据媒体报道， OpenAI近日向投资者透露，到2030年的总算力支出目标约为6000亿美元。这与该公司 CEO阿尔特曼此前宣称的1.4万亿美元基础设施投入承诺相比大幅减少，引发了关于AI投资是否会大幅 缩减的激烈讨论。 需要留意的是，这两组数据并无直接的可比性。1.4万亿美元计划是从2025年至2033年为期8年的长期承 诺，覆盖人工智能全栈基础设施（computing infrastructure），既涵盖算力硬件，也包含数据中心、能源 等所有方面的开支；而6000亿美元计划，周期缩减至2025年至2030年的6年，仅聚焦于算力（total compute spend）。 虽然无法直接得出AI投资规模"腰斩"的结论，但OpenAI显然对其"烧钱"计划做出了调整。这释放出一 个明确的信号：将聚焦于算力这一核心要素，提升基座大模型的能力，巩固技术优势以应对竞争，进而 获取可持续的财务收益。 其一，投资规模需与财务、融资及上市计划相匹配。自具有划时代意义的GPT-3.5发布以来，AI大模型 已迅猛发展近三年。当前，资本市场对大模型企业的关注点已从模型的先进性转变为投入产出比。不久 前，IBM针对2000名企业高管 ...

其一，投资规模需与财务、融资及上市计划相匹配。自具有划时代意义的GPT-3.5发布以来，AI大模型 已迅猛发展近三年。当前，资本市场对大模型企业的关注点已从模型的先进性转变为投入产出比。不久 前，IBM针对2000名企业高管开展了一项关于2030年AI期望的调查，结果显示，79%的人预计AI将显著 提高其收入，但仅有24%的人能明确知晓这些收入的来源。也就是说，AI对企业效益的提升目前仍停留 在"想象层面"，而非实际的收益。 公开数据显示，OpenAI在2025年的收入为130亿美元，但现金亏损高达80亿美元，这种"高收入、高亏 损"的模式难以持续。1.4万亿美元的投资计划与当前的财务状况脱节，引发了投资者对其资金链的质 疑，进而阻碍了融资与上市进程。 目前，OpenAI正在推进千亿美元级别的融资，投后估值有望突破8500亿美元，同时也在筹备于2026年 实现IPO（首次公开募股）。6000亿美元的聚焦型计划，能够让投资者清晰地将算力投入与远期收入联 系起来，减少不确定性，增强融资的吸引力。 据媒体报道，OpenAI近日向投资者透露，到2030年的总算力支出目标约为6000亿美元。这与该公司 CEO阿尔特曼此 ...

智通财经APP获悉，国金证券发布研报称，字节跳动推出的AI视频生成模型Seedance2.0火爆出圈，大幅 降低了高质量视频内容的创作门槛，或成为AI影视发展的重要节点。元宝、千问等大模型新春活动加 速入口流量争夺。国内AI应用正加速向垂直领域渗透，形成技术与产业深度融合的新格局。在政策驱 动下，智能体技术成为核心增长引擎，已规模化应用于工业质检、医疗诊断等场景，推动AI从"感 知"向"决策"升级。展望2026年，将是AI应用从"技术验证"迈向"商业推广"的关键之年。 国金证券主要观点如下： AI应用产业趋势确立，2026年有望迎来双击 1)2026年应用是"必修课"。2026年无论是从算力的ROI诉求角度，还是互联网/软件/端侧公司做产品的角 度。 1)超级入口：大模型量收共振，流量枢纽地位确立。大模型已超越技术底座范畴，演进为AI时代具统治 力的流量入口。全球领军者商业化进程呈非线性加速：OpenAI ARR预计2025年底突破200亿美元，远 期锚定"数千亿美元"量级；Google Gemini 2025年10月单月Token使用量达1.3千万亿个，呈指数级跃 升；Anthropic Claude AR ...

