Executive Summary - Despite $30–40 billion in enterprise investment into GenAI, 95% of organizations are seeing zero return, leading to the phenomenon termed the GenAI Divide [6][12] - Only 5% of integrated AI pilots are generating significant value, while the majority fail to impact P&L [6][12] - The divide is attributed not to model quality or regulation, but to the approach taken by organizations [6][8] Adoption and Transformation - Over 80% of organizations have explored or piloted tools like ChatGPT, but these primarily enhance individual productivity rather than P&L performance [7][12] - 60% of organizations evaluated enterprise-grade systems, but only 20% reached pilot stage and just 5% reached production [7][12] - Most implementations do not lead to headcount reduction, but organizations crossing the divide see selective workforce impacts in specific functions [12][33] Industry-Level Transformation - Only two out of eight major sectors (Tech and Media) show meaningful structural change due to GenAI, while seven sectors remain stagnant [10][15] - A composite AI Market Disruption Index was developed to quantify disruption across industries, revealing limited transformation despite high investment [17][22] Pilot-to-Production Rates - The GenAI Divide is most evident in deployment rates, with only 5% of custom enterprise AI tools reaching production [24][28] - Generic LLM chatbots have high pilot-to-implementation rates (~83%), but this masks a deeper split in perceived value [29][30] - Enterprises with over $100 million in annual revenue lead in pilot count but report the lowest rates of pilot-to-scale conversion [32] Investment Patterns - 50% of GenAI budgets are allocated to sales and marketing, despite back-office automation often yielding better ROI [40][41] - The focus on visible functions over high-ROI back-office opportunities perpetuates the GenAI Divide [40][46] Learning Gap - The primary barrier to scaling is a learning gap; most GenAI systems do not retain feedback or adapt to context [8][48] - Users prefer consumer-grade tools like ChatGPT for simple tasks but abandon them for critical workflows due to lack of memory and customization [48][61] Successful Strategies - Organizations crossing the GenAI Divide build adaptive systems that learn from feedback and integrate deeply into workflows [71][75] - Successful vendors focus on narrow, high-value use cases and prioritize customization over broad feature sets [73][75] Buyer Practices - Successful organizations treat AI vendors as business service providers, demanding deep customization and holding them accountable to business metrics [96][108] - External partnerships with learning-capable tools see a deployment success rate of ~67%, compared to ~33% for internal builds [96][100] Workforce Impact - GenAI is starting to impact workforce dynamics, particularly in customer support and administrative functions, but not through widespread layoffs [112][114] - Organizations crossing the divide report measurable savings from reduced external spending rather than significant internal headcount reductions [110][116] Future Trends - The emergence of an Agentic Web, where autonomous systems can coordinate across the internet, represents the next evolution beyond the current GenAI Divide [121][123] - Organizations that quickly adopt learning-capable tools will establish competitive advantages as the window to cross the divide narrows [89][127]

Core Insights - Pinterest's CEO Bill Ready described the platform as an "AI-enabled shopping assistant," but believes the concept of an agentic web where AI shops on behalf of users is still a long way off [1][3] Company Positioning - Pinterest positions itself at the early stages of the shopping journey, where users seek ideas that may lead to purchases, raising concerns that AI could redirect users to personalized recommendations instead of using Pinterest [2] AI Integration - The company referred to the current exploration of AI-driven experiences as a "Cambrian moment," highlighting its use of AI for recommendation systems, multimodal AI, visual search, conversational search, and advertising efficiencies [5] User Experience - Ready emphasized that Pinterest's proactive recommendations resonate with users, who feel that "Pinterest just gets me," akin to a personal shopping assistant [4] User Concerns - There are growing user concerns regarding the prevalence of AI-generated low-quality content on Pinterest, leading to the introduction of tools to label AI-generated images and filter out such content [8] Talent Acquisition - Pinterest aims to attract AI talent focused on using AI responsibly and positively, distinguishing itself from other social media platforms [9] Financial Performance - Pinterest reported Q2 revenue of $998 million, exceeding expectations, but earnings per share of 33 cents fell short of the anticipated 35 cents, leading to a drop in stock price [10]

Core Viewpoint - The article discusses the emergence of the "Agentic Web," a new paradigm in internet usage where AI agents autonomously complete tasks based on user-defined goals, marking a significant shift from traditional web interactions [3][6][57]. Group 1: Paradigm Shifts in the Web - The internet has undergone three major paradigm shifts: from a keyword-driven "directory web" in the PC era, to a recommendation-driven "content explosion" in the mobile era, and now to an "action network" driven by AI agents [8][9][15]. - In the Agentic Web, the role of the web transitions from being an information repository to an ecosystem of actionable resources for AI agents [13][15]. Group 2: Definition and Structure of Agentic Web - The Agentic Web is defined as a distributed, interactive ecosystem where AI agents, powered by large language models (LLMs), continuously plan, coordinate, and execute goal-oriented tasks [16][17]. - Users interact with the web by delegating tasks to AI agents, which autonomously handle the execution of these tasks [20][21]. Group 3: Core Dimensions of Agentic Web - The structure of the Agentic Web can be understood through three core dimensions: intelligence, interaction, and economy [24][28]. - The "Agent Attention Economy" signifies a shift in focus from human clicks to AI agent interactions, changing the metrics of commercial competition [29]. Group 4: Application Scenarios - The capabilities of the Agentic Web can be categorized into transactional, informational, and communicational tasks, enabling AI agents to perform a wide range of functions from booking tickets to conducting research [30][31]. - In transactional tasks, users can simply state their needs, and AI agents will autonomously complete the entire process, enhancing efficiency [33]. Group 5: Challenges Ahead - The implementation of the Agentic Web faces systemic challenges, including the need for improved AI capabilities, robust network infrastructure, and a redefined economic model [42][43]. - Key challenges include ensuring the reasoning and memory capabilities of AI agents, managing security risks associated with external tools, and establishing effective communication protocols for multi-agent collaboration [44][51]. Group 6: Socio-Economic Implications - The rise of the Agentic Web poses significant implications for traditional advertising models, labor markets, and economic structures, necessitating a reevaluation of how value is created and distributed in the digital economy [56][58]. - As AI agents become more prevalent, there is a pressing need to address the potential displacement of jobs and ensure equitable economic benefits [56][58].

