancing user experience through advanced AI interactions [8][9] Future Outlook - The company anticipates continued growth in its smart software and AI sectors, driven by advancements in AI mobile technology and heterogeneous computing [8] - The focus will remain on building a robust AI ecosystem that supports smart homes and AI hardware, ultimately aiming for a fully interconnected AI environment [8][10] - The company is committed to maintaining a strong global presence with R&D teams across 16 countries, ensuring responsiveness to local market needs [10]

Group 1: Financial Performance - In H1 2025, the company reported revenue of 3.299 billion yuan, a year-on-year increase of 37.4%, and a net profit attributable to shareholders of 158 million yuan, up 51.8% year-on-year [1] - In Q2 2025, revenue reached 1.831 billion yuan, reflecting a year-on-year growth of 49.7%, while net profit attributable to shareholders surged to 66 million yuan, a remarkable increase of 384.2% year-on-year [1] Group 2: Business Segments - The smart IoT business experienced significant growth, with revenue of 1.270 billion yuan in H1 2025, representing a year-on-year increase of 136.1%. This growth was driven by enhanced delivery capabilities in diverse handheld terminals and deep integration of AI technology [1] - The smart automotive segment generated revenue of 1.189 billion yuan in H1 2025, marking a year-on-year increase of 7.9%. The company introduced the AI-native vehicle operating system, Drip OS, aimed at becoming the intelligent core of vehicles in the AI era [2] - The smart software segment saw revenue of 841 million yuan in H1 2025, reflecting a year-on-year growth of 10.5%, continuing the recovery trend since Q4 2024 [2] Group 3: International Expansion - The company's overseas revenue reached 1.558 billion yuan in H1 2025, a year-on-year increase of 81.4%, with revenue from Europe and America alone surging by 151.1% to 1.105 billion yuan [3] - The demand for automotive intelligence and IoT solutions in overseas markets is robust, and the company supports enterprises in their global expansion through a "globalization + localization" strategy [3] Group 4: Future Outlook - The overall gross margin is under pressure, with revised forecasts predicting a gross margin of 35.0% for 2025 and 35.1% for 2026, down from previous estimates of 37.4% and 37.8% respectively [3] - Revenue forecasts for 2025 and 2026 have been adjusted to 6.402 billion yuan and 7.047 billion yuan, respectively, with net profit forecasts of 510 million yuan and 618 million yuan [3] - The company is expected to enter an operational upcycle, maintaining an "overweight" rating [3]