Morgan Brennan is at Goldman Sachs's Communicopia Technology Conference out in San Francisco and she's joined by the chairman and CEO of Service Now, Bill McDermott. Morgan, I'll send things down to you. All right, Dom, thank you.And Bill, it's great to be sitting here with you at this conference. Thank you, Morgan. Great to be with you.What a day to be talking about AI and and tech more broadly. Obviously, this monster move in Oracle. Um, speaking to the capacity constraints we're seeing in in AI infrastru ...

Core Insights - Service Now is positioned as a leader in the agentic AI revolution, emphasizing the need for machines to enhance productivity in enterprises [2][4] - A significant challenge in digital transformation is the lack of integration, with only 25% of companies achieving a positive ROI and just 5% benefiting from agentic AI [3][4] - Service Now's platform offers a customizable, single-tenant solution that integrates various data sources and cloud services, facilitating business transformation [6][7] AI Capabilities - The recent Zurich release introduced 1,200 new agentic AI capabilities, enhancing functionalities such as employee onboarding and data security compliance [6][7] - Autonomous business processes are a key feature, allowing for seamless operations across different functions and data sources [7][8] - The platform can autonomously manage tasks like credit card fraud prevention, showcasing the practical applications of AI in enhancing productivity [8][10] Market Position and Strategy - Service Now has been a first mover in the AI space, collaborating with Nvidia and securing significant contracts, including a deal with the U.S. government [9][10] - The demand for fewer platforms that deliver more functionality is high among CEOs and technical leaders, which aligns with Service Now's offerings [11] - The company has successfully reduced operational costs while increasing headcount, demonstrating the effectiveness of its AI platform in driving productivity and profitability [12]

Core Viewpoint - The demand for AI training and inference integrated machines is increasing as AI applications become more prevalent, with nearly a hundred manufacturers launching related products in the domestic market this year [1][2]. Market Demand and Trends - The sales of training and inference integrated machines have shown significant growth, with companies like Digital China and ZTE reporting strong market performance [2][7]. - The shift in demand from training to inference is driven by the lower barriers to entry for AI, particularly after the rise of DeepSeek, which has encouraged many small and medium enterprises to develop their own AI applications [2][3]. - The integrated machines are designed to support the entire process of large model training, inference, and application development, catering to the need for ready-to-use solutions [2][3]. Industry Applications - The integrated machines are being adopted across various sectors, including government, education, healthcare, and telecommunications, with ZTE reporting sales covering 15 industries [2][8]. - Specific applications include AI education platforms, medical diagnostic tools, and automotive design solutions, showcasing the versatility of these machines in different fields [7]. Future Market Outlook - The market for training and inference integrated machines is expected to grow significantly, with IDC predicting a 260% increase in the intelligent agent market by 2025 [4][5]. - The integration of AI capabilities into business processes is seen as essential for future development, with a focus on personalized solutions for various industries [5][6]. Challenges and Considerations - The deployment of integrated machines faces challenges related to the complexity of AI ecosystems and the need for deep integration of hardware and software [9][10]. - Companies are advised to enhance the scalability of integrated machines and incorporate cloud management systems to better support the development of AI models and applications [9][10].

[Music] Thank you. Thank you. And uh a very good afternoon to all of you.Voices of change. I'm going to speak about boundaries not balance. How to integrate work and life without burning out.And that's going to be the voice of change that I'm going to speak about by seeing how AI can come into this and make this happen. It's about work life balance. We are all aware that there are great industry stalwarts who spoke about the number of hours that is expected from the workforce and then happened a lot of deba ...