Financial Data and Key Metrics Changes - Cloudflare achieved an annual run rate revenue exceeding $2 billion, with quarterly revenue of $512.3 million, reflecting a 28% year-over-year growth, up from 26.5% in the first quarter [5][24] - The company reported a gross margin of 76.3%, consistent with its long-term target range of 75% to 77% [6][28] - Operating income was $72.3 million, representing a 27% increase year-over-year, with an operating margin of 14.1% [31] Business Line Data and Key Metrics Changes - The number of customers paying over $100,000 annually increased by 22% year-over-year, totaling 3,712 customers [6][26] - Revenue contribution from large customers grew to 71% of total revenue, up from 67% in the same quarter last year [6][26] - Dollar-based net retention rate improved to 114%, up 3% quarter-over-quarter [6][27] Market Data and Key Metrics Changes - The U.S. market accounted for 49% of total revenue, increasing by 22% year-over-year, while EMEA represented 28% of revenue with a 29% increase, and APAC represented 15% with a 44% increase [25][26] - The total number of paying customers reached approximately 266,000, marking a 27% year-over-year increase [26] Company Strategy and Development Direction - Cloudflare is focusing on reaccelerating growth through innovation and operational efficiency, with a strong emphasis on large enterprise customers and expanding its sales capacity [5][24] - The company is positioning itself to capitalize on the shift from traditional web models to AI-driven business models, particularly in the publishing sector [20][21] - Cloudflare is developing its product offerings into distinct acts, with Act Four focusing on enabling transactions in an AI-driven web [22] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the company's ability to maintain growth momentum, driven by strong performance in large customer segments and ongoing innovation [24][34] - The management highlighted the strategic importance of partnerships with media and AI companies to adapt to changing market dynamics [20][21] Other Important Information - Cloudflare issued $2 billion in convertible senior notes due in 2030, enhancing its financial flexibility [32] - The company ended the quarter with $4 billion in cash and equivalents, indicating a strong liquidity position [32] Q&A Session Summary Question: Insights on the business model for the Agentic web - Management indicated that various business models will likely emerge, emphasizing the need for a level playing field in content access and monetization [37][40] Question: Discussion on Act One products and their performance - Management highlighted the architectural advantages of Cloudflare's network, which allows for efficient handling of DDoS attacks without increasing costs [46][50] Question: Update on large deal momentum and partner growth - Management noted that the go-to-market strategy has shifted to a partner-first approach, which is driving growth in larger deals [60][62] Question: Media sector's attractiveness and Cloudflare's strategy - Management discussed the strategic importance of media partnerships and the potential for free services to create long-term value [75][76] Question: Relationship with foundation model vendors - Management confirmed that a significant portion of major AI companies are Cloudflare customers, utilizing its security and inference capabilities [80][82]

Investment Rating - The investment rating for the company is maintained as BUY with a target price of USD 564.57, indicating a potential upside of 20% from the closing price of USD 470.38 as of June 6, 2025 [1][8]. Core Insights - The company is leveraging its enterprise capabilities and Azure product advantages to establish a foundational platform for the Agentic Web, which is expected to accelerate the development of Agent applications and increase AI inference demand for its cloud business [1][2]. - The company has completed the infrastructure for Agentic Web development, focusing on both edge and cloud-side toolchains, which enhances development capabilities and supports third-party integrations [2]. - The cloud business has shown strong growth, with Azure and other cloud services revenue increasing by 33% year-over-year in 3QFY25, driven by AI contributions [3]. - The commercialization of AI applications in the US is accelerating, with strategic collaborations between the company and software vendors like SAP and ServiceNow enhancing cloud service reliance [4]. - Earnings forecasts project revenue growth for FY25E/FY26E/FY27E at USD 278.8 billion, USD 320.2 billion, and USD 368.8 billion respectively, with EPS expected to rise to USD 13.77, USD 16.12, and USD 18.85 [5]. Summary by Sections Development and Infrastructure - The company is enhancing its development capabilities with the launch of the GitHub Coding Agent and a complete enterprise-grade Agent customization system, which supports multi-Agent orchestration and flexible model selection [2]. - The introduction of Windows AI Foundry supports local Agent development, creating a more complete development ecosystem [2]. Financial Performance - The cloud business revenue growth of 33% year-over-year in 3QFY25 was significantly supported by AI, which contributed 16% to Azure's revenue growth [3]. - The company processed over 100 trillion tokens in 3QFY25, reflecting a fivefold increase year-over-year, indicating strong demand for AI inference [3]. Earnings and Valuation - The company maintains its earnings forecasts with projected revenues and EPS growth over the next three fiscal years, reflecting confidence in its competitive edge in AI and cloud business [5]. - The stock is valued at 41x FY25E PE, which is above the peers' average of 29.8x, justifying the BUY rating [5].