HubSpot FY Conference Summary Company Overview - **Company**: HubSpot (NYSE:HUBS) - **Event**: Ninth Annual Wells Fargo TMT Conference - **Date**: November 19, 2025 Key Points Industry Context - The software industry is currently facing challenges but presents significant opportunities, particularly in AI integration [8][9][10] AI Strategy - HubSpot's AI strategy focuses on embedding AI into all products to assist small and medium businesses (SMBs) in growth [8][9] - Three featured AI agents were launched: Customer Agent, Prospecting Agent, and Data Agent, along with a Breeze Assistant for go-to-market employees [9][10] - The differentiation of HubSpot's AI offerings lies in its deep understanding of SMBs and the context it provides for sales and marketing activities [10][11] Customer Base and Adoption - HubSpot has 280,000 customers utilizing its platform for marketing, sales, and service, which enhances AI effectiveness through contextual data [11] - AI adoption is driven by the presence of C-suite leaders prioritizing AI initiatives within companies, rather than company size [58][59] Data Management - The transition to an agentic architecture allows HubSpot to handle unstructured data, enhancing the context and quality of AI outputs [22][23] - Data Hub was rebranded to emphasize its role in improving data quality and facilitating AI workflows [66][67] Monetization Strategy - HubSpot employs a hybrid monetization strategy for AI, charging for both seats and credits based on the type of AI usage [48][49] - The Customer Agent is the leading consumer of credits, resolving over 60% of support tickets for more than 6,000 customers [49][50] Marketing and SEO Adaptation - HubSpot has diversified its marketing channels in response to changes in lead generation, focusing on platforms like YouTube and podcasts [94][95] - The concept of Answer Engine Optimization (AEO) is emerging as a new strategy to capture leads through AI-driven responses [95][96] Future Outlook - The adoption curve for AI technologies is expected to improve as customers gain trust in data quality and the effectiveness of AI solutions [102][103] - HubSpot aims to continue enhancing its AI capabilities and providing value to its customers through iterative feedback and improvements [102][103] Conclusion - HubSpot is well-positioned to leverage its extensive customer base, data management capabilities, and AI integration to drive growth and enhance customer experiences in the evolving software landscape [72][73]

Financial Data and Key Metrics Changes - Revenue grew 18.4% year over year in constant currency, reaching $810 million, with an operating margin of 20% [4][18] - Subscription revenue increased by 21% year over year, while services and other revenue rose by 19% [18] - Net income was $140 million in Q3, translating to $2.66 per fully diluted share, with free cash flow at $147 million, or 18% of revenue [19] - Customer dollar retention remained in the high 80s, and net revenue retention was flat at 103% [18][19] - Calculated billings were $804 million, growing 19% year over year in constant currency [19] Business Line Data and Key Metrics Changes - Total customers increased by 10,900 in Q3, bringing the total to 279,000, a 17% year-over-year growth [18] - Multi-hub adoption is evident, with 43% of Pro Plus customers subscribing to three core hubs, up 4% year over year [5] - Deals over $5,000 in monthly recurring revenue grew 35% year over year, indicating strong up-market momentum [6] Market Data and Key Metrics Changes - Domestic revenue grew 17% year over year, while international revenue growth was 20% in constant currency [18] - International revenue represented 49% of total revenue, highlighting the company's global reach [18] Company Strategy and Development Direction - The company is focused on platform consolidation, multi-hub adoption, and up-market momentum as key growth drivers [5][15] - AI innovation is central to the company's strategy, with new features launched across all hubs to enhance customer experience [8][9] - The introduction of the Loop playbook aims to guide customers in leveraging AI for growth, with significant engagement metrics reported [12][13] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the company's ability to achieve durable growth, emphasizing the strength of core growth drivers and emerging levers [25][17] - The company anticipates a step-up in net revenue retention in Q4, with expectations for a couple of points improvement for the full year of 2025 [18][19] - Management highlighted the importance of AI as a multi-year tailwind for growth, with ongoing investments in product innovation [24][17] Other Important Information - The company repurchased 780,000 shares of common stock under its share repurchase program, representing $375 million [19] - The acquisition of XFunnel aims to enhance brand visibility in AI-generated answers, integrating it into HubSpot's platform [14] Q&A Session Summary Question: Path to 20% Growth - Management believes it can grow faster than current levels, focusing on durable growth through strong core drivers and emerging growth levers [22][23] Question: Impact of AI Overviews - Management sees AI overviews as a significant opportunity for customers to enhance visibility and engagement through new marketing strategies [26][27] Question: Platform Consolidation Drivers - Customers cite total cost of ownership, unified data access, and AI adoption as key reasons for consolidating on HubSpot's platform [29][30] Question: Billings Growth and Revenue Inflection - Management noted that billings growth is influenced by various factors, including mix shifts and duration, and expects revenue growth to follow net new ARR trends [31][32] Question: Customer Adoption of AI Products - The company is seeing strong adoption of AI features, with positive trends in credit consumption and customer engagement [38][39] Question: ASP Growth Challenges - ASP growth has been impacted by new customer pricing and robust starter additions, but there are tailwinds from large deals and multi-hub adoption [45] Question: Customer Data Quality - The state of customer data varies, with higher quality observed among those using multiple hubs, and the introduction of Data Hub aims to improve data quality [52][54]