Core Insights - Cisco Systems, Inc. is recognized as a leading cybersecurity stock, particularly following its recent expansion of the Secure AI Factory in collaboration with Nvidia [1][2] - The new solution aims to enhance data extraction and retrieval for agentic AI workloads, integrating VAST Data's InsightEngine with Cisco AI PODs [2] - Cisco's advancements are positioned to meet the increasing demand for AI application performance enhancement, significantly reducing RAG pipeline latency and enabling real-time AI responses [3] Company Overview - Cisco provides a wide range of cybersecurity solutions through its Cisco Security Cloud platform, focusing on network, cloud, endpoint, and email security [4]

场景描述 特赞科技致力于开发企业级内容人工智能系统（Enterprise AI+Content Platform），打造了一站式创意内 容服务方案、企业数据管理等多款解决方案，可为企业更高效、优质的内容生产、管理、分发服务。该 公司认为企业客户在进行深度商业研讨时面临的4大痛点： 因此，特赞科技希望通过广泛可用的混合推理模型，依托Agentic AI突破4大挑战，为商业研究提供更多 可能。 2025年4月，特赞科技发布了首个商业研究Agent框架atypica.AI。该Agent框架使用了Amazon Bedrock Claude作为底层大模型之一。该Agent上线后，特赞科技发现应用了Agentic AI后，有效解决了以上挑 战。 解决方案 特赞科技将atypica.AI构建在基于亚马逊云科技的现代化、高可用云原生架构之上，并以Amazon Bedrock Claude作为核心AI引擎，在AI引擎、基础设施、容器化编排、数据库和安全性等方面获得了全 面技术支持，有效保障Agent运行的稳定性、连续性与安全性： 在此基础上，特赞科技还将Agent 能力延伸至： 成效 1.加速产品上市：Amazon Bedro ...

Core Viewpoint - The article discusses the recent transformation of AI startup you.com from a search engine to an AI infrastructure company following a $100 million Series C funding round. This shift aligns with the "product-driven infrastructure" strategy and reflects a broader trend of commercializing Agentic AI from laboratory settings [1]. Group 1: Agent Environment and Its Evolution - The focus of artificial intelligence is shifting from content creation to goal-driven, autonomous Agentic AI, driven by rapid advancements in the field [4]. - AI agents are expected to become the new interface for human-computer interaction, allowing users to issue commands in natural language without needing to write code [5]. - Companies like Cursor, Bolt, and Mercor have achieved significant revenue growth by leveraging unique intelligent agent products [6]. Group 2: Development of Agent Environment - The development of a suitable "Agent Environment" is crucial for modern intelligent applications, balancing the need for freedom in code execution with security and isolation [7]. - Companies like E2B and Modal Labs are providing secure, isolated cloud environments (sandboxes) for running AI-generated code [7]. - The concept of Agent Environment can be traced back to reinforcement learning, where it serves as a simulated space for training agents through trial and error [8]. Group 3: Real-World Application and Safety - As LLM-based agents advance, the requirements for their environments are evolving from training spaces to operational zones, necessitating safe access to real-world tools [9]. - Different types of agents require distinct environments, such as physical environments for robots and digital environments for virtual assistants [10].

Core Insights - Enterprises are cautious about adopting agentic artificial intelligence browsers due to concerns over technology autonomy and security [1][2] - The CEO of Palo Alto Networks, Nikesh Arora, emphasized the need for built-in controls for agentic browsers to ensure enterprise security [3] Company Developments - Palo Alto Networks announced a planned $25 billion acquisition of cybersecurity company CyberArk to enhance enterprise protection against credential theft [3][4] - The acquisition is expected to close during fiscal year 2026, pending regulatory approvals [4] Industry Trends - There is a resurgence in high-profile cybersecurity mergers and acquisitions, with Google acquiring cloud security firm Wiz for $32 billion [4][5] - Trust issues regarding accountability and compliance are causing firms to be cautious about agentic AI rollouts, with 80% of high-automation enterprises citing data security and privacy as their top concern [6][7]