Core Insights - Generative AI is transitioning from "technically feasible" to "value feasible," entering a critical validation period for its practical application [1] Group 1: Consensus on AI Implementation - The bottleneck for AI deployment has shifted from the supply side to the demand side, with 88% of surveyed medium to large enterprises using AI in at least one business function, but only one-third achieving large-scale deployment [2] - The high customization requirement for AI solutions poses challenges, with about 70% needing customization and only 30% being standardizable, leading to difficulties in monetization and product capability accumulation [3] - The commercial model for AI applications remains unproven, with significant price competition pressures, particularly in the B2B sector, where API prices have dropped by 95%-99% since 2024 [4][5] Group 2: Divergences in AI Development - The extent to which intelligent agents can evolve by 2026 is uncertain, with significant advancements in task completion capabilities but still facing challenges in high-risk scenarios like finance and healthcare [6] - The competition for computing power is shifting from training to inference, with a focus on optimizing inference efficiency and cost, which will redefine market dynamics for chip manufacturers and cloud service providers [7][8] - The evolution of the AI ecosystem is complex, with debates on data flow rules and privacy concerns, indicating a need for a new regulatory framework to address these challenges [9][10] Group 3: Recommendations for Future Actions - Companies should prioritize application scenarios that demonstrate real value, focusing on areas with good data foundations and manageable risks [11] - Standardization efforts are needed to reduce customization costs and foster replicable product capabilities, particularly in key industries [12] - High-risk AI applications require robust quality supervision and safety audits to mitigate systemic uncertainties [13] - Encouraging diverse commercial models is essential to avoid detrimental price competition and foster long-term industry health [14]

Core Insights - Comscore reports significant growth in mobile and desktop visitation to AI assistant destinations, with mobile unique visitors reaching 54.3 million, a 107% increase year-over-year, while desktop unique visitors grew to 83.0 million, an 18% increase year-over-year [1][2]. Mobile and Desktop Growth - Mobile visitation to AI assistants has surged, indicating a shift towards mobile as the primary access point for these services [3][7]. - Desktop visitation growth is strong but more concentrated, particularly with ChatGPT leading the gains [7]. AI Assistant Rankings - OpenAI ChatGPT leads with 34.5 million unique visitors, reflecting an 84% year-over-year increase, followed by Google Gemini at 12.8 million (+137% YoY) and Microsoft Copilot at 10.6 million (+246% YoY) [6]. - In December 2025, OpenAI ChatGPT reached 56.4 million unique visitors, while Google Gemini saw a remarkable increase to 12.3 million (+648% YoY) [6]. Industry Trends - The rapid integration of AI experiences into mobile applications is driving sustained audience growth, with clear leaders emerging across devices [3]. - The data suggests that AI assistants are becoming essential tools in everyday life, highlighting the importance of understanding consumer behavior and adoption trends in 2026 [3].

策略研究 | 证券研究报告 — 点评报告 2026 年 1 月 28 日 策略点评 Clawdbot 重塑个人 AI 助理新范式 Clawdbot 以创新设计推动 AI 向主动执行进化，凸显 AI agent 投资潜力。 中银国际证券股份有限公司 具备证券投资咨询业务资格 策略研究 证券分析师：王君 (8610)66229061 jun.wang@bocichina.com 证券投资咨询业务证书编号：S1300519060003 尽管 Clawdbot 仍面临一定的商业化瓶颈，但其生态位价值意义重大，关注 AI agent 投资机会。 Clawdbot 建立在开源大模型和开源框架之下，这表明未来 AI 代理生态中，核心价值可能从"模型本 身"上移至"代理框架"和"应用层"，而这一雏形产品受到市场和用户的广泛关注表明此类个人 AI 助 手在 AI 应用生态位中具备较高商业价值，也侧面反映了高价值量 AI agent 的增长潜力，关注 AI agent 及相关产业链如云服务、算力、存储、大模型厂商等投资机会。 风险提示 1）政策落地不及预期，宏观经济波动超预期。2）市场波动风险。3）海外经济超预期衰退、流动性 风 ...

