Zoom Communications Q2 FY26 Earnings August 21, 2025 © 2025 Zoom Communications, Inc. Use of non-GAAP financial measures In addition to the financials presented in accordance with U.S. generally accepted accounting principles ("GAAP"), this presentation includes the following non-GAAP metrics: Revenue in Constant Currency, non-GAAP gross profit, non-GAAP gross margin, non-GAAP R&D expense, non-GAAP S&M expense, non-GAAP G&A expense, non-GAAP operating margin, non-GAAP income from operations, non-GAAP net in ...

Summary of Key Points from the Conference Call Industry Overview - The report focuses on the state of Generative AI (GenAI) in business as of 2025, highlighting a significant divide in the effectiveness of AI implementations across organizations [10][11]. Core Findings - Despite an investment of $30–40 billion in enterprise GenAI, 95% of organizations report no return on investment, leading to the term "GenAI Divide" [10]. - Only 5% of integrated AI pilots are generating substantial value, while the majority fail to impact profit and loss (P&L) [10]. - High adoption rates of tools like ChatGPT and Copilot (over 80% explored, nearly 40% deployed) do not translate to improved P&L performance [10]. - The divide is attributed not to model quality or regulation but to the approach taken by organizations [10][11]. Patterns Defining the GenAI Divide - The primary barrier to scaling AI is learning; most systems lack the ability to retain feedback and adapt to context [11]. - Successful organizations demand process-specific customization and evaluate tools based on business outcomes rather than software benchmarks [12]. - Limited disruption is observed, with only 2 out of 8 major sectors showing meaningful structural change [13]. - Enterprises lead in pilot volume but lag in scaling up, with 60% evaluating enterprise-grade systems but only 20% reaching pilot stage [13]. Implementation Insights - Organizations that have crossed the GenAI Divide report selective workforce impacts, particularly in customer support and administrative functions, with measurable savings in back-office operations [15]. - The highest-performing organizations see improved customer retention and sales conversion through automated outreach systems [15]. Investment Patterns - Investment allocation reveals a bias towards sales and marketing functions, capturing approximately 70% of AI budgets, despite back-office automation often yielding better ROI [47][48]. - This bias perpetuates the GenAI Divide by focusing resources on visible but less transformative use cases [53]. Barriers to Adoption - The learning gap is the primary factor keeping organizations on the wrong side of the GenAI Divide, with tools that do not adapt or integrate well into workflows facing resistance [55][57]. - Users prefer consumer-grade tools like ChatGPT for simple tasks but abandon them for critical work due to lack of memory and adaptability [70]. Shadow AI Economy - A "shadow AI economy" is emerging, where employees use personal AI tools to automate tasks, often achieving better ROI than formal initiatives [40][41]. - Over 90% of surveyed employees reported using personal AI tools regularly, while only 40% of companies purchased official LLM subscriptions [43][44]. Future Outlook - The window for crossing the GenAI Divide is narrowing as enterprises increasingly demand systems that adapt over time [102]. - Startups that build adaptive agents capable of learning from feedback and integrating deeply into workflows are likely to succeed [104][105]. Conclusion - The GenAI Divide highlights a critical challenge in AI adoption, where high investment does not equate to transformation. Organizations must focus on learning-capable systems and address the barriers to integration to realize the full potential of AI technologies [10][12][55].

Forward-Looking Statements Investor Presentation Second Quarter 2025 Earnings August 6, 2025 August 2025 / © 2025 Remitly Inc. Disclosures This presentation contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. All statements other than statements of historical fact are forward-looking statements. These statements include, but are not limited to, statements regarding our future events or our future results of operations and financial position, includ ...