Core Viewpoint - The implementation of Data Agents appears straightforward but is fraught with challenges, primarily due to software engineering difficulties. A unified semantic layer is crucial for success, and neglecting aspects like scenario focus, iterative technical frameworks, or semantic models can lead to stagnation in prototype stages [2][6][12]. Group 1: Importance of Semantic Layer - The significance of building a semantic layer for Data Agents is widely recognized, with both domestic and international investments increasing in this area. Tencent Cloud WeData has been an early investor in this domain [7][12]. - The semantic layer encompasses four main aspects: concepts, data relationships, metrics, and dimensions, which are essential for providing accurate and unified data access interfaces for Agents [8][12]. Group 2: Technical Challenges and Solutions - The primary technical challenges in integrating Data Agents into existing enterprise platforms include data governance issues and the difficulty in evaluating the effectiveness of Data Agents [14][15]. - To address these challenges, a focus on specific scenarios for unified semantic layer construction and evaluation systems is recommended [15][18]. Group 3: Future of Data Roles - Data Agents are not expected to replace data engineers or scientists but will automate some execution tasks. This will lead to a fusion of roles, requiring professionals to possess a broader skill set related to Agents and large language models (LLMs) [10][11]. - Understanding the basic principles of Agents and LLMs is essential for effectively utilizing large model technologies [11]. Group 4: Recommendations for Enterprises - Companies are advised to focus on scenario-specific semantic abstraction and address existing data governance issues to build a robust semantic layer [16][17]. - It is crucial to establish an iterative technical framework and a comprehensive Agent operation system to monitor, evaluate, and modify the Data Agent effectively [18].

Core Insights - The article discusses the upgrades and new features of Volcano Engine's AI tools, including PromptPilot and Data Agent, aimed at enhancing AI application efficiency and data utilization for enterprises [1][2][4]. Group 1: PromptPilot Upgrade - PromptPilot has been upgraded to support prompt optimization for any model, including public cloud models, private models, and custom-trained models [2][3]. - The tool utilizes natural language interaction to understand user needs, extract evaluation criteria, and generate improved prompts, thereby continuously optimizing based on online traffic and bad case analysis [2][3]. - The integration with Volcano Engine's knowledge base allows for precise content retrieval, enhancing the model's understanding and output in specialized fields [3]. Group 2: Data Agent and One-Customer-One-Strategy - Data Agent is a vertical intelligence tool that deeply understands and utilizes enterprise data assets, enabling proactive analysis and action [5]. - The "One-Customer-One-Strategy" feature allows for personalized marketing by analyzing multi-dimensional data related to customers, internal knowledge bases, and public data, generating tailored marketing plans [5][6]. - This feature has shown significant results, with conversion efficiency from Marketing Qualified Leads (MQL) to Sales Qualified Leads (SQL) increasing by up to 300%, and data utilization rising from 10% to 95% [6]. Group 3: AI Data Lake Service and AI Operator Square - The AI Data Lake service has been enhanced with the launch of "AI Operator Square," which facilitates the management of multi-modal data, including text, images, and audio-visual content [7]. - The AI Operator Square provides over 100 standardized operators for various data processing tasks, allowing users to create modular workflows through a visual drag-and-drop interface [8][9]. - This upgrade aims to transform scattered data into knowledge assets, promoting automated circulation and value addition of knowledge assets [9].

Core Insights - The article discusses the upgrades of Volcano Engine's PromptPilot and Data Agent, which enhance AI applications and data management for enterprises. Group 1: PromptPilot Upgrade - PromptPilot has been upgraded to support prompt optimization for any model, including public cloud models, private models, and custom-trained models [2][3] - The tool utilizes natural language interaction to understand user needs, extract evaluation criteria, and generate better prompts, thus improving AI application performance [2][3] - After deployment, PromptPilot can sample online traffic, analyze bad cases, and autonomously optimize prompts, creating a cycle of continuous improvement [2][3] Group 2: Data Agent and Multi-modal Data Lake - Data Agent is introduced as a vertical intelligent agent that deeply understands and utilizes enterprise data assets, enabling proactive analysis and action [4] - The "One Customer, One Strategy" capability of the intelligent marketing agent integrates three types of data for precise customer profiling and targeted marketing strategies [4][5] - The effectiveness of "One Customer, One Strategy" includes a 300% increase in conversion efficiency from MQL to SQL, a rise in data utilization from 10% to 95%, and a reduction in customer analysis time from 30 minutes to 2 minutes [5] Group 3: AI Operator Square - The AI Data Lake service has launched the "AI Operator Square," which integrates management of multi-modal data, including text, images, and audio-visual content [5][6] - The platform offers over 100 standardized operators and supports the integration of mainstream open-source operators, providing a comprehensive framework for custom operator development [6] - Users can visually drag and drop to quickly assemble modular workflows, transforming scattered data into knowledge assets for automated circulation and value addition [7]

