Microsoft(MSFT) - 2026 Q2 - Earnings Call Transcript

Financial Data and Key Metrics Changes - Revenue for the quarter was $81.3 billion, up 17% year-over-year and 15% in constant currency [25] - Gross margin dollars increased by 16% and 14% in constant currency, while operating income rose by 21% and 19% in constant currency [25] - Earnings per share was $4.14, an increase of 24% and 21% in constant currency when adjusted for the impact from the investment in OpenAI [25][26] - Company gross margin percentage was 68%, slightly down year-over-year due to investments in AI infrastructure [26] Business Line Data and Key Metrics Changes - Microsoft Cloud revenue reached $51.5 billion, growing 26% and 24% in constant currency [31] - Revenue from productivity and business processes was $34.1 billion, growing 16% and 14% in constant currency [31] - M365 Commercial Cloud revenue increased by 17% and 14% in constant currency, driven by strong Copilot results [31] - Dynamics 365 revenue increased by 19% and 17% in constant currency, with growth across all workloads [32] Market Data and Key Metrics Changes - Commercial bookings increased by 230% and 228% in constant currency, driven by large Azure commitments from OpenAI [30] - Commercial remaining performance obligation increased to $625 billion, up 110% year-over-year [30] - Microsoft Cloud gross margin percentage was slightly better than expected at 67% [31] Company Strategy and Development Direction - The company is focusing on AI diffusion and its impact on GDP, with a substantial growth in total addressable market (TAM) across the tech stack [6] - Investments are being made in AI infrastructure to support high-scale workloads and improve performance [7][9] - The introduction of Agent 365 aims to extend governance and security controls to agents built on the cloud [15] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the growth of AI and its integration into various business lines, emphasizing the importance of long-term investments in infrastructure [6][25] - The company anticipates continued strong growth across commercial businesses, partially offset by consumer business performance [38] - Management highlighted the need to balance incoming supply with growing demand, particularly in Azure [40] Other Important Information - Capital expenditures for the quarter were $37.5 billion, with a significant portion allocated to short-lived assets like GPUs and CPUs [27] - Cash flow from operations was $35.8 billion, up 60%, driven by strong cloud billings and collections [29] - The company returned $12.7 billion to shareholders through dividends and share repurchases, an increase of 32% year-over-year [29] Q&A Session Summary Question: Concerns about CapEx growth and Azure revenue - Analyst raised concerns about CapEx growing faster than expected and its impact on Azure revenue growth [47] - Management explained that CapEx is necessary for long-term growth and is allocated to meet increasing demand for Azure and AI products [48][50] Question: Understanding CapEx investment to revenue and margins - Analyst inquired about the relationship between hardware CapEx investment and revenue growth [57] - Management clarified that much of the capital spent is already contracted for the useful life of the hardware, reducing risk [60][61] Question: Durability of OpenAI-related backlog - Analyst asked about the exposure related to the 45% backlog tied to OpenAI [67] - Management emphasized the diversification of the RPO balance and expressed confidence in the partnership with OpenAI [70] Question: Capacity additions and future growth - Analyst requested comments on the magnitude of capacity additions in upcoming quarters [73] - Management confirmed efforts to add capacity globally and emphasized the importance of meeting customer demand [75] Question: Core competency in silicon development - Analyst asked about the significance of the Maia 200 accelerator and its implications for gross margins [79] - Management highlighted the long-term investment in silicon development and its optimization for AI workloads [81]