Summary of European AI & Semis Symposium Industry Overview - The symposium focused on the **semiconductor** and **artificial intelligence (AI)** industries, featuring over **500 participants** and **25+ speakers** from various sectors including academia, corporate, and private companies [1][2] Key Insights - **Cost Efficiency of Generative AI**: The capabilities of Generative AI are being validated as cost efficiency improves, with some large language models being up to **400x cheaper** for training and inference compared to earlier versions [4][5] - **Productivity Gains**: AI is driving measurable productivity gains, with claims that AI can equate to a workforce with **6x the bandwidth** in software development tasks [4][5] - **AI Workload Management**: At Salesforce, AI now handles up to **50% of workloads**, while at Google, AI accounts for **25% of new code lines** [4][5] - **Advancements in AI Capabilities**: OpenAI and DeepMind's models have achieved gold-medal scores at the International Mathematical Olympiad, outperforming most human participants [4][5] - **Multi-modality in AI**: AI is evolving to execute algorithms and generate various sensory inputs and outputs, expanding its applications across sectors like healthcare, retail, and industrials [5][6] Sector-Specific Applications - **Healthcare**: AI is significantly impacting healthcare, with **95% of certain cancer procedures** in the US being performed robotically. AI-linked mirrors can detect **90% of known diseases** [7][8] - **Entertainment**: Netflix reported a **10x acceleration** in production through the integration of Generative AI into its visual effects pipeline [7][8] - **Retail**: AI-powered robots in retail can perform stock-taking and predictive analysis, achieving a **3x to 4x ROI** within **3 to 4 months** [7][8] - **Education Technology (EdTech)**: AI created **1,200 learning modules** of **35 hours each** in just three days, enhancing consumer engagement by **3x** through AI-driven media solutions [7][8] Challenges and Solutions - **Power Consumption**: Data centers may require as much power as **New York City**, necessitating new power generation sources and more efficient distribution grids [6][9] - **Guardrails for AI Content**: The need for human reviewers and technical safeguards is emphasized to mitigate risks associated with AI-generated content [9][10] - **Transparency and Bias Mitigation**: Mechanisms to explain AI's decision-making processes and avoid bias are crucial for reliability [9][10] Technological Innovations - **Photonics and Quantum Computing**: These technologies are seen as transformative, with potential for **70% reduction** in energy consumption for certain accelerators [9][10] - **EUV Lithography Tools**: ASML's tools are essential for improving yield and reducing cycle time in semiconductor production, supporting AI training with increased power and reduced latency [9][10] - **Hybrid Bonding**: This technology offers advantages in chip performance and thermal efficiency, critical for data centers [9][10] Company Highlights - **Logitech**: Leveraging thousands of autonomous robots for continuous operation, resulting in clear cost benefits [7][8] - **Infineon**: Positioned positively due to its role in providing AI power semiconductors with a reliable track record [9][10] - **Nebius**: Focused on cost and energy-efficient GPUs, indicating a strong market position [9][10] Conclusion The symposium underscored the rapid advancements in AI and semiconductor technologies, highlighting both the opportunities for productivity gains and the challenges that need to be addressed for sustainable growth in these sectors [1][2][8]

Group 1 - The core viewpoint of the article highlights the challenges and opportunities for Chinese companies going global in 2025, emphasizing a shift from anxiety to proactive responses in the face of international uncertainties [3][5] - A report indicates that 81% of global respondents are open to considering new brands, with the figure rising to 85% in the U.S., suggesting a growing acceptance of new value propositions beyond just low prices [5][19] - The marketing landscape is evolving with AI transforming search from keyword-based to intent-based understanding, allowing for more nuanced consumer engagement [7][10] Group 2 - Google introduced three AI-driven search experiences: AI Overviews, AI Mode, and Agentic AI, which enhance user interaction and advertising effectiveness [10][11] - The Power Pack toolset, including PMax and AI Max, allows advertisers to automate audience targeting and optimize campaigns based on business goals, leading to improved conversion rates [11][14] - Case studies, such as AliExpress, demonstrate significant increases in conversion rates and return on ad spend through AI-driven insights and advertising strategies [13][14] Group 3 - YouTube is positioned as a unique platform for brands to connect with global audiences, emphasizing the importance of building emotional connections over mere visibility [17][19] - The shift from product-focused to brand-focused strategies among Chinese companies reflects a deeper understanding of the need for long-term brand equity and consumer loyalty [24][25] - Trust and authenticity are critical in creator partnerships, as highlighted by Mr. Beast's approach to brand collaborations, which emphasizes genuine product endorsement over monetary incentives [26][28]