Core Viewpoint - The article discusses the evolution and differentiation between AI Agents and Agentic AI, highlighting their respective roles in automating tasks and collaborating on complex objectives, with a focus on the advancements since the introduction of ChatGPT in November 2022 [2][10][57]. Group 1: Evolution of AI Technology - The emergence of ChatGPT in November 2022 marked a pivotal moment in AI development, leading to increased interest in AI Agents and Agentic AI [2][4]. - The historical context of AI Agents dates back to the 1970s with systems like MYCIN and DENDRAL, which were limited to rule-based operations without learning capabilities [10][11]. - The transition to AI Agents occurred with the introduction of frameworks like AutoGPT and BabyAGI in 2023, enabling these agents to autonomously complete multi-step tasks by integrating LLMs with external tools [12][13]. Group 2: Definition and Characteristics of AI Agents - AI Agents are defined as modular systems driven by LLMs and LIMs for task automation, addressing the limitations of traditional automation scripts [13][16]. - Three core features distinguish AI Agents: autonomy, task specificity, and reactivity [16][17]. - The dual-engine capability of LLMs and LIMs is essential for AI Agents, allowing them to operate independently and adapt to dynamic environments [17][21]. Group 3: Transition to Agentic AI - Agentic AI represents a shift from individual AI Agents to collaborative systems that can tackle complex tasks through multi-agent cooperation [24][27]. - The key difference between AI Agents and Agentic AI lies in the introduction of system-level intelligence, enabling broader autonomy and the management of multi-step tasks [27][29]. - Agentic AI systems utilize a coordination layer and shared memory to enhance collaboration and task management among multiple agents [33][36]. Group 4: Applications and Use Cases - The article outlines various applications of Agentic AI, including automated fund application writing, collaborative agricultural harvesting, and clinical decision support in healthcare [37][43]. - In these scenarios, Agentic AI systems demonstrate their ability to manage complex tasks efficiently through specialized agents working in unison [38][43]. Group 5: Challenges and Future Directions - The article identifies key challenges facing AI Agents and Agentic AI, including causal reasoning deficits, coordination bottlenecks, and the need for improved interpretability [48][50]. - Proposed solutions include enhancing retrieval-augmented generation (RAG), implementing causal modeling, and establishing governance frameworks to address ethical concerns [52][53]. - Future development paths for AI Agents and Agentic AI focus on scaling multi-agent collaboration, domain customization, and evolving into human collaborative partners [56][59].

Core Insights - Innodata (INOD) and Veritone (VERI) are both positioned in the rapidly growing AI data services sector, assisting companies in developing AI solutions [1] - Innodata has reported significant revenue growth and profitability, while Veritone is undergoing a strategic turnaround with improving financial metrics [2] Company Overview - Innodata is a 35-year-old data engineering firm specializing in high-quality training data and AI model support, benefiting from the generative AI trend [4] - Veritone has transitioned to a pure-play AI software company after divesting its non-AI media business, focusing on AI products and services [7] Financial Performance - Innodata's Q2 2025 revenues increased by 79% year-over-year to $58.4 million, with full-year 2025 organic revenue growth guidance raised to at least 45% [4][8] - Veritone's total revenues remained steady at $24 million in Q2 2025, with core AI software revenues growing over 45% year-over-year, driven by SaaS adoption [7][10] Strategic Positioning - Innodata is aligning with the "agentic AI" era, focusing on providing critical data for testing and improving AI models at scale [5] - Veritone is capitalizing on increasing public sector demand, with government revenue nearly doubling in Q2 2025, supported by a U.S. Air Force contract [9] Market Valuation - Innodata has a market cap of approximately $1.3 billion, with a forward 12-month price/sales (P/S) ratio of 4.64, indicating high growth expectations [16] - Veritone's market cap is around $152.6 million, with a forward 12-month P/S ratio of 1.23, suggesting a lower valuation compared to Innodata [17] Growth Outlook - Innodata's strong pipeline and cash balance of $59.8 million provide financial flexibility for continued growth, despite concentration risk from reliance on a few large clients [6] - Veritone's annual recurring revenue reached $62.6 million, with management expecting revenues of $108–115 million for 2025, reflecting a year-over-year growth of approximately 20% [10][12] Investment Considerations - Innodata is recognized for its explosive growth and profitability, but its high valuation may limit short-term upside [28] - Veritone, while still loss-making, shows potential for significant appreciation due to its low valuation and improving revenue growth [28]