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]

Our next guest had an advanced look at GPT5's enhanced capabilities. His company integrating that tech into their suite of enterprise tools as of this afternoon. So for more on that and how these fastmoving developments in AI will change the business world.Let's bring in Aaron Levy, CEO and co-founder of Box. Aaron, it's great to speak with you today. Yeah, thanks for having me on.Appreciate it. So let's start right there. GPT5, what are your takeaways so far.Yeah, this is a very big deal. uh and it's I thi ...

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]

Company Overview - Box is a B2B company operating as an unstructured data platform, serving large enterprises including Fortune 500 companies [1][2] - Box has over 115,000 companies as customers, tens of millions of users, and manages over 1 exabyte of data [2] - Box is often the first AI deployed within large enterprises due to existing trust relationships [3] Data Extraction Evolution - Box initially used a straightforward architecture for data extraction involving pre-processing, OCR, and large language models [8] - The initial AI deployment processed 10 million pages, but encountered challenges with complex documents, OCR accuracy, language variations, and the need for confidence scores [9][10][11] - The company experienced a "trough of disillusionment" as the initial AI solution proved insufficient for diverse customer needs [12] Agentic Approach Implementation - Box re-architected its data extraction process using a multi-agent approach, separating problems into sub-agents [12] - The agentic system intelligently groups related fields, dynamically determines data extraction methods, and incorporates a quality feedback loop for continuous improvement [13] - This approach allows for easier updates and specialization, enabling the company to quickly adapt to new document types and customer requirements [13] Engineering and Customer Impact - Building agentic systems helps engineers think about AI and agentic workflows, leading to better understanding of customer needs [13] - This approach facilitates the development of tools that integrate with customer-built agents, enhancing the overall ecosystem [13] - The company advises building agentic systems early when developing intelligent features [14]