Core Insights - The report outlines the practical guide for building Agentic AI applications, emphasizing its role as an autonomous software system based on large language models (LLMs) that can automate complex tasks through perception, reasoning, planning, and tool invocation [1][5]. Group 1: Agentic AI Technology Architecture and Key Technologies - Agentic AI has evolved from rule-based engines to goal-oriented architectures, with core capabilities including natural language understanding, autonomous planning, and tool integration [3][5]. - The technology architecture consists of single-agent systems for simple tasks and multi-agent systems for complex tasks, utilizing protocols for agent communication and tool integration [3][4]. Group 2: Building Solutions and Scenario Adaptation - Amazon Web Services offers three types of building solutions: dedicated agents for specific tasks, fully managed agent services, and completely self-built agents, allowing enterprises to choose based on their needs for task certainty and flexibility [1][4]. - The report highlights various application scenarios, such as optimizing ERP systems and automating document processing, showcasing the effectiveness of Agentic AI in reducing manual operations and improving response times [4][5]. Group 3: Industry Applications and Value Validation - Case studies include Kingdee International's ERP system optimization and Formula 1's root cause analysis acceleration, demonstrating the practical benefits of Agentic AI in different sectors [2][4]. - The manufacturing and financial sectors are also highlighted for their use of Agentic AI in automating contract processing and generating visual reports, respectively, which enhances decision-making efficiency [4][5]. Group 4: Future Trends and Challenges - The report discusses future trends indicating that Agentic AI will penetrate various fields, driven by advancements in model capabilities and standardized protocols [5]. - Challenges include ensuring the stability of planning capabilities, improving multi-agent collaboration efficiency, and addressing the "hallucination" problem in output credibility [4][5].

Core Insights - Walmart is reengineering its global supply chain with a focus on automation and real-time artificial intelligence (AI) [1] - The company is implementing proven U.S. technologies globally to enhance operational efficiency [2] - Walmart's innovations include self-healing inventory systems and agentic AI, which allow for quick adaptation to local needs while maintaining a unified tech stack [2] Supply Chain Innovations - Walmart's perishable distribution center in Coyol, Costa Rica, utilizes predictive warehouse and transportation management systems to optimize delivery routes and align orders with store demand [2] - The "Self-Healing Inventory" system in Mexico City automatically reroutes supplies in case of overstocks, saving the company over $55 million [3] - The company is also cutting out the middleman in beef sourcing by opening its first proprietary meat-processing facility, indicating a trend towards greater control over supply chains [5] Competitive Landscape - The rivalry between Walmart and Amazon is intensifying as both companies invest billions in AI, warehouse robotics, predictive logistics, and generative tools to enhance operations and reduce labor costs [4] - The competition is shifting from a simple eCommerce versus big-box store dynamic to a focus on whose algorithms and data infrastructure can more efficiently manage the movement of goods [5] - Companies that own their logistics and can optimize costs at every stage of the supply chain will have a competitive advantage, especially in the context of inflation affecting consumer behavior [6]

