Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 00:00:00 OpenAI to prioritize enterprise in 2026 00:02:01 Could OpenAI's enterprise push help it fund infrastructure? 00:05:18 Is the AGI dream over? 00:10:29 Alex is on team product? 00:18:12 Can OpenAI design for consumer and enterprise at the same time? 00:21:36 Erotic ChatGPT is coming in Q1 00:23:03 Disney and OpenAI ink a groundbreaking deal 00:24:59 Why Disney wins from giving up some control 00:35:52 The AI ...

For more, let's bring in Alex Caneritz, the founder of Big Technology and a CNBC contributor, and Gil Laura, who is an analyst at DA Davidson. Welcome to both of you. Alex, how significant are the OpenAI announcements today.>> They're pretty significant, and that is because OpenAI is now taking a strategic pivot in a way where they were previously really focused on consumer, now they're all in on enterprise. I have some reporting that earlier this week, Sam Alman sat with heads of New York publications and ...

Tokenization - Avalanche (AVAX) is being used for tokenization across various sectors [1] - Tokenization use cases include Dinari, Frontier stable token, KRW1, Inversion, Apollo, BlackRock, Franklin Templeton, KKR, Diamond Standard, Republic Note, Wine Capital, Homium, Intain, Citi, Watr, Littio, ParaFi, Balcony, Misyon Bank, Lemonade, and Re [1] Payments - Avalanche (AVAX) is facilitating payments through platforms like StraitsX, Fonbnk, Visa card, and Nonco [1] Enterprise Solutions - Avalanche (AVAX) is being adopted by enterprises such as CA DMV, Deloitte, Chainlink/Balcony, Bergen County, Kinexys/JPM, and Blockticity [1] Consumer Applications - Avalanche (AVAX) is used in consumer applications including OTG, Maplestory, Zero One, Uptop, Youmio, film, SI Tickets, Tixbase, SK Planet, FIFA, Bowmore/Suntory, and Fanhub [2]

Overview - VeChain is an enterprise-focused blockchain platform [1] Analysis Focus - The analysis provides a closer look at VeChain [1]

Overview - VeChain is enterprise-focused [1] Deep Dive Analysis - The report provides a closer look at VeChain [1]

Hiring Opportunity - Arkham is seeking an Enterprise Sales Lead to expand its product suite [1] - The role involves structuring, negotiating, and closing contracts with enterprise clients [1] - Excellent communication and relationship management skills are essential for engaging with senior leaders [1]

Marketing & Sales - Enterprise 版本限时五折优惠 [1] - 鼓励用户点击链接了解更多信息 (Read here → https://t.co/Sy7IMvVp7g) [1] Product Information - 该报告为企业专供 (Enterprise gated) [1]

Financial Data and Key Metrics Changes - Atlassian reported over $5.2 billion in revenue and over $1.4 billion in free cash flow for FY 2025, achieving a balanced rule of 40 plus performance [6][10] - Free cash flow for the quarter was $360 million, down 13% year over year, primarily due to strong collections in the prior year [24][25] - The company achieved a cloud net revenue retention rate of 120% [7] Business Line Data and Key Metrics Changes - The Teamwork platform now serves over 300,000 customers, with significant growth in AI usage, reaching 2.3 million AI users, a 50% increase from the previous quarter [6][7] - Core applications such as Jira, Confluence, and Jira Service Management are growing in line or faster than total company revenue [10] - The Teamwork collection has exceeded expectations since its launch, contributing to strong revenue growth [10] Market Data and Key Metrics Changes - Atlassian's enterprise sales teams executed a record number of deals greater than $1 million in annual contract value (ACV), more than doubling year over year [6][10] - Data center to cloud migrations increased by 60% year over year, indicating strong customer commitment to the cloud platform [42][69] Company Strategy and Development Direction - The company is focusing on serving the enterprise, delivering AI innovations, and connecting teams through the Atlassian system of work [7][10] - A partnership with Google Cloud aims to enhance the AI-powered Teamwork platform and accelerate cloud transformation [8][10] - Atlassian is committed to a long-term growth strategy, targeting a compounded annual growth rate of 20% from FY 2024 to FY 2027 [33][34] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the company's ability to achieve long-term growth targets despite macroeconomic uncertainties [33][34] - The company sees AI as a significant tailwind for business growth, with expectations of increased software creation and collaboration [20][21] - Management highlighted the importance of customer-centric processes and partnerships to drive enterprise growth [71][72] Other Important Information - Anu, the President of Atlassian, will transition away from her role in December after nearly twelve years [10][11] - The company is investing heavily in R&D and sales to support its strategic priorities [101][102] Q&A Session Summary Question: Concerns about tool generation tools and developer roles - Management stated that they are not seeing any negative impact on growth rates or adoption from the integration of code-generating AI tools, and user growth remains healthy [15][16] Question: Free cash flow trajectory for 2026 - Free cash flow for FY 2025 was flat at $1.4 billion, with expectations that cash flow will correlate with non-GAAP operating income trends moving forward [24][25][26] Question: Potential inflection points for revenue growth - Management highlighted multiple growth levers, including paid seat expansion, cross-sell, upsell, and new customer growth, with AI opening up additional opportunities [33][34] Question: Guidance for data center segment growth - Management explained that Q1 guidance reflects a smaller expiration base and headwinds from prior programmatic changes, leading to a cautious outlook [78][79] Question: Driving wall-to-wall deployments in enterprises - Management emphasized ongoing consolidation efforts and the growth of business user segments, indicating strong momentum in expanding usage across non-technical roles [81][84]

GPT-5 Capabilities & Enterprise Impact - GPT-5 represents a significant advancement, particularly for enterprises dealing with large volumes of unstructured data [2][3] - GPT-5 demonstrates improved accuracy compared to GPT-4, making it a leading model for enterprise applications [4] - Box is integrating GPT-5 into its AI studio, offering it to enterprise customers [4] Use Cases & Productivity - AI agents, powered by models like GPT-5, can automate tasks such as analyzing contracts and legal briefings, increasing productivity for professionals like lawyers [8][9] - The value proposition for customers includes increased productivity and potential for increased revenue or efficiency [7][9] - Enterprises can leverage AI to extract information, summarize documents, and perform calculations on data [6] Data & Model Evaluation - Box evaluates AI models using typical enterprise data like contracts, research data, invoices, resumes, and agreements [6] - The evaluation focuses on the accuracy of the model's responses to questions like extracting data or summarizing information [6]

MCP and AI Agent Development - MCP is presented as a way of interfacing between AI and external resources, enabling functionalities like database access and complex computations [3] - The industry is currently focused on building internal demos and connecting them to APIs, but needs to move towards robust authentication and authorization [9][10] - The industry needs to adapt existing tooling for MCP due to its dynamic client registration, which can flood developer dashboards [12] Enterprise Readiness and Security - Scaling MCP servers requires addressing free credit abuse, bot blocking, and robust access controls [12] - Selling MCP solutions to enterprises necessitates SSO, lifecycle management, provisioning, fine-grained access controls, audit logs, and data loss prevention [12] - Regulations like GDPR impose specific logging requirements for AI workloads, which are not widely supported [12] Challenges and Future Development - Passing scope and access control between different AI workloads remains a significant challenge [13] - The MCP spec is actively developing, with features like elicitation (AI asking humans for input) still unstable [13] - Cloud vendors are solving cloud hosting, but authorization and access control are the hardest parts of enterprise deployment [13]