以下文章来源于网易科技 ，作者小小 网易科技 . 网易科技频道，有态度的科技门户。 来源丨网易科技（ tech_163 ） 作者丨 小小 编辑丨 王凤枝 "我们已经投进去的几千亿美元，只是道开胃菜。要把这套架构真正搭起来，后面还得再砸几万亿美 元。" 1月21日，在达沃斯的聚光灯下，英伟达掌门人黄仁勋与贝莱德（BlackRock）掌门人拉里·芬克 （Larry Fink）展开了一场长达半小时的巅峰对话。面对华尔街最关心的"资金黑洞"问题，黄仁勋抛 出了上述论断。 就在全世界都在担忧 "AI是不是过热了"的时候，他给出了一个截然不同的定义： "我们遇上的不是什 么AI泡沫，而是人类历史上最大的一场基建热潮。" 目前英伟达的 GPU依然一芯难求，就连几年前老款型号的租金都在飞涨。 为了解释这笔钱到底要花在哪，黄仁勋将整个 AI体系比作一个庞大的"五层蛋糕"：最底层是能源， 往上依次是芯片、云服务、AI模型，而最上面那层才是各行各业的具体应用。 要把这块蛋糕每一层都 填满，现有的投入确实仅仅是个开始。 而对于 "AI抢人饭碗"这个引发全球焦虑的话题，黄仁勋觉得大家可能都担心反了： "AI非但没有制造 失业，反而正在 ...

Core Insights - Despite the ongoing rise of artificial intelligence technology, the large-scale deployment of enterprise AI applications is progressing slowly, with only 17% of surveyed companies achieving large-scale production, a slight increase from 14% in March 2023 [1] Group 1: Market Leaders and Trends - Microsoft, OpenAI, and Nvidia continue to dominate the enterprise AI market, with Microsoft Azure leading in cloud infrastructure and OpenAI's GPT models occupying three of the top five spots in large language models [3] - Microsoft M365 Copilot remains the preferred enterprise AI tool, although OpenAI's ChatGPT commercial version is rapidly closing the gap [3][10] - The survey indicates a significant preference for self-built AI applications, with 60% of companies opting for a hybrid model of self-building or fully self-building, compared to only 34% relying entirely on third-party software vendors [4][5] Group 2: Deployment Challenges and Workforce Impact - The main challenges for AI deployment include unclear ROI, cited by 59% of respondents, up from 50% in March 2023, followed by compliance concerns (45%) and a lack of internal expertise (43%) [3] - AI applications are not leading to mass layoffs; 40% of companies expect AI to drive employee growth, while only 31% anticipate a reduction in workforce [3] Group 3: AI Agent Deployment and Market Outlook - The deployment of AI agents is still in its early stages, with only 5% of companies achieving large-scale production, while 71% are in pilot or small-scale production phases [9] - The slow progress in AI agent deployment supports the view that AI agents will not significantly replace human labor in the short term, and investors should maintain realistic revenue expectations for related technology suppliers [9] Group 4: Data Infrastructure and Spending Trends - There is a notable increase in demand for data infrastructure driven by AI projects, with an average of 52% of respondents expecting to increase spending across various data software categories [12] - The cloud data warehouse sector is expected to benefit significantly, with 69% of respondents anticipating increased spending, and 25% expecting substantial growth [12][14] - In contrast, the operational database sector shows a more moderate AI-driven spending increase, with only 10% of respondents expecting significant growth [14]