Core Insights - C3.ai, Inc. is focusing on the Agentic AI segment, which is becoming a significant revenue contributor with an annualized run rate of $60 million in fiscal Q4 2025 [1][8] - The company holds an early patent on agentic AI, differentiating itself in a competitive market by providing production-grade AI agents for autonomous decision-making across over 100 use cases [2][8] - C3.ai is leveraging strategic partnerships, including a new alliance with PwC, to enhance the reach of its Agentic AI solutions in various sectors [3] Company Developments - The Agentic AI business is considered undervalued, with management suggesting it could achieve a valuation multiple exceeding the current market cap if separated [4] - C3.ai has established customer references with major companies such as Shell, Dow, Cargill, and the U.S. Navy, indicating strong traction in enterprise use cases [4] Competitive Landscape - Cadence Design Systems is integrating agentic AI into semiconductor design, reporting that over 50% of customer chip designs now utilize AI-enabled workflows [5] - SoundHound AI is focusing on conversational AI with its Amelia 7.0 platform, which is deployed in various sectors but remains consumer-centric [6] Market Performance - C3.ai shares have increased by 39.7% over the past three months, outperforming the industry growth of 9.5% [7] - The company trades at a forward price-to-sales ratio of 7.43X, significantly lower than the industry average of 18.24X, indicating potential for valuation improvement [10] Earnings Estimates - The Zacks Consensus Estimate for C3.ai's fiscal 2026 and 2027 earnings per share (EPS) suggests year-over-year growth of 9.8% and 56.9%, respectively, with recent upward revisions in EPS estimates [11]

Industry Overview - The cybersecurity industry is poised for robust growth in the second half of 2025 and beyond, driven by increasing complexity in digital systems, greater use of digitized resources, and rising costs associated with cybersecurity attacks [1][2] - The cost of cybercrime is projected to exceed $10.5 trillion in 2025, creating a significant incentive for businesses to invest in cybersecurity solutions [2] Company Highlights - Palo Alto Networks is recognized as the industry leader in terms of revenue and market capitalization, holding a 1.2% share of the fragmented market [5] - The company's strategy for 2025 focuses on platformization, which aims to unify its services into a more user-friendly system, contributing to its high-double-digit growth rate [5] - Analysts at Wedbush have ranked Palo Alto Networks as a top pick, citing its strong growth and profitability exceeding estimates [5][6] Analyst Sentiment - Analysts express a generally bullish outlook for Palo Alto Networks, with a Moderate Buy rating and a price target of $208.00, although some caution is noted in the data [6] - Zscaler is highlighted as another growth stock with solid performance, driven by its new Z-Flex program, which enhances client flexibility and service penetration [8] - CrowdStrike is also noted as a potential winner for the second half of the year, but its stock price has already advanced significantly, limiting further upside potential [11][12]

Group 1 - The core viewpoint emphasizes the increasing importance of data infrastructure in driving business innovation and ensuring compliance in the context of digital transformation accelerated by AI and cloud technologies [1] - According to Gartner, global public cloud service end-user spending is projected to reach $723.4 billion by 2025, reflecting a 21.5% increase from 2024, with IaaS and PaaS expected to grow by 24.8% and 21.6% respectively [1] - The rise of data privacy and security regulations, such as China's PIPL and Europe's GDPR, is significantly impacting multinational companies' data strategies, making data localization a critical issue [1] Group 2 - Pfizer's digital delivery head in China highlighted the necessity of a highly scalable, secure, and stable cloud infrastructure as a fundamental consensus for modern data strategies [2] - The challenges of cross-border data transmission and management have become a "lifeline" for companies operating in specific markets due to increasing data sovereignty awareness and regulatory developments [2] - Pfizer recognizes that data localization is not only a regulatory requirement but also essential for stable development in the Chinese market, allowing better adaptation to rapid market changes [2] Group 3 - Pfizer's core needs for data infrastructure include the necessity for global collaboration, agility to respond to market changes, and strong compliance capabilities [3] - The partnership with Amazon Web Services enables Pfizer to build a comprehensive digital ecosystem, enhancing decision-making and business operations through AI capabilities [3] - Pfizer's collaboration with Amazon in biopharmaceutical R&D has led to significant cost savings, estimated between $750 million and $1 billion annually, by leveraging data analysis and machine learning [4] Group 4 - Pfizer aims for a modern data strategy that integrates various aspects, including personnel, technology, and processes, to drive business innovation and process reengineering [4] - Continuous investment in data infrastructure and strategy, combined with AI empowerment, is expected to enhance R&D efficiency, reduce operational costs, and improve market responsiveness for pharmaceutical companies [5] - This approach is anticipated to serve as a reference for other multinational companies looking to implement data strategies in China [5]