Core Insights - The article emphasizes the transformative role of Data Agents in the era of Generative AI, highlighting their ability to compress the data lifecycle into a rapid "data → insight → action" loop, achieving over 60% efficiency gains and significant cost savings in the millions of dollars [3][4][10]. Industry Trends - Data Agents redefine "Data" as any digital asset that can be accessed and utilized in real-time, moving away from traditional static databases [5][7]. - The global data volume is projected to reach 149 ZB in 2024 and exceed 181 ZB in 2025, with approximately 80% being unstructured data that requires immediate structuring for algorithmic use [5][7]. - Generative AI is expected to contribute an additional $2.6 to $4.4 trillion in value annually, with nearly 75% of this value coming from functions heavily reliant on structured data [5][7]. Data Agent Definition and Functionality - Data Agents are AI entities that automate the entire data lifecycle, capable of planning, executing, and verifying tasks based on natural language inputs [7][8]. - They are positioned as core infrastructure rather than mere BI tools, directly impacting business KPIs and productivity [7][8]. Efficiency Gains and Market Acceptance - Early adopters of Data Agents have reported productivity increases of over 60% and annual savings of millions of dollars [7][8]. - The cost of LLM inference has dramatically decreased from $60 per million tokens to $0.06, indicating a significant technological shift [10][13]. - AI search and query traffic in the U.S. has reached 5.6%, reflecting a growing acceptance of natural language interactions for structured answers [13][14]. Market Demand and Investment Trends - The demand for Data Agents has surged, with a 900% increase in global search interest for "AI agent" and a tripling of investment in the AI Agent sector, reaching $3.8 billion in 2024 [45][46]. - Major acquisitions by companies like Databricks and Snowflake indicate a strong focus on data-driven AI platforms [13][14]. Development Stages of Data Agents - The evolution of Data Agents is expected to occur in three stages: 1. Human-led with AI empowerment, transforming data interaction and decision-making processes [36][37]. 2. Scenario-driven applications that allow for rapid development of customized systems based on existing data [38][40]. 3. Autonomous intelligence where Data Agents manage data collection, governance, and analysis, acting as a digital COO [41][42]. Conclusion and Future Outlook - The current landscape presents a unique opportunity for Data Agents to become the default interface for digital work, akin to the Office suite in the 1990s [45][46]. - The integration of Data Agents into business processes is anticipated to enhance organizational efficiency and responsiveness, marking a significant shift in how data is utilized across industries [48][49].

Group 1 - The core viewpoint is that the commercialization of AI technology is steadily advancing, with 2025 expected to be the year of large-scale commercial deployment of AI Agents [1] - ByteDance has upgraded multiple models and launched the all-scenario intelligent agent Data Agent, enhancing its capabilities in video and music generation [1] - The report emphasizes that AI technology iteration and the deployment of Agents are progressing across major companies, indicating ongoing development in AI commercialization [1] Group 2 - The China Securities Regulatory Commission (CSRC) has revised the Major Asset Restructuring Management Measures, aiming to deepen the reform of the mergers and acquisitions market [2] - Key changes include establishing a phased payment mechanism for restructuring shares and increasing regulatory tolerance for financial condition changes and related transactions [2] - The CSRC's actions are expected to invigorate the mergers and acquisitions market and accelerate the integration of segments driven by digital transformation in the computing sector [2] Group 3 - Google DeepMind has launched the general-purpose AI system AlphaEvolve, which autonomously generates and improves algorithmic code [3] - AlphaEvolve has successfully addressed significant challenges in mathematics and computer science, demonstrating its potential in enhancing AI chip design and optimizing global computing resource utilization [3] - The consensus among major tech companies is that the deployment of Agents is becoming a reality, with expectations for accelerated future development [3]

