Mobile usage climbs +107% YoY to 54.3M unique visitors; desktop grows +18% YoY to 83.0M as ChatGPT drives PC gainsRESTON, Va., Jan. 29, 2026 (GLOBE NEWSWIRE) -- Comscore (Nasdaq: SCOR), a global leader in measuring and analyzing consumer behaviors, today released new data showing continued momentum for AI assistant destinations across devices heading into 2026. Using Comscore’s cross-platform measurement of unique visitors, total mobile visitation to the leading AI assistant destinations reached 54.3M in De ...

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]

Core Insights - Amazon is struggling to attract top AI talent due to its unique compensation structure, reputation for being behind in AI, and strict return-to-office policies [1][4][7] Compensation Challenges - The internal document highlights that Amazon's fixed salary ranges and egalitarian pay philosophy result in lower compensation compared to competitors, making it less attractive for top tech talent [4][10] - The company has not increased salary ranges for key positions in recent years, which has hindered its ability to recruit top AI talent [4] - Amazon's stock vesting plan, which defers more compensation to later years, is less appealing to new hires, including executives who often do not receive cash bonuses [4] Perception of AI Lag - Amazon is perceived as lagging in the AI field, particularly in generative AI, which has intensified competition for specialized talent [5][6] - Reports indicate that Amazon's engineer retention rate is lower than that of competitors like Meta, OpenAI, and Anthropic [5] - Concerns about Amazon's market share being eroded by competitors were raised during a recent earnings call, leading to a decline in the company's stock price [6] Return-to-Office Policy - Amazon's strict return-to-office policy has created logistical challenges and limited its ability to recruit high-demand talent with generative AI skills [7][9] - The policy requires employees to relocate to designated office centers, which has led to job offer rejections from potential candidates [8][9] - Reports indicate that Oracle has successfully recruited over 600 employees from Amazon in the past two years, largely due to this strict policy [9] Recruitment Strategy Adjustments - In response to these challenges, Amazon plans to optimize its compensation and location strategies and establish specialized recruitment teams for generative AI [6][8] - The company is exploring the possibility of offering more flexible work location positions to attract talent [7][8]

Core Insights - OpenAI has shifted its strategy by re-entering the open-source domain with the release of two models, gpt-oss-120b and gpt-oss-20b, marking a significant change from its previous closed-source approach [2][5][17] - The open-source models are designed to cater to different use cases, with gpt-oss-120b focusing on high inference needs and gpt-oss-20b aimed at localized applications [8][15] - OpenAI's decision to open-source these models is seen as a response to increasing competition in the AI space, particularly from companies like Anthropic and Google, which are gaining market share in the enterprise sector [3][22] OpenAI's Market Position - As of August, ChatGPT boasts 700 million weekly active users, a fourfold increase year-on-year, with daily message volume exceeding 3 billion [3] - OpenAI's paid user base has grown from 3 million to 5 million, with Pro and enterprise users contributing over 60% of revenue [3] - Despite its consumer market dominance, OpenAI faces challenges in the enterprise market, where competitors are encroaching on its share [3][22] Open-Source Strategy - OpenAI's initial open-source philosophy has evolved, with a notable shift to a closed-source model in 2020, which drew criticism for deviating from its mission to benefit humanity [5][16] - The newly released models follow a permissive Apache 2.0 license, allowing for extensive commercial and research use, which contrasts with the previous API-dependent model [14][15] - The open-source models are expected to enhance OpenAI's market influence, as they can now be deployed on major cloud platforms like Amazon AWS, allowing for broader accessibility [17][19] Competitive Landscape - The rise of open-source models has led to a more competitive environment, with companies like DeepSeek and Alibaba's Qwen series gaining traction in the market [18][22] - OpenAI's re-entry into open-source is anticipated to reshape the competitive dynamics, as more companies adopt a hybrid approach of open and closed models [17][22] - The trend indicates that open-source models are becoming increasingly viable, with the performance gap between open-source and closed-source models narrowing [17][18] Financial Implications - OpenAI is projected to achieve an annual recurring revenue (ARR) of $12 billion by the end of July, significantly outpacing its closest competitor, Anthropic, which is expected to reach $5 billion [19][22] - The financial model of open-source remains challenging, as companies may hesitate to adopt open-source strategies due to the lack of direct revenue generation from model usage [19][22]

Core Insights - The article highlights the dual nature of AI in the workplace, where it is seen as a tool for efficiency but also as a cover for ongoing layoffs, creating a disparity between corporate cost savings and the actual financial burden of maintaining AI systems [1][2][3] Group 1: AI Implementation and Challenges - Many companies are increasingly adopting AI tools across various operations, with 78% of businesses using AI in at least one area as of last year, a significant rise from 55% in 2023 [3] - Despite the widespread adoption, the average cost reduction achieved is less than 10%, and revenue increases are also below 5%, indicating a gap between AI usage and its effectiveness [3] - Companies are facing challenges with AI-generated content, leading to additional costs for revisions and corrections, as seen in the experiences of freelance writers and digital marketing firms [2] Group 2: Workforce Impact and Future Outlook - AI is predicted to potentially replace up to 50% of entry-level jobs within the next 1 to 5 years, which could drive the unemployment rate in the U.S. to between 10% and 20% [1] - The introduction of AI in customer service has resulted in various issues, including miscommunication and increased workload for human agents who must manage AI errors [2] - Companies are beginning to recognize the risks associated with AI, with Amazon hiring a manager specifically for AGI risk management to address technical and societal risks [3]

Group 1 - The core viewpoint is that while AI is seen as a tool for efficiency and cost-saving by companies, it often leads to increased expenses in managing AI-related issues and correcting its mistakes [1][2][4][7] - Many companies are experiencing a rise in costs associated with maintaining AI systems, including content review and compliance, which can exceed initial budget expectations [1][4][8] - AI's integration into various business functions has not resulted in significant cost reductions or revenue increases, with average cost savings reported at less than 10% and revenue growth under 5% [7] Group 2 - The emergence of new job roles focused on correcting AI errors indicates a shift in workforce dynamics, as companies must now invest in human resources to manage AI shortcomings [1][8] - AI's application in customer service has revealed numerous challenges, including miscommunication and increased pressure on human staff to rectify AI errors [4][8] - The narrative of AI replacing human jobs is becoming a double-edged sword, as consumer backlash against AI-driven services is growing, leading companies to reconsider their reliance on AI [8][9]