Why Microsoft stock dropped after earnings

Key Concepts

• Capex (Capital Expenditure): Investments in fixed assets like data centers, crucial for expanding capacity, but can initially depress free cash flow.
• Azure: Microsoft’s cloud computing platform, a key growth driver, but facing capacity constraints.
• ARR (Annual Recurring Revenue): A metric used to measure the predictable revenue stream, particularly important for subscription-based services like Azure.
• AI (Artificial Intelligence): A major investment area for Microsoft and other tech companies, driving demand for capacity and impacting growth expectations.
• Model Agnostic/Multimodal: The ability of software to function with different AI models (OpenAI, Anthropic, open-source), reducing dependence on a single provider.
• Free Cash Flow (FCF): The cash a company generates after accounting for capital expenditures; a key metric for investor confidence.
• RPO (Remaining Performance Obligation): A measure of future revenue under contract, providing insight into backlog and future growth.
• Agentic AI: AI systems capable of autonomous action and decision-making, a key focus for Salesforce and ServiceNow.
• TAM (Total Addressable Market): The overall market demand for a product or service.

Microsoft Earnings & Market Reaction

The discussion centers around Microsoft’s recent earnings report and the surprisingly negative market reaction, despite meeting or exceeding some estimates. Shares fell over 11% due to concerns about increased capital expenditure (capex) plans. Rishi Gerria (RBC Capital Markets) argues the market is overreacting, framing it as a “missing the forest for the trees” moment. While capex is higher than expected (in line with Gerria’s expectations, but above street consensus), it’s a necessary investment to address capacity constraints and bring third-party capacity (Oracle, CoreWeave, Nebas) in-house. Microsoft has explicitly stated they are capacity constrained, and this spending is intended to alleviate that.

Azure growth, at 38% constant currency, was strong, adding approximately $9 billion in constant currency ARR sequentially. This growth was broad-based, not solely driven by OpenAI. The concern isn’t necessarily the Azure number itself, but the fact that it wasn’t higher given the significant investment in AI and cloud infrastructure. The market is impatient for capex to translate into revenue, a process that takes time. Gerria views the dip as a buying opportunity.

Capacity Constraints & Expansion Timeline

The core issue is Microsoft’s current capacity limitations. Expanding capacity is a multi-faceted strategy involving:

• Building new data centers: Long-term ROI, offering flexible architecture for various AI workloads (training, inference, post-training).
• Third-party leases: Utilizing existing data center capacity quickly.
• Third-party data centers: A combination of approaches to rapidly increase capacity.

Microsoft doesn’t expect to resolve capacity constraints until at least the end of their fiscal year (June), potentially later depending on the rate of AI demand. The current surge in AI demand, driven by both model providers and enterprise adoption, is exacerbating the problem. The increasing enterprise AI demand is a positive sign, suggesting a broader adoption beyond just model training.

OpenAI & Microsoft’s AI Strategy

OpenAI currently represents 45% of Microsoft’s commercial Remaining Performance Obligation (RPO). While this is a significant concentration, Gerria argues it’s a strength, not a liability. Microsoft maintains research rights to OpenAI until 2028 and IP rights (including AGI) until 2032, with no gross margin drag. Crucially, Microsoft’s applications (M365 Copilot, GitHub Copilot, Dynamics Security) are built in a “model-agnostic” or “multimodal” fashion, allowing them to switch between OpenAI, Anthropic, or open-source models. This hedges against dependence on a single AI provider. The success of Microsoft isn’t solely tied to OpenAI’s performance, but to the broader evolution of AI technology.

Transparency & Future Data Needs

The discussion highlights the value of increased transparency from Microsoft, particularly regarding paid user numbers (e.g., 15 million M365 Copilot subscriptions) and metrics like Microsoft Fabric reaching $2 billion ARR (60% growth in under two years). However, Gerria emphasizes the need for more forward-looking indicators:

• User Engagement Metrics: Data showing increased time spent and engagement with Office suite features due to Copilot.
• Developer Activity: Metrics demonstrating increased code contribution on GitHub due to Copilot.

These metrics would provide leading indicators of future monetization and validate the AI investment.

Broader Software Landscape & AI Disruption

Kim Forest (Bokeh Partners) is quoted expressing skepticism about the idea that AI will eliminate the need for enterprise software companies, stating that while AI can assist with coding, it doesn’t replace the need for robust software solutions. Gerria agrees, noting that barriers to entry in software have lowered, potentially expanding the Total Addressable Market (TAM). However, this also puts pressure on companies like Salesforce to innovate and justify pricing. The key is for companies to “double down on innovation” and develop “AI-first, AI-native” products that deliver significantly increased customer lifetime value.

Market Sentiment & Buying Opportunities

The market is described as “fickle and impatient.” The panelists believe the current pullback in software stocks presents a buying opportunity, particularly for companies like HubSpot and Microsoft. Two key factors will shift market sentiment:

1. Accelerating Growth: Strong growth rates are essential to overcome negative narratives.
2. Proof Points Around AI: Demonstrable evidence that AI is driving user engagement, value, and ultimately, revenue.

ServiceNow & Salesforce Analysis

Alex (unspecified affiliation) provides analysis on ServiceNow and Salesforce:

• ServiceNow: Positioned to become the platform for managing the “agentic swarm” of AI agents within enterprises. Recent acquisitions and strategic pivots are seen as positive, and the stock is considered a buying opportunity. The key is demonstrating margin improvement alongside growth.
• Salesforce: Needs to demonstrate accelerating growth and improved profitability following recent M&A activity and a strategic shift towards Agent Force. Concrete examples of AI adoption and its impact on revenue are crucial to regain investor confidence.

Tesla & the Shift to Robotics

Elon Musk’s announcement to scale back production of Model S and X vehicles to focus on humanoid robots is discussed. While unconventional, the panelists believe Musk has a track record of delivering on ambitious promises. The shift to robotics is seen as a long-term opportunity, and the market may not immediately penalize Tesla for the increased capital expenditure. Ellen notes Tesla has historically traded on factors beyond traditional financial metrics.

Conclusion

The discussion paints a picture of a complex market grappling with the implications of AI. While increased capital expenditure and capacity constraints are causing short-term concerns, the panelists generally believe the long-term outlook for companies investing in AI is positive. The key is demonstrating tangible results – accelerating growth, improved profitability, and clear evidence of AI driving value for customers. The current market pullback is viewed as a potential buying opportunity for investors willing to look beyond short-term volatility.