Core Insights - The article discusses the emergence of intelligent agents based on language models that possess reasoning, planning, and action capabilities, highlighting a new paper from Google that establishes quantitative scaling principles for these agents [1][7]. Group 1: Scaling Principles - Google defines scaling in terms of the interaction between the number of agents, collaboration structure, model capabilities, and task attributes [3]. - The research evaluated four benchmark tests: Finance-Agent, BrowseComp-Plus, PlanCraft, and Workbench, using five typical agent architectures and three LLM families [4][5]. Group 2: Experimental Findings - The study involved 180 controlled experiments across various scenarios, demonstrating that the effectiveness of multi-agent collaboration varies significantly depending on the task [10][11]. - In finance tasks, centralized architectures can enhance performance by 80.9%, while in game planning tasks, multi-agent systems can lead to performance drops of 39% to 70% due to high communication costs [14]. Group 3: Factors Affecting Agent Performance - Three core factors hindering agent scalability were identified: 1. The more tools required, the harder collaboration becomes, leading to inefficiencies [15]. 2. If a single agent is already sufficiently capable, adding more agents can yield negative returns [16]. 3. Without a centralized commander, errors can amplify significantly, highlighting the importance of architectural design [18]. Group 4: Model Characteristics - Different models exhibit distinct collaborative characteristics: - Google Gemini excels in hierarchical management, showing a 164.3% performance increase in centralized structures [19]. - OpenAI GPT performs best in hybrid architectures, leveraging complex communication effectively [20]. - Anthropic Claude is sensitive to communication complexity and performs best in simple centralized structures [20]. Group 5: Predictive Model Development - Google derived a predictive model based on efficiency, overhead, and error amplification, achieving an 87% accuracy rate in predicting the best architecture for unseen tasks [22][25]. - This marks a transition from an era of "alchemy" in agent system design to a more calculable and predictable "chemistry" era [26].

Core Insights - The North American AI narrative has evolved over the past three years, with a shift from FOMO-driven capital expenditures (Capex) to a focus on return on investment (ROI) as the market matures [3][5][8] - The total Capex for major cloud and internet companies is projected to reach $554 billion in FY26, representing a year-over-year increase of 38% [3][18] - The top three AI model providers are narrowing the performance gap, with Anthropic focusing on B-end programming and Google’s Gemini gaining market share [3][25][27] Cloud Computing - Capex in cloud computing is expected to continue expanding in 2026, but ROI is anticipated to vary among companies [24][46] - Google Cloud (GCP) and Amazon AWS are expected to accelerate growth driven by demand from Anthropic and Gemini [15][18] - The Capex of major cloud providers is projected to be $554 billion in FY26, with Google showing the healthiest Capex to operating cash flow ratio [18][19] AI Models - The competitive landscape among AI models is diversifying, with a focus on commercial acceleration [24][46] - Anthropic is expected to achieve positive cash flow by 2027, with a revenue forecast of $70 billion by 2028 [34][45] - OpenAI's revenue strategy balances B-end and C-end markets, with a valuation of $500 billion as of October 2025 [39][40] AI Applications - AI applications are witnessing rapid commercialization, particularly in programming and advertising, with expected revenues in the hundreds of billions [51][54] - AI video applications are nearing a commercialization tipping point, supported by increased computational power [54][55] - The enterprise AI sector is expected to accelerate in 2026 as foundational work in data governance and workflow integration is completed [54] AI Computing Power - The focus of competition is shifting towards developing ecosystems, with significant advancements in hardware and software performance [3][24] - The supply of AI computing power is diversifying, with Google’s TPU hardware gaining traction and AMD and Amazon's Tranium ecosystems maturing [3][24] AI Networks - The network architecture is transitioning from scale-out to scale-up, with a focus on optical communication and power supply solutions [3][24] - 2026 is anticipated to be a critical year for the explosion of silicon photonics solutions and the introduction of CPO networks [3][24] Key Company Valuations - Recommended stocks include Google and Amazon in the AI-internet and cloud computing sectors, with a focus on Snowflake and ServiceNow in software [3][24] - In the semiconductor space, Broadcom is highlighted, with Nvidia and AMD as companies to watch [3][24]