Investment Rating - The report maintains an "Increase" rating for the industry [7] Core Insights - Major players like Alibaba and Tencent are significantly increasing their capital expenditures (Capex) for AI infrastructure, indicating a positive outlook for the industry [12][16] - The demand for high-performance computing is rapidly increasing, driven by AI applications, which is expected to further expand cloud computing needs [12][16] - The report emphasizes that computing power is a critical infrastructure for the development of AI agents, which will support long-term growth in the industry [42][51] Summary by Sections Capital Expenditure Growth - Alibaba's Capex for Q1 2025 reached 24.612 billion RMB, a year-on-year increase of 120.68%, with cloud revenue of 30.127 billion RMB, up 17.71% [13][16] - Tencent's Capex for Q1 2025 was 27.476 billion RMB, a 91.35% increase from 14.4 billion RMB in Q1 2024 [16][19] AI Application Acceleration - Major cloud providers are enhancing their capabilities to accelerate AI application deployment, with significant upgrades announced at various conferences [21][26] - Alibaba Cloud's ninth-generation ECS has improved computing power by up to 20% while reducing prices by 5% [28][30] - Huawei Cloud introduced the CloudMatrix 384 super node, designed to meet the massive computing demands of the AI era [36][39] Computing Power as a Key Driver - The report identifies several reasons for the high demand for computing power in AI agents, including the need for long context processing, external data integration, and complex task verification [42][51] - The increasing complexity of AI models and the need for high concurrency access further exacerbate the demand for computing resources [51] Investment Opportunities - The report suggests focusing on companies involved in computing power such as Cambricon, Alibaba, and Inspur, as well as those in the AI agent space like Kingsoft Office and Kingdee International [4][53][54]

Core Insights - Volcano Engine held an AI innovation exhibition in Shanghai, launching several models including Seedance 1.0 lite for video generation and the upgraded Doubao 1.5 visual deep thinking model, aiming to enhance the application chain from business to intelligent agents [1][2] - The Seedance 1.0 lite model supports text-to-video and image-to-video generation, achieving significant improvements in video quality and generation speed, making it suitable for various applications such as e-commerce advertising and entertainment [1] - The Doubao 1.5 model demonstrates strong multi-modal understanding and reasoning capabilities, ranking in the top tier across 38 out of 60 public evaluation benchmarks [1][2] Model Upgrades and Applications - The Doubao music model was upgraded to support English song creation and can automatically adapt background music based on video understanding, now fully launched [2] - Data Agent, a new enterprise data intelligent agent, can analyze and generate professional research reports by integrating structured and unstructured data [2] - Doubao models have been widely adopted across industries including automotive, finance, education, and retail, covering nearly 400 million devices and major companies [2] Industry Collaborations - Giant Network announced a collaboration with Volcano Engine to enhance AI gameplay in their social deduction game "Space Kill" using Doubao models [2][3] - Eli Lilly has developed a dedicated AI application platform in partnership with Volcano Engine, facilitating innovations in drug development and disease diagnosis [3] - Volcano Engine emphasizes the importance of a three-stage journey for AI implementation, focusing on investment returns, model infrastructure, and the lifecycle of intelligent agents [3][4] Model Service Matrix - Volcano Engine has established a comprehensive model service matrix covering various fields such as language, deep thinking, vision, and speech, continuously optimizing model capabilities to meet specific business needs [4]

Core Insights - ByteDance has launched an advanced visual-language multimodal model, Seed 1.5-VL, showcasing significant improvements in multimodal understanding and reasoning capabilities [1][2][3]. Group 1: Model Features - Seed 1.5-VL demonstrates enhanced visual localization and reasoning, with the ability to quickly and accurately identify various elements in images and videos [3][4]. - The model can process a single image and a prompt to identify and classify multiple objects, providing precise coordinates [4]. - It can analyze video footage to answer specific questions, showcasing its advanced video understanding capabilities [5]. Group 2: Performance Metrics - Despite having only 20 billion activation parameters, Seed 1.5-VL performs comparably to Gemini 2.5 Pro, achieving state-of-the-art results in 38 out of 60 public evaluation benchmarks [6]. - The inference cost is competitive, with input priced at 0.003 yuan per 1,000 tokens and output at 0.009 yuan per 1,000 tokens [7]. Group 3: Practical Applications - Developers can access Seed 1.5-VL through an API, enabling the creation of AI visual assistants, inspection systems, and interactive agents [7]. - The model's capabilities extend to complex tasks such as identifying emotions in images and solving visual puzzles, demonstrating its versatility [17][20]. Group 4: Technical Architecture - Seed 1.5-VL consists of three core components: a visual encoding module (SeedViT), a multi-layer perceptron (MLP) adapter, and a large language model (Seed1.5-LLM) [27]. - The model has undergone a unique training process, including multi-modal pre-training and reinforcement learning strategies, enhancing its performance while reducing inference costs [29][30]. Group 5: Industry Impact - The advancements presented at the Shanghai event indicate that ByteDance is building a comprehensive AI ecosystem, integrating various technologies from video generation to deep visual understanding [32]. - The emergence of Seed 1.5-VL signifies a step towards a true multimodal intelligent era, reshaping interactions with visual